Open AccessArticle
Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis
Remote Sens. 2017, 9(11), 1185; doi:10.3390/rs9111185 (registering DOI) -
Abstract
The L-band radiometry data and in-situ ground and snow measurements performed during the 2016/2017 winter campaign at the Davos-Laret remote sensing field laboratory are presented and discussed. An improved version of the procedure for the computation of L-band brightness temperatures from ELBARA radiometer
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The L-band radiometry data and in-situ ground and snow measurements performed during the 2016/2017 winter campaign at the Davos-Laret remote sensing field laboratory are presented and discussed. An improved version of the procedure for the computation of L-band brightness temperatures from ELBARA radiometer raw data is introduced. This procedure includes a thorough explanation of the calibration and filtering including a refined radio frequency interference (RFI) mitigation approach. This new mitigation approach not only performs better than conventional “normality” tests (kurtosis and skewness) but also allows for the quantification of measurement uncertainty introduced by non-thermal noise contributions. The brightness temperatures of natural snow covered areas and areas with a reflector beneath the snow are simulated for varying amounts of snow liquid water content distributed across the snow profile. Both measured and simulated brightness temperatures emanating from natural snow covered areas and areas with a reflector beneath the snow reveal noticeable sensitivity with respect to snow liquid water. This indicates the possibility of estimating snow liquid water using L-band radiometry. It is also shown that distinct daily increases in brightness temperatures measured over the areas with the reflector placed on the ground indicate the onset of the snow melting season, also known as “early-spring snow”. Full article
Open AccessArticle
Evaluating the Applicability of Four Latest Satellite–Gauge Combined Precipitation Estimates for Extreme Precipitation and Streamflow Predictions over the Upper Yellow River Basins in China
Remote Sens. 2017, 9(11), 1176; doi:10.3390/rs9111176 (registering DOI) -
Abstract
This study aimed to statistically and hydrologically assess the performance of the four latest and widely used satellite–gauge combined precipitation estimates (SGPEs), namely CRT (CMORPH CRT), BLD (CMORPH BLD), CDR (PERSIANN CDR), 3B42 (TMPA 3B42 version 7) over the upper yellow river basins
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This study aimed to statistically and hydrologically assess the performance of the four latest and widely used satellite–gauge combined precipitation estimates (SGPEs), namely CRT (CMORPH CRT), BLD (CMORPH BLD), CDR (PERSIANN CDR), 3B42 (TMPA 3B42 version 7) over the upper yellow river basins (UYRB) in china during 2001–2012 time period. The performances of the SGPEs were compared with the Chinese Meteorological Administration (CMA) datasets using the hydrologic model called Variable Infiltration Capacity (VIC) which is known as a land surface hydrologic model. Results indicated that irrespective of the slight underestimation in the western mountains and overestimation in the southeast, the four SGPEs could generally captured the spatial distribution of precipitation well. Although 3B42 exhibited a better performance in capturing the spatial distribution of daily average precipitation, BLD agreed best with CMA in the time series of watershed average precipitation, which resulted in BLD having a comparable performance to the CMA in the long-term hydrological simulations. Moreover, the potential for disastrous heavy rain mainly occurs in southeastern corner of the basin, and CRT and BLD comparisons showed to be closer to the CMA in the distribution of extreme precipitation events while 3B42 and CDR overestimated the extreme precipitation especially over the southeast of UYRB region. Therefore, CRT and BLD were able to match the high peak discharges very well for the wet seasons, while 3B42 and CDR overrated the high peak discharges. In addition, the four SGPEs performed well for the 2005 flood event but exhibited poorly when tested for the 2012 flood event. Results indicate that the application of the four SGPEs should be used with caution in simulating massive flood events over UYRB region. Full article
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Open AccessArticle
Stochastic Models of Very High-Rate (50 Hz) GPS/BeiDou Code and Phase Observations
Remote Sens. 2017, 9(11), 1188; doi:10.3390/rs9111188 (registering DOI) -
Abstract
In recent years, very high-rate (10–50 Hz) Global Navigation Satellite System (GNSS) has gained a rapid development and has been widely applied in seismology, natural hazard early warning system and structural monitoring. However, existing studies on stochastic models of GNSS observations are limited
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In recent years, very high-rate (10–50 Hz) Global Navigation Satellite System (GNSS) has gained a rapid development and has been widely applied in seismology, natural hazard early warning system and structural monitoring. However, existing studies on stochastic models of GNSS observations are limited to sampling rates not higher than 1 Hz. To support very high-rate GNSS applications, we assess the precisions, cross correlations and time correlations of very high-rate (50 Hz) Global Positioning System (GPS)/BeiDou code and phase observations. The method of least-squares variance component estimation is applied with the geometry-based functional model using the GNSS single-differenced observations. The real-data experimental results show that the precisions are elevation-dependent at satellite elevation angles below 40° and nearly constant at satellite elevation angles above 40°. The precisions of undifferenced observations are presented, exhibiting different patterns for different observation types and satellites, especially for BeiDou because different types of satellites are involved. GPS and BeiDou have comparable precisions at high satellite elevation angles, reaching 0.91–1.26 mm and 0.13–0.17 m for phase and code, respectively, while, at low satellite elevation angles, GPS precisions are generally lower than BeiDou ones. The cross correlation between dual-frequency phase is very significant, with the coefficients of 0.773 and 0.927 for GPS and BeiDou, respectively. The cross correlation between dual-frequency code is much less significant, and no correlation can be found between phase and code. Time correlations exist for GPS/BeiDou phase and code at time lags within 1 s. At very small time lags of 0.02–0.12 s, time correlations of 0.041–0.293 and 0.858–0.945 can be observed for phase and code observations, respectively, indicating that the correlations in time should be taken into account in very high-rate applications. Full article
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Open AccessArticle
Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal
Remote Sens. 2017, 9(11), 1197; doi:10.3390/rs9111197 (registering DOI) -
Abstract
The spatiotemporal pattern of soil moisture is of great significance for the understanding of the water exchange between the land surface and the atmosphere. The two-satellite constellation of the Sentinel-1 mission provides C-band synthetic aperture radar (SAR) observations with high spatial and temporal
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The spatiotemporal pattern of soil moisture is of great significance for the understanding of the water exchange between the land surface and the atmosphere. The two-satellite constellation of the Sentinel-1 mission provides C-band synthetic aperture radar (SAR) observations with high spatial and temporal resolutions, which are suitable for soil moisture monitoring. In this paper, we aim to assess the capability of pattern analysis based on the soil moisture retrieved from Sentinel-1 time-series data of Dahra in Senegal. The look-up table (LUT) method is used in the retrieval with the backscattering coefficients that are simulated by the advanced integrated equation Model (AIEM) for the soil layer and the Michigan microwave canopy scattering (MIMICS) model for the vegetation layer. The temporal trend of Sentinel-1A soil moisture is evaluated by the ground measurements from the site at Dahra, with an unbiased root-mean-squared deviation (ubRMSD) of 0.053 m3/m3, a mean average deviation (MAD) of 0.034 m3/m3, and an R value of 0.62. The spatial variation is also compared with the existing microwave products at a coarse scale, which confirms the reliability of the Sentinel-1A soil moisture. The spatiotemporal patterns are analyzed by empirical orthogonal functions (EOF), and the geophysical factors that are affecting soil moisture are discussed. The first four EOFs of soil moisture explain 77.2% of the variance in total and the primary EOF explains 66.2%, which shows the dominant pattern at the study site. Soil texture and the normalized difference vegetation index are more closely correlated with the primary pattern than the topography and temperature in the study area. The investigation confirms the potential for soil moisture retrieval and spatiotemporal pattern analysis using Sentinel-1 images. Full article
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Open AccessArticle
Airport Detection Using End-to-End Convolutional Neural Network with Hard Example Mining
Remote Sens. 2017, 9(11), 1198; doi:10.3390/rs9111198 (registering DOI) -
Abstract
Deep convolutional neural network (CNN) achieves outstanding performance in the field of target detection. As one of the most typical targets in remote sensing images (RSIs), airport has attracted increasing attention in recent years. However, the essential challenge for using deep CNN to
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Deep convolutional neural network (CNN) achieves outstanding performance in the field of target detection. As one of the most typical targets in remote sensing images (RSIs), airport has attracted increasing attention in recent years. However, the essential challenge for using deep CNN to detect airport is the great imbalance between the number of airports and background examples in large-scale RSIs, which may lead to over-fitting. In this paper, we develop a hard example mining and weight-balanced strategy to construct a novel end-to-end convolutional neural network for airport detection. The initial motivation of the proposed method is that backgrounds contain an overwhelming number of easy examples and a few hard examples. Therefore, we design a hard example mining layer to automatically select hard examples by their losses, and implement a new weight-balanced loss function to optimize CNN. Meanwhile, the cascade design of proposal extraction and object detection in our network releases the constraint on input image size and reduces spurious false positives. Compared with geometric characteristics and low-level manually designed features, the hard example mining based network could extract high-level features, which is more robust for airport detection in complex environment. The proposed method is validated on a multi-scale dataset with complex background collected from Google Earth. The experimental results demonstrate that our proposed method is robust, and superior to the state-of-the-art airport detection models. Full article
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Open AccessArticle
Estimating Snow Depth Using Multi-Source Data Fusion Based on the D-InSAR Method and 3DVAR Fusion Algorithm
Remote Sens. 2017, 9(11), 1195; doi:10.3390/rs9111195 (registering DOI) -
Abstract
Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the
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Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the accuracy of snow depth inversion to a certain extent. This study proposes a scheme to estimate spatial snow depth that combines remote sensing with site observation. On the one hand, this scheme adopts the Sentinel-1 C-band of the European Space Agency (ESA), making use of the two-pass method of differential interferometry for inversion of spatial snow depth. On the other hand, the 3DVAR (three dimensional variational) fusion algorithm is used to integrate actual snow depth data of virtual stations and real-world observation stations into the snow depth inversion results. Thus, the accuracy of snow inversion will be improved. This scheme is applied in the study area of Bayanbulak Basin, which is located in the central hinterland of Tianshan Mountains in Xinjiang, China. Observation data from stations in different altitudes are selected to test the fusion method. According to the results, most of the obtained snow depth values using interferometry are lower than the observed ones. However, after the fusion using the 3DVAR algorithm, the snow depth accuracy is slightly higher than it was in the inversion results (R2 = 0.31 vs. R2 = 0.50, RMSE = 2.51 cm vs. RMSE = 1.96 cm; R2 = 0.27 vs. R2 = 0.46, RMSE = 4.04 cm vs. RMSE = 3.65 cm). When compared with the inversion results, the relative error (RE) improved by 6.97% and 3.59%, respectively. This study shows that the scheme can effectively improve the accuracy of regional snow depth estimation. Therefore, its future application is of great potential. Full article
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Open AccessArticle
Developing a Random Forest Algorithm for MODIS Global Burned Area Classification
Remote Sens. 2017, 9(11), 1193; doi:10.3390/rs9111193 (registering DOI) -
Abstract
This paper aims to develop a global burned area (BA) algorithm for MODIS BRDF-corrected images based on the Random Forest (RF) classifier. Two RF models were generated, including: (1) all MODIS reflective bands; and (2) only the red (R) and near infrared (NIR)
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This paper aims to develop a global burned area (BA) algorithm for MODIS BRDF-corrected images based on the Random Forest (RF) classifier. Two RF models were generated, including: (1) all MODIS reflective bands; and (2) only the red (R) and near infrared (NIR) bands. Active fire information, vegetation indices and auxiliary variables were taken into account as well. Both RF models were trained using a statistically designed sample of 130 reference sites, which took into account the global diversity of fire conditions. For each site, fire perimeters were obtained from multitemporal pairs of Landsat TM/ETM+ images acquired in 2008. Those fire perimeters were used to extract burned and unburned areas to train the RF models. Using the standard MD43A4 resolution (500 × 500 m), the training dataset included 48,365 burned pixels and 6,293,205 unburned pixels. Different combinations of number of trees and number of parameters were tested. The final RF models included 600 trees and 5 attributes. The RF full model (considering all bands) provided a balanced accuracy of 0.94, while the RF RNIR model had 0.93. As a first assessment of these RF models, they were used to classify daily MCD43A4 images in three test sites for three consecutive years (2006–2008). The selected sites included different ecosystems: Australia (Tropical), Boreal (Canada) and Temperate (California), and extended coverage (totaling more than 2,500,000 km2). Results from both RF models for those sites were compared with national fire perimeters, as well as with two existing BA MODIS products; the MCD45 and MCD64. Considering all three years and three sites, commission error for the RF Full model was 0.16, with an omission error of 0.23. For the RF RNIR model, these errors were 0.19 and 0.21, respectively. The existing MODIS BA products had lower commission errors, but higher omission errors (0.09 and 0.33 for the MCD45 and 0.10 and 0.29 for the MCD64) than those obtained with the RF models, and therefore they showed less balanced accuracies. The RF models developed here should be applicable to other biomes and years, as they were trained with a global set of reference BA sites. Full article
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Open AccessArticle
Photochemical Reflectance Index (PRI) for Detecting Responses of Diurnal and Seasonal Photosynthetic Activity to Experimental Drought and Warming in a Mediterranean Shrubland
Remote Sens. 2017, 9(11), 1189; doi:10.3390/rs9111189 (registering DOI) -
Abstract
Climatic warming and drying are having profound impacts on terrestrial carbon cycling by altering plant physiological traits and photosynthetic processes, particularly for species in the semi-arid Mediterranean ecosystems. More effective methods of remote sensing are needed to accurately assess the physiological responses and
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Climatic warming and drying are having profound impacts on terrestrial carbon cycling by altering plant physiological traits and photosynthetic processes, particularly for species in the semi-arid Mediterranean ecosystems. More effective methods of remote sensing are needed to accurately assess the physiological responses and seasonal photosynthetic activities of evergreen species to climate change. We evaluated the stand reflectance in parallel to the diurnal and seasonal changes in gas exchange, fluorescence and water contents of leaves and soil for a Mediterranean evergreen shrub, Erica multiflora, submitted to long-term experimental warming and drought. We also calculated a differential photochemical reflectance index (ΔPRI, morning PRI subtracted from midday PRI) to assess the diurnal responses of photosynthesis (ΔA) to warming and drought. The results indicated that the PRI, but not the normalized difference vegetation index (NDVI), was able to assess the seasonal changes of photosynthesis. Changes in water index (WI) were consistent with seasonal foliar water content (WC). In the warming treatment, ΔA value was higher than control in winter but ΔYield was significantly lower in both summer and autumn, demonstrating the positive effect of the warming on the photosynthesis in winter and the negative effect in summer and autumn, i.e., increased photosynthetic midday depression in summer and autumn, when temperatures were much higher than in winter. Drought treatment increased the midday depression of photosynthesis in summer. Importantly, ΔPRI was significantly correlated with ΔA both under warming and drought, indicating the applicability of ΔPRI for tracking the midday depression of photosynthetic processes. Using PRI and ΔPRI to monitor the variability in photosynthesis could provide a simple method to remotely sense photosynthetic seasonality and midday depression in response to ongoing and future environmental stresses. Full article
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Open AccessArticle
Advancing the PROSPECT-5 Model to Simulate the Spectral Reflectance of Copper-Stressed Leaves
Remote Sens. 2017, 9(11), 1191; doi:10.3390/rs9111191 (registering DOI) -
Abstract
This paper proposes a modified model based on the PROSPECT-5 model to simulate the spectral reflectance of copper-stressed leaves. Compared with PROSPECT-5, the modified model adds the copper content of leaves as one of input variables, and the specific absorption coefficient related to
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This paper proposes a modified model based on the PROSPECT-5 model to simulate the spectral reflectance of copper-stressed leaves. Compared with PROSPECT-5, the modified model adds the copper content of leaves as one of input variables, and the specific absorption coefficient related to copper (Kcu) was estimated and fixed in the modified model. The specific absorption coefficients of other biochemical components (chlorophyll, carotenoid, water, dry matter) were the same as those in PROSPECT-5. Firstly, based on PROSPECT-5, we estimated the leaf structure parameters (N), using biochemical contents (chlorophyll, carotenoid, water, and dry matter) and the spectra of all the copper-stressed leaves (samples). Secondly, the specific absorption coefficient related to copper (Kcu) was estimated by fitting the simulated spectra to the measured spectra using 22 samples. Thirdly, other samples were used to verify the effectiveness of the modified model. The spectra with the new model are closer to the measured spectra when compared to that with PROSPECT-5. Moreover, for all the datasets used for validation and calibration, the root mean square errors (RMSEs) from the new model are less than that from PROSPECT-5. The differences between simulated reflectance and measured reflectance at key wavelengths with the new model are nearer to zero than those with the PROSPECT-5 model. This study demonstrated that the modified model could get more accurate spectral reflectance from copper-stressed leaves when compared with PROSPECT-5, and would provide theoretical support for monitoring the vegetation stressed by copper using remote sensing. Full article
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Open AccessArticle
Sparse Weighted Constrained Energy Minimization for Accurate Remote Sensing Image Target Detection
Remote Sens. 2017, 9(11), 1190; doi:10.3390/rs9111190 (registering DOI) -
Abstract
Target detection is an important task for remote sensing images, while it is still difficult to obtain satisfied performance when some images possess complex and confusion spectrum information, for example, the high similarity between target and background spectrum under some circumstance. Traditional detectors
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Target detection is an important task for remote sensing images, while it is still difficult to obtain satisfied performance when some images possess complex and confusion spectrum information, for example, the high similarity between target and background spectrum under some circumstance. Traditional detectors always detect target without any preprocessing procedure, which can increase the difference between target spectrum and background spectrum. Therefore, these methods could not discriminate the target from complex or similar background effectively. In this paper, sparse representation was introduced to weight each pixel for further increasing the difference between target and background spectrum. According to sparse reconstruction error matrix of pixels on images, adaptive weights will be assigned to each pixel for improving the difference between target and background spectrum. Furthermore, the sparse weighted-based constrained energy minimization method only needs to construct target dictionary, which is easier to acquire. Then, according to more distinct spectrum characteristic, the detectors can distinguish target from background more effectively and efficiency. Comparing with state-of-the-arts of target detection on remote sensing images, the proposed method can obtain more sensitive and accurate detection performance. In addition, the method is more robust to complex background than the other methods. Full article
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Open AccessArticle
Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction
Remote Sens. 2017, 9(11), 1187; doi:10.3390/rs9111187 (registering DOI) -
Abstract
Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture
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Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by ~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments. Full article
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Open AccessArticle
Wave Height Estimation from First-Order Backscatter of a Dual-Frequency High Frequency Radar
Remote Sens. 2017, 9(11), 1186; doi:10.3390/rs9111186 (registering DOI) -
Abstract
Second-order scattering based wave height measurement with high-frequency (HF) radar has always been subjected to problems such as distance limitation and external interference especially under low or moderate sea state. The performance is further exacerbated for a compact system with small antennas. First-order
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Second-order scattering based wave height measurement with high-frequency (HF) radar has always been subjected to problems such as distance limitation and external interference especially under low or moderate sea state. The performance is further exacerbated for a compact system with small antennas. First-order Bragg scattering has been investigated to relate wave height to the stronger Bragg backscatter, but calibrating the echo power along distance and direction is challenging. In this paper, a new method is presented to deal with the calibration and improve the Bragg scattering based wave height estimation from dual-frequency radar data. The relative difference of propagation attenuation and directional spreading between two operating frequencies has been found to be identifiable along range and almost independent of direction, and it is employed to effectively reduce the fitting requirements of in situ wave buoys. A 20-day experiment was performed over the Taiwan Strait of China to validate this method. Comparison of wave height measured by radar and buoys at distance of 15 km and 70 km shows that the root-mean-square errors are 0.34 m and 0.56 m, respectively, with correlation coefficient of 0.82 and 0.84. Full article
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Open AccessArticle
Data Assimilation to Extract Soil Moisture Information from SMAP Observations
Remote Sens. 2017, 9(11), 1179; doi:10.3390/rs9111179 -
Abstract
This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the
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This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE) by 0.005 m3 m3 and 0.001 m3 m3, respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m3 m3, but increased the root zone bias by 0.014 m3 m3. Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to skill degradation in other land surface variables. Full article
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Open AccessArticle
Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters
Remote Sens. 2017, 9(11), 1177; doi:10.3390/rs9111177 -
Abstract
In this paper, we present a novel approach for automatically detecting buildings from multiple heterogeneous and uncalibrated very high-resolution (VHR) satellite images for a rapid response to natural disasters. In the proposed method, a simple and efficient visual attention method is first used
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In this paper, we present a novel approach for automatically detecting buildings from multiple heterogeneous and uncalibrated very high-resolution (VHR) satellite images for a rapid response to natural disasters. In the proposed method, a simple and efficient visual attention method is first used to extract built-up area candidates (BACs) from each multispectral (MS) satellite image. After this, morphological building indices (MBIs) are extracted from all the masked panchromatic (PAN) and MS images with BACs to characterize the structural features of buildings. Finally, buildings are automatically detected in a hierarchical probabilistic model by fusing the MBI and masked PAN images. The experimental results show that the proposed method is comparable to supervised classification methods in terms of recall, precision and F-value. Full article
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Open AccessArticle
Source Parameters of the 2016–2017 Central Italy Earthquake Sequence from the Sentinel-1, ALOS-2 and GPS Data
Remote Sens. 2017, 9(11), 1182; doi:10.3390/rs9111182 -
Abstract
In this study, joint inversions of Synthetic Aperture Radar (SAR) and Global Position System (GPS) measurements are used to investigate the source parameters of four Mw > 5 events of the 2016–2017 Central Italy earthquake sequence. The results show that the four events
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In this study, joint inversions of Synthetic Aperture Radar (SAR) and Global Position System (GPS) measurements are used to investigate the source parameters of four Mw > 5 events of the 2016–2017 Central Italy earthquake sequence. The results show that the four events are all associated with a normal fault striking northwest–southeast and dipping southwest. The observations, in all cases, are consistent with slip on a rupture plane, with strike in the range of 157° to 164° and dip in the range of 39° to 44° that penetrates the uppermost crust to a depth of 0 to 8 km. The primary characteristics of these four events are that the 24 August 2016 Mw 6.2 Amatrice earthquake had pronounced heterogeneity of the slip distribution marked by two main slip patches, the 26 October 2016 Mw 6.1 Visso earthquake had a concentrated slip at 3–6 km, and the predominant slip of the 30 October 2016 Mw 6.6 Norcia earthquake occurred on the fault with a peak magnitude of 2.5 m at a depth of 0–6 km, suggesting that the rupture may have reached the surface, and the 18 January 2017 Mw 5.7 Campotosto earthquake had a large area of sliding at depth 3–9 km. The positive static stress changes on the fault planes of the latter three events demonstrate that the 24 August 2016 Amatrice earthquake may have triggered a cascading failure of earthquakes along the complex normal fault system in Central Italy. Full article
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Open AccessArticle
Tree-Species Classification in Subtropical Forests Using Airborne Hyperspectral and LiDAR Data
Remote Sens. 2017, 9(11), 1180; doi:10.3390/rs9111180 -
Abstract
Accurate classification of tree-species is essential for sustainably managing forest resources and effectively monitoring species diversity. In this study, we used simultaneously acquired hyperspectral and LiDAR data from LiCHy (Hyperspectral, LiDAR and CCD) airborne system to classify tree-species in subtropical forests of southeast
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Accurate classification of tree-species is essential for sustainably managing forest resources and effectively monitoring species diversity. In this study, we used simultaneously acquired hyperspectral and LiDAR data from LiCHy (Hyperspectral, LiDAR and CCD) airborne system to classify tree-species in subtropical forests of southeast China. First, each individual tree crown was extracted using the LiDAR data by a point cloud segmentation algorithm (PCS) and the sunlit portion of each crown was selected using the hyperspectral data. Second, different suites of hyperspectral and LiDAR metrics were extracted and selected by the indices of Principal Component Analysis (PCA) and the mean decrease in Gini index (MDG) from Random Forest (RF). Finally, both hyperspectral metrics (based on whole crown and sunlit crown) and LiDAR metrics were assessed and used as inputs to Random Forest classifier to discriminate five tree-species at two levels of classification. The results showed that the tree delineation approach (point cloud segmentation algorithm) was suitable for detecting individual tree in this study (overall accuracy = 82.9%). The classification approach provided a relatively high accuracy (overall accuracy > 85.4%) for classifying five tree-species in the study site. The classification using both hyperspectral and LiDAR metrics resulted in higher accuracies than only hyperspectral metrics (the improvement of overall accuracies = 0.4–5.6%). In addition, compared with the classification using whole crown metrics (overall accuracies = 85.4–89.3%), using sunlit crown metrics (overall accuracies = 87.1–91.5%) improved the overall accuracies of 2.3%. The results also suggested that fewer of the most important metrics can be used to classify tree-species effectively (overall accuracies = 85.8–91.0%). Full article
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Open AccessTechnical Note
Identification of C-Band Radio Frequency Interferences from Sentinel-1 Data
Remote Sens. 2017, 9(11), 1183; doi:10.3390/rs9111183 -
Abstract
We propose the use of Sentinel-1 Synthetic Aperture Radar (SAR) to provide a continuous and global monitoring of Radio Frequency Interferences (RFI) in C-band. We take advantage of the first 8–10 echo measures at the beginning of each burst, a 50–70 MHz wide
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We propose the use of Sentinel-1 Synthetic Aperture Radar (SAR) to provide a continuous and global monitoring of Radio Frequency Interferences (RFI) in C-band. We take advantage of the first 8–10 echo measures at the beginning of each burst, a 50–70 MHz wide bandwidth and a ground beam coverage of ~25 km (azimuth) by 70 km (range). Such observations can be repeated with a frequency better than three days, by considering two satellites and both ascending and descending passes. These measures can be used to qualify the same Sentinel-1 (S1) dataset as well as to monitor the availability and the use of radio frequency spectrum for present and future spaceborne imagers and for policy makers. In the paper we investigate the feasibility and the limits of this approach, and we provide a first Radio Frequency Interference (RFI) map with continental coverage over Europe. Full article
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Open AccessArticle
In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest
Remote Sens. 2017, 9(11), 1184; doi:10.3390/rs9111184 -
Abstract
Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of
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Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery. Full article
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Open AccessArticle
Evaluation and Aggregation Properties of Thermal Infra-Red-Based Evapotranspiration Algorithms from 100 m to the km Scale over a Semi-Arid Irrigated Agricultural Area
Remote Sens. 2017, 9(11), 1178; doi:10.3390/rs9111178 -
Abstract
Evapotranspiration (ET) estimates are particularly needed for monitoring the available water of arid lands. Remote sensing data offer the ideal spatial and temporal coverage needed by irrigation water management institutions to deal with increasing pressure on available water. Low spatial resolution (LR) products
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Evapotranspiration (ET) estimates are particularly needed for monitoring the available water of arid lands. Remote sensing data offer the ideal spatial and temporal coverage needed by irrigation water management institutions to deal with increasing pressure on available water. Low spatial resolution (LR) products present strong advantages. They cover larger zones and are acquired more frequently than high spatial resolution (HR) products. Current sensors such as Moderate-Resolution Imaging Spectroradiometer (MODIS) offer a long record history. However, validation of ET products at LR remains a difficult task. In this context, the objective of this study is to evaluate scaling properties of ET fluxes obtained at high and low resolution by two commonly used Energy Balance models, the Surface Energy Balance System (SEBS) and the Two-Source Energy Balance model (TSEB). Both are forced by local meteorological observations and remote sensing data in Visible, Near Infra-Red and Thermal Infra-Red spectral domains. Remotely sensed data stem from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS sensors, respectively, resampled at 100 m and 1000 m resolutions. The study zone is a square area of 4 by 4 km2 located in a semi-arid irrigated agricultural zone in the northwest of Mexico. Wheat is the dominant crop, followed by maize and vegetables. The HR ASTER dataset includes seven dates between the 30 December 2007 and 13 May 2008 and the LR MODIS products were retrieved for the same overpasses. ET retrievals from HR ASTER products provided reference ET maps at LR once linearly aggregated at the km scale. The quality of this retrieval was assessed using eddy covariance data at seven locations within the 4 by 4 km2 square. To investigate the impact of input aggregation, we first compared to the reference dataset all fluxes obtained by running TSEB and SEBS models using ASTER reflectances and radiances previously aggregated at the km scale. Second, we compared to the same reference dataset all fluxes obtained with SEBS and TSEB models using MODIS data. LR fluxes obtained by both models driven by aggregated ASTER input data compared well with the reference simulations and illustrated the relatively good accuracy achieved using aggregated inputs (relative bias of about 3.5% for SEBS and decreased to less than 1% for TSEB). Results also showed that MODIS ET estimates compared well with the reference simulation (relative bias was down to about 2% for SEBS and 3% for TSEB). Discrepancies were mainly related to fraction cover mapping for TSEB and to surface roughness length mapping for SEBS. This was consistent with the sensitivity analysis of those parameters previously published. To improve accuracy from LR estimates obtained using the 1 km surface temperature product provided by MODIS, we tested three statistical and one deterministic aggregation rules for the most sensible input parameter, the surface roughness length. The harmonic and geometric averages appeared to be the most accurate. Full article
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Open AccessArticle
An Evaluation of Four MODIS Collection 6 Aerosol Products in a Humid Subtropical Region
Remote Sens. 2017, 9(11), 1173; doi:10.3390/rs9111173 -
Abstract
Moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) products have been widely used to characterize the temporal variations and spatial distributions of atmospheric aerosols. In the present study, we evaluate the performance of four Terra and Aqua MODIS Collection 6 (C6) quality
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Moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) products have been widely used to characterize the temporal variations and spatial distributions of atmospheric aerosols. In the present study, we evaluate the performance of four Terra and Aqua MODIS Collection 6 (C6) quality assured AOD products in the Pearl River Delta (PRD) region, a humid subtropical region. The 10 km AOD products retrieved by the Dark Target (DT) and Deep Blue (DB) algorithms, the merged DT/DB (DTDB) 10 km product, and the DT 3 km AOD product were obtained for 2006–2015. These products were compared with Aerosol Robotic Network (AERONET) observations, and with each other. The Terra- and Aqua-derived AODs are quantitatively similar. However, there are significant differences among the four AOD products. The DT 10 km product correlates more closely with AERONET AOD observations than does the DB 10 km product. The latter tends to underestimate the AOD, whereas the former typically overestimates it for highly urbanized areas. The DTDB 10 km product is mainly derived from the DT 10 km product; it does not provide a gap-filled data set, because valid DB 10 km retrievals are not included in the merged product even when DT 10 km retrievals are unavailable. Therefore, the DT/DB merging protocol should be improved. The DT 3 km AOD product closely mimics the DT 10 km product; however, it contains fewer data than the DT 10 km product over water-contaminated areas. In addition, although the quality assured AOD products are recommended for use in quantitative applications by the MODIS aerosol science team, the sampling frequency of these products is generally lower than 25%. Thus, the sampling issues of these products should be considered in humid subtropical areas. Full article
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