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Remote Sens., Volume 9, Issue 5 (May 2017)

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Cover Story Passive detection of sun-induced chlorophyll fluorescence has arisen as the most powerful remote [...] Read more.
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Open AccessArticle Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data
Remote Sens. 2017, 9(5), 398; doi:10.3390/rs9050398
Received: 7 March 2017 / Revised: 12 April 2017 / Accepted: 21 April 2017 / Published: 25 April 2017
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Abstract
Recently, several methods have been introduced and applied to estimate daily air surface temperature (Ta) using MODIS land surface temperature data (MODIS LST). Among these methods, the most common used method is statistical modeling, and the most applied algorithms are linear/multiple
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Recently, several methods have been introduced and applied to estimate daily air surface temperature (Ta) using MODIS land surface temperature data (MODIS LST). Among these methods, the most common used method is statistical modeling, and the most applied algorithms are linear/multiple linear regression models (LM). There are only a handful of studies using machine learning algorithm models such as random forest (RF) or cubist regression (CB). In particular, there is no study comparing different combinations of four MODIS LST datasets with or without auxiliary data using different algorithms such as multiple linear regression, random forest, and cubist regression for daily Ta-max, Ta-min, and Ta-mean estimation. Our study examines the mentioned combinations of four MODIS-LST datasets and shows that different combinations and differently applied algorithms produce various Ta estimation accuracies. Additional analysis of daily data from three climate stations in the mountain area of North West of Vietnam for the period of five years (2009 to 2013) with four MODIS LST datasets (AQUA daytime, AQUA nighttime, TERRA daytime, and TERRA nighttime) and two additional auxiliary datasets (elevation and Julian day) shows that CB and LM should be applied if MODIS LST data is used solely. If MODIS LST is used together with auxiliary data, especially in mountainous areas, CB or RF is highly recommended. This study proved that the very high accuracy of Ta estimation (R2 > 0.93/0.80/0.89 and RMSE ~1.5/2.0/1.6 °C of Ta-max, Ta-min, and Ta-mean, respectively) could be achieved with a simple combination of four LST data, elevation, and Julian day data using a suitable algorithm. Full article
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Open AccessArticle Evaluation of MODIS Albedo Product over Ice Caps in Iceland and Impact of Volcanic Eruptions on Their Albedo
Remote Sens. 2017, 9(5), 399; doi:10.3390/rs9050399
Received: 17 February 2017 / Revised: 31 March 2017 / Accepted: 13 April 2017 / Published: 25 April 2017
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Abstract
Albedo is a key variable in the response of glaciers to climate. In Iceland, large albedo variations of the ice caps may be caused by the deposition of volcanic ash (tephra). Sparse in situ measurements are insufficient to characterize the spatial variation of
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Albedo is a key variable in the response of glaciers to climate. In Iceland, large albedo variations of the ice caps may be caused by the deposition of volcanic ash (tephra). Sparse in situ measurements are insufficient to characterize the spatial variation of albedo over the ice caps due to their large size. Here we evaluated the latest MCD43 MODIS albedo product (collection 6) to monitor albedo changes over the Icelandic ice caps using albedo measurements from ten automatic weather stations on Vatnajökull and Langjökull. Furthermore, we examined the influence of the albedo variability within MODIS pixels by comparing the results with a collection of Landsat scenes. The results indicate a good ability of the MODIS product to characterize the seasonal and interannual albedo changes with correlation coefficients ranging from 0.47 to 0.90 (median 0.84) and small biases ranging from −0.07 to 0.09. The root-mean square errors (RMSE) ranging from 0.08 to 0.21, are larger than that from previous studies, but we did not discard the retrievals flagged as bad quality to maximize the amount of observations given the frequent cloud obstruction in Iceland. We found a positive but non-significant relationship between the RMSE and the subpixel variability as indicated by the standard deviation of the Landsat albedo within a MODIS pixel (R = 0.48). The summer albedo maps and time series computed from the MODIS product show that the albedo decreased significantly after the 2010 Eyjafjallajökull and 2011 Grímsvötn eruptions on all the main ice caps except the northernmost Drangajökull. A strong reduction of the summer albedo by up to 0.6 is observed over large regions of the accumulation areas. These data can be assimilated in an energy and mass balance model to better understand the relative influence of the volcanic and climate forcing to the ongoing mass losses of Icelandic ice caps. Full article
(This article belongs to the Special Issue Remote Sensing of Glaciers)
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Open AccessArticle Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data
Remote Sens. 2017, 9(5), 400; doi:10.3390/rs9050400
Received: 16 February 2017 / Revised: 19 April 2017 / Accepted: 21 April 2017 / Published: 26 April 2017
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Abstract
Fusion of ALS and hyperspectral data can offer a powerful basis for the discrimination of tree species and enables an accurate prediction of species-specific attributes. In this study, the fused airborne laser scanning (ALS) data and hyperspectral images were used to model and
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Fusion of ALS and hyperspectral data can offer a powerful basis for the discrimination of tree species and enables an accurate prediction of species-specific attributes. In this study, the fused airborne laser scanning (ALS) data and hyperspectral images were used to model and predict the total and species-specific volumes based on three forest inventory approaches, namely the individual tree crown (ITC) approach, the semi-ITC approach, and the area-based approach (ABA). The performances of these inventory approaches were analyzed and compared at the plot level in a complex Alpine forest in Italy. For the ITC and semi-ITC approaches, an ITC delineation algorithm was applied. With the ITC approach, the species-specific volumes were predicted with allometric models for each crown segment and aggregated to the total volume. For the semi-ITC and ABA, a multivariate k-most similar neighbor method was applied to simultaneously predict the total and species-specific volumes using leave-one-out cross-validation at the plot level. In both methods, the ALS and hyperspectral variables were important for volume modeling. The total volume of the ITC, semi-ITC, and ABA resulted in relative root mean square errors (RMSEs) of 25.31%, 17.41%, 30.95% of the mean and systematic errors (mean differences) of 21.59%, −0.27%, and −2.69% of the mean, respectively. The ITC approach achieved high accuracies but large systematic errors for minority species. For majority species, the semi-ITC performed slightly better compared to the ABA, resulting in higher accuracies and smaller systematic errors. The results indicated that the semi-ITC outperformed the two other inventory approaches. To conclude, we suggest that the semi-ITC method is further tested and assessed with attention to its potential in operational forestry applications, especially in cases for which accurate species-specific forest biophysical attributes are needed. Full article
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
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Open AccessArticle Comparison of Two Data Assimilation Methods for Improving MODIS LAI Time Series for Bamboo Forests
Remote Sens. 2017, 9(5), 401; doi:10.3390/rs9050401
Received: 19 January 2017 / Revised: 21 April 2017 / Accepted: 22 April 2017 / Published: 25 April 2017
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Abstract
Bamboo forests, especially the Moso bamboo forest (MBF) and the Lei bamboo forest (LBF), have a strong carbon sequestration capability and play an important role in the global forest carbon cycle. The leaf area index (LAI) is an important structural parameter for simulating
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Bamboo forests, especially the Moso bamboo forest (MBF) and the Lei bamboo forest (LBF), have a strong carbon sequestration capability and play an important role in the global forest carbon cycle. The leaf area index (LAI) is an important structural parameter for simulating the spatiotemporal pattern of the carbon cycle in bamboo forests. However, current LAI products suffer from substantial noise and errors, and data assimilation methods are the most appropriate way to improve the accuracy of LAI data. In this study, two data assimilation methods (the Dual Ensemble Kalman filter (DEnKF) and Particle filter (PF) methods) were applied to improve the quality of MODIS LAI time-series data, which removed noises and smoothed the results using a locally adjusted cubic-spline capping method for the MBF and LBF during 2014–2015. The method with the highest correlation coefficient (r) and lowest root-mean-square error (RMSE) was used to generate highly accurate LAI products of bamboo forests in Zhejiang Province. The results show that the LAI assimilated using two methods saw greatly reduced fluctuations in the MODIS LAI product for both the MBF and the LBF. The LAI assimilated using DEnKF significantly correlated with the observed LAI, with an r value of 0.90 and 0.95, and an RMSE value of 0.42 and 0.42, for the MBF and the LBF, respectively. The PF algorithm achieved a better accuracy than the DEnKF algorithm, with an average increase in r of 8.78% and an average decrease in the RMSE of 33.33%. Therefore, the PF method was applied for LAI assimilation in Zhejiang Province, and the assimilated LAI of bamboo forests achieved a reasonable spatiotemporal pattern in Zhejiang Province. The PF algorithm greatly improves the accuracy of MODIS LAI products and provides a reliable structural parameter for the large-scale simulation of the carbon cycle in bamboo forest ecosystems. Full article
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Open AccessArticle Evaluation of Multiple Downscaled Microwave Soil Moisture Products over the Central Tibetan Plateau
Remote Sens. 2017, 9(5), 402; doi:10.3390/rs9050402
Received: 22 February 2017 / Revised: 3 April 2017 / Accepted: 21 April 2017 / Published: 25 April 2017
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Abstract
Downscaled microwave soil moisture (SM) products with a fine resolution are of great importance for both local and regional studies. However, few studies have explored the merits of multiple downscaled microwave SM products. An evaluation of the different products could help to advance
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Downscaled microwave soil moisture (SM) products with a fine resolution are of great importance for both local and regional studies. However, few studies have explored the merits of multiple downscaled microwave SM products. An evaluation of the different products could help to advance knowledge of the downscaled microwave SM products and help researchers to choose the appropriate downscaled SM products for use in further studies. In this research, five microwave SM products derived from Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), AMSR2, and Soil Moisture and Ocean Salinity (SMOS) data were downscaled via the back-propagation neural network (BPNN). The BPNN was chosen because it can effectively simulate the nonlinear relationship between SM and the land surface temperature (LST)/vegetation index (VI). The different downscaled SM products were evaluated with in-situ SM data from the central Tibetan Plateau Soil Moisture/Temperature Monitoring Network (SMTMN) during the period from 1 August 2010 to 31 December 2012. Compared with the regression technique, the downscaled correlation coefficient (r) is significantly improved by the BPNN. The downscaled root-mean-square error (RMSE) and bias are comparable for the two techniques. As expected, LST and enhanced VI (EVI) are physically related to SM, and this is the most suitable combination for SM downscaling. Except for the ascending node of SMOS and AMSR2, the downscaled r is closely related to the original RMSE, and a lower original RMSE for the SM product results in a higher downscaled r. The BPNN-downscaled SMOS product in descending node is the closest to the in-situ SM among the different downscaled microwave SM products. The temporal variations and ranges of the microwave SM products are well maintained by the BPNN downscaling. Furthermore, the evaluations against in-situ SM reveal that the overall accuracies of the BPNN-downscaled SM products are very close to the original microwave SM products. Full article
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Open AccessArticle Validation of VIIRS AOD through a Comparison with a Sun Photometer and MODIS AODs over Wuhan
Remote Sens. 2017, 9(5), 403; doi:10.3390/rs9050403
Received: 22 February 2017 / Revised: 15 April 2017 / Accepted: 21 April 2017 / Published: 25 April 2017
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Abstract
Visible Infrared Imaging Radiometer Suite (VIIRS) is a next-generation polar-orbiting operational environmental sensor with a capability for global aerosol observations. A comprehensive validation of VIIRS products is significant for improving product quality, assessing environment quality for human life, and studying regional climate change.
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Visible Infrared Imaging Radiometer Suite (VIIRS) is a next-generation polar-orbiting operational environmental sensor with a capability for global aerosol observations. A comprehensive validation of VIIRS products is significant for improving product quality, assessing environment quality for human life, and studying regional climate change. In this study, three-year (from 1 January 2014 to 31 December 2016) records of VIIRS Intermediate Product (IP) data and Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals on aerosol optical depth (AOD) at 550 nm were evaluated by comparing them to ground sun photometer measurements over Wuhan. Results indicated that VIIRS IP retrievals were underestimated by 5% for the city. A comparison of VIIRS IP retrievals and ground sun photometer measurements showed a lower R2 of 0.55 (0.79 for Terra-MODIS and 0.76 for Aqua-MODIS), with only 52% of retrievals falling within the expected error range established by MODIS over land (i.e., ±(0.05 + 0.15AOD)). Bias analyses with different Ångström exponents (AE) demonstrated that land aerosol model selection of the VIIRS retrieval over Wuhan was appropriate. However, the larger standard deviations (i.e., uncertainty) of VIIRS AODs than MODIS AODs could be attributed to the less robust retrieval algorithm. Monthly variations displayed largely underestimated AODs of VIIRS in winter, which could be caused by a large positive bias in surface reflectance estimation due to the sparse vegetation and greater surface brightness of Wuhan in this season. The spatial distribution of VIIRS and MODIS AOD observations revealed that the VIIRS IP AODs over high-pollution areas (AOD > 0.8) with sparse vegetation were underestimated by more than 20% in Wuhan, and 40% in several regions. Analysis of several clear rural areas (AOD < 0.2) with native vegetation indicated an overestimation of about 20% in the northeastern region of the city. These findings showed that the VIIRS IP AOD at 550 nm can provide a solid dataset with a high resolution (750 m) for quantitative scientific investigations and environmental monitoring over Wuhan. However, the performance of dark target algorithms in VIIRS was associated with aerosol types and ground vegetation conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)
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Open AccessArticle An ML-Based Radial Velocity Estimation Algorithm for Moving Targets in Spaceborne High-Resolution and Wide-Swath SAR Systems
Remote Sens. 2017, 9(5), 404; doi:10.3390/rs9050404
Received: 28 February 2017 / Revised: 20 April 2017 / Accepted: 21 April 2017 / Published: 26 April 2017
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Abstract
Multichannel synthetic aperture radar (SAR) is a significant breakthrough to the inherent limitation between high-resolution and wide-swath (HRWS) compared with conventional SAR. Moving target indication (MTI) is an important application of spaceborne HRWS SAR systems. In contrast to previous studies of SAR MTI,
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Multichannel synthetic aperture radar (SAR) is a significant breakthrough to the inherent limitation between high-resolution and wide-swath (HRWS) compared with conventional SAR. Moving target indication (MTI) is an important application of spaceborne HRWS SAR systems. In contrast to previous studies of SAR MTI, the HRWS SAR mainly faces the problem of under-sampled data of each channel, causing single-channel imaging and processing to be infeasible. In this study, the estimation of velocity is equivalent to the estimation of the cone angle according to their relationship. The maximum likelihood (ML) based algorithm is proposed to estimate the radial velocity in the existence of Doppler ambiguities. After that, the signal reconstruction and compensation for the phase offset caused by radial velocity are processed for a moving target. Finally, the traditional imaging algorithm is applied to obtain a focused moving target image. Experiments are conducted to evaluate the accuracy and effectiveness of the estimator under different signal-to-noise ratios (SNR). Furthermore, the performance is analyzed with respect to the motion ship that experiences interference due to different distributions of sea clutter. The results verify that the proposed algorithm is accurate and efficient with low computational complexity. This paper aims at providing a solution to the velocity estimation problem in the future HRWS SAR systems with multiple receive channels. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop
Remote Sens. 2017, 9(5), 405; doi:10.3390/rs9050405
Received: 29 March 2017 / Revised: 13 April 2017 / Accepted: 13 April 2017 / Published: 25 April 2017
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Abstract
Leaf area index (LAI) and chlorophyll content, at leaf and canopy level, are important variables for agricultural applications because of their crucial role in photosynthesis and in plant functioning. The goal of this study was to test the hypothesis that LAI, leaf chlorophyll
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Leaf area index (LAI) and chlorophyll content, at leaf and canopy level, are important variables for agricultural applications because of their crucial role in photosynthesis and in plant functioning. The goal of this study was to test the hypothesis that LAI, leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) of a potato crop can be estimated by vegetation indices for the first time using Sentinel-2 satellite images. In 2016 ten plots of 30 × 30 m were designed in a potato field with different fertilization levels. During the growing season approximately 10 daily radiometric field measurements were used to determine LAI, LCC, and CCC. These radiometric determinations were extensively calibrated against LAI2000 and chlorophyll meter (SPAD, soil plant analysis development) measurements for potato crops grown in the years 2010–2014. Results for Sentinel-2 showed that the weighted difference vegetation index (WDVI) using bands at 10 m spatial resolution can be used for estimating the LAI (R2 of 0.809; root mean square error of prediction (RMSEP) of 0.36). The ratio of the transformed chlorophyll in reflectance index and the optimized soil-adjusted vegetation index (TCARI/OSAVI) showed to be a good linear estimator of LCC at 20 m (R2 of 0.696; RMSEP of 0.062 g·m−2). The performance of the chlorophyll vegetation index (CVI) at 10 m spatial resolution was slightly worse (R2 of 0.656; RMSEP of 0.066 g·m−2) compared to TCARI/OSAVI. Finally, results showed that the green chlorophyll index (CIgreen) was an accurate and linear estimator of CCC at 10 m (R2 of 0.818; RMSEP of 0.29 g·m−2). Results for CIgreen were better than for the red-edge chlorophyll index (CIred-edge, R2 of 0.576, RMSE of 0.43 g·m−2). Our results show that Sentinel-2 bands at 10 m spatial resolution are suitable for estimating LAI, LCC, and CCC, avoiding the need for red-edge bands that are only available at 20 m. This is an important finding for applying Sentinel-2 data in precision agriculture. Full article
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Open AccessArticle The Role of Emissivity in the Detection of Arctic Night Clouds
Remote Sens. 2017, 9(5), 406; doi:10.3390/rs9050406
Received: 13 February 2017 / Revised: 19 April 2017 / Accepted: 22 April 2017 / Published: 26 April 2017
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Abstract
Detection of clouds over polar areas from satellite radiometric measurements in the visible and IR atmospheric window region is rather difficult because of the high albedo of snow, possible ice covered surfaces, very low humidity, and the usual presence of atmospheric temperature inversion.
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Detection of clouds over polar areas from satellite radiometric measurements in the visible and IR atmospheric window region is rather difficult because of the high albedo of snow, possible ice covered surfaces, very low humidity, and the usual presence of atmospheric temperature inversion. Cold and highly reflective polar surfaces provide little thermal and visible contrast between clouds and the background surface. Moreover, due to the presence of temperature inversion, clouds are not always identifiable as being colder than the background. In addition, low humidity often causes polar clouds to be optically thin. Finally, polar clouds are usually composed of a mixture of ice and water, which leads to an unclear spectral signature. Single and bi-spectral threshold methods are sometimes inappropriate due to a large variability of surface emissivity and cloud conditions. The objective of this study is to demonstrate the crucial role played by surface emissivity in the detection of polar winter clouds and the potential improvement offered by infrared hyperspectral observations, such as from the Infrared Atmospheric Sounding Interferometer (IASI). In this paper a new approach for cloud detection is proposed and validated exploiting active measurements from satellite sensors, i.e., the CloudSat cloud profiling radar (CPR) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For a homogenous IASI field of view (FOVs), the proposed cloud detection scheme tallies with the combined CPR and CALIOP product in classifying 98.11% of the FOVs as cloudy and also classifies 97.54% of the FOVs as clear. The Hansen Kuipers discriminant reaches 0.95. Full article
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Open AccessArticle Spring and Autumn Phenological Variability across Environmental Gradients of Great Smoky Mountains National Park, USA
Remote Sens. 2017, 9(5), 407; doi:10.3390/rs9050407
Received: 27 February 2017 / Revised: 19 April 2017 / Accepted: 21 April 2017 / Published: 26 April 2017
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Abstract
Mountainous regions experience complex phenological behavior along climatic, vegetational and topographic gradients. In this paper, we use a MODIS time series of the Normalized Difference Vegetation Index (NDVI) to understand the causes of variations in spring and autumn timing from 2000 to 2015,
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Mountainous regions experience complex phenological behavior along climatic, vegetational and topographic gradients. In this paper, we use a MODIS time series of the Normalized Difference Vegetation Index (NDVI) to understand the causes of variations in spring and autumn timing from 2000 to 2015, for a landscape renowned for its biological diversity. By filtering for cover type, topography and disturbance history, we achieved an improved understanding of the effects of seasonal weather variation on land surface phenology (LSP). Elevational effects were greatest in spring and were more important than site moisture effects. The spring and autumn NDVI of deciduous forests were found to increase in response to antecedent warm temperatures, with evidence of possible cross-seasonal lag effects, including possible accelerated green-up after cold Januarys and early brown-down following warm springs. Areas that were disturbed by the hemlock woolly adelgid and a severe tornado showed a weaker sensitivity to cross-year temperature and precipitation variation, while low severity wildland fire had no discernable effect. Use of ancillary datasets to filter for disturbance and vegetation type improves our understanding of vegetation’s phenological responsiveness to climate dynamics across complex environmental gradients. Full article
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Open AccessArticle Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network
Remote Sens. 2017, 9(5), 408; doi:10.3390/rs9050408
Received: 27 January 2017 / Revised: 17 April 2017 / Accepted: 21 April 2017 / Published: 26 April 2017
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Abstract
In this study, a convolutional neural network (CNN) is used to estimate sea ice concentration using synthetic aperture radar (SAR) scenes acquired during freeze-up in the Gulf of St. Lawrence on the east coast of Canada. The ice concentration estimates from the CNN
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In this study, a convolutional neural network (CNN) is used to estimate sea ice concentration using synthetic aperture radar (SAR) scenes acquired during freeze-up in the Gulf of St. Lawrence on the east coast of Canada. The ice concentration estimates from the CNN are compared to those from a neural network (multi-layer perceptron or MLP) that uses hand-crafted features as input and a single layer of hidden nodes. The CNN is found to be less sensitive to pixel level details than the MLP and produces ice concentration that is less noisy and in closer agreement with that from image analysis charts. This is due to the multi-layer (deep) structure of the CNN, which enables abstract image features to be learned. The CNN ice concentration is also compared with ice concentration estimated from passive microwave brightness temperature data using the ARTIST sea ice (ASI) algorithm. The bias and RMS of the difference between the ice concentration from the CNN and that from image analysis charts is reduced as compared to that from either the MLP or ASI algorithm. Additional results demonstrate the impact of varying the input patch size, varying the number of CNN layers, and including the incidence angle as an additional input. Full article
(This article belongs to the collection Learning to Understand Remote Sensing Images)
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Open AccessArticle Spatiotemporal Variability of Lake Water Quality in the Context of Remote Sensing Models
Remote Sens. 2017, 9(5), 409; doi:10.3390/rs9050409
Received: 25 February 2017 / Revised: 7 April 2017 / Accepted: 21 April 2017 / Published: 26 April 2017
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Abstract
This study demonstrates a number of methods for using field sampling and observed lake characteristics and patterns to improve techniques for development of algae remote sensing models and applications. As satellite and airborne sensors improve and their data are more readily available, applications
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This study demonstrates a number of methods for using field sampling and observed lake characteristics and patterns to improve techniques for development of algae remote sensing models and applications. As satellite and airborne sensors improve and their data are more readily available, applications of models to estimate water quality via remote sensing are becoming more practical for local water quality monitoring, particularly of surface algal conditions. Despite the increasing number of applications, there are significant concerns associated with remote sensing model development and application, several of which are addressed in this study. These concerns include: (1) selecting sensors which are suitable for the spatial and temporal variability in the water body; (2) determining appropriate uses of near-coincident data in empirical model calibration; and (3) recognizing potential limitations of remote sensing measurements which are biased toward surface and near-surface conditions. We address these issues in three lakes in the Great Salt Lake surface water system (namely the Great Salt Lake, Farmington Bay, and Utah Lake) through sampling at scales that are representative of commonly used sensors, repeated sampling, and sampling at both near-surface depths and throughout the water column. The variability across distances representative of the spatial resolutions of Landsat, SENTINEL-2 and MODIS sensors suggests that these sensors are appropriate for this lake system. We also use observed temporal variability in the system to evaluate sensors. These relationships proved to be complex, and observed temporal variability indicates the revisit time of Landsat may be problematic for detecting short events in some lakes, while it may be sufficient for other areas of the system with lower short-term variability. Temporal variability patterns in these lakes are also used to assess near-coincident data in empirical model development. Finally, relationships between the surface and water column conditions illustrate potential issues with near-surface remote sensing, particularly when there are events that cause mixing in the water column. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessArticle Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China
Remote Sens. 2017, 9(5), 410; doi:10.3390/rs9050410
Received: 4 February 2017 / Revised: 7 April 2017 / Accepted: 19 April 2017 / Published: 27 April 2017
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Abstract
Air temperature (Tair) near the ground surface is a fundamental descriptor of terrestrial environment conditions and one of the most widely used climatic variables in global change studies. The main objective of this study was to explore the possibility of retrieving
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Air temperature (Tair) near the ground surface is a fundamental descriptor of terrestrial environment conditions and one of the most widely used climatic variables in global change studies. The main objective of this study was to explore the possibility of retrieving high-resolution Tair from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products, covering complex terrain in Northeast China. The All Subsets Regression (ASR) method was adopted to select the predictors and build optimal multiple linear regression models for estimating maximum (Tmax), minimum (Tmin), and mean (Tmean) air temperatures. The relative importance of predictors in these models was evaluated via the Standardized Regression Coefficients (SRCs) method. The results indicated that the optimal models could estimate the Tmax, Tmin, and Tmean with relatively high accuracies (Model Efficiency ≥ 0.90). Both LST and day length (DL) predictors were important in estimating Tmax (SRCs: daytime LST = 0.53, DL = 0.35), Tmin (SRCs: nighttime LST = 0.74, DL = 0.23), and Tmean (SRCs: nighttime LST = 0.72, DL = 0.28). Models predicting Tmin and Tmean had better performance than the one predicting Tmax. Nighttime LST was better at predicting Tmin and Tmean than daytime LST data at predicting Tmax. Land covers had noticeable influences on estimating Tair, and even seasonal vegetation greening could result in temporal variations of model performance. Air temperature could be accurately estimated using remote sensing, but the model performance was varied across different spatial and temporal scales. More predictors should be incorporated for the purpose of improving the estimation of near surface Tair from the MODIS LST production. Full article
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Open AccessArticle Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach
Remote Sens. 2017, 9(5), 411; doi:10.3390/rs9050411
Received: 8 April 2017 / Revised: 8 April 2017 / Accepted: 22 April 2017 / Published: 30 April 2017
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Abstract
Hemispherical (fisheye) photography is a well-established approach for estimating the sky view factor (SVF). High-resolution urban models from LiDAR and oblique airborne photogrammetry can provide continuous SVF estimates over a large urban area, but such data are not always available and are difficult
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Hemispherical (fisheye) photography is a well-established approach for estimating the sky view factor (SVF). High-resolution urban models from LiDAR and oblique airborne photogrammetry can provide continuous SVF estimates over a large urban area, but such data are not always available and are difficult to acquire. Street view panoramas have become widely available in urban areas worldwide: Google Street View (GSV) maintains a global network of panoramas excluding China and several other countries; Baidu Street View (BSV) and Tencent Street View (TSV) focus their panorama acquisition efforts within China, and have covered hundreds of cities therein. In this paper, we approach this issue from a big data perspective by presenting and validating a method for automatic estimation of SVF from massive amounts of street view photographs. Comparisons were made with SVF estimates derived from two independent sources: a LiDAR-based Digital Surface Model (DSM) and an oblique airborne photogrammetry-based 3D city model (OAP3D), resulting in a correlation coefficient of 0.863 and 0.987, respectively. The comparisons demonstrated the capacity of the proposed method to provide reliable SVF estimates. Additionally, we present an application of the proposed method with about 12,000 GSV panoramas to characterize the spatial distribution of SVF over Manhattan Island in New York City. Although this is a proof-of-concept study, it has shown the potential of the proposed approach to assist urban climate and urban planning research. However, further development is needed before this approach can be finally delivered to the urban climate and urban planning communities for practical applications. Full article
(This article belongs to the Special Issue Remote Sensing for 3D Urban Morphology)
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Open AccessArticle EO-1 Data Quality and Sensor Stability with Changing Orbital Precession at the End of a 16 Year Mission
Remote Sens. 2017, 9(5), 412; doi:10.3390/rs9050412
Received: 30 January 2017 / Revised: 6 April 2017 / Accepted: 19 April 2017 / Published: 27 April 2017
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Abstract
The Earth Observing One (EO-1) satellite has completed 16 years of Earth observations in early 2017. What started as a technology mission to test various new advancements turned into a science and application mission that extended many years beyond the satellite’s planned life
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The Earth Observing One (EO-1) satellite has completed 16 years of Earth observations in early 2017. What started as a technology mission to test various new advancements turned into a science and application mission that extended many years beyond the satellite’s planned life expectancy. EO-1’s primary instruments are spectral imagers: Hyperion, the only civilian full spectrum spectrometer (430–2400 nm) in orbit, and the Advanced Land Imager (ALI), the prototype for Landsat-8’s pushbroom imaging technology. Both Hyperion and ALI instruments have continued to perform well, but in February 2011, the satellite ran out of the fuel necessary to maintain orbit, which initiated a change in precession rate that led to increasingly earlier equatorial crossing times during its last five years. The change from EO-1’s original orbit, when it was formation flying with Landsat-7 at a 10:01 a.m. equatorial overpass time, to earlier overpass times results in image acquisitions with increasing solar zenith angles (SZAs). This study takes several approaches to characterize data quality as SZAs increased. The results show that for both EO-1 sensors, atmospherically corrected reflectance products, are within 5 to 10% of mean pre-drift products. No marked trend in decreasing quality in ALI or Hyperion is apparent through 2016, and these data remain a high quality resource through the end of the mission. Full article
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Open AccessArticle Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites
Remote Sens. 2017, 9(5), 413; doi:10.3390/rs9050413
Received: 16 January 2017 / Revised: 5 April 2017 / Accepted: 21 April 2017 / Published: 27 April 2017
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Abstract
Photogrammetric point clouds (PPCs) provide a source of three-dimensional (3-D) remote sensing data that is well-suited to use over small areas that are within the scope of observation by unmanned aerial vehicles (UAVs). We compared PPC-based structural metrics to traditional ground surveys conducted
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Photogrammetric point clouds (PPCs) provide a source of three-dimensional (3-D) remote sensing data that is well-suited to use over small areas that are within the scope of observation by unmanned aerial vehicles (UAVs). We compared PPC-based structural metrics to traditional ground surveys conducted by field personnel in order to assess the capacity of PPC data to contribute to vegetation-reclamation surveys. We found good statistical agreement between key structural vegetation parameters, such as mean and maximum vegetation height, with PPC metrics successfully predicting most height and tree-diameter metrics using multivariate linear regression. However, PPC metrics were not as useful for estimating ground-measured vegetation cover. We believe that part of the issue lies in the mismatch between PPC- and ground-based measurement approaches, including subjective judgement on behalf of ground crews: a topic that requires more investigation. Our work highlights the emerging value of UAV-based PPCs to complement, and in some cases supplement, traditional ground-based sources of measured vegetation structure. Full article
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Open AccessArticle A Low Cost UWB Based Solution for Direct Georeferencing UAV Photogrammetry
Remote Sens. 2017, 9(5), 414; doi:10.3390/rs9050414
Received: 1 January 2017 / Revised: 20 April 2017 / Accepted: 21 April 2017 / Published: 27 April 2017
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Abstract
Thanks to their flexibility and availability at reduced costs, Unmanned Aerial Vehicles (UAVs) have been recently used on a wide range of applications and conditions. Among these, they can play an important role in monitoring critical events (e.g., disaster monitoring) when the presence
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Thanks to their flexibility and availability at reduced costs, Unmanned Aerial Vehicles (UAVs) have been recently used on a wide range of applications and conditions. Among these, they can play an important role in monitoring critical events (e.g., disaster monitoring) when the presence of humans close to the scene shall be avoided for safety reasons, in precision farming and surveying. Despite the very large number of possible applications, their usage is mainly limited by the availability of the Global Navigation Satellite System (GNSS) in the considered environment: indeed, GNSS is of fundamental importance in order to reduce positioning error derived by the drift of (low-cost) Micro-Electro-Mechanical Systems (MEMS) internal sensors. In order to make the usage of UAVs possible even in critical environments (when GNSS is not available or not reliable, e.g., close to mountains or in city centers, close to high buildings), this paper considers the use of a low cost Ultra Wide-Band (UWB) system as the positioning method. Furthermore, assuming the use of a calibrated camera, UWB positioning is exploited to achieve metric reconstruction on a local coordinate system. Once the georeferenced position of at least three points (e.g., positions of three UWB devices) is known, then georeferencing can be obtained, as well. The proposed approach is validated on a specific case study, the reconstruction of the façade of a university building. Average error on 90 check points distributed over the building façade, obtained by georeferencing by means of the georeferenced positions of four UWB devices at fixed positions, is 0.29 m. For comparison, the average error obtained by using four ground control points is 0.18 m. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Multiangular Observation of Canopy Sun-Induced Chlorophyll Fluorescence by Combining Imaging Spectroscopy and Stereoscopy
Remote Sens. 2017, 9(5), 415; doi:10.3390/rs9050415
Received: 16 February 2017 / Revised: 19 April 2017 / Accepted: 23 April 2017 / Published: 28 April 2017
Cited by 1 | PDF Full-text (28105 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The effect that the canopy structure and the viewing geometry have on the intensity and the spatial distribution of passively measured sun-induced chlorophyll fluorescence at canopy scale is still not well understood. These uncertainties constrain the potential use of fluorescence to quantify photosynthesis
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The effect that the canopy structure and the viewing geometry have on the intensity and the spatial distribution of passively measured sun-induced chlorophyll fluorescence at canopy scale is still not well understood. These uncertainties constrain the potential use of fluorescence to quantify photosynthesis at this level. Using a novel technique, we evaluated the diurnal changes in the spatial distribution of sun-induced fluorescence at 760 nm (F760) within the canopy as a consequence of the spatial disposition of the leaves and the viewing angle of the sensor. High resolution spectral and stereo images of a full sugar beet canopy were recorded simultaneously in the field to estimate maps of F760 and the surface angle distribution, respectively. A dedicated algorithm was used to align both maps in the post-processing and its accuracy was evaluated using a sensitivity test. The relative angle between sun and the leaf surfaces primarily determined the amount of incident Photosynthetic Active Radiation (PAR), which in turn was reflected in different values of F760, with the highest values occurring in leaf surfaces that are perpendicularly oriented to the sun. The viewing angle of the sensor also had an impact in the intensity of the recorded F760. Higher viewing angles generally resulted in higher values of F760. We attribute these changes to a direct effect of the vegetation directional reflectance response on fluorescence retrieval. Consequently, at leaf surface level, the spatio-temporal variations of F760 were mainly explained by the sun–leaf–sensor geometry rather than directionality of the fluorescence emission. At canopy scale, the diurnal patterns of F760 observed on the top-of-canopy were attributed to the complex interplay between the light penetration into the canopy as a function of the display of the various leaves and the fluorescence emission of each leaf which is modulated by the exposure of the individual leaf patch to the incoming light and the functional status of photosynthesis. We expect that forward modeling can help derive analytical simplified skeleton assumptions to scale canopy measurements to the leaf functional properties. Full article
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Open AccessArticle The Dynamic Analysis between Urban Nighttime Economy and Urbanization Using the DMSP/OLS Nighttime Light Data in China from 1992 to 2012
Remote Sens. 2017, 9(5), 416; doi:10.3390/rs9050416
Received: 27 March 2017 / Revised: 17 April 2017 / Accepted: 22 April 2017 / Published: 28 April 2017
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Abstract
Along with rapid urbanization, nighttime activities from places, such as restaurants, pubs and bars, and theatres, have created enormous economic and social benefits. The nighttime economy (NTE), as a newly developed social phenomenon, has been used to describe economic activities at night. However,
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Along with rapid urbanization, nighttime activities from places, such as restaurants, pubs and bars, and theatres, have created enormous economic and social benefits. The nighttime economy (NTE), as a newly developed social phenomenon, has been used to describe economic activities at night. However, few studies have investigated urban nighttime economy and its relation to urbanization from nighttime light (NTL) data perspective. To fill this gap, this study proposed a nighttime light economy index (NLEI). The correlation analysis was performed between the NLEI and economic indicators at both the city and provincial levels in China from 1992 to 2012 using the DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) time series data. Results revealed that correlations between the NLEI and all kinds of economic indicators were statistically significant. It was observed that both the urbanization and nighttime economy levels increased greatly from 1992 to 2012 in China. Cities and provinces in east China displayed relatively higher annual growth rates of NLEI compared to those in southwest and northwest China. Based on the quadrant map of urbanization and nighttime economy levels, most of the provincial capitals and provinces in east China were in the advanced coordination pattern while those in west China in the low-level coordination pattern. Full article
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Open AccessArticle Mapping Reflectance Anisotropy of a Potato Canopy Using Aerial Images Acquired with an Unmanned Aerial Vehicle
Remote Sens. 2017, 9(5), 417; doi:10.3390/rs9050417
Received: 10 March 2017 / Revised: 12 April 2017 / Accepted: 22 April 2017 / Published: 29 April 2017
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Abstract
Viewing and illumination geometry has a strong influence on optical measurements of natural surfaces due to their anisotropic reflectance properties. Typically, cameras on-board unmanned aerial vehicles (UAVs) are affected by this because of their relatively large field of view (FOV) and thus large
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Viewing and illumination geometry has a strong influence on optical measurements of natural surfaces due to their anisotropic reflectance properties. Typically, cameras on-board unmanned aerial vehicles (UAVs) are affected by this because of their relatively large field of view (FOV) and thus large range of viewing angles. In this study, we investigated the magnitude of reflectance anisotropy effects in the 500–900 nm range, captured by a frame camera mounted on a UAV during a standard mapping flight. After orthorectification and georeferencing of the images collected by the camera, we calculated the viewing geometry of all observations of each georeferenced ground pixel, forming a dataset with multi-angular observations. We performed UAV flights on two days during the summer of 2016 over an experimental potato field where different zones in the field received different nitrogen fertilization treatments. These fertilization levels caused variation in potato plant growth and thereby differences in structural properties such as leaf area index (LAI) and canopy cover. We fitted the Rahman–Pinty–Verstraete (RPV) model through the multi-angular observations of each ground pixel to quantify, interpret, and visualize the anisotropy patterns in our study area. The Θ parameter of the RPV model, which controls the proportion of forward and backward scattering, showed strong correlation with canopy cover, where in general an increase in canopy cover resulted in a reduction of backward scattering intensity, indicating that reflectance anisotropy contains information on canopy structure. In this paper, we demonstrated that anisotropy data can be extracted from measurements using a frame camera, collected during a typical UAV mapping flight. Future research will focus on how to use the anisotropy signal as a source of information for estimation of physical vegetation properties. Full article
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Open AccessArticle Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data
Remote Sens. 2017, 9(5), 418; doi:10.3390/rs9050418
Received: 22 March 2017 / Revised: 17 April 2017 / Accepted: 27 April 2017 / Published: 29 April 2017
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Abstract
The latest MODIS GPP (gross primary productivity) product, MOD17A2H, has great advantages over the previous version, MOD17A2, because the resolution increased from 1000 m to 500 m. In this study, MOD17A2H GPP was assessed using the latest eddy covariance (EC) flux data (FLUXNET2015
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The latest MODIS GPP (gross primary productivity) product, MOD17A2H, has great advantages over the previous version, MOD17A2, because the resolution increased from 1000 m to 500 m. In this study, MOD17A2H GPP was assessed using the latest eddy covariance (EC) flux data (FLUXNET2015 Dataset) at eighteen sites in six ecosystems across the globe. The sensitivity of MOD17A2H GPP to the meteorology dataset and the fractional photosynthetically- active radiation (FPAR) product was explored by introducing site meteorology observations and improved Global Land Surface Satellite (GLASS) Leaf Area Index (LAI) products. The results showed that MOD17A2H GPP underestimated flux-derived GPP at most sites. Its performance in estimating annual GPP was poor (R2 = 0.62) and even worse over eight days (R2 = 0.52). For the MOD17A2H algorithm, replacing the reanalysis meteorological datasets with the site meteorological measurements failed to improve the estimation accuracies. However, great improvements in estimating the site-based GPP were gained by replacing MODIS FPAR with GLASS FPAR. This indicated that in the existing MOD17A2H product, the errors were originated more from FPAR than the meteorological data. We further examined the potential error contributions from land cover classification and maximum light use efficiency (εmax). It was found that the current land cover classification scheme exhibited frequent misclassification errors. Moreover, the εmax value assigned in MOD17A2H was much smaller than the inferred εmax value. Therefore, the qualities of FPAR and land cover classification datasets should be upgraded, and the εmax value needs to be adjusted to provide more accurate GPP estimates using MOD17A2H for global ecosystems. Full article
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Open AccessArticle Scrubbing Up: Multi-Scale Investigation of Woody Encroachment in a Southern African Savannah
Remote Sens. 2017, 9(5), 419; doi:10.3390/rs9050419
Received: 2 February 2017 / Revised: 14 April 2017 / Accepted: 22 April 2017 / Published: 29 April 2017
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Abstract
Changes in the extent of woody vegetation represent a major conservation question in many savannah systems around the globe. To address the problem of the current lack of broad-scale cost-effective tools for land cover monitoring in complex savannah environments, we use a multi-scale
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Changes in the extent of woody vegetation represent a major conservation question in many savannah systems around the globe. To address the problem of the current lack of broad-scale cost-effective tools for land cover monitoring in complex savannah environments, we use a multi-scale approach to quantifying vegetation change in Kruger National Park (KNP), South Africa. We test whether medium spatial resolution satellite data (Landsat, existing back to the 1970s), which have pixel sizes larger than typical vegetation patches, can nevertheless capture the thematic detail required to detect woody encroachment in savannahs. We quantify vegetation change over a 13-year period in KNP, examine the changes that have occurred, assess the drivers of these changes, and compare appropriate remote sensing data sources for monitoring change. We generate land cover maps for three areas of southern KNP using very high resolution (VHR) and medium resolution satellite sensor imagery from February 2001 to 2014. Considerable land cover change has occurred, with large increases in shrubs replacing both trees and grassland. Examination of exclosure areas and potential environmental driver data suggests two mechanisms: elephant herbivory removing trees and at least one separate mechanism responsible for conversion of grassland to shrubs, theorised to be increasing atmospheric CO2. Thus, the combination of these mechanisms causes the novel two-directional shrub encroachment that we observe (tree loss and grassland conversion). Multi-scale comparison of classifications indicates that although spatial detail is lost when using medium resolution rather than VHR imagery for land cover classification (e.g., Landsat imagery cannot readily distinguish between tree and shrub classes, while VHR imagery can), the thematic detail contained within both VHR and medium resolution classifications is remarkably congruent. This suggests that medium resolution imagery contains sufficient thematic information for most broad-scale land cover monitoring requirements in heterogeneous savannahs, while having the benefits of being cost-free and providing a longer historical archive of data than VHR sources. We conclude that monitoring of broad-scale land cover change using remote sensing has considerable potential as a cost-effective tool for both better informing land management practitioners, and for monitoring the future landscape-scale impacts of management policies in savannahs. Full article
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Open AccessArticle An Optical Classification Tool for Global Lake Waters
Remote Sens. 2017, 9(5), 420; doi:10.3390/rs9050420
Received: 28 February 2017 / Revised: 11 April 2017 / Accepted: 23 April 2017 / Published: 29 April 2017
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Abstract
Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global
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Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral remote sensing reflectance spectra collected by partners and advisors of the European Union 7th Framework Programme (FP7) Global Lakes Sentinel Services (GLaSS) project. The method has been integrated in the Envisat-BEAM software and the Sentinel Application Platform (SNAP) and generates maps of water types from image data. Two variations of water type classification are provided: one based on area-normalized spectral reflectance focusing on spectral shape (6CN, six-class normalized) and one that retains magnitude with no modification to the reflectance signal (6C). This resulted in a protocol, or processing scheme, that can also be applied or adapted for Sentinel-3 Ocean and Land Colour Imager (OLCI) datasets. We apply both treatments to MERIS imagery of a variety of European lakes to demonstrate its applicability. The studied target lakes cover a range of biophysical types, from shallow turbid to deep and clear, as well as eutrophic and dark absorbing waters, rich in colored dissolved organic matter (CDOM). In shallow, high-reflecting Dutch and Estonian lakes with high sediment load, 6C performed better, while in deep, low-reflecting clear Italian and Swedish lakes, 6CN performed better. The 6CN classification of in situ data is promising for very dark, high CDOM, absorbing lakes, but we show that our atmospheric correction of the imagery was insufficient to corroborate this. We anticipate that the application of the protocol to other lakes with unknown in-water characterization, but with comparable biophysical properties will suggest similar atmospheric correction (AC) and in-water retrieval algorithms for global lakes. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessArticle Terrestrial Hyperspectral Image Shadow Restoration through Lidar Fusion
Remote Sens. 2017, 9(5), 421; doi:10.3390/rs9050421
Received: 2 March 2017 / Revised: 20 April 2017 / Accepted: 27 April 2017 / Published: 29 April 2017
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Abstract
Acquisition of hyperspectral imagery (HSI) from cameras mounted on terrestrial platforms is a relatively recent development that enables spectral analysis of dominantly vertical structures. Although solar shadowing is prevalent in terrestrial HSI due to the vertical scene geometry, automated shadow detection and restoration
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Acquisition of hyperspectral imagery (HSI) from cameras mounted on terrestrial platforms is a relatively recent development that enables spectral analysis of dominantly vertical structures. Although solar shadowing is prevalent in terrestrial HSI due to the vertical scene geometry, automated shadow detection and restoration algorithms have not yet been applied to this capture modality. We investigate the fusion of terrestrial laser scanning (TLS) spatial information with terrestrial HSI for geometric shadow detection on a rough vertical surface and examine the contribution of radiometrically calibrated TLS intensity, which is resistant to the influence of solar shadowing, to HSI shadow restoration. Qualitative assessment of the shadow detection results indicates pixel level accuracy, which is indirectly validated by shadow restoration improvements when sub-pixel shadow detection is used in lieu of single pixel detection. The inclusion of TLS intensity in existing shadow restoration algorithms that use regions of matching material in sun and shade exposures was found to have a marginal positive influence on restoring shadow spectrum shape, while a proposed combination of TLS intensity with passive HSI spectra boosts restored shadow spectrum magnitude precision by 40% and band correlation with respect to a truth image by 45% compared to existing restoration methods. Full article
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Open AccessArticle Sea Surface Currents Estimated from Spaceborne Infrared Images Validated against Reanalysis Data and Drifters in the Mediterranean Sea
Remote Sens. 2017, 9(5), 422; doi:10.3390/rs9050422
Received: 14 March 2017 / Revised: 19 April 2017 / Accepted: 27 April 2017 / Published: 29 April 2017
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Abstract
Near-real time sea surface current information is needed for ocean operations. On a global scale, only satellites can provide such measurements. This can be done with data from infrared radiometers, available on several satellites, thus giving several images a day. This work analyses
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Near-real time sea surface current information is needed for ocean operations. On a global scale, only satellites can provide such measurements. This can be done with data from infrared radiometers, available on several satellites, thus giving several images a day. This work analyses the accuracy of such an estimation of surface current fields retrieved with the maximum cross correlation (MCC) method, here used to track patterns of Advanced Very High Resolution Radiometer (AVHRR) brightness temperature between 224 pairs of consecutive images taken between January and December 2015 in the western Mediterranean Sea. Comparison with in-situ drifters shows that relatively small patterns, moving at a slow speed, tracked between images separated by less than four hours give the best agreement. The agreement was strongest in summer, and consistent with low wind, non-eddying situations. When compared to a daily reanalysis field, the averaged satellite-retrieved fields showed good agreement, but not the in-situ drifter data. Drifter data should hence be used to complement satellite-retrieved currents rather than to validate them, since they may measure different components of the surface currents. Full article
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Open AccessArticle 3D Digitisation of Large-Scale Unstructured Great Wall Heritage Sites by a Small Unmanned Helicopter
Remote Sens. 2017, 9(5), 423; doi:10.3390/rs9050423
Received: 14 March 2017 / Revised: 24 April 2017 / Accepted: 27 April 2017 / Published: 29 April 2017
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Abstract
The ancient Great Wall of China has long suffered from damage due to natural factors and human activities. A small low-cost unmanned helicopter system with a laser scanner and a digital camera is developed to efficiently visualize the status of the huge Great
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The ancient Great Wall of China has long suffered from damage due to natural factors and human activities. A small low-cost unmanned helicopter system with a laser scanner and a digital camera is developed to efficiently visualize the status of the huge Great Wall area. The goal of the system is to achieve 3D digitisation of the large-scale Great Wall using a combination of fly-hover-scan and flying-scan modes. However, pose uncertainties of the unmanned helicopter could cause mismatching among point clouds acquired by each hovering-scan. This problem would become more severe as the target area becomes larger and more unstructured. Therefore, a hierarchical optimization framework is proposed in this paper to achieve 3D digitisation of the large-scale unstructured Great Wall with unpredictable pose uncertainties of the unmanned helicopter. In this framework, different optimization methodologies are proposed for the fly-hover-scan and flying-scan modes, respectively, because different scan modes would result in different features of point clouds. Moreover, a user-friendly interface based on WebGL has been developed for 3D model visualization and comparison. Experimental results demonstrate the feasibility of the proposed framework for 3D digitisation of the Great Wall segments. Full article
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Open AccessArticle Estimating FAPAR of Rice Growth Period Using Radiation Transfer Model Coupled with the WOFOST Model for Analyzing Heavy Metal Stress
Remote Sens. 2017, 9(5), 424; doi:10.3390/rs9050424
Received: 10 March 2017 / Revised: 21 April 2017 / Accepted: 27 April 2017 / Published: 29 April 2017
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Abstract
Timely assessment of crop growth conditions under heavy metal pollution is of great significance for agricultural decision-making and estimation of crop productivity. The object of this study is to assess the effects of heavy metal stress on physiological functions of rice through the
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Timely assessment of crop growth conditions under heavy metal pollution is of great significance for agricultural decision-making and estimation of crop productivity. The object of this study is to assess the effects of heavy metal stress on physiological functions of rice through the spatial-temporal analysis of the fraction of absorbed photosynthetically active radiation (FAPAR). The calculation of daily FAPAR is conducted based on a coupled model consisting of the leaf-canopy radiative transfer model and World Food Study Model (WOFOST). These two models are connected by leaf area index (LAI) and a fraction of diffused incoming solar radiation (SKYL) in the rice growth period. The input parameters of the coupled model are obtained from measured data and GF-1 images. Meanwhile, in order to improve accuracy of FAPAR, the crop growth model is optimized by data assimilation. The validation result shows that the correlation between the simulated FAPAR and the measured data is strong in the rice growth period, with the correlation coefficients being above 7.5 for two areas. The discrepancy of FAPAR between two areas of different stress levels is visualized by spatial-temporal analysis. FAPAR discrepancy starts to appear in the jointing-booting period and experiences a gradual rise, reaching its maximum in the heading-flowering stage. This study suggests that the coupled model, consisting of the leaf-canopy radiative transfer model and the WOFOST model, is able to accurately simulate daily FAPAR during crop growth period and FAPAR can be used as a potential indicator to reflect the impact of heavy metal stress on crop growth. Full article
(This article belongs to the Special Issue Ecophysiological Remote Sensing)
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Open AccessArticle Observational Quantification of Climatic and Human Influences on Vegetation Greening in China
Remote Sens. 2017, 9(5), 425; doi:10.3390/rs9050425
Received: 21 March 2017 / Revised: 23 April 2017 / Accepted: 27 April 2017 / Published: 30 April 2017
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Abstract
This study attempts to quantify the relative contributions of vegetation greening in China due to climatic and human influences from multiple observational datasets. Satellite measured vegetation greenness, Normalized Difference Vegetation Index (NDVI), and relevant climate, land cover, and socioeconomic data since 1982 are
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This study attempts to quantify the relative contributions of vegetation greening in China due to climatic and human influences from multiple observational datasets. Satellite measured vegetation greenness, Normalized Difference Vegetation Index (NDVI), and relevant climate, land cover, and socioeconomic data since 1982 are analyzed using a multiple linear regression (MLR) method. A statistically significant positive trend of average growing-season (April–October) NDVI is found over more than 34% of the vegetated areas, mainly in North China, while significant decreases in NDVI are only seen in less than 5% of the areas. The relationships between vegetation and climate (temperature, precipitation, and radiation) vary by geographical location and vegetation type. We estimate the NDVI changes in association with the non-climatic effects by removing the climatic effects from the original NDVI time series using the MLR analysis. Our results indicate that land use change is the dominant factor driving the long-term changes in vegetation greenness. The significant greening in North China is due to the increase in crops, grasslands, and forests. The socioeconomic datasets provide consistent and supportive results for the non-climatic effects at the provincial level that afforestation and reduced fire events generally have a major contribution. This study provides a basis for quantifying the non-climatic effects due to possible human influences on the vegetation greening in China. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle Shallow Water Measurements Using a Single Green Laser Corrected by Building a Near Water Surface Penetration Model
Remote Sens. 2017, 9(5), 426; doi:10.3390/rs9050426
Received: 25 February 2017 / Revised: 12 April 2017 / Accepted: 27 April 2017 / Published: 30 April 2017
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Abstract
To reduce the size and cost of an integrated infrared (IR) and green airborne LiDAR bathymetry (ALB) system, and improve the accuracy of the green ALB system, this study proposes a method to accurately determine water surface and water bottom heights using a
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To reduce the size and cost of an integrated infrared (IR) and green airborne LiDAR bathymetry (ALB) system, and improve the accuracy of the green ALB system, this study proposes a method to accurately determine water surface and water bottom heights using a single green laser corrected by the near water surface penetration (NWSP) model. The factors that influence the NWSP of green laser are likewise analyzed. In addition, an NWSP modeling method is proposed to determine the relationship between NWSP and the suspended sediment concentration (SSC) of the surface layer, scanning angle of a laser beam and sensor height. The water surface and water bottom height models are deduced by considering NWSP and using only green laser based on the measurement principle of the IR laser and green laser, as well as employing the relationship between NWSP and the time delay of the surface return of the green laser. Lastly, these methods and models are applied to a practical ALB measurement. Standard deviations of 3.0, 5.3, and 1.3 cm are obtained by the NWSP, water-surface height, and water-bottom height models, respectively. Several beneficial conclusions and recommendations are drawn through the experiments and discussions. Full article
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Open AccessArticle A Novel Classification Technique of Landsat-8 OLI Image-Based Data Visualization: The Application of Andrews’ Plots and Fuzzy Evidential Reasoning
Remote Sens. 2017, 9(5), 427; doi:10.3390/rs9050427
Received: 21 February 2017 / Revised: 20 April 2017 / Accepted: 28 April 2017 / Published: 30 April 2017
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Abstract
Andrews first proposed an equation to visualize the structures within data in 1972. Since then, this equation has been used for data transformation and visualization in a wide variety of fields. However, it has yet to be applied to satellite image data. The
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Andrews first proposed an equation to visualize the structures within data in 1972. Since then, this equation has been used for data transformation and visualization in a wide variety of fields. However, it has yet to be applied to satellite image data. The effect of unwanted, or impure, pixels occurring in these data varies with their distribution in the image; the effect is greater if impurity pixels are included in a classifier’s training set. Andrews’ curves enable the interpreter to select outlier or impurity data that can be grouped into a new category for classification. This study overcomes the above-mentioned problem and illustrates the novelty of applying Andrews’ plots to satellite image data, and proposes a robust method for classifying the plots that combines Dempster-Shafer theory with fuzzy set theory. In addition, we present an example, obtained from real satellite images, to demonstrate the application of the proposed classification method. The accuracy and robustness of the proposed method are investigated for different training set sizes and crop types, and are compared with the results of two traditional classification methods. We find that outlier data are easily eliminated by examining Andrews’ curves and that the proposed method significantly outperforms traditional methods when considering the classification accuracy. Full article
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Open AccessArticle Quantifying Sub-Pixel Surface Water Coverage in Urban Environments Using Low-Albedo Fraction from Landsat Imagery
Remote Sens. 2017, 9(5), 428; doi:10.3390/rs9050428
Received: 14 February 2017 / Revised: 8 April 2017 / Accepted: 16 April 2017 / Published: 1 May 2017
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Abstract
The problem of mixed pixels negatively affects the delineation of accurate surface water in Landsat Imagery. Linear spectral unmixing has been demonstrated to be a powerful technique for extracting surface materials at a sub-pixel scale. Therefore, in this paper, we propose an innovative
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The problem of mixed pixels negatively affects the delineation of accurate surface water in Landsat Imagery. Linear spectral unmixing has been demonstrated to be a powerful technique for extracting surface materials at a sub-pixel scale. Therefore, in this paper, we propose an innovative low albedo fraction (LAF) method based on the idea of unconstrained linear spectral unmixing. The LAF stands on the “High Albedo-Low Albedo-Vegetation” model of spectral unmixing analysis in urban environments, and investigates the urban surface water extraction problem with the low albedo fraction map. Three experiments are carefully designed using Landsat TM/ETM+ images on the three metropolises of Wuhan, Shanghai, and Guangzhou in China, and per-pixel and sub-pixel accuracies are estimated. The results are compared against extraction accuracies from three popular water extraction methods including the normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and automated water extraction index (AWEI). Experimental results show that LAF achieves a better accuracy when extracting urban surface water than both MNDWI and AWEI do, especially in boundary mixed pixels. Moreover, the LAF has the smallest threshold variations among the three methods, and the fraction threshold of 1 is a proper choice for LAF to obtain good extraction results. Therefore, the LAF is a promising approach for extracting urban surface water coverage. Full article
(This article belongs to the collection Learning to Understand Remote Sensing Images)
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Open AccessArticle A Satellite-Based Sunshine Duration Climate Data Record for Europe and Africa
Remote Sens. 2017, 9(5), 429; doi:10.3390/rs9050429
Received: 17 March 2017 / Revised: 21 April 2017 / Accepted: 27 April 2017 / Published: 2 May 2017
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Abstract
Besides 2 m - temperature and precipitation, sunshine duration is one of the most important and commonly used parameter in climatology, with measured time series of partly more than 100 years in length. EUMETSAT’s Satellite Application Facility on Climate Monitoring (CM SAF) presents
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Besides 2 m - temperature and precipitation, sunshine duration is one of the most important and commonly used parameter in climatology, with measured time series of partly more than 100 years in length. EUMETSAT’s Satellite Application Facility on Climate Monitoring (CM SAF) presents a climate data record for daily and monthly sunshine duration (SDU) for Europe and Africa. Basis for the advanced retrieval is a highly resolved satellite product of the direct solar radiation from measurements by Meteosat satellites 2 to 10. The data record covers the time period 1983 to 2015 with a spatial resolution of 0.05° × 0.05°. The comparison against ground-based data shows high agreement but also some regional differences. Sunshine duration is overestimated by the satellite-based data in many regions, compared to surface data. In West and Central Africa, low clouds seem to be the reason for a stronger overestimation of sunshine duration in this region (up to 20% for monthly sums). For most stations, the overestimation is low, with a bias below 7.5 h for monthly sums and below 0.4 h for daily sums. A high correlation of 0.91 for daily SDU and 0.96 for monthly SDU also proved the high agreement with station data. As SDU is based on a stable and homogeneous climate data record of more than 30 years length, it is highly suitable for climate applications, such as trend estimates. Full article
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Open AccessArticle An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa
Remote Sens. 2017, 9(5), 430; doi:10.3390/rs9050430
Received: 31 October 2016 / Revised: 26 April 2017 / Accepted: 28 April 2017 / Published: 2 May 2017
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Abstract
Accurate and consistent rainfall observations are vital for climatological studies in support of better agricultural and water management decision-making and planning. In East Africa, accurate rainfall estimation with an adequate spatial distribution is limited due to sparse rain gauge networks. Satellite rainfall products
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Accurate and consistent rainfall observations are vital for climatological studies in support of better agricultural and water management decision-making and planning. In East Africa, accurate rainfall estimation with an adequate spatial distribution is limited due to sparse rain gauge networks. Satellite rainfall products can potentially play a role in increasing the spatial coverage of rainfall estimates; however, their performance needs to be understood across space–time scales and factors relating to their errors. This study assesses the performance of seven satellite products: Tropical Applications of Meteorology using Satellite and ground-based observations (TAMSAT), African Rainfall Climatology And Time series (TARCAT), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Centre (CPC) Morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP), and Global Precipitation Climatology Project (GPCP), using locally developed gridded (0.05°) rainfall data for 15 years (1998–2012) over East Africa. The products’ assessments were done at monthly and yearly timescales and were remapped to the gridded rain gauge data spatial scale during the March to May (MAM) and October to December (OND) rainy seasons. A grid-based statistical comparison between the two datasets was used, but only pixel values located at the rainfall stations were considered for validation. Additionally, the impact of topography on the performance of the products was assessed by analyzing the pixels in areas of highest negative bias. All the products could substantially replicate rainfall patterns, but their differences are mainly based on retrieving high rainfall amounts, especially of localized orographic types. The products exhibited systematic errors, which decreased with an increase in temporal resolution from a monthly to yearly scale. Challenges in retrieving orographic rainfall, especially during the OND season, were identified as the main cause of high underestimations. Underestimation was observed when elevation was <2500 m and above this threshold; overestimation was evident in mountainous areas. CMORPH, CHIRPS, and TRMM showed consistently high performance during both seasons, and this was attributed to their ability to retrieve rainfall of different rainfall regimes. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle Limitations and Improvements of the Leaf Optical Properties Model Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields (LIBERTY)
Remote Sens. 2017, 9(5), 431; doi:10.3390/rs9050431
Received: 18 January 2017 / Revised: 19 April 2017 / Accepted: 27 April 2017 / Published: 3 May 2017
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Abstract
Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields (LIBERTY) models the effects of leaf biochemical concentrations on reflectance spectra on the basis of Melamed theory, which has several limitations. These are: (1) the radiation components are not treated satisfactorily; (2) the directional changes
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Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields (LIBERTY) models the effects of leaf biochemical concentrations on reflectance spectra on the basis of Melamed theory, which has several limitations. These are: (1) the radiation components are not treated satisfactorily; (2) the directional changes of both particle and sublayer scattering ratios are not considered; and (3) the boundary constraint which makes needle leaves different from broadleaves is not included. Proofs of these limitations as well as theoretical improvements are given in this study. Global sensitivity analysis (SA) of three models: the original LIBERTY, our improved LIBERTY (LIBERTYim) and The optical PROperties SPECTra model (PROSPECT) suggests that compared with LIBERTY, the global reflectance and transmittance of LIBERTYim are more sensitive to diametrical absorbance α d —a parameter related to leaf biochemistry. Moreover, the global reflectance and transmittance of LIBERTYim and PROSPECT had similar sensitivity patterns to the input variables, demonstrating indirectly the validity of our improvements over LIBERTY. However, neither LIBERTY nor LIBERTYim considers boundary constraints, which limits their applications in modelling needle leaf optical properties. We introduced a particle string model, which might be used to simulate needle leaf optical properties in the future. Full article
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Open AccessArticle Evaluation of Aerosol Optical Depth and Aerosol Models from VIIRS Retrieval Algorithms over North China Plain
Remote Sens. 2017, 9(5), 432; doi:10.3390/rs9050432
Received: 13 February 2017 / Revised: 24 April 2017 / Accepted: 27 April 2017 / Published: 2 May 2017
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Abstract
The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on Suomi National Polar-orbiting Partnership (S-NPP) satellite in late 2011. Similar to the Moderate resolution Imaging Spectroradiometer (MODIS), VIIRS observes top-of-atmosphere spectral reflectance and is potentially suitable for retrieval of the aerosol optical
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The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on Suomi National Polar-orbiting Partnership (S-NPP) satellite in late 2011. Similar to the Moderate resolution Imaging Spectroradiometer (MODIS), VIIRS observes top-of-atmosphere spectral reflectance and is potentially suitable for retrieval of the aerosol optical depth (AOD). The VIIRS Environmental Data Record data (VIIRS_EDR) is produced operationally by NOAA, and is based on the MODIS atmospheric correction algorithm. The “MODIS-like” VIIRS data (VIIRS_ML) are being produced experimentally at NASA, from a version of the “dark-target” algorithm that is applied to MODIS. In this study, the AOD and aerosol model types from these two VIIRS retrieval algorithms over the North China Plain (NCP) are evaluated using the ground-based CE318 Sunphotometer (CE318) measurements during 2 May 2012–31 March 2014 at three sites. These sites represent three different surface types: urban (Beijing), suburban (XiangHe) and rural (Xinglong). Firstly, we evaluate the retrieved spectral AOD. For the three sites, VIIRS_EDR AOD at 550 nm shows a positive mean bias (MB) of 0.04–0.06 and the correlation of 0.83–0.86, with the largest MB (0.10–0.15) observed in Beijing. In contrast, VIIRS_ML AOD at 550 nm has overall higher positive MB of 0.13–0.14 and a higher correlation (0.93–0.94) with CE318 AOD. Secondly, we evaluate the aerosol model types assumed by each algorithm, as well as the aerosol optical properties used in the AOD retrievals. The aerosol model used in VIIRS_EDR algorithm shows that dust and clean urban models were the dominant model types during the evaluation period. The overall accuracy rate of the aerosol model used in VIIRS_ML over NCP three sites (0.48) is higher than that of VIIRS_EDR (0.27). The differences in Single Scattering Albedo (SSA) at 670 nm between VIIRS_ML and CE318 are mostly less than 0.015, but high seasonal differences are found especially over the Xinglong site. The values of SSA from VIIRS_EDR are higher than that observed by CE318 over all sites and all assumed aerosol modes, with a positive bias of 0.02–0.04 for fine mode, 0.06–0.12 for coarse mode and 0.03–0.05 for bi-mode at 440 nm. The overestimation of SSA but positive AOD MB of VIIRS_EDR indicate that other factors (e.g., surface reflectance characterization or cloud contamination) are important sources of error in the VIIRS_EDR algorithm, and their effects on aerosol retrievals may override the effects from non-ideality in these aerosol models. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)
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Open AccessArticle An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells
Remote Sens. 2017, 9(5), 433; doi:10.3390/rs9050433
Received: 13 March 2017 / Revised: 9 April 2017 / Accepted: 30 April 2017 / Published: 3 May 2017
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Abstract
Plane segmentation is a basic task in the automatic reconstruction of indoor and urban environments from unorganized point clouds acquired by laser scanners. As one of the most common plane-segmentation methods, standard Random Sample Consensus (RANSAC) is often used to continually detect planes
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Plane segmentation is a basic task in the automatic reconstruction of indoor and urban environments from unorganized point clouds acquired by laser scanners. As one of the most common plane-segmentation methods, standard Random Sample Consensus (RANSAC) is often used to continually detect planes one after another. However, it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points. An improved RANSAC method based on Normal Distribution Transformation (NDT) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation. A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface. The 3D NDT represents the point cloud with a set of NDT cells and models the observed points with a normal distribution within each cell. The geometric appearances of NDT cells are used to classify the NDT cells into planar and non-planar cells. The proposed method is verified on three indoor scenes. The experimental results show that the correctness exceeds 88.5% and the completeness exceeds 85.0%, which indicates that the proposed method identifies more reliable and accurate planes than standard RANSAC. It also executes faster. These results validate the suitability of the method. Full article
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
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Open AccessArticle Potential Benefits of Combining Anomaly Detection with View Planning for UAV Infrastructure Modeling
Remote Sens. 2017, 9(5), 434; doi:10.3390/rs9050434
Received: 11 March 2017 / Revised: 19 April 2017 / Accepted: 28 April 2017 / Published: 3 May 2017
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Abstract
This paper presents a novel method for UAV-based 3D modeling of large infrastructure objects, such as pipelines, canals and levees, that combines anomaly detection with automatic on-board 3D view planning. The study begins by assuming that anomaly detections are possible and focuses on
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This paper presents a novel method for UAV-based 3D modeling of large infrastructure objects, such as pipelines, canals and levees, that combines anomaly detection with automatic on-board 3D view planning. The study begins by assuming that anomaly detections are possible and focuses on quantifying the potential benefits of the combined method and the view planning algorithm. A simulated canal environment is constructed, and several simulated anomalies are created and marked. The algorithm is used to plan inspection flights for the anomaly locations, and simulated images from the flights are rendered and processed to construct 3D models of the locations of interest. The new flights are compared to traditional flights in terms of flight time, data collected and 3D model accuracy. When compared to a low speed, low elevation traditional flight, the proposed method is shown in simulation to decrease total flight time by up to 55%, while reducing the amount of image data to be processed by 89% and maintaining 3D model accuracy at areas of interest. Full article
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Open AccessArticle Change Detection in SAR Images Based on Deep Semi-NMF and SVD Networks
Remote Sens. 2017, 9(5), 435; doi:10.3390/rs9050435
Received: 31 December 2016 / Revised: 17 April 2017 / Accepted: 28 April 2017 / Published: 4 May 2017
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Abstract
With the development of Earth observation programs, more and more multi-temporal synthetic aperture radar (SAR) data are available from remote sensing platforms. Therefore, it is demanding to develop unsupervised methods for SAR image change detection. Recently, deep learning-based methods have displayed promising performance
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With the development of Earth observation programs, more and more multi-temporal synthetic aperture radar (SAR) data are available from remote sensing platforms. Therefore, it is demanding to develop unsupervised methods for SAR image change detection. Recently, deep learning-based methods have displayed promising performance for remote sensing image analysis. However, these methods can only provide excellent performance when the number of training samples is sufficiently large. In this paper, a novel simple method for SAR image change detection is proposed. The proposed method uses two singular value decomposition (SVD) analyses to learn the non-linear relations between multi-temporal images. By this means, the proposed method can generate more representative feature expressions with fewer samples. Therefore, it provides a simple yet effective way to be designed and trained easily. Firstly, deep semi-nonnegative matrix factorization (Deep Semi-NMF) is utilized to select pixels that have a high probability of being changed or unchanged as samples. Next, image patches centered at these sample pixels are generated from the input multi-temporal SAR images. Then, we build SVD networks, which are comprised of two SVD convolutional layers and one histogram feature generation layer. Finally, pixels in both multi-temporal SAR images are classified by the SVD networks, and then the final change map can be obtained. The experimental results of three SAR datasets have demonstrated the effectiveness and robustness of the proposed method. Full article
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Open AccessArticle Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations
Remote Sens. 2017, 9(5), 436; doi:10.3390/rs9050436
Received: 7 March 2017 / Revised: 21 April 2017 / Accepted: 1 May 2017 / Published: 5 May 2017
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Abstract
California growers face challenges with water shortages and there is a strong need to use the least amount of water while optimizing yield. Timely information on evapotranspiration (ET), a dominant component of crop consumptive water use, is critical for growers to tailor irrigation
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California growers face challenges with water shortages and there is a strong need to use the least amount of water while optimizing yield. Timely information on evapotranspiration (ET), a dominant component of crop consumptive water use, is critical for growers to tailor irrigation management based on in-field spatial variability and in-season variations. We evaluated the performance of a remote sensing-based approach, Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), in mapping ET over an almond orchard in California, driven by Landsat satellite observations. Reference ET from a network of weather stations over well-watered grass (ETo) was used for the internal calibration and for deriving ET at daily and extended time period, instead of alfalfa based reference evapotranspiration (ETr). Our study showed that METRIC daily ET estimates during Landsat overpass dates agreed well with the field measurements. During 2009–2012, a root mean square error (RMSE) of 0.53 mm/day and a coefficient of determination (R2) of 0.87 were found between METRIC versus observed daily ET. Monthly ET estimates had a higher accuracy, with a RMSE of 12.08 mm/month, a R2 of 0.90, and a relatively small relative mean difference (RMD) of 9.68% during 2009–2012 growing seasons. Net radiation and Normalized Difference Vegetation Index (NDVI) from remote sensing observations were highly correlated with spatial and temporal ET estimates. An empirical model was developed to estimate daily ET using NDVI, net radiation (Rn), and vapor pressure deficit (VPD). The validation showed that the accuracy of this easy-to-use empirical method was slightly lower than that of METRIC but still reasonable, with a RMSE of 0.71 mm/day when compared to ground measurements. The remote sensing based ET estimate will support a variety of State and local interests in water use and irrigation management, for both planning and regulatory/compliance purposes, and it provides the farmers observation-based guidance for site-specific and time-sensitive irrigation management. Full article
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Open AccessArticle Fine-Resolution Repeat Topographic Surveying of Dryland Landscapes Using UAS-Based Structure-from-Motion Photogrammetry: Assessing Accuracy and Precision against Traditional Ground-Based Erosion Measurements
Remote Sens. 2017, 9(5), 437; doi:10.3390/rs9050437
Received: 10 March 2017 / Revised: 12 April 2017 / Accepted: 21 April 2017 / Published: 3 May 2017
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Abstract
Structure-from-motion (SfM) photogrammetry from unmanned aerial system (UAS) imagery is an emerging tool for repeat topographic surveying of dryland erosion. These methods are particularly appealing due to the ability to cover large landscapes compared to field methods and at reduced costs and finer
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Structure-from-motion (SfM) photogrammetry from unmanned aerial system (UAS) imagery is an emerging tool for repeat topographic surveying of dryland erosion. These methods are particularly appealing due to the ability to cover large landscapes compared to field methods and at reduced costs and finer spatial resolution compared to airborne laser scanning. Accuracy and precision of high-resolution digital terrain models (DTMs) derived from UAS imagery have been explored in many studies, typically by comparing image coordinates to surveyed check points or LiDAR datasets. In addition to traditional check points, this study compared 5 cm resolution DTMs derived from fixed-wing UAS imagery with a traditional ground-based method of measuring soil surface change called erosion bridges. We assessed accuracy by comparing the elevation values between DTMs and erosion bridges along thirty topographic transects each 6.1 m long. Comparisons occurred at two points in time (June 2014, February 2015) which enabled us to assess vertical accuracy with 3314 data points and vertical precision (i.e., repeatability) with 1657 data points. We found strong vertical agreement (accuracy) between the methods (RMSE 2.9 and 3.2 cm in June 2014 and February 2015, respectively) and high vertical precision for the DTMs (RMSE 2.8 cm). Our results from comparing SfM-generated DTMs to check points, and strong agreement with erosion bridge measurements suggests repeat UAS imagery and SfM processing could replace erosion bridges for a more synoptic landscape assessment of shifting soil surfaces for some studies. However, while collecting the UAS imagery and generating the SfM DTMs for this study was faster than collecting erosion bridge measurements, technical challenges related to the need for ground control networks and image processing requirements must be addressed before this technique could be applied effectively to large landscapes. Full article
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Open AccessArticle Unsupervised Change Detection for Multispectral Remote Sensing Images Using Random Walks
Remote Sens. 2017, 9(5), 438; doi:10.3390/rs9050438
Received: 14 February 2017 / Revised: 18 April 2017 / Accepted: 21 April 2017 / Published: 4 May 2017
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Abstract
In this paper, the change detection of Multi-Spectral (MS) remote sensing images is treated as an image segmentation issue. An unsupervised method integrating histogram-based thresholding and image segmentation techniques is proposed. In order to overcome the poor performance of thresholding techniques for strongly
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In this paper, the change detection of Multi-Spectral (MS) remote sensing images is treated as an image segmentation issue. An unsupervised method integrating histogram-based thresholding and image segmentation techniques is proposed. In order to overcome the poor performance of thresholding techniques for strongly overlapped change/non-change signals, a Gaussian Mixture Model (GMM) with three components, including non-change, non-labeling and change, is adopted to model the statistical characteristics of the different images between two multi-temporal MS images. The non-labeling represents the pixels that are difficult to be classified. A random walk based segmentation method is applied to solve this problem, in which the different images are modeled as graphs and the classification results of GMM are imported as the labeling seeds. The experimental results of three remote sensing image pairs acquired by different sensors suggest a superiority of the proposed approach comparing with the existing unsupervised change detection methods. Full article
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Open AccessArticle Remote Sensing Image Registration with Line Segments and Their Intersections
Remote Sens. 2017, 9(5), 439; doi:10.3390/rs9050439
Received: 22 March 2017 / Revised: 26 April 2017 / Accepted: 30 April 2017 / Published: 4 May 2017
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Abstract
Image registration is a basic but essential step for remote sensing image processing, and finding stable features in multitemporal images is one of the most considerable challenges in the field. The main shape contours of artificial objects (e.g., roads, buildings, farmlands, and airports)
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Image registration is a basic but essential step for remote sensing image processing, and finding stable features in multitemporal images is one of the most considerable challenges in the field. The main shape contours of artificial objects (e.g., roads, buildings, farmlands, and airports) can be generally described as a group of line segments, which are stable features, even in images with evident background changes (e.g., images taken before and after a disaster). In this study, a registration method that uses line segments and their intersections is proposed for multitemporal remote sensing images. First, line segments are extracted in image pyramids to unify the scales of the reference image and the test image. Then, a line descriptor based on the gradient distribution of local areas is constructed, and the segments are matched in image pyramids. Lastly, triplets of intersections of matching lines are selected to estimate affine transformation between two images. Additional corresponding intersections are provided based on the estimated transformation, and an iterative process is adopted to remove outliers. The performance of the proposed method is tested on a variety of optical remote sensing image pairs, including synthetic and real data. Compared with existing methods, our method can provide more accurate registration results, even in images with significant background changes. Full article
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Open AccessArticle Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data
Remote Sens. 2017, 9(5), 440; doi:10.3390/rs9050440
Received: 10 February 2017 / Revised: 11 April 2017 / Accepted: 27 April 2017 / Published: 4 May 2017
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Abstract
We present an earth observation based approach to detect aquaculture ponds in coastal areas with dense time series of high spatial resolution Sentinel-1 SAR data. Aquaculture is one of the fastest-growing animal food production sectors worldwide, contributes more than half of the total
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We present an earth observation based approach to detect aquaculture ponds in coastal areas with dense time series of high spatial resolution Sentinel-1 SAR data. Aquaculture is one of the fastest-growing animal food production sectors worldwide, contributes more than half of the total volume of aquatic foods in human consumption, and offers a great potential for global food security. The key advantages of SAR instruments for aquaculture mapping are their all-weather, day and night imaging capabilities which apply particularly to cloud-prone coastal regions. The different backscatter responses of the pond components (dikes and enclosed water surface) and aquaculture’s distinct rectangular structure allow for separation of aquaculture areas from other natural water bodies. We analyzed the large volume of free and open Sentinel-1 data to derive and map aquaculture pond objects for four study sites covering major river deltas in China and Vietnam. SAR image data were processed to obtain temporally smoothed time series. Terrain information derived from DEM data and accurate coastline data were utilized to identify and mask potential aquaculture areas. An open source segmentation algorithm supported the extraction of aquaculture ponds based on backscatter intensity, size and shape features. We were able to efficiently map aquaculture ponds in coastal areas with an overall accuracy of 0.83 for the four study sites. The approach presented is easily transferable in time and space, and thus holds the potential for continental and global mapping. Full article
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Open AccessArticle Image Registration and Fusion of Visible and Infrared Integrated Camera for Medium-Altitude Unmanned Aerial Vehicle Remote Sensing
Remote Sens. 2017, 9(5), 441; doi:10.3390/rs9050441
Received: 27 March 2017 / Revised: 27 April 2017 / Accepted: 28 April 2017 / Published: 5 May 2017
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Abstract
This study proposes a novel method for image registration and fusion via commonly used visible light and infrared integrated cameras mounted on medium-altitude unmanned aerial vehicles (UAVs).The innovation of image registration lies in three aspects. First, it reveals how complex perspective transformation can
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This study proposes a novel method for image registration and fusion via commonly used visible light and infrared integrated cameras mounted on medium-altitude unmanned aerial vehicles (UAVs).The innovation of image registration lies in three aspects. First, it reveals how complex perspective transformation can be converted to simple scale transformation and translation transformation between two sensor images under long-distance and parallel imaging conditions. Second, with the introduction of metadata, a scale calculation algorithm is designed according to spatial geometry, and a coarse translation estimation algorithm is presented based on coordinate transformation. Third, the problem of non-strictly aligned edges in precise translation estimation is solved via edge–distance field transformation. A searching algorithm based on particle swarm optimization is introduced to improve efficiency. Additionally, a new image fusion algorithm is designed based on a pulse coupled neural network and nonsubsampled contourlet transform to meet the special requirements of preserving color information, adding infrared brightness information, improving spatial resolution, and highlighting target areas for unmanned aerial vehicle (UAV) applications. A medium-altitude UAV is employed to collect datasets. The result is promising, especially in applications that involve other medium-altitude or high-altitude UAVs with similar system structures. Full article
(This article belongs to the collection Learning to Understand Remote Sensing Images)
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Open AccessArticle Glacier Surface Velocity Retrieval Using D-InSAR and Offset Tracking Techniques Applied to Ascending and Descending Passes of Sentinel-1 Data for Southern Ellesmere Ice Caps, Canadian Arctic
Remote Sens. 2017, 9(5), 442; doi:10.3390/rs9050442
Received: 31 January 2017 / Revised: 28 April 2017 / Accepted: 1 May 2017 / Published: 5 May 2017
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Abstract
The Terrain Observation by Progressive Scans (TOPS) acquisition mode of the Sentinel-1 mission provides a wide coverage per acquisition with resolutions of 5 m in range and 20 m in azimuth, which makes this acquisition mode attractive for glacier velocity monitoring. Here, we
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The Terrain Observation by Progressive Scans (TOPS) acquisition mode of the Sentinel-1 mission provides a wide coverage per acquisition with resolutions of 5 m in range and 20 m in azimuth, which makes this acquisition mode attractive for glacier velocity monitoring. Here, we retrieve surface velocities from the southern Ellesmere Island ice caps (Canadian Arctic) using both offset tracking and Differential Interferometric Synthetic Aperture Radar (D-InSAR) techniques and combining ascending and descending passes. We optimise the offset tracking technique by omitting the azimuth offsets. By doing so, we are able to improve the final resolution of the velocity product, as Sentinel-1 shows a lower resolution in the azimuth direction. Simultaneously, we avoid the undesired ionospheric effect manifested in the data as azimuth streaks. The D-InSAR technique shows its merits when applied to slow-moving areas, while offset tracking is more suitable for fast-moving areas. This research shows that the methods used here are complementary and the use of both to determine glacier velocities is better than only using one or the other. We observe glacier surface velocities of up to 1200 m year 1 for the fastest tidewater glaciers. The land-terminating glaciers show typical velocities between 12 and 33 m year 1 , though with peaks up to 150 m year 1 in narrowing zones of the confining valleys. Full article
(This article belongs to the Special Issue Remote Sensing of Glaciers)
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Open AccessArticle Revealing Implicit Assumptions of the Component Substitution Pansharpening Methods
Remote Sens. 2017, 9(5), 443; doi:10.3390/rs9050443
Received: 22 March 2017 / Revised: 28 April 2017 / Accepted: 3 May 2017 / Published: 5 May 2017
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Abstract
The component substitution (CS) pansharpening methods have been developed for almost three decades and have become better understood recently by generalizing them into one framework. However, few studies focus on the statistical assumptions implicit in the CS methods. This paper reveals their implicit
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The component substitution (CS) pansharpening methods have been developed for almost three decades and have become better understood recently by generalizing them into one framework. However, few studies focus on the statistical assumptions implicit in the CS methods. This paper reveals their implicit statistical assumptions from a Bayesian data fusion framework and suggests best practices for histogram matching of the panchromatic image to the intensity image, a weighted summation of the multispectral images, to better satisfy these assumptions. The purpose of histogram matching was found to make the difference between the high-resolution panchromatic and intensity images as small as possible, as one implicit assumption claims their negligible difference. The statistical relationship between the high-resolution panchromatic and intensity images and the relationship between their corresponding low-resolution images are the same, as long as the low resolution panchromatic image is derived by considering the modulation transfer functions of the multispectral sensors. Hence, the histogram-matching equation should be derived from the low-resolution panchromatic and intensity images, but not derived from the high-resolution panchromatic and expanded low-resolution intensity images. Experiments using three example CS methods, each using the two different histogram-matching equations, was conducted on the four-band QuickBird and eight-band WorldView-2 top-of-atmosphere reflectance data. The results verified the best practices and showed that the histogram-matching equation derived from the high-resolution panchromatic and expanded low-resolution intensity images provides more-blurred histogram-matched panchromatic image and, hence less-sharpened pansharpened images than that derived from the low-resolution image pair. The usefulness of the assumptions revealed in this study for method developers is discussed. For example, the CS methods can be improved by satisfying the assumptions better, e.g., classifying the images into homogenous areas before pansharpening, and by changing the assumptions to be more general to address their deficiencies. Full article
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Open AccessArticle Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean
Remote Sens. 2017, 9(5), 444; doi:10.3390/rs9050444
Received: 3 January 2017 / Revised: 21 April 2017 / Accepted: 1 May 2017 / Published: 5 May 2017
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Abstract
Changes in marine environments affect fishery resources at different spatial and temporal scales in marine ecosystems. Predictions from species distribution models are available to parameterize the environmental characteristics that influence the biology, range, and habitats of the species of interest. This study used
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Changes in marine environments affect fishery resources at different spatial and temporal scales in marine ecosystems. Predictions from species distribution models are available to parameterize the environmental characteristics that influence the biology, range, and habitats of the species of interest. This study used generalized additive models (GAMs) fitted to two spatiotemporal fishery data sources, namely 1° spatial grid and observer record longline fishery data from 2006 to 2010, to investigate the relationship between catch rates of yellowfin tuna and oceanographic conditions by using multispectral satellite images and to develop a habitat preference model. The results revealed that the cumulative deviances obtained using the selected GAMs were 33.6% and 16.5% in the 1° spatial grid and observer record data, respectively. The environmental factors in the study were significant in the selected GAMs, and sea surface temperature explained the highest deviance. The results suggest that areas with a higher sea surface temperature, a sea surface height anomaly of approximately −10.0 to 20 cm, and a chlorophyll-a concentration of approximately 0.05–0.25 mg/m3 yield higher catch rates of yellowfin tuna. The 1° spatial grid data had higher cumulative deviances, and the predicted relative catch rates also exhibited a high correlation with observed catch rates. However, the maps of observer record data showed the high-quality spatial resolutions of the predicted relative catch rates in the close-view maps. Thus, these results suggest that models of catch rates of the 1° spatial grid data that incorporate relevant environmental variables can be used to infer possible responses in the distribution of highly migratory species, and the observer record data can be used to detect subtle changes in the target fishing grounds. Full article
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
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Open AccessArticle Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data
Remote Sens. 2017, 9(5), 445; doi:10.3390/rs9050445
Received: 26 February 2017 / Revised: 2 May 2017 / Accepted: 4 May 2017 / Published: 5 May 2017
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Abstract
Microwave remote sensing can measure surface geometry. Via the processing of the Synthetic Aperture Radar (SAR) data, the earth surface geometric parameters can be provided for geoscientific studies, especially in geological mapping. For this purpose, it is necessary to model the surface roughness
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Microwave remote sensing can measure surface geometry. Via the processing of the Synthetic Aperture Radar (SAR) data, the earth surface geometric parameters can be provided for geoscientific studies, especially in geological mapping. For this purpose, it is necessary to model the surface roughness against microwave signal backscattering. Of the available models, the Integral Equation Model (IEM) for co-polarized data has been the most frequently used model. Therefore, by the processing of the SAR data using this model, the surface geometry can be studied. In the IEM, the surface roughness geometry is calculable via the height statistical parameter, the rms-height. However, this parameter is not capable enough to represent surface morphology, since it only measures the surface roughness in the vertical direction, while the roughness dispersion on the surface is not included. In this paper, using the random fractal geometry capability, via the implementation of the power-law roughness spectrum, the precision and correctness of the surface roughness estimation has been improved by up to 10%. Therefore, the random fractal geometry is implemented through the calculation of the input geometric parameters of the IEM using the power-law surface spectrum and the spectral slope. In this paper, the in situ roughness measurement data, as well as SAR images at frequencies of L, C, and X, have been used to implement and evaluate the proposed method. Surface roughness, according to the operational frequencies, exhibits a fractal or a diffractal behavior. Full article
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
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Open AccessArticle Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images
Remote Sens. 2017, 9(5), 446; doi:10.3390/rs9050446
Received: 2 April 2017 / Revised: 27 April 2017 / Accepted: 1 May 2017 / Published: 5 May 2017
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Abstract
Semantic segmentation is a fundamental task in remote sensing image processing. The large appearance variations of ground objects make this task quite challenging. Recently, deep convolutional neural networks (DCNNs) have shown outstanding performance in this task. A common strategy of these methods (e.g.,
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Semantic segmentation is a fundamental task in remote sensing image processing. The large appearance variations of ground objects make this task quite challenging. Recently, deep convolutional neural networks (DCNNs) have shown outstanding performance in this task. A common strategy of these methods (e.g., SegNet) for performance improvement is to combine the feature maps learned at different DCNN layers. However, such a combination is usually implemented via feature map summation or concatenation, indicating that the features are considered indiscriminately. In fact, features at different positions contribute differently to the final performance. It is advantageous to automatically select adaptive features when merging different-layer feature maps. To achieve this goal, we propose a gated convolutional neural network to fulfill this task. Specifically, we explore the relationship between the information entropy of the feature maps and the label-error map, and then a gate mechanism is embedded to integrate the feature maps more effectively. The gate is implemented by the entropy maps, which are generated to assign adaptive weights to different feature maps as their relative importance. Generally, the entropy maps, i.e., the gates, guide the network to focus on the highly-uncertain pixels, where detailed information from lower layers is required to improve the separability of these pixels. The selected features are finally combined to feed into the classifier layer, which predicts the semantic label of each pixel. The proposed method achieves competitive segmentation accuracy on the public ISPRS 2D Semantic Labeling benchmark, which is challenging for segmentation by only using the RGB images. Full article
(This article belongs to the collection Learning to Understand Remote Sensing Images)
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Open AccessArticle Intercomparison of Ozone Vertical Profile Measurements by Differential Absorption Lidar and IASI/MetOp Satellite in the Upper Troposphere–Lower Stratosphere
Remote Sens. 2017, 9(5), 447; doi:10.3390/rs9050447
Received: 10 February 2017 / Revised: 24 April 2017 / Accepted: 27 April 2017 / Published: 8 May 2017
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Abstract
This paper introduces the technique of retrieving the profiles of vertical distribution of ozone considering temperature and aerosol correction in DIAL sounding of the atmosphere. The authors determine wavelengths, which are promising for measurements of ozone profiles in the upper troposphere–lower stratosphere. An
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This paper introduces the technique of retrieving the profiles of vertical distribution of ozone considering temperature and aerosol correction in DIAL sounding of the atmosphere. The authors determine wavelengths, which are promising for measurements of ozone profiles in the upper troposphere–lower stratosphere. An ozone differential absorption lidar is designed for the measurements. The results of applying the developed technique to the retrieval of the vertical profiles of ozone considering temperature and aerosol correction in the altitude range 6–15 km in DIAL sounding of the atmosphere confirm the prospects of ozone sounding at selected wavelengths of 341 and 299 nm with the proposed lidar. The 2015 ozone profiles retrieved were compared with satellite IASI data and the Kruger model. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)
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Open AccessArticle Quantifying Changes of Villages in the Urbanizing Beijing Metropolitan Region: Integrating Remote Sensing and GIS Analysis
Remote Sens. 2017, 9(5), 448; doi:10.3390/rs9050448
Received: 15 March 2017 / Revised: 16 April 2017 / Accepted: 3 May 2017 / Published: 6 May 2017
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Abstract
Rapid urbanization has resulted in great changes in rural landscapes globally. Using remote sensing data to quantify the distribution of rural settlements and their changes has received increasing attention in the past three decades, but remains a challenge. Previous studies mostly focused on
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Rapid urbanization has resulted in great changes in rural landscapes globally. Using remote sensing data to quantify the distribution of rural settlements and their changes has received increasing attention in the past three decades, but remains a challenge. Previous studies mostly focused on the residential changes within a grid or administrative boundary, but not at the individual village level. This paper presents a new change detection approach for rural residential settlements, which can identify different types of rural settlement changes at the individual village level by integrating remote sensing and Geographic Information System (GIS) analyses. Using multi-temporal Landsat TM image data, this approach classifies villages into five types: “no change”, “totally lost”, “shrinking”, “expanding”, and “merged”, in contrast to the commonly used “increase” and “decrease”. This approach was tested in the Beijing metropolitan area from 1984 to 2010. Additionally, the drivers of such changes were investigated using multinomial logistic regression models. The results revealed that: (1) 36% of the villages were lost, but the total area of developed lands in existing villages increased by 34%; (2) Changes were dominated by the type of ‘expansion’ in 1984–1990 (accounted for 43.42%) and 1990–2000 (56.21%). However, from 2000 to 2010, 49.73% of the villages remained unchanged; (3) Both topographical factors and distance factors had significant effects on whether the villages changed or not, but their impacts changed through time. The topographical driving factors showed decreasing effects on the loss of rural settlements, while distance factors had increasing impacts on settlement expansion and merging. This approach provides a useful tool for better understanding the changes in rural residential settlements and their associations with urbanization. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Ecology)
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Open AccessArticle Precision Near-Field Reconstruction in the Time Domain via Minimum Entropy for Ultra-High Resolution Radar Imaging
Remote Sens. 2017, 9(5), 449; doi:10.3390/rs9050449
Received: 12 December 2016 / Revised: 26 April 2017 / Accepted: 4 May 2017 / Published: 6 May 2017
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Abstract
Ultra-high resolution (UHR) radar imaging is used to analyze the internal structure of objects and to identify and classify their shapes based on ultra-wideband (UWB) signals using a vector network analyzer (VNA). However, radar-based imaging is limited by microwave propagation effects, wave scattering,
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Ultra-high resolution (UHR) radar imaging is used to analyze the internal structure of objects and to identify and classify their shapes based on ultra-wideband (UWB) signals using a vector network analyzer (VNA). However, radar-based imaging is limited by microwave propagation effects, wave scattering, and transmit power, thus the received signals are inevitably weak and noisy. To overcome this problem, the radar may be operated in the near-field. The focusing of UHR radar signals over a close distance requires precise geometry in order to accommodate the spherical waves. In this paper, a geometric estimation and compensation method that is based on the minimum entropy of radar images with sub-centimeter resolution is proposed and implemented. Inverse synthetic aperture radar (ISAR) imaging is used because it is applicable to several fields, including medical- and security-related applications, and high quality images of various targets have been produced to verify the proposed method. For ISAR in the near-field, the compensation for the time delay depends on the distance from the center of rotation and the internal RF circuits and cables. Required parameters for the delay compensation algorithm that can be used to minimize the entropy of the radar images are determined so that acceptable results can be achieved. The processing speed can be enhanced by performing the calculations in the time domain without the phase values, which are removed after upsampling. For comparison, the parameters are also estimated by performing random sampling in the data set. Although the reduced data set contained only 5% of the observed angles, the parameter optimization method is shown to operate correctly. Full article
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
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Open AccessArticle A Software-Defined GNSS Reflectometry Recording Receiver with Wide-Bandwidth, Multi-Band Capability and Digital Beam-Forming
Remote Sens. 2017, 9(5), 450; doi:10.3390/rs9050450
Received: 10 March 2017 / Revised: 26 April 2017 / Accepted: 1 May 2017 / Published: 6 May 2017
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Abstract
In this paper, we present the Software PARIS Interferometric Receiver (SPIR), a high-speed GNSS reflectometry recording receiver which has been designed and implemented with the primary goal of demonstrating the synoptic capabilities of the interferometric technique in GNSS Reflectrometry. Thanks to the use
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In this paper, we present the Software PARIS Interferometric Receiver (SPIR), a high-speed GNSS reflectometry recording receiver which has been designed and implemented with the primary goal of demonstrating the synoptic capabilities of the interferometric technique in GNSS Reflectrometry. Thanks to the use of large bandwidth GNSS signals, this technique is advantageous in comparison to the so-called clean-replica processing, when sea surface altimetric applications are pursued. The SPIR receiver down-converts, samples, and records the GNSS signals acquired by the sixteen elements of two antenna arrays. It can operate at any of the common GNSS L1, L2, or L5 bands. Digital beam-forming and signal processing is performed off-line by its dedicated signal processor, so that the GNSS reflectometry can be applied to different transmitting satellites using the same set of recorded signals. Alternatively, different processing techniques can be compared by applying them to exactly the same signals. This article focuses on the SPIR instrument hardware and software, as well as the remote sensing observables that can be obtained using this equipment. Full article
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Open AccessArticle Hyperspectral Alteration Information from Drill Cores and Deep Uranium Exploration in the Baiyanghe Uranium Deposit in the Xuemisitan Area, Xinjiang, China
Remote Sens. 2017, 9(5), 451; doi:10.3390/rs9050451
Received: 13 March 2017 / Revised: 29 April 2017 / Accepted: 4 May 2017 / Published: 6 May 2017
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Abstract
The Baiyanghe uranium deposit is a currently important medium-sized deposit in the Xuemisitan area, Xinjiang. The hydrothermal alteration in this deposit is closely related to the uranium mineralization of the deposit. In this study, hyperspectral data are collected from drill cores in the
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The Baiyanghe uranium deposit is a currently important medium-sized deposit in the Xuemisitan area, Xinjiang. The hydrothermal alteration in this deposit is closely related to the uranium mineralization of the deposit. In this study, hyperspectral data are collected from drill cores in the Baiyanghe uranium deposit using a FieldSpec4 visible-shortwave infrared spectrometer to study the hydrothermal alteration. The results reveal that the altered mineral assemblages have obvious zonation characteristics: (1) the upper section comprises long-wavelength illite and minor hematite and montmorillonite; (2) the middle section contains three types of illite (long-, medium- and short-wavelength illite) and hematite; and (3) the lower section includes short-wavelength illite, chlorite and carbonate. Additionally, the variety in the characteristic absorption-peak wavelength of illite at 2200 nm gradually shifts to shorter wavelength and ranges between 2195 nm and 2220 nm with increasing depth, while the SWIR-IC (short-wavelength infrared illite crystallinity, a dimensionless quantity) of the drill holes gradually increases from 0.2 to 2.1. These patterns reflect the hydrothermal fluid activity in the deposit, which features relatively high-temperature, high-pressure hydrothermal fluid in the deeper section and low-temperature, low-pressure hydrothermal fluid in the shallower section. Additionally, the uranium mineralization is located near the fracture zone, which represents the center of hydrothermal fluid activity or mineralization. This area has abundant alteration minerals, and the minerals illite (short- and medium-wavelength), hematite and fluorite can be used as uranium-prospecting indicators for uranium exploration in the deeper sections of the Baiyanghe uranium deposit. Full article
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Open AccessArticle Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis
Remote Sens. 2017, 9(5), 452; doi:10.3390/rs9050452
Received: 14 March 2017 / Revised: 28 April 2017 / Accepted: 3 May 2017 / Published: 6 May 2017
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Abstract
Recently, sparse and low-rank graph-based discriminant analysis (SLGDA) has yielded satisfactory results in hyperspectral image (HSI) dimensionality reduction (DR), for which sparsity and low-rankness are simultaneously imposed to capture both local and global structure of hyperspectral data. However, SLGDA fails to exploit the
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Recently, sparse and low-rank graph-based discriminant analysis (SLGDA) has yielded satisfactory results in hyperspectral image (HSI) dimensionality reduction (DR), for which sparsity and low-rankness are simultaneously imposed to capture both local and global structure of hyperspectral data. However, SLGDA fails to exploit the spatial information. To address this problem, a tensor sparse and low-rank graph-based discriminant analysis (TSLGDA) is proposed in this paper. By regarding the hyperspectral data cube as a third-order tensor, small local patches centered at the training samples are extracted for the TSLGDA framework to maintain the structural information, resulting in a more discriminative graph. Subsequently, dimensionality reduction is performed on the tensorial training and testing samples to reduce data redundancy. Experimental results of three real-world hyperspectral datasets demonstrate that the proposed TSLGDA algorithm greatly improves the classification performance in the low-dimensional space when compared to state-of-the-art DR methods. Full article
(This article belongs to the collection Learning to Understand Remote Sensing Images)
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Open AccessArticle Evaluation of Urbanization Dynamics and its Impacts on Surface Heat Islands: A Case Study of Beijing, China
Remote Sens. 2017, 9(5), 453; doi:10.3390/rs9050453
Received: 23 January 2017 / Revised: 24 April 2017 / Accepted: 5 May 2017 / Published: 7 May 2017
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Abstract
As the capital of China, Beijing has experienced a continued and rapid urbanization process in the past few decades. One of the key environmental impacts of rapid urbanization is the effect of urban heat island (UHI). The objective of this study was to
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As the capital of China, Beijing has experienced a continued and rapid urbanization process in the past few decades. One of the key environmental impacts of rapid urbanization is the effect of urban heat island (UHI). The objective of this study was to estimate the urbanization indexes of Beijing from 1992 to 2013 based on the stable nighttime light (NTL) data derived from the Defense Meteorological Satellite Program’s Operational Line Scanner System (DMSP/OLS), which has became a widely used remote sensing database after decades of development. The annual average value nighttime light Digital Number (NTL-DN), and the total lit number and urban area proportion within Beijing’s boundary were calculated and compared with social-economic statistics parameters to estimate the correlation between them. Four Landsat thematic mapper (TM) images acquired in 1995 and 2009 were applied to estimate the normalized difference vegetation index (NDVI) and normalized land surface temperature (LSTnor), and spatial correlation analysis was then carried out to investigate the relationship between the urbanization level and NDVI and LSTnor. Our results showed a strong negative linear relationship between the NTL-DN value and NDVI; however, in contrast, a strong positive linear relationship between existed between the NTL-DN value and LSTnor. By conducting a spatial comparison analysis of 1995 and 2009, the vegetation coverage change and surface temperature difference were calculated and compared with the NTL-DN difference. Our result revealed that the regions of fast urbanization resulted in a decrease of NDVI and increase of LSTnor. In addition, choropleth maps showing the spatial pattern of urban heat island zones were produced based on different temperatures, and the analysis result indicated that the spatial distribution of surface temperature was closely related with the NTL-DN and NDVI. These findings are helpful for understanding the urbanization process as well as urban ecology, which both have significant implications for urban planning and minimize the potential environmental impacts of urbanization in Beijing. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Land Surface Temperature and Emissivity Retrieval from Field-Measured Hyperspectral Thermal Infrared Data Using Wavelet Transform
Remote Sens. 2017, 9(5), 454; doi:10.3390/rs9050454
Received: 9 March 2017 / Revised: 21 April 2017 / Accepted: 3 May 2017 / Published: 7 May 2017
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Abstract
Currently, the main difficulty in separating the land surface temperature (LST) and land surface emissivity (LSE) from field-measured hyperspectral Thermal Infrared (TIR) data lies in solving the radiative transfer equation (RTE). Based on the theory of wavelet transform (WT), this paper proposes a
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Currently, the main difficulty in separating the land surface temperature (LST) and land surface emissivity (LSE) from field-measured hyperspectral Thermal Infrared (TIR) data lies in solving the radiative transfer equation (RTE). Based on the theory of wavelet transform (WT), this paper proposes a method for accurately and effectively separating LSTs and LSEs from field-measured hyperspectral TIR data. We show that the number of unknowns in the RTE can be reduced by decomposing and reconstructing the LSE spectrum, thus making the RTE solvable. The final results show that the errors introduced by WT are negligible. In addition, the proposed method usually achieves a greater accuracy in a wet-warm atmosphere than that in a dry-cold atmosphere. For the results under instrument noise conditions (NE∆T = 0.2 K), the overall accuracy of the LST is approximately 0.1–0.3 K, while the Root Mean Square Error (RMSE) of the LSEs is less than 0.01. In contrast to the effects of instrument noise, our method is quite insensitive to noises from atmospheric downwelling radiance, and all the RMSEs of our method are approximately zero for both the LSTs and the LSEs. When we used field-measured data to better evaluate our method’s performance, the results showed that the RMSEs of the LSTs and LSEs were approximately 1.1 K and 0.01, respectively. The results from both simulated data and field-measured data demonstrate that our method is promising for decreasing the number of unknowns in the RTE. Furthermore, the proposed method overcomes some known limitations of current algorithms, such as singular values and the loss of continuity in the spectrum of the retrieved LSEs. Full article
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Open AccessArticle Comparative Assessment of Two Vegetation Fractional Cover Estimating Methods and Their Impacts on Modeling Urban Latent Heat Flux Using Landsat Imagery
Remote Sens. 2017, 9(5), 455; doi:10.3390/rs9050455
Received: 8 December 2016 / Revised: 14 April 2017 / Accepted: 1 May 2017 / Published: 8 May 2017
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Abstract
Quantifying vegetation fractional cover (VFC) and assessing its role in heat fluxes modeling using medium resolution remotely sensed data has received less attention than it deserves in heterogeneous urban regions. This study examined two approaches (Normalized Difference Vegetation Index (NDVI)-derived and Multiple Endmember
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Quantifying vegetation fractional cover (VFC) and assessing its role in heat fluxes modeling using medium resolution remotely sensed data has received less attention than it deserves in heterogeneous urban regions. This study examined two approaches (Normalized Difference Vegetation Index (NDVI)-derived and Multiple Endmember Spectral Mixture Analysis (MESMA)-derived methods) that are commonly used to map VFC based on Landsat imagery, in modeling surface heat fluxes in urban landscape. For this purpose, two different heat flux models, Two-source energy balance (TSEB) model and Pixel Component Arranging and Comparing Algorithm (PCACA) model, were adopted for model evaluation and analysis. A comparative analysis of the NDVI-derived and MESMA-derived VFCs showed that the latter achieved more accurate estimates in complex urban regions. When the two sources of VFCs were used as inputs to both TSEB and PCACA models, MESMA-derived urban VFC produced more accurate urban heat fluxes (Bowen ratio and latent heat flux) relative to NDVI-derived urban VFC. Moreover, our study demonstrated that Landsat imagery-retrieved VFC exhibited greater uncertainty in obtaining urban heat fluxes for the TSEB model than for the PCACA model. Full article
(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)
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Open AccessArticle Improving the Impervious Surface Estimation from Hyperspectral Images Using a Spectral-Spatial Feature Sparse Representation and Post-Processing Approach
Remote Sens. 2017, 9(5), 456; doi:10.3390/rs9050456
Received: 26 February 2017 / Revised: 1 May 2017 / Accepted: 4 May 2017 / Published: 8 May 2017
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Abstract
Impervious surfaces have been widely recognized as an indicator for urbanization and environment monitoring. Plenty of methods have been proposed to extract impervious surfaces using remote sensing images. However, accurately extracting impervious surface is still a challenging task due to the confusion between
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Impervious surfaces have been widely recognized as an indicator for urbanization and environment monitoring. Plenty of methods have been proposed to extract impervious surfaces using remote sensing images. However, accurately extracting impervious surface is still a challenging task due to the confusion between impervious surface and bare soil. Thus, this paper presents a hybrid approach consisting of spectral-spatial feature sparse representation (SS-SR) and post-processing to extract urban impervious surface from hyperspectral images. We first extracted spectral and spatial features from hyperspectral images. Then, the spectral and spatial information of a pixel is represented by the vector stacking strategy. Each pixel vector can be represented by a linear combination of a few atoms from a learned dictionary, which is more suitable for impervious surface estimation. The sparse coefficients were automatically learned and then used for extracting impervious surface. The proposed impervious surface extraction method was evaluated with four hyperspectral datasets. We compared our algorithms with the state-of-the-art per-pixel based impervious surface extraction methods. The encouraging experimental results demonstrate the SS-SR algorithm generally outperforms the classic support vector machines and random forest. The improvement is more significant when combining SS-SR with post-classification approach. Full article
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Open AccessArticle SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product
Remote Sens. 2017, 9(5), 457; doi:10.3390/rs9050457
Received: 1 March 2017 / Revised: 26 April 2017 / Accepted: 3 May 2017 / Published: 9 May 2017
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Abstract
The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band.
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The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and τ. In this study, we present an alternative SMOS product that was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d’Etudes Spatiales de la BIOsphère). One of the main goals of this SMOS-INRA-CESBIO (SMOS-IC) product is to be as independent as possible from auxiliary data. The SMOS-IC product provides daily SM and τ at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. Specifically, SMOS-IC is much simpler and does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary datasets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A six-year (2010–2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ, SMOS-IC τ was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and the northern mid-latitudes. Full article
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Open AccessArticle Airborne LiDAR and Aerial Imagery to Assess Potential Burrow Locations for the Desert Tortoise (Gopherus agassizii)
Remote Sens. 2017, 9(5), 458; doi:10.3390/rs9050458
Received: 26 January 2017 / Revised: 6 April 2017 / Accepted: 1 May 2017 / Published: 8 May 2017
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Abstract
The Southwestern United States desert serves as the host for several threatened and endangered species, one of which is the desert tortoise (Gopherus agassizii). The goal of this study was to develop a fine-scale, remote-sensing-based approach that indicates favorable burrow locations
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The Southwestern United States desert serves as the host for several threatened and endangered species, one of which is the desert tortoise (Gopherus agassizii). The goal of this study was to develop a fine-scale, remote-sensing-based approach that indicates favorable burrow locations for G. agassizii in the Boulder City (Nevada) Conservation Easement area (35,500 ha). This was done by analyzing airborne LiDAR data (5–7 points/m2) and color imagery (four bands, 0.15-m resolution) and determining the percent vegetation cover; shrub height and area; Normalized Difference Vegetation Index (NDVI); and several geomorphic characteristics including slope, azimuth, and roughness. Other field data used herein include estimates of canopy area and species richness using 1271 line transects, and shrub height and canopy area using plant-specific measurements of ~200 plants. Larrea tridentata and Ambrosia dumosa shrubs were identified using an algorithm that obtained an optimum combination of NDVI and average reflectance of the four bands (IR, R, G, and B) from pixels in each image. The results, which identified more than 65 million shrubs across the study area, indicate that percent vegetation cover from aerial imagery across the site (13.92%) compared favorably (14.52%) to the estimate obtained from line transects, though the LiDAR method yielded shrub heights approximately 60% those of measured shrub heights. Landscape and plant properties were combined with known locations of tortoise burrows, as visually observed in 2014. Masks were created using roughness coefficient, slope percent, azimuth of burrow openings, elevation, and percent vegetation cover to isolate areas more likely to host burrows. Combined, the masks isolated 55% of the total survey area, which will help target future field surveys. Overall, the approach provides areas where tortoise burrows are more likely to be found, though additional ecological data would help refine the overall method. Full article
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Open AccessArticle Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion
Remote Sens. 2017, 9(5), 460; doi:10.3390/rs9050460
Received: 30 March 2017 / Revised: 26 April 2017 / Accepted: 3 May 2017 / Published: 10 May 2017
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Abstract
Rice crops are important in the global food economy, and new techniques are being implemented for their effective management. These techniques rely mainly on the changes in the phenological cycle, which can be investigated by remote sensing systems. High frequency and high spatial
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Rice crops are important in the global food economy, and new techniques are being implemented for their effective management. These techniques rely mainly on the changes in the phenological cycle, which can be investigated by remote sensing systems. High frequency and high spatial resolution Synthetic Aperture Radar (SAR) sensors have great potential in all-weather conditions for detecting temporal phenological changes. This study focuses on a novel approach for growth stage determination of rice fields from SAR data using a parameter space search algorithm. The method employs an inversion scheme for a morphology-based electromagnetic backscattering model. Since such a morphology-based model is complicated and computationally expensive, a surrogate metamodel-based inversion algorithm is proposed for the growth stage estimation. The approach is designed to provide estimates of crop morphology and corresponding growth stage from a continuous growth scale. The accuracy of the proposed method is tested with ground measurements from Turkey and Spain using the images acquired by the TerraSAR-X (TSX) sensor during a full growth cycle of rice crops. The analysis shows good agreement for both datasets. The results of the proposed method emphasize the effectiveness of X-band PolSAR data for morphology-based growth stage determination of rice crops. Full article
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
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Open AccessArticle Characterizing Land Cover Impacts on the Responses of Land Surface Phenology to the Rainy Season in the Congo Basin
Remote Sens. 2017, 9(5), 461; doi:10.3390/rs9050461
Received: 28 February 2017 / Revised: 4 May 2017 / Accepted: 5 May 2017 / Published: 9 May 2017
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Abstract
Knowledge of how rainfall seasonality affects land surface phenology has important implications on understanding ecosystem resilience to future climate change in the Congo Basin. We studied the impacts of land cover on the response of the canopy greenness cycle (CGC) to the rainy
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Knowledge of how rainfall seasonality affects land surface phenology has important implications on understanding ecosystem resilience to future climate change in the Congo Basin. We studied the impacts of land cover on the response of the canopy greenness cycle (CGC) to the rainy season in the Congo Basin on a yearly basis during 2006–2013. Specifically, we retrieved CGC from the time series of two-band enhanced vegetation index (EVI2) acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI). We then detected yearly onset (ORS) and end (ERS) of the rainy season using a modified Climatological Anomalous Accumulation (CAA) method based on the daily rainfall time series provided by the Tropical Rainfall Measurement Mission. We further examined the timing differences between CGC and the rainy season across different types of land cover, and investigated the relationship between spatial variations in CGC and rainy season timing. Results show that the rainy season in the equatorial Congo Basin was regulated by a distinct bimodal rainfall regime. The spatial variation in the rainy season timing presented distinct latitudinal gradients whereas the variation in CGC timing was relatively small. Moreover, the inter-annual variation in the rainy season timing could exceed 40 days whereas it was predominantly less than 20 days for CGC timing. The response of CGC to the rainy season varied with land cover. The lead time of CGC onset prior to ORS was longer in tropical woodlands and forests, whereas it became relatively short in grasslands and shrublands. Further, the spatial variation in CGC onset had a stronger correlation with that of ORS in grasslands and shrublands than in tropical woodlands and forests. In contrast, the lag of CGC end behind ERS was widespread across the Congo Basin, which was longer in grasslands and shrublands than that in tropical woodlands and forests. However, no significant relationship was identified between spatial variations in ERS and CGC end. Full article
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Open AccessArticle High Frequency Field Measurements of an Undular Bore Using a 2D LiDAR Scanner
Remote Sens. 2017, 9(5), 462; doi:10.3390/rs9050462
Received: 24 March 2017 / Revised: 28 April 2017 / Accepted: 3 May 2017 / Published: 10 May 2017
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Abstract
The secondary wave field associated with undular tidal bores (known as whelps) has been barely studied in field conditions: the wave field can be strongly non-hydrostatic, and the turbidity is generally high. In situ measurements based on pressure or acoustic signals can therefore
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The secondary wave field associated with undular tidal bores (known as whelps) has been barely studied in field conditions: the wave field can be strongly non-hydrostatic, and the turbidity is generally high. In situ measurements based on pressure or acoustic signals can therefore be limited or inadequate. The intermittent nature of this process in the field and the complications encountered in the downscaling to laboratory conditions also render its study difficult. Here, we present a new methodology based on LiDAR technology to provide high spatial and temporal resolution measurements of the free surface of an undular tidal bore. A wave-by-wave analysis is performed on the whelps, and comparisons between LiDAR, acoustic and pressure-derived measurements are used to quantify the non-hydrostatic nature of this phenomenon. A correction based on linear wave theory applied on individual wave properties improves the results from the pressure transducer (Root mean square error, R M S E of 0 . 19 m against 0 . 38 m); however, more robust data is obtained from an upwards-looking acoustic sensor despite high turbidity during the passage of the whelps ( R M S E of 0 . 05 m). Finally, the LiDAR scanner provides the unique possibility to study the wave geometry: the distribution of measured wave height, period, celerity, steepness and wavelength are presented. It is found that the highest wave from the whelps can be steeper than the bore front, explaining why breaking events are sometimes observed in the secondary wave field of undular tidal bores. Full article
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Open AccessArticle Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments
Remote Sens. 2017, 9(5), 463; doi:10.3390/rs9050463
Received: 24 February 2017 / Revised: 27 April 2017 / Accepted: 2 May 2017 / Published: 10 May 2017
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Abstract
Livestock plays an important economic role in Niger, especially in the semi-arid regions, while being highly vulnerable as a result of the large inter-annual variability of precipitation and, hence, rangeland production. This study aims to support effective rangeland management by developing an approach
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Livestock plays an important economic role in Niger, especially in the semi-arid regions, while being highly vulnerable as a result of the large inter-annual variability of precipitation and, hence, rangeland production. This study aims to support effective rangeland management by developing an approach for mapping rangeland biomass production. The observed spatiotemporal variability of biomass production is utilised to build a model based on ground and remote sensing data for the period 2001 to 2015. Once established, the model can also be used to estimate herbaceous biomass for the current year at the end of the season without the need for new ground data. The phenology-based seasonal cumulative Normalised Difference Vegetation Index (cNDVI), computed from 10-day image composites of the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data, was used as proxy for biomass production. A linear regression model was fitted with multi-annual field measurements of herbaceous biomass at the end of the growing season. In addition to a general model utilising all available sites for calibration, different aggregation schemes (i.e., grouping of sites into calibration units) of the study area with a varying number of calibration units and different biophysical meaning were tested. The sampling sites belonging to a specific calibration unit of a selected scheme were aggregated to compute the regression. The different aggregation schemes were evaluated with respect to their predictive power. The results gathered at the different aggregation levels were subjected to cross-validation (cv), applying a jackknife technique (leaving out one year at a time). In general, the model performance increased with increasing model parameterization, indicating the importance of additional unobserved and spatially heterogeneous agro-ecological effects (which might relate to grazing, species composition, optical soil properties, etc.) in modifying the relationship between cNDVI and herbaceous biomass at the end of the season. The biophysical aggregation scheme, the calibration units for which were derived from an unsupervised ISODATA classification utilising 10-day NDVI images taken between January 2001 and December 2015, showed the best performance in respect to the predictive power (R2cv = 0.47) and the cross-validated root-mean-square error (398 kg·ha−1) values, although it was not the model with the highest number of calibration units. The proposed approach can be applied for the timely production of maps of estimated biomass at the end of the growing season before field measurements are made available. These maps can be used for the improved management of rangeland resources, for decisions on fire prevention and aid allocation, and for the planning of more in-depth field missions. Full article
(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)
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Open AccessArticle Multimode Hybrid Geometric Calibration of Spaceborne SAR Considering Atmospheric Propagation Delay
Remote Sens. 2017, 9(5), 464; doi:10.3390/rs9050464
Received: 10 February 2017 / Revised: 4 May 2017 / Accepted: 7 May 2017 / Published: 10 May 2017
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Abstract
The atmospheric propagation delay of radar signals is a systematic error that occurs in the atmospheric environment, and is a key issue in the high-precision geometric calibration of spaceborne SAR. A multimode hybrid geometric calibration method for spaceborne SAR that considers the atmospheric
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The atmospheric propagation delay of radar signals is a systematic error that occurs in the atmospheric environment, and is a key issue in the high-precision geometric calibration of spaceborne SAR. A multimode hybrid geometric calibration method for spaceborne SAR that considers the atmospheric propagation delay is proposed in this paper. Error sources that affect the accuracy of the geometric calibration were systematically analyzed. Based on correction of the atmospheric propagation delay, a geometric calibration model for spaceborne SAR was established. The high precision geometric calibration scheme for spaceborne SAR was explored by considering the pulse-width and bandwidth of the signal. A series of experiments were carried out based on high-resolution Yaogan 13 (YG-13) SAR satellite data and ground control data. The experimental results demonstrated that the proposed method is effective. The plane positioning accuracy of YG-13 in stripmap mode without control points is better than 3 m, and the accuracy of the sliding spotlight mode is better than 1.5 m. Full article
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Open AccessArticle Evaluating Consistency of Snow Water Equivalent Retrievals from Passive Microwave Sensors over the North Central U. S.: SSM/I vs. SSMIS and AMSR-E vs. AMSR2
Remote Sens. 2017, 9(5), 465; doi:10.3390/rs9050465
Received: 27 March 2017 / Revised: 1 May 2017 / Accepted: 6 May 2017 / Published: 10 May 2017
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Abstract
For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals
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For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Open AccessArticle The CM SAF TOA Radiation Data Record Using MVIRI and SEVIRI
Remote Sens. 2017, 9(5), 466; doi:10.3390/rs9050466
Received: 17 February 2017 / Revised: 21 April 2017 / Accepted: 5 May 2017 / Published: 10 May 2017
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Abstract
The CM SAF Top of Atmosphere (TOA) Radiation MVIRI/SEVIRI Data Record provides a homogenised satellite-based climatology of TOA Reflected Solar (TRS) and Emitted Thermal (TET) radiation in all-sky conditions over the Meteosat field of view. The continuous monitoring of these two components of
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The CM SAF Top of Atmosphere (TOA) Radiation MVIRI/SEVIRI Data Record provides a homogenised satellite-based climatology of TOA Reflected Solar (TRS) and Emitted Thermal (TET) radiation in all-sky conditions over the Meteosat field of view. The continuous monitoring of these two components of the Earth Radiation Budget is of prime importance to study climate variability and change. Combining the Meteosat MVIRI and SEVIRI instruments allows an unprecedented temporal (30 min/15 min) and spatial (2.5 km/3 km) resolution compared to, e.g., the CERES products. It also opens the door to the generation of a long data record covering a 32 years time period and extending from 1 February 1983 to 30 April 2015. The retrieval method used to process the CM SAF TOA Radiation MVIRI/SEVIRI Data Record is discussed. The overlap between the MVIRI and GERB instruments in the period 2004–2006 is used to derive empirical narrowband to broadband regressions. The CERES TRMM angular dependency models and theoretical models are respectively used to compute the TRS and TET fluxes from the broadband radiances. The TOA radiation products are issued as daily means, monthly means and monthly averages of the hourly integrated values (diurnal cycle). The data is provided on a regular grid at a spatial resolution of 0.05 degrees and covers the region 70 N–70 S and 70 W–70 E. The quality of the data record has been evaluated by intercomparison with several references. In general, the stability in time of the data record is found better than 4 Wm 2 and most products fulfill the predefined accuracy requirements. Full article
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Open AccessArticle Ground Ammonia Concentrations over China Derived from Satellite and Atmospheric Transport Modeling
Remote Sens. 2017, 9(5), 467; doi:10.3390/rs9050467
Received: 27 March 2017 / Revised: 2 May 2017 / Accepted: 7 May 2017 / Published: 15 May 2017
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Abstract
As a primary basic gas in the atmosphere, atmospheric ammonia (NH3) plays an important role in determining air quality, environmental degradation, and climate change. However, the limited ground observation currently presents a barrier to estimating ground NH3 concentrations on a
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As a primary basic gas in the atmosphere, atmospheric ammonia (NH3) plays an important role in determining air quality, environmental degradation, and climate change. However, the limited ground observation currently presents a barrier to estimating ground NH3 concentrations on a regional scale, thus preventing a full understanding of the atmospheric processes in which this trace gas is involved. This study estimated the ground NH3 concentrations over China, combining the Infrared Atmospheric Sounding Interferometer (IASI) satellite NH3 columns and NH3 profiles from an atmospheric chemistry transport model (CTM). The estimated ground NH3 concentrations showed agreement with the variability in annual ground NH3 measurements from the Chinese Nationwide Nitrogen Deposition Monitoring Network (NNDMN). Great spatial heterogeneity of ground NH3 concentrations was found across China, and high ground NH3 concentrations were found in Northern China, Southeastern China, and some areas in Xinjiang Province. The maximum ground NH3 concentrations over China occurred in summer, followed by spring, autumn, and winter seasons, which were in agreement with the seasonal patterns of NH3 emissions in China. This study suggested that a combination of NH3 profiles from CTMs and NH3 columns from satellite obtained reliable ground NH3 concentrations over China. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)
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