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Remote Sens., Volume 9, Issue 12 (December 2017)

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Editorial

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Open AccessFeature PaperEditorial Editorial for Special Issue “Radar Systems for the Societal Challenges”
Remote Sens. 2017, 9(12), 1284; doi:10.3390/rs9121284
Received: 8 December 2017 / Revised: 8 December 2017 / Accepted: 9 December 2017 / Published: 11 December 2017
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Abstract
The special issue (SI) “Radar Systems for the Societal Challenges” is an updated survey of recent advances in radar systems, encompassing several application fields and related to the impact on society [...]
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(This article belongs to the Special Issue Radar Systems for the Societal Challenges)

Research

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Open AccessArticle Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States
Remote Sens. 2017, 9(12), 1181; doi:10.3390/rs9121181
Received: 21 September 2017 / Revised: 9 October 2017 / Accepted: 2 November 2017 / Published: 29 November 2017
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Abstract
Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for ~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to
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Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for ~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to significant advances in the frequency and spatial resolution of ET estimates, derived from energy balance principles with variables such as temperature used to estimate surface latent heat flux. Although remote sensing methods excel at depicting spatial and temporal variability, estimation of ET independently of other water budget components can lead to inconsistency with other budget terms. Methods that rely on ground-based data better constrain long-term ET, but are unable to provide the same temporal resolution. Here we combine long-term ET estimates from a water-balance approach with the SSEBop (operational Simplified Surface Energy Balance) remote sensing-based ET product for 2000–2015. We test the new combined method, the original SSEBop product, and another remote sensing ET product (MOD16) against monthly measurements from 119 flux towers. The new product showed advantages especially in non-irrigated areas where the new method showed a coefficient of determination R2 of 0.44, compared to 0.41 for SSEBop or 0.35 for MOD16. The resulting monthly data set will be a useful, unique contribution to ET estimation, due to its combination of remote sensing-based variability and ground-based long-term water balance constraints. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle Contributions of Actual and Simulated Satellite SAR Data for Substrate Type Differentiation and Shoreline Mapping in the Canadian Arctic
Remote Sens. 2017, 9(12), 1206; doi:10.3390/rs9121206
Received: 5 October 2017 / Revised: 18 November 2017 / Accepted: 20 November 2017 / Published: 23 November 2017
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Abstract
Detailed information on the land cover types present and the horizontal position of the land–water interface is needed for sensitive coastal ecosystems throughout the Arctic, both to establish baselines against which the impacts of climate change can be assessed and to inform response
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Detailed information on the land cover types present and the horizontal position of the land–water interface is needed for sensitive coastal ecosystems throughout the Arctic, both to establish baselines against which the impacts of climate change can be assessed and to inform response operations in the event of environmental emergencies such as oil spills. Previous work has demonstrated potential for accurate classification via fusion of optical and SAR data, though what contribution either makes to model accuracy is not well established, nor is it clear what shorelines can be classified using optical or SAR data alone. In this research, we evaluate the relative value of quad pol RADARSAT-2 and Landsat 5 data for shoreline mapping by individually excluding both datasets from Random Forest models used to classify images acquired over Nunavut, Canada. In anticipation of the RADARSAT Constellation Mission (RCM), we also simulate and evaluate dual and compact polarimetric imagery for shoreline mapping. Results show that SAR data is needed for accurate discrimination of substrates as user’s and producer’s accuracies were 5–24% higher for models constructed with quad pol RADARSAT-2 and DEM data than models constructed with Landsat 5 and DEM data. Models based on simulated RCM and DEM data achieved significantly lower overall accuracies (71–77%) than models based on quad pol RADARSAT-2 and DEM data (80%), with Wetland and Tundra being most adversely affected. When classified together with Landsat 5 and DEM data, however, model accuracy was less affected by the SAR data type, with multiple polarizations and modes achieving independent overall accuracies within a range acceptable for operational mapping, at 89–91%. RCM is expected to contribute positively to ongoing efforts to monitor change and improve emergency preparedness throughout the Arctic. Full article
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Open AccessArticle Spatial-Temporal Simulation of LAI on Basis of Rainfall and Growing Degree Days
Remote Sens. 2017, 9(12), 1207; doi:10.3390/rs9121207
Received: 11 October 2017 / Revised: 1 November 2017 / Accepted: 20 November 2017 / Published: 23 November 2017
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Abstract
The dimensionless Leaf Area Index (LAI) is widely used to characterize vegetation cover. With recent remote sensing developments LAI is available for large areas, although not continuous. However, in practice, continuous spatial-temporal LAI datasets are required for many environmental models. We investigate the
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The dimensionless Leaf Area Index (LAI) is widely used to characterize vegetation cover. With recent remote sensing developments LAI is available for large areas, although not continuous. However, in practice, continuous spatial-temporal LAI datasets are required for many environmental models. We investigate the relationship between LAI and climatic variable rainfall and Growing Degree Days (GDD) on the basis of data of a cold semi-arid region in Southwest Iran. For this purpose, monthly rainfall and temperature data were collected from ground stations between 2003 and 2015; LAI data were obtained from MODIS for the same period. The best relationship for predicting the monthly LAI values was selected from a set of single- and two-variable candidate models by considering their statistical goodness of fit (correlation coefficients, Nash-Sutcliffe coefficients, Root Mean Square Error and mean absolute error). Although various forms of linear and nonlinear relationships were tested, none showed a statistically meaningful relationship between LAI and rainfall for the study area. However, a two-variable nonlinear function was selected based on an iterative procedure linking rainfall and GDD to the expected LAI. By taking advantage of map algebra tools, this relationship can be used to predict missing LAI data for time series simulations. It is also concluded that the relationship between MODIS LAI and modeled LAI on basis of climatic variables shows a higher correlation for the wet season than for dry season. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)
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Open AccessArticle Online Global Land Surface Temperature Estimation from Landsat
Remote Sens. 2017, 9(12), 1208; doi:10.3390/rs9121208
Received: 29 September 2017 / Revised: 15 November 2017 / Accepted: 15 November 2017 / Published: 23 November 2017
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Abstract
This study explores the estimation of land surface temperature (LST) for the globe from Landsat 5, 7 and 8 thermal infrared sensors, using different surface emissivity sources. A single channel algorithm is used for consistency among the estimated LST products, whereas the option
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This study explores the estimation of land surface temperature (LST) for the globe from Landsat 5, 7 and 8 thermal infrared sensors, using different surface emissivity sources. A single channel algorithm is used for consistency among the estimated LST products, whereas the option of using emissivity from different sources provides flexibility for the algorithm’s implementation to any area of interest. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, allows the estimation of LST products for the globe, covering the time period from 1984 to present. To evaluate the method, the estimated LST products were compared against two reference datasets: (a) LST products derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), as higher-level products based on the temperature-emissivity separation approach; (b) Landsat LST data that have been independently produced, using different approaches. An overall RMSE (root mean square error) of 1.52 °C was observed and it was confirmed that the accuracy of the LST product is dependent on the emissivity; different emissivity sources provided different LST accuracies, depending on the surface cover. The LST products, for the full Landsat 5, 7 and 8 archives, are estimated “on-the-fly” and are available on-line via a web application. Full article
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Open AccessArticle New Scheme for Validating Remote-Sensing Land Surface Temperature Products with Station Observations
Remote Sens. 2017, 9(12), 1210; doi:10.3390/rs9121210
Received: 27 September 2017 / Revised: 10 November 2017 / Accepted: 20 November 2017 / Published: 24 November 2017
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Abstract
Continuous land-surface temperature (LST) observations from ground-based stations are an important reference dataset for validating remote-sensing LST products. However, a lack of evaluations of the representativeness of station observations limits the reliability of validation results. In this study, a new practical validation scheme
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Continuous land-surface temperature (LST) observations from ground-based stations are an important reference dataset for validating remote-sensing LST products. However, a lack of evaluations of the representativeness of station observations limits the reliability of validation results. In this study, a new practical validation scheme is presented for validating remote-sensing LST products that includes a key step: assessing the spatial representativeness of ground-based LST measurements. Three indicators, namely, the dominant land-cover type (DLCT), relative bias (RB), and average structure scale (ASS), are established to quantify the representative levels of station observations based on the land-cover type (LCT) and LST reference maps with high spatial resolution. We validated MODIS LSTs using station observations from the Heihe River Basin (HRB) in China. The spatial representative evaluation steps show that the representativeness of observations greatly differs among stations and varies with different vegetation growth and other factors. Large differences in the validation results occur when using different representative level observations, which indicates a large potential for large error during the traditional T-based validation scheme. Comparisons show that the new validation scheme greatly improves the reliability of LST product validation through high-level representative observations. Full article
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Open AccessArticle Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery
Remote Sens. 2017, 9(12), 1211; doi:10.3390/rs9121211
Received: 25 July 2017 / Revised: 20 November 2017 / Accepted: 21 November 2017 / Published: 24 November 2017
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Abstract
The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and
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The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R2 = 0.60–0.62, while models for the Hyperion sensor showed R2 = 0.49–0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600–700 nm), and the short wave infrared region between ~2000–2250 nm. The use of SMLR and spectral indices based on reference channels at ~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle Moisture Content Measurement of Broadleaf Litters Using Near-Infrared Spectroscopy Technique
Remote Sens. 2017, 9(12), 1212; doi:10.3390/rs9121212
Received: 18 October 2017 / Revised: 14 November 2017 / Accepted: 20 November 2017 / Published: 24 November 2017
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Abstract
Near-infrared spectroscopy (NIRS) was implemented to monitor the moisture content of broadleaf litters. Partial least-squares regression (PLSR) models, incorporating optimal wavelength selection techniques, have been proposed to better predict the litter moisture of forest floor. Three broadleaf litters were used to sample the
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Near-infrared spectroscopy (NIRS) was implemented to monitor the moisture content of broadleaf litters. Partial least-squares regression (PLSR) models, incorporating optimal wavelength selection techniques, have been proposed to better predict the litter moisture of forest floor. Three broadleaf litters were used to sample the reflection spectra corresponding the different degrees of litter moisture. The maximum normalization preprocessing technique was successfully applied to remove unwanted noise from the reflectance spectra of litters. Four variable selection methods were also employed to extract the optimal subset of measured spectra for establishing the best prediction model. The results showed that the PLSR model with the peak of beta coefficients method was the best predictor among all of the candidate models. The proposed NIRS procedure is thought to be a suitable technique for on-the-spot evaluation of litter moisture. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Geomorphological Dating of Pleistocene Conglomerates in Central Slovenia Based on Spatial Analyses of Dolines Using LiDAR and Ground Penetrating Radar
Remote Sens. 2017, 9(12), 1213; doi:10.3390/rs9121213
Received: 23 October 2017 / Revised: 15 November 2017 / Accepted: 17 November 2017 / Published: 24 November 2017
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Abstract
On Kranjsko polje in central Slovenia, carbonate conglomerates have been dated to several Pleistocene glacial phases by relative dating based on the morphostratigrafic mapping and borehole data, and by paleomagnetic and 10Be analyses. To define how the age of conglomerates determines the
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On Kranjsko polje in central Slovenia, carbonate conglomerates have been dated to several Pleistocene glacial phases by relative dating based on the morphostratigrafic mapping and borehole data, and by paleomagnetic and 10Be analyses. To define how the age of conglomerates determines the geomorphological characteristics of karst surface features, morphometrical and distributive spatial analyses of dolines were performed on three test sites including old, middle, and young Pleistocene conglomerates. As dolines on conglomerates are covered by a thick soil cover and show a strong human influence, the ground penetrating radar (GPR) method was first applied to select dolines appropriate for further morphometrical and distributive analyses. A considerable modification of natural morphology was revealed for cultivated dolines, excluding this type of depression from spatial analyses. Input parameters for spatial analyses (doline rim and deepest point) were manually extracted from the 1 × 1 m grid digital elevation model (DEM) originating from the high-resolution LiDAR (Light Detection and Ranging) data. Basic geomorphological characteristics, namely circularity index, planar size, depth, and density index of dolines were calculated for each relative age of conglomerates, and common characteristics were determined from these data to establish a general surface typology for a particular conglomerate. The obtained surface typologies were spatially extrapolated to the wider conglomerate area in central Slovenia to test the existent geological dating. Spatial analyses generally confirmed previous dating, while in four areas the geomorphological characteristics of dolines did not correspond to the existing dating and require further revision and modification. Doline populations exhibit specific and common morphometrical and distributive characteristics on conglomerates of a particular age and can be a reliable and fast indicator for their dating. Full article
(This article belongs to the Special Issue Recent Advances in GPR Imaging)
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Open AccessArticle A New Method for Acquisition of High-Resolution Seabed Topography by Matching Seabed Classification Images
Remote Sens. 2017, 9(12), 1214; doi:10.3390/rs9121214
Received: 13 October 2017 / Revised: 11 November 2017 / Accepted: 22 November 2017 / Published: 24 November 2017
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Abstract
The multibeam echo sounders (MBES) can acquire accurate positional but low-resolution seabed terrain and images, whereas side scan sonars (SSS) can only acquire inaccurate positional but high-resolution seabed images. In this study, a new method for superimposing corrected-positional SSS images on multibeam bathymetric
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The multibeam echo sounders (MBES) can acquire accurate positional but low-resolution seabed terrain and images, whereas side scan sonars (SSS) can only acquire inaccurate positional but high-resolution seabed images. In this study, a new method for superimposing corrected-positional SSS images on multibeam bathymetric terrain is proposed to obtain high-resolution and accurate-positional seabed topography using traditional MBES and SSS. Three steps, including the normalization by the z-score, sediment classification by the k-means++ algorithm, and denoising processing using morphological operations, are processed for both MBES and SSS images to obtain the corresponding sediment images. Next, a segmented matching method is given based on the common sediment distributions and features of MBES and SSS sediment images. The two kinds of sediment images are matched segmentally using the speeded up robust features algorithm and random sample consensus algorithm. Then, the positions of SSS images are corrected segmentally using thin plate splines based on matching points. Finally, the corrected SSS image is superimposed on MBES bathymetric terrain, based on positional relationship. The proposed method was verified through experiments, and high image resolution and high position accuracy seabed topography were obtained. Moreover, the performances of the method are discussed, and some conclusions are drawn according to the experiments and discussions. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Preliminary Analysis of Chinese GF-3 SAR Quad-Polarization Measurements to Extract Winds in Each Polarization
Remote Sens. 2017, 9(12), 1215; doi:10.3390/rs9121215
Received: 30 October 2017 / Revised: 18 November 2017 / Accepted: 22 November 2017 / Published: 25 November 2017
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Abstract
This study analyzed the noise equivalent sigma zero (NESZ) and ocean wind sensitivity for Chinese C-band Gaofen-3 (GF-3) quad-polarization synthetic aperture radar (SAR) measurements to facilitate further operational wind extraction from GF-3 data. Data from the GF-3 quad-polarization SAR and collocated winds from
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This study analyzed the noise equivalent sigma zero (NESZ) and ocean wind sensitivity for Chinese C-band Gaofen-3 (GF-3) quad-polarization synthetic aperture radar (SAR) measurements to facilitate further operational wind extraction from GF-3 data. Data from the GF-3 quad-polarization SAR and collocated winds from both NOAA/NCEP Global Forecast System (GFS) atmospheric model and National Data Buoy Center (NDBC) buoys were used in the analysis. For NESZ, the co-polarization was slightly higher compared to the cross-polarization. Regarding co-polarization and cross-polarization, NESZ was close to RadarSAT-2 and Sentinel-1 A. Wind sensitivity was analyzed by evaluating the dependence on winds in terms of normalized radar cross-sections (NRCS) and polarization combinations. The closest geophysical model function (GMF) and the polarization ratio (PR) model to GF-3 data were determined by comparing data and the model results. The dependence of co-polarized NRCS on wind speed and azimuth angle was consistent with the proposed GMF models. The combination of CMOD5 and CMOD5.N was considered to be the closest GMF in co-polarization. The cross-polarized NRCS exhibited a strong linear relationship with moderate wind speeds higher than 4 m·s−1, but a weak correlation with the azimuth angle. The proposed model was considered as the closest GMF in cross-polarization. For polarization combinations, PR and polarization difference (PD) were considered. PR increased only with the incidence angle, whereas PD increased with wind speed and varied with azimuth angle. There were three very close PR models and each can be considered as the closest. Preliminary results indicate that GF-3 quad-polarization data are valid and have the ability to extract winds in each polarization. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Tensor-Based Sparse Representation Classification for Urban Airborne LiDAR Points
Remote Sens. 2017, 9(12), 1216; doi:10.3390/rs9121216
Received: 22 October 2017 / Revised: 17 November 2017 / Accepted: 24 November 2017 / Published: 27 November 2017
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Abstract
The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. In many methods, only the features of each point are used, regardless of their spatial distribution within a
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The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. In many methods, only the features of each point are used, regardless of their spatial distribution within a certain neighborhood. This paper proposes a tensor-based sparse representation classification (TSRC) method for airborne LiDAR (Light Detection and Ranging) points. To keep features arranged in their spatial arrangement, each LiDAR point is represented as a 4th-order tensor. Then, TSRC is performed for point classification based on the 4th-order tensors. Firstly, a structured and discriminative dictionary set is learned by using only a few training samples. Subsequently, for classifying a new point, the sparse tensor is calculated based on the tensor OMP (Orthogonal Matching Pursuit) algorithm. The test tensor data is approximated by sub-dictionary set and its corresponding subset of sparse tensor for each class. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on eight real LiDAR point clouds whose result shows that objects can be distinguished by TSRC successfully. The overall accuracy of all the datasets is beyond 80% by TSRC. TSRC also shows a good improvement on LiDAR points classification when compared with other common classifiers. Full article
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Open AccessArticle Spatiotemporally Representative and Cost-Efficient Sampling Design for Validation Activities in Wanglang Experimental Site
Remote Sens. 2017, 9(12), 1217; doi:10.3390/rs9121217
Received: 12 September 2017 / Revised: 22 November 2017 / Accepted: 23 November 2017 / Published: 26 November 2017
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Abstract
Spatiotemporally representative Elementary Sampling Units (ESUs) are required for capturing the temporal variations in surface spatial heterogeneity through field measurements. Since inaccessibility often coexists with heterogeneity, a cost-efficient sampling design is mandatory. We proposed a sampling strategy to generate spatiotemporally representative and cost-efficient
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Spatiotemporally representative Elementary Sampling Units (ESUs) are required for capturing the temporal variations in surface spatial heterogeneity through field measurements. Since inaccessibility often coexists with heterogeneity, a cost-efficient sampling design is mandatory. We proposed a sampling strategy to generate spatiotemporally representative and cost-efficient ESUs based on the conditioned Latin hypercube sampling scheme. The proposed strategy was constrained by multi-temporal Normalized Difference Vegetation Index (NDVI) imagery, and the ESUs were limited within a sampling feasible region established based on accessibility criteria. A novel criterion based on the Overlapping Area (OA) between the NDVI frequency distribution histogram from the sampled ESUs and that from the entire study area was used to assess the sampling efficiency. A case study in Wanglang National Nature Reserve in China showed that the proposed strategy improves the spatiotemporally representativeness of sampling (mean annual OA = 74.7%) compared to the single-temporally constrained (OA = 68.7%) and the random sampling (OA = 63.1%) strategies. The introduction of the feasible region constraint significantly reduces in-situ labour-intensive characterization necessities at expenses of about 9% loss in the spatiotemporal representativeness of the sampling. Our study will support the validation activities in Wanglang experimental site providing a benchmark for locating the nodes of automatic observation systems (e.g., LAINet) which need a spatially distributed and temporally fixed sampling design. Full article
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Open AccessArticle Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery
Remote Sens. 2017, 9(12), 1218; doi:10.3390/rs9121218
Received: 9 October 2017 / Revised: 18 November 2017 / Accepted: 23 November 2017 / Published: 26 November 2017
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Abstract
Spatial regularization based sparse unmixing has attracted much attention in the hyperspectral remote sensing image processing field, which combines spatial information consideration with a sparse unmixing model, and has achieved improved fractional abundance results. However, the traditional spatial sparse unmixing approaches can suppress
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Spatial regularization based sparse unmixing has attracted much attention in the hyperspectral remote sensing image processing field, which combines spatial information consideration with a sparse unmixing model, and has achieved improved fractional abundance results. However, the traditional spatial sparse unmixing approaches can suppress discrete wrong unmixing points and smooth an abundance map with low-contrast changes, and it has no concept of scale difference. In this paper, to better extract the different levels of spatial details, rolling guidance based scale-aware spatial sparse unmixing (namely, Rolling Guidance Sparse Unmixing (RGSU)) is proposed to extract and recover the different levels of important structures and details in the hyperspectral remote sensing image unmixing procedure, as the different levels of structures and edges in remote sensing imagery have different meanings and importance. Differing from the existing spatial regularization based sparse unmixing approaches, the proposed method considers the different levels of edges by combining a Gaussian filter-like method to realize small-scale structure removal with a joint bilateral filtering process to account for the spatial domain and range domain correlations. The proposed method is based on rolling guidance spatial regularization in a traditional spatial regularization sparse unmixing framework, and it accomplishes scale-aware sparse unmixing. The experimental results obtained with both simulated and real hyperspectral images show that the proposed method achieves visual effects better and produces higher quantitative results (i.e., higher SRE values) when compared to the current state-of-the-art sparse unmixing algorithms, which illustrates the effectiveness of the rolling guidance based scale aware method. In the future work, adaptive scale-aware spatial sparse unmixing framework will be studied and developed to enhance the current idea. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study
Remote Sens. 2017, 9(12), 1220; doi:10.3390/rs9121220
Received: 23 September 2017 / Revised: 22 November 2017 / Accepted: 23 November 2017 / Published: 26 November 2017
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Abstract
There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high
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There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image often cannot be extrapolated to a different image. Recently, deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in computer vision and are offering promising results in land cover mapping. This paper analyzes the potential of CNN-based methods for detection of plant species of conservation concern using free high-resolution Google Earth TM images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. Compared to the best performing OBIA-method, the best CNN-detector achieved up to 12% better precision, up to 30% better recall and up to 20% better balance between precision and recall. Besides, the knowledge that CNNs acquired in the first image can be re-utilized in other regions, which makes the detection process very fast. A natural conclusion of this work is that including CNN-models as classifiers, e.g., ResNet-classifier, could further improve OBIA methods. The provided methodology can be systematically reproduced for other species detection using our codes available through (https://github.com/EGuirado/CNN-remotesensing). Full article
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Open AccessArticle Ionosphere Model for European Region Based on Multi-GNSS Data and TPS Interpolation
Remote Sens. 2017, 9(12), 1221; doi:10.3390/rs9121221
Received: 23 October 2017 / Revised: 22 November 2017 / Accepted: 23 November 2017 / Published: 27 November 2017
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Abstract
The ionosphere is still considered one of the most significant error sources in precise Global Navigation Satellite Systems (GNSS) positioning. On the other hand, new satellite signals and data processing methods allow for a continuous increase in the accuracy of the available ionosphere
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The ionosphere is still considered one of the most significant error sources in precise Global Navigation Satellite Systems (GNSS) positioning. On the other hand, new satellite signals and data processing methods allow for a continuous increase in the accuracy of the available ionosphere models derived from GNSS observables. Therefore, many research groups around the world are conducting research on the development of precise ionosphere products. This is also reflected in the establishment of several ionosphere-related working groups by the International Association of Geodesy. Whilst a number of available global ionosphere maps exist today, dense regional GNSS networks often offer the possibility of higher accuracy regional solutions. In this contribution, we propose an approach for regional ionosphere modelling based on un-differenced multi-GNSS carrier phase data for total electron content (TEC) estimation, and thin plate splines for TEC interpolation. In addition, we propose a methodology for ionospheric products self-consistency analysis based on calibrated slant TEC. The results of the presented approach are compared to well-established global ionosphere maps during varied ionospheric conditions. The initial results show that the accuracy of our regional ionospheric vertical TEC maps is well below 1 TEC unit, and that it is at least a factor of 2 better than the global products. Full article
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Open AccessArticle Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods
Remote Sens. 2017, 9(12), 1222; doi:10.3390/rs9121222
Received: 6 November 2017 / Revised: 22 November 2017 / Accepted: 25 November 2017 / Published: 27 November 2017
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Abstract
Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and
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Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence. The abundance and minimum noise fraction (MNF) results that were obtained from mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D) Terrain model, which was created using an image fusion digital elevation model (DEM), to select training samples (ROIs), and improve ROI separability. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method increased by 0.093% and 10%, respectively, as compared with the original decision tree method. This proposed method could effectively eliminate the influence of mixed pixels and improve the accuracy in complex LULC classifications. Full article
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Open AccessArticle Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia
Remote Sens. 2017, 9(12), 1223; doi:10.3390/rs9121223
Received: 6 October 2017 / Revised: 21 November 2017 / Accepted: 21 November 2017 / Published: 27 November 2017
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Abstract
Accurate pre-harvest estimation of avocado (Persea americana cv. Haas) yield offers a range of benefits to industry and growers. Currently there is no commercial yield monitor available for avocado tree crops and the manual count method used for yield forecasting can be
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Accurate pre-harvest estimation of avocado (Persea americana cv. Haas) yield offers a range of benefits to industry and growers. Currently there is no commercial yield monitor available for avocado tree crops and the manual count method used for yield forecasting can be highly inaccurate. Remote sensing using satellite imagery offers a potential means to achieve accurate pre-harvest yield forecasting. This study evaluated the accuracies of high resolution WorldView (WV) 2 and 3 satellite imagery and targeted field sampling for the pre-harvest prediction of total fruit weight (kg·tree−1) and average fruit size (g) and for mapping the spatial distribution of these yield parameters across the orchard block. WV 2 satellite imagery was acquired over two avocado orchards during 2014, and WV3 imagery was acquired in 2016 and 2017 over these same two orchards plus an additional three orchards. Sample trees representing high, medium and low vigour zones were selected from normalised difference vegetation index (NDVI) derived from the WV images and sampled for total fruit weight (kg·tree−1) and average fruit size (g) per tree. For each sample tree, spectral reflectance data was extracted from the eight band multispectral WV imagery and 18 vegetation indices (VIs) derived. Principal component analysis (PCA) and non-linear regression analysis was applied to each of the derived VIs to determine the index with the strongest relationship to the measured total fruit weight and average fruit size. For all trees measured over the three year period (2014, 2016, and 2017) a consistent positive relationship was identified between the VI using near infrared band one and the red edge band (RENDVI1) to both total fruit weight (kg·tree−1) (R2 = 0.45, 0.28, and 0.29 respectively) and average fruit size (g) (R2 = 0.56, 0.37, and 0.29 respectively) across all orchard blocks. Separate analysis of each orchard block produced higher R2 values as well as identifying different optimal VIs for each orchard block and year. This suggests orchard location and growing season are influencing the relationship of spectral reflectance to total fruit weight and average fruit size. Classified maps of avocado yield (kg·tree−1) and average fruit size per tree (g) were produced using the relationships developed for each orchard block. Using the relationships derived between the measured yield parameters and the optimal VIs, total fruit yield (kg) was calculated for each of the five sampled blocks for the 2016 and 2017 seasons and compared to actual yield at time of harvest and pre-season grower estimates. Prediction accuracies achieved for each block far exceeded those provided by the grower estimates. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Joint Local Abundance Sparse Unmixing for Hyperspectral Images
Remote Sens. 2017, 9(12), 1224; doi:10.3390/rs9121224
Received: 20 October 2017 / Revised: 14 November 2017 / Accepted: 22 November 2017 / Published: 27 November 2017
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Abstract
Sparse unmixing is widely used for hyperspectral imagery to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. This abundance has a unique property, i.e., high spatial correlation in local regions.
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Sparse unmixing is widely used for hyperspectral imagery to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. This abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region are highly correlated. This implies the low-rankness of the abundance in terms of the endmember. From this prior knowledge, it is expected that considering the low-rank local abundance to the sparse unmixing problem improves estimation performance. In this study, we propose an algorithm that exploits the low-rank local abundance by applying the nuclear norm to the abundance matrix for local regions of spatial and abundance domains. In our optimization problem, the local abundance regularizer is collaborated with the L 2 , 1 norm and the total variation for sparsity and spatial information, respectively. We conducted experiments for real and simulated hyperspectral data sets assuming with and without the presence of pure pixels. The experiments showed that our algorithm yields competitive results and performs better than the conventional algorithms. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Open AccessArticle Remote Sensing in Human Health: A 10-Year Bibliometric Analysis
Remote Sens. 2017, 9(12), 1225; doi:10.3390/rs9121225
Received: 28 September 2017 / Revised: 20 November 2017 / Accepted: 23 November 2017 / Published: 28 November 2017
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Abstract
A mixed methods bibliometric analysis was performed to ascertain the characteristic of scientific literature published in a 10-year period (2007–2016) regarding the application of remote sensing data in human health. A search was performed on the Scopus database, followed by manual revision using
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A mixed methods bibliometric analysis was performed to ascertain the characteristic of scientific literature published in a 10-year period (2007–2016) regarding the application of remote sensing data in human health. A search was performed on the Scopus database, followed by manual revision using synthesis studies’ techniques, requiring the authors to sort through more than 8000 medical concepts to create the query, and to manually select relevant papers from over 2000 documents. From the initial 2752 papers identified, 520 articles were selected for analysis, showing that the United States ranked first, with a total of 250 (48.1% of the total) documents, followed by France and the United Kingdom, with 67 (12.9% of the total) and 54 (10.4% of the total) documents, respectively. When considering authorship, the top three authors were Vounatsou P (22 articles), Utzinger J (19 articles), and Vignolles C (13 articles). Regarding disease-specific keywords, malaria, dengue, and schistosomiasis were the most frequent keywords, occurring 142, 34, and 24 times, respectively. For some infectious diseases and other highly pathogenic or emerging infectious diseases, remote sensing has become a very powerful instrument. Also, several studies relate different environmental factors retrieved by remote sensing data with other diseases, such as asthma exacerbations. Health-related remote sensing publications are increasing and this paper highlights the importance of these related technologies toward better information and, ideally, better provision of healthcare. On the other hand, this paper provides an overall picture of the state of the research regarding the application of remote sensing data in human health and identifies the most active stakeholders e.g., authors and institutions in the field, informing possible new collaboration research groups. Full article
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Open AccessArticle Mini N2-Raman Lidar Onboard Ultra-Light Aircraft for Aerosol Measurements: Demonstration and Extrapolation
Remote Sens. 2017, 9(12), 1226; doi:10.3390/rs9121226
Received: 8 September 2017 / Revised: 20 November 2017 / Accepted: 22 November 2017 / Published: 28 November 2017
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Abstract
Few airborne aerosol research experiments have deployed N2-Raman Lidar despite its capability to retrieve aerosol optical properties without ambiguity. Here, we show the high scientific potential of this instrument when used with specific flight plans. Our demonstration is based on (i)
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Few airborne aerosol research experiments have deployed N2-Raman Lidar despite its capability to retrieve aerosol optical properties without ambiguity. Here, we show the high scientific potential of this instrument when used with specific flight plans. Our demonstration is based on (i) a field-experiment conducted in June 2015 in southern France, involving a N2-Raman Lidar embedded on an ultra-light aircraft (ULA); and (ii) an appropriate algorithmic approach using two-level flight levels, aiming to solve the notorious instability of the airborne Lidar inversion for the retrieval of aerosol optical properties. The Lidar measurements include the determination of the aerosol extinction coefficient along ~500 m horizontal line of sight, and this value is used as a reference to validate the proposed algorithm. The Lidar-derived vertical profiles obtained during the flights are used as an input in a Monte Carlo simulation in order to compute the error budget in terms of biases and standard deviations on the retrieved aerosol extinction coefficient profile, as well as the subsequent optical thickness. The influence of the Lidar ratio (i.e., between aerosol extinction and backscatter) on the error budget is further discussed. Finally, from this end-to-end modeling, an optimal N2-Raman Lidar is proposed for airborne experiments, adapted to both small and large carriers. Full article
(This article belongs to the Special Issue Aerosol Remote Sensing)
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Open AccessArticle Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems
Remote Sens. 2017, 9(12), 1227; doi:10.3390/rs9121227
Received: 25 October 2017 / Revised: 23 November 2017 / Accepted: 27 November 2017 / Published: 28 November 2017
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Abstract
Arctic tundra ecosystems are a major source of methane (CH4), the variability of which is affected by local environmental and climatic factors, such as water table depth, microtopography, and the spatial heterogeneity of the vegetation communities present. There is a disconnect
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Arctic tundra ecosystems are a major source of methane (CH4), the variability of which is affected by local environmental and climatic factors, such as water table depth, microtopography, and the spatial heterogeneity of the vegetation communities present. There is a disconnect between the measurement scales for CH4 fluxes, which can be measured with chambers at one-meter resolution and eddy covariance towers at 100–1000 m, whereas model estimates are typically made at the ~100 km scale. Therefore, it is critical to upscale site level measurements to the larger scale for model comparison. As vegetation has a critical role in explaining the variability of CH4 fluxes across the tundra landscape, we tested whether remotely-sensed maps of vegetation could be used to upscale fluxes to larger scales. The objectives of this study are to compare four different methods for mapping and two methods for upscaling plot-level CH4 emissions to the measurements from EC towers. We show that linear discriminant analysis (LDA) provides the most accurate representation of the tundra vegetation within the EC tower footprints (classification accuracies of between 65% and 88%). The upscaled CH4 emissions using the areal fraction of the vegetation communities showed a positive correlation (between 0.57 and 0.81) with EC tower measurements, irrespective of the mapping method. The area-weighted footprint model outperformed the simple area-weighted method, achieving a correlation of 0.88 when using the vegetation map produced with the LDA classifier. These results suggest that the high spatial heterogeneity of the tundra vegetation has a strong impact on the flux, and variation indicates the potential impact of environmental or climatic parameters on the fluxes. Nonetheless, assimilating remotely-sensed vegetation maps of tundra in a footprint model was successful in upscaling fluxes across scales. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle TCCON Philippines: First Measurement Results, Satellite Data and Model Comparisons in Southeast Asia
Remote Sens. 2017, 9(12), 1228; doi:10.3390/rs9121228
Received: 8 September 2017 / Revised: 11 November 2017 / Accepted: 22 November 2017 / Published: 28 November 2017
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Abstract
The Total Carbon Column Observing Network (TCCON) is a global network dedicated to the precise and accurate measurements of greenhouse gases (GHG) in the atmosphere. The TCCON station in Burgos, Ilocos Norte, Philippines was established with the primary purpose of validating the upcoming
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The Total Carbon Column Observing Network (TCCON) is a global network dedicated to the precise and accurate measurements of greenhouse gases (GHG) in the atmosphere. The TCCON station in Burgos, Ilocos Norte, Philippines was established with the primary purpose of validating the upcoming Greenhouse gases Observing SATellite-2 (GOSAT-2) mission and in general, to respond to the need for reliable ground-based validation data for satellite GHG observations in the region. Here, we present the first 4 months of data from the new TCCON site in Burgos, initial comparisons with satellite measurements of C O 2 and model simulations of C O . A nearest sounding from Japan’s GOSAT as well as target mode observations from NASA’s Orbiting Carbon Observatory 2 (OCO-2) showed very good consistency in the retrieved column-averaged dry air mole fractions of C O 2 , yielding TCCON - satellite differences of 0.86 ± 1.06 ppm for GOSAT and 0.83 ± 1.22 ppm for OCO-2. We also show measurements of enhanced C O , probably from East Asia. GEOS-Chem model simulations were used to study the observed C O variability. However, despite the model capturing the pattern of the C O variability, there is an obvious underestimation in the C O magnitude in the model. We conclude that more measurements and modeling are necessary to adequately sample the variability over different seasons and to determine the suitability of current inventories. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)
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Open AccessArticle Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography
Remote Sens. 2017, 9(12), 1229; doi:10.3390/rs9121229
Received: 14 September 2017 / Revised: 14 September 2017 / Accepted: 2 November 2017 / Published: 28 November 2017
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Abstract
Synthetic Aperture Radar Tomography (TomoSAR) allows the reconstruction of the 3D reflectivity of natural volume scatterers such as forests, thus providing an opportunity to infer structure information in 3D. In this paper, the potential of TomoSAR data at L-band to monitor temporal variations
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Synthetic Aperture Radar Tomography (TomoSAR) allows the reconstruction of the 3D reflectivity of natural volume scatterers such as forests, thus providing an opportunity to infer structure information in 3D. In this paper, the potential of TomoSAR data at L-band to monitor temporal variations of forest structure is addressed using simulated and experimental datasets. First, 3D reflectivity profiles were extracted by means of TomoSAR reconstruction based on a Compressive Sensing (CS) approach. Next, two complementary indices for the description of horizontal and vertical forest structure were defined and estimated by means of the distribution of local maxima of the reconstructed reflectivity profiles. To assess the sensitivity and consistency of the proposed methodology, variations of these indices for different types of forest changes in simulated as well as in real scenarios were analyzed and assessed against different sources of reference data: airborne Lidar measurements, high resolution optical images, and forest inventory data. The forest structure maps obtained indicated the potential to distinguish between different forest stages and the identification of different types of forest structure changes induced by logging, natural disturbance, or forest management. Full article
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
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Open AccessArticle Using Copernicus Atmosphere Monitoring Service Products to Constrain the Aerosol Type in the Atmospheric Correction Processor MAJA
Remote Sens. 2017, 9(12), 1230; doi:10.3390/rs9121230
Received: 3 October 2017 / Revised: 9 November 2017 / Accepted: 22 November 2017 / Published: 28 November 2017
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Abstract
The quantitative use of space-based optical imagery requires atmospheric correction to separate the contributions from the surface and the atmosphere. The MACCS (Multi-sensor Atmospheric Correction and Cloud Screening)-ATCOR (Atmospheric and Topographic Correction) Joint Algorithm, called MAJA, is a numerical tool designed to perform
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The quantitative use of space-based optical imagery requires atmospheric correction to separate the contributions from the surface and the atmosphere. The MACCS (Multi-sensor Atmospheric Correction and Cloud Screening)-ATCOR (Atmospheric and Topographic Correction) Joint Algorithm, called MAJA, is a numerical tool designed to perform cloud detection and atmospheric correction. For the correction of aerosols effects, MAJA makes an estimate of the aerosol optical thickness (AOT) based on multi-temporal and multi-spectral criteria, but there is insufficient information to infer the aerosol type. The current operational version of MAJA uses an aerosol type which is constant with time, and this assumption impacts the quality of the atmospheric correction. In this study, we assess the potential of using an aerosol type derived from the Copernicus Atmosphere Monitoring Service (CAMS) operational analysis. The performances, with and without the CAMS information, are evaluated. Firstly, in terms of the aerosol optical thickness retrievals, a comparison against sunphotometer measurements over several sites indicates an improvement over arid sites, with a root-mean-square error (RMSE) reduced by 28% (from 0.095 to 0.068), although there is a slight degradation over vegetated sites (RMSE increased by 13%, from 0.054 to 0.061). Secondly, a direct validation of the retrieved surface reflectances at the La Crau station (France) indicates a reduction of the relative bias by 2.5% on average over the spectral bands. Thirdly, based on the assumption that surface reflectances vary slowly with time, a noise criterion was set up, exhibiting no improvement over the spectral bands and the validation sites when using CAMS data, partly explained by a slight increase in the surface reflectances themselves. Finally, the new method presented in this study provides a better way of using the MAJA processor in an operational environment because the aerosol type used for the correction is automatically inferred from CAMS data, and is no longer a parameter to be defined in advance. Full article
(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)
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Open AccessArticle Investigation of Water Temperature Variations and Sensitivities in a Large Floodplain Lake System (Poyang Lake, China) Using a Hydrodynamic Model
Remote Sens. 2017, 9(12), 1231; doi:10.3390/rs9121231
Received: 19 October 2017 / Revised: 20 November 2017 / Accepted: 27 November 2017 / Published: 28 November 2017
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Abstract
Although changes in water temperature influence the rates of many ecosystem processes in lakes, knowledge of the water temperature regime for large floodplain lake systems subjected to multiple stressors has received little attention. The coupled models can serve to derive more knowledge on
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Although changes in water temperature influence the rates of many ecosystem processes in lakes, knowledge of the water temperature regime for large floodplain lake systems subjected to multiple stressors has received little attention. The coupled models can serve to derive more knowledge on the water temperature impact on lake ecosystems. For this purpose, we used a physically-based hydrodynamic model coupled with a transport model to examine the spatial and temporal behavior and primary causal factors of water temperature within the floodplain of Poyang Lake that is representative of shallow and large lakes in China. Model performance is assessed through comparison with field observations and remote sensing data. The daily water temperature variations within Poyang Lake were reproduced reasonably well by the hydrodynamic model, with the root mean square errors of 1.5–1.9 °C. The modeling results indicate that the water temperature exhibits distinct spatial and temporal variability. The mean seasonal water temperatures vary substantially from 29.1 °C in summer to 7.7 °C in winter, with the highest value in August and the lowest value in January. Although the degree of spatial variability differed considerably between seasons, the water temperature generally decreases from the shallow floodplains to the main flow channels of the lake. As expected, the lake water temperature is primarily affected by the air temperature, solar radiation, wind speed and the inflow temperature, whereas other factors such as cloud cover, relative humidity, precipitation, evaporation and lake topography may play a complementary role in influencing temperature. The current work presents a first attempt to use a coupled model approach, which is therefore a useful tool to investigate the water temperature behavior and its major causal factors for a large floodplain lake system. It would have implications for improving the understanding of Poyang Lake water temperature and supporting planning and management of the lake, its water quality and ecosystem functioning. Full article
(This article belongs to the Special Issue Remote Sensing of Floodpath Lakes and Wetlands)
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Open AccessArticle High Resolution Mapping of Cropping Cycles by Fusion of Landsat and MODIS Data
Remote Sens. 2017, 9(12), 1232; doi:10.3390/rs9121232
Received: 9 November 2017 / Revised: 23 November 2017 / Accepted: 27 November 2017 / Published: 29 November 2017
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Abstract
Multiple cropping, a common practice of intensive agriculture that grows crops multiple times in the agricultural land in one growing season, is an effective way to fulfill the food demand given limited cropland areas. Deriving cropping cycles from satellite data provides the spatial
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Multiple cropping, a common practice of intensive agriculture that grows crops multiple times in the agricultural land in one growing season, is an effective way to fulfill the food demand given limited cropland areas. Deriving cropping cycles from satellite data provides the spatial distribution of cropping intensities that allows for monitoring of the multiple cropping activities over large areas. Although efforts have been made to map cropping cycles at 500 m or coarser resolution, producing cropping cycle maps at high resolution remain challenging because data from single satellite sensor do not provide sufficient spatiotemporal observations. In this paper, we generate dense time series of satellite data at 30 m resolution by fusion of Landsat and MODIS data, and derive the cropping cycles from the fused time series data. The method achieves overall accuracies of 92.5% and 89.2%, respectively, for two typical regions of multiple cropping in China using samples identified based on satellite time series data, and an overall accuracy of 81.2% for four subregions using all samples identified based on multi-temporal high resolution images. The mapped crop cycles show to be reasonable geographically and agree with the national census data. The fusion approach provides a feasible way to map cropping cycles at 30 m resolution and enables improved depiction of the spatial distribution of multiple cropping. Full article
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Open AccessArticle Change Detection Using High Resolution Remote Sensing Images Based on Active Learning and Markov Random Fields
Remote Sens. 2017, 9(12), 1233; doi:10.3390/rs9121233
Received: 15 October 2017 / Revised: 25 November 2017 / Accepted: 27 November 2017 / Published: 29 November 2017
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Abstract
Change detection has been widely used in remote sensing, such as for disaster assessment and urban expansion detection. Although it is convenient to use unsupervised methods to detect changes from multi-temporal images, the results could be further improved. In supervised methods, heavy data
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Change detection has been widely used in remote sensing, such as for disaster assessment and urban expansion detection. Although it is convenient to use unsupervised methods to detect changes from multi-temporal images, the results could be further improved. In supervised methods, heavy data labelling tasks are needed, and the sample annotation process with real categories is tedious and costly. To relieve the burden of labelling and to obtain satisfactory results, we propose an interactive change detection framework based on active learning and Markov random field (MRF). More specifically, a limited number of representative objects are found in an unsupervised way at the beginning. Then, the very limited samples are labelled as “change” or “no change” to train a simple binary classification model, i.e., a Gaussian process model. By using this model, we then select and label the most informative samples by “the easiest” sample selection strategy to update the former weak classification model until the detection results do not change notably. Finally, the maximum a posteriori (MAP) change detection is efficiently computed via the min-cut-based integer optimization algorithm. The time consuming and laborious manual labelling process can be reduced substantially, and a desirable detection result can be obtained. The experiments on several WorldView-2 images demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Normalized Difference Vegetation Index as an Estimator for Abundance and Quality of Avian Herbivore Forage in Arctic Alaska
Remote Sens. 2017, 9(12), 1234; doi:10.3390/rs9121234
Received: 29 September 2017 / Revised: 9 November 2017 / Accepted: 13 November 2017 / Published: 29 November 2017
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Abstract
Tools that can monitor biomass and nutritional quality of forage plants are needed to understand how arctic herbivores may respond to the rapidly changing environment at high latitudes. The Normalized Difference Vegetation Index (NDVI) has been widely used to assess changes in abundance
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Tools that can monitor biomass and nutritional quality of forage plants are needed to understand how arctic herbivores may respond to the rapidly changing environment at high latitudes. The Normalized Difference Vegetation Index (NDVI) has been widely used to assess changes in abundance and distribution of terrestrial vegetative communities. However, the efficacy of NDVI to measure seasonal changes in biomass and nutritional quality of forage plants in the Arctic remains largely un-evaluated at landscape and fine-scale levels. We modeled the relationships between NDVI and seasonal changes in aboveground biomass and nitrogen concentration in halophytic graminoids, a key food source for arctic-nesting geese. The model was calibrated based on data collected at one site and validated using data from another site. Effects of spatial scale on model accuracy were determined by comparing model predictions between NDVI derived from moderate resolution (250 × 250 m pixels) satellite data and high resolution (20 cm diameter area) handheld spectrometer data. NDVI derived from the handheld spectrometer was a superior estimator (R2 ≥ 0.67) of seasonal changes in aboveground biomass compared to satellite-derived NDVI (R2 ≤ 0.40). The addition of temperature and precipitation variables to the model for biomass improved fit, but provided minor gains in predictive power beyond that of the NDVI-only model. This model, however, was only a moderately accurate estimator of biomass in an ecologically-similar halophytic graminoid wetland located 100 km away, indicating the necessity for site-specific validation. In contrast to assessments of biomass, satellite-derived NDVI was a better estimator for the timing of peak percent of nitrogen than NDVI derived from the handheld spectrometer. We confirmed that the date when NDVI reached 50% of its seasonal maximum was a reasonable approximation of the period of peak spring vegetative green-up and peak percent nitrogen. This study demonstrates the importance of matching the scale of NDVI measurements to the vegetation properties of biomass and nitrogen phenology. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessFeature PaperArticle An Integrated Procedure to Assess the Stability of Coastal Rocky Cliffs: From UAV Close-Range Photogrammetry to Geomechanical Finite Element Modeling
Remote Sens. 2017, 9(12), 1235; doi:10.3390/rs9121235
Received: 29 September 2017 / Revised: 24 November 2017 / Accepted: 27 November 2017 / Published: 29 November 2017
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Abstract
The present paper explores the combination of unmanned aerial vehicle (UAV) photogrammetry and three-dimensional geomechanical modeling in the investigation of instability processes of long sectors of coastal rocky cliffs. The need of a reliable and detailed reconstruction of the geometry of the cliff
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The present paper explores the combination of unmanned aerial vehicle (UAV) photogrammetry and three-dimensional geomechanical modeling in the investigation of instability processes of long sectors of coastal rocky cliffs. The need of a reliable and detailed reconstruction of the geometry of the cliff surfaces, beside the geomechanical characterization of the rock materials, could represent a very challenging requirement for sub-vertical coastal cliffs overlooking the sea. Very often, no information could be acquired by alternative surveying methodologies, due to the absence of vantage points, and the fieldwork could pose a risk for personnel. The case study is represented by a 600 m long sea cliff located at Sant’Andrea (Melendugno, Apulia, Italy). The cliff is characterized by a very complex geometrical setting, with a suggestive alternation of 10 to 20 m high vertical walls, with frequent caves, arches and rock-stacks. Initially, the rocky cliff surface was reconstructed at very fine spatial resolution from the combination of nadir and oblique images acquired by unmanned aerial vehicles. Successively, a limited area has been selected for further investigation. In particular, data refinement/decimation procedure has been assessed to find a convenient three-dimensional model to be used in the finite element geomechanical modeling without loss of information on the surface complexity. Finally, to test integrated procedure, the potential modes of failure of such sector of the investigated cliff were achieved. Results indicate that the most likely failure mechanism along the sea cliff examined is represented by the possible propagation of shear fractures or tensile failures along concave cliff portions or over-hanging due to previous collapses or erosion of the underlying rock volumes. The proposed approach to the investigation of coastal cliff stability has proven to be a possible and flexible tool in the rapid and highly-automated investigation of hazards to slope failure in coastal areas. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle The Impact of Different Support Vectors on GOSAT-2 CAI-2 L2 Cloud Discrimination
Remote Sens. 2017, 9(12), 1236; doi:10.3390/rs9121236
Received: 17 October 2017 / Revised: 17 November 2017 / Accepted: 28 November 2017 / Published: 30 November 2017
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Abstract
Greenhouse gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two sensors: the Thermal and Near-infrared Sensor for Carbon Observation (TANSO)-Fourier Transform Spectrometer 2 (FTS-2) and the TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 is a
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Greenhouse gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two sensors: the Thermal and Near-infrared Sensor for Carbon Observation (TANSO)-Fourier Transform Spectrometer 2 (FTS-2) and the TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 is a push-broom imaging sensor that has forward- and backward-looking bands to observe the optical properties of aerosols and clouds and to monitor the status of urban air pollution and transboundary air pollution over oceans, such as PM2.5 (particles less than 2.5 micrometers in diameter). CAI-2 has important applications for cloud discrimination in each direction. The Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1), which applies sequential threshold tests to features is used for GOSAT CAI L2 cloud flag processing. If CLAUDIA1 is used with CAI-2, it is necessary to optimize the thresholds in accordance with CAI-2. However, CLAUDIA3 with support vector machines (SVM), a supervised pattern recognition method, was developed, and then we applied CLAUDIA3 for GOSAT-2 CAI-2 L2 cloud discrimination processing. Thus, CLAUDIA3 can automatically find the optimized boundary between clear and cloudy areas. Improvements in CLAUDIA3 using CAI (CLAUDIA3-CAI) continue to be made. In this study, we examined the impact of various support vectors (SV) on GOSAT-2 CAI-2 L2 cloud discrimination by analyzing (1) the impact of the choice of different time periods for the training data and (2) the impact of different generation procedures for SV on the cloud discrimination efficiency. To generate SV for CLAUDIA3-CAI from MODIS data, there are two times at which features are extracted, corresponding to CAI bands. One procedure is equivalent to generating SV using CAI data. Another procedure generates SV for MODIS cloud discrimination at the beginning, and then extracts decision function, thresholds, and SV corresponding to CAI bands. Our results indicated the following. (1) For the period from November to May, it is more effective to use SV generated from training data from February while for the period from June to October it is more effective to use training data from August; (2) In the preparation of SV, features obtained using MODIS bands are more effective than those obtained using the corresponding GOSAT CAI bands to automatically extract cloud training samples. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Hyperspectral Image Segmentation via Frequency-Based Similarity for Mixed Noise Estimation
Remote Sens. 2017, 9(12), 1237; doi:10.3390/rs9121237
Received: 17 October 2017 / Revised: 25 November 2017 / Accepted: 29 November 2017 / Published: 30 November 2017
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Abstract
Accurate approximation of the signal-independent (SI) and signal-dependent (SD) mixed noise from hyperspectral (HS) images is a critical task for many image processing applications where the detection of homogeneous regions plays a key role. Most of the conventional methods empirically divide images into
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Accurate approximation of the signal-independent (SI) and signal-dependent (SD) mixed noise from hyperspectral (HS) images is a critical task for many image processing applications where the detection of homogeneous regions plays a key role. Most of the conventional methods empirically divide images into rectangular blocks and then select the homogeneous ones, but it might result in erroneous homogeneity detection, especially for highly textured HS images. To address this challenge, a superpixel segmentation algorithm is proposed in this paper, which can decompose a noisy HS image into patches that adhere to the local structures and hence persist in homogeneous characteristic. A novel spectral similarity measure is defined in the frequency domain to make the superpixel segmentation algorithm more robust to the mixed noise. Combined with an improved scatter-plot-based homogeneous superpixel selection and a multiple linear regression-based noise parameter calculation, our method can accurately estimate SD and SI noise variances from HS images with different noise conditions and various image complexities. We evaluate the proposed method with both synthetic and real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) HS images. Experimental results demonstrate that the proposed noise estimation method outperforms the state-of-the-art methods. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle River Levels Derived with CryoSat-2 SAR Data Classification—A Case Study in the Mekong River Basin
Remote Sens. 2017, 9(12), 1238; doi:10.3390/rs9121238
Received: 13 October 2017 / Revised: 23 November 2017 / Accepted: 27 November 2017 / Published: 30 November 2017
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Abstract
In this study we use CryoSat-2 SAR (delay-Doppler synthetic-aperture radar) data in the Mekong River Basin to estimate water levels. Compared to classical pulse limited radar altimetry, medium- and small-sized inland waters can be observed with CryoSat-2 SAR data with a higher accuracy
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In this study we use CryoSat-2 SAR (delay-Doppler synthetic-aperture radar) data in the Mekong River Basin to estimate water levels. Compared to classical pulse limited radar altimetry, medium- and small-sized inland waters can be observed with CryoSat-2 SAR data with a higher accuracy due to the smaller along track footprint. However, even with this SAR data the estimation of water levels over a medium-sized river (width less than 500 m) is still challenging with only very few consecutive observations over the water. The target identification with land–water masks tends to fail as the river becomes smaller. Therefore, we developed a classification approach to divide the observations into water and land returns based solely on the data. The classification is done with an unsupervised classification algorithm, and it is based on features derived from the SAR and range-integrated power (RIP) waveforms. After the classification, classes representing water and land are identified. Better results are obtained when the Mekong River Basin is divided into different geographical regions: upstream, middle stream, and downstream. The measurements classified as water are used in a next step to estimate water levels for each crossing over a river in the Mekong River network. The resulting water levels are validated and compared to gauge data, Envisat data, and CryoSat-2 water levels derived with a land–water mask. The CryoSat-2 water levels derived with the classification lead to more valid observations with fewer outliers in the upstream region than with a land–water mask (1700 with 2% outliers vs. 1500 with 7% outliers). The median of the annual differences that is used in the validation is in all test regions smaller for the CryoSat-2 classification results than for Envisat or CryoSat-2 land–water mask results (for the entire study area: 0.76 m vs. 0.96 m vs. 0.83 m, respectively). Overall, in the upstream region with small- and medium-sized rivers the classification approach is more effective for deriving reliable water level observations than in the middle stream region with wider rivers. Full article
(This article belongs to the Special Issue Satellite Altimetry for Earth Sciences)
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Open AccessArticle GPS and BeiDou Differential Code Bias Estimation Using Fengyun-3C Satellite Onboard GNSS Observations
Remote Sens. 2017, 9(12), 1239; doi:10.3390/rs9121239
Received: 6 November 2017 / Revised: 20 November 2017 / Accepted: 28 November 2017 / Published: 1 December 2017
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Abstract
Differential code biases (DCBs) are important parameters in GNSS (Global Navigation Satellite System) applications such as positioning as well as ionosphere remote sensing. In comparison to the conventional approach, which utilizes ground-based observations and parameterizes global ionosphere maps together with DCBs, a method
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Differential code biases (DCBs) are important parameters in GNSS (Global Navigation Satellite System) applications such as positioning as well as ionosphere remote sensing. In comparison to the conventional approach, which utilizes ground-based observations and parameterizes global ionosphere maps together with DCBs, a method is presented for GPS and BeiDou system (BDS) satellite DCB estimation using onboard observations from the Chinese Fengyun-3C (FY3C) satellite. One month worth of GPS and BDS data during March 2015 was exploited and the GPS C1C-C2W and BDS C2I-C7I DCBs were explored. To improve DCB estimation precision, the dual frequency carrier phase measurements leveled by code measurements were used to form basic observation equation. Code multipath errors of the FY3C onboard GPS/BDS observations were assessed and modeled as grid maps, and their impact on DCB estimation was analyzed. By correcting code multipath errors, the stability of DCB estimates was improved by 5.0%, 3.1%, 16.2% and 13.6% for GPS, and BDS geosynchronous orbit satellites (GEOs), inclined geosynchronous satellite orbit satellites (IGSOs) and medium Earth orbit satellites (MEOs), respectively. The monthly stability of FY3C-based DCBs was at the order of 0.1 ns for GPS satellites, 0.2 ns for BDS GEOs and 0.1 ns for BDS IGSOs and MEOs. By comparison to the ground-based DCB products issued by other institutions, FY3C-based DCBs showed stability degradation for BDS C02 and C05 satellites, while, for other satellites, the stability reached a similar or even superior level. The estimated FY3C receiver DCB stability was at the order of 0.2 ns for both GPS and BDS. In addition to the DCB estimates, the obtained vertical total electron content above the FY3C satellite orbit was also investigated and its realism was examined in physical and numerical aspects. Full article
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Open AccessArticle Validation of Satellite Rainfall Products over a Mountainous Watershed in a Humid Subtropical Climate Region of Brazil
Remote Sens. 2017, 9(12), 1240; doi:10.3390/rs9121240
Received: 28 August 2017 / Revised: 14 November 2017 / Accepted: 27 November 2017 / Published: 1 December 2017
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Abstract
Remote sensing allows for the continuous and repetitive measurement of rainfall values. Satellite rainfall products such as Tropical Rainfall Measurement Mission (TRMM) 3B42 and the Hydroestimator (Hydroe) can be potential sources of data for hydrologic applications, mainly in areas with irregular and sparse
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Remote sensing allows for the continuous and repetitive measurement of rainfall values. Satellite rainfall products such as Tropical Rainfall Measurement Mission (TRMM) 3B42 and the Hydroestimator (Hydroe) can be potential sources of data for hydrologic applications, mainly in areas with irregular and sparse spatial distributions of traditional rain gauge stations. However, the accuracy of these satellite rainfall products over different spatial and temporal scales is unknown. In this study, we examined the potential of the TRMM 3B42 and Hydroe rainfall products to provide reliable rainfall estimates for a mountainous watershed in a humid subtropical climate region of Brazil. The purpose was to develop useful guidelines for future hydrologic studies on the potential and uncertainties of the rainfall products at different spatial and temporal resolutions. We compared the satellite products to reference rainfall data collected at 11 rain gauge stations irregularly distributed in the area. The results showed different levels of accuracy for each temporal scale evaluated. TRMM 3B42 performed better at the daily, monthly, and seasonal scales than Hydroe, while Hydroe presented a better correlation at the annual scale. In general, TRMM 3B42 overestimated the rainfall over the watershed at all evaluated temporal scales, whereas Hydroe underestimated it except for June–August at the seasonal scale. An evaluation based on contingency tables indicated that TRM 3B42 was better able to represent the local rainfall than Hydroe. The findings of this study indicate that satellite rainfall products are better suited for applications at the monthly and annual scales rather than the daily scale. Full article
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Open AccessArticle Evaluation of Seed Emergence Uniformity of Mechanically Sown Wheat with UAV RGB Imagery
Remote Sens. 2017, 9(12), 1241; doi:10.3390/rs9121241
Received: 29 September 2017 / Revised: 27 November 2017 / Accepted: 28 November 2017 / Published: 1 December 2017
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Abstract
The uniformity of wheat seed emergence is an important characteristic used to evaluate cultivars, cultivation mode and field management. Currently, researchers typically investigated the uniformity of seed emergence by manual measurement, a time-consuming and laborious process. This study employed field RGB images from
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The uniformity of wheat seed emergence is an important characteristic used to evaluate cultivars, cultivation mode and field management. Currently, researchers typically investigated the uniformity of seed emergence by manual measurement, a time-consuming and laborious process. This study employed field RGB images from unmanned aerial vehicles (UAVs) to obtain information related to the uniformity of wheat seed emergence and missing seedlings. The calculation of the length of areas with missing seedlings in both drill and broadcast sowing can be achieved by using an area localization algorithm, which facilitated the comprehensive evaluation of uniformity of seed emergence. Through a comparison between UAV images and the results of manual surveys used to gather data on the uniformity of seed emergence, the root-mean-square error (RMSE) was 0.44 for broadcast sowing and 0.64 for drill sowing. The RMSEs of the numbers of missing seedling regions for broadcast and drill sowing were 1.39 and 3.99, respectively. The RMSEs of the lengths of the missing seedling regions were 12.39 cm for drill sowing and 0.20 cm2 for broadcast sowing. The UAV image-based method provided a new and greatly improved method for efficiently measuring the uniformity of wheat seed emergence. The proposed method could provide a guideline for the intelligent evaluation of the uniformity of wheat seed emergence. Full article
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Open AccessArticle Investigating the Influence of Variable Freshwater Ice Types on Passive and Active Microwave Observations
Remote Sens. 2017, 9(12), 1242; doi:10.3390/rs9121242
Received: 1 September 2017 / Revised: 22 November 2017 / Accepted: 27 November 2017 / Published: 1 December 2017
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Abstract
Dual-polarized airborne passive microwave (PM) brightness temperatures (Tb) at 6.9 GHz H/V, 19 GHz H/V and 37 GHz H/V and spaceborne active microwave (AM) X-band (9.65 GHz VV, VH) backscatter (σ0) are observed coincident to in situ snow
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Dual-polarized airborne passive microwave (PM) brightness temperatures (Tb) at 6.9 GHz H/V, 19 GHz H/V and 37 GHz H/V and spaceborne active microwave (AM) X-band (9.65 GHz VV, VH) backscatter (σ0) are observed coincident to in situ snow and lake-ice measurements collected over two lakes near Inuvik, Canada. Lake-ice thickness is found to be positively correlated with 19 GHz V emission (R = 0.67) and negatively with 19 GHz H emission (R = −0.79), indicating surface ice conditions influence microwave interaction. Lake ice types are delineated from TerraSAR-X synthetic aperture radar (SAR) images using the iterative region growing with semantics (IRGS) segmentation algorithm implemented in the MAGIC (MAp Guided Ice Classification) system. The spatial extent of derived ice type classes correspond well to in situ observations. The overall magnitude of emission at 19 GHz H and X-band VH σ0 increase with the scattering potential of associated ice types (grey/rafted ice). Transects of 6.9 GHz PM and 19 GHz PM exhibit positive relationships with VH σ0 over freshwater lake ice, with the greatest R coefficients at H-pol (R = 0.64, 0.46). Conversely, 6.9 GHz Tb and 19 GHz Tb exhibit negative R coefficients in regions of brackish water due to tubular bubble and brine inclusions in the ice. This study identifies congruency between PM and AM scattering mechanisms over lake ice for the purpose of identifying the influence of ice types on overall microwave interaction within the lake-ice system. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle Improved DisTrad for Downscaling Thermal MODIS Imagery over Urban Areas
Remote Sens. 2017, 9(12), 1243; doi:10.3390/rs9121243
Received: 22 October 2017 / Revised: 20 November 2017 / Accepted: 26 November 2017 / Published: 1 December 2017
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Abstract
Spaceborne thermal sensors provide important physical parameters for urban studies. However, due to technical constraints, spaceborne thermal sensors yield a trade-off between their spatial and temporal resolution. The aims of this study are (1) to downscale the three originally low spatial resolution (960
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Spaceborne thermal sensors provide important physical parameters for urban studies. However, due to technical constraints, spaceborne thermal sensors yield a trade-off between their spatial and temporal resolution. The aims of this study are (1) to downscale the three originally low spatial resolution (960 m) Moderate Resolution Imaging Spectroradiometer (MODIS/Terra) land surface temperature image products (MOD11_L2, MOD11A1 and MOD11A2) to resolutions of 60, 90, 120, 240 and 480 m; and (2) to propose an improved version of the DisTrad method for downscaling the MODIS/Terra land surface temperature products over urban areas. The proposed improved DisTrad is based on a better parameterization of the original DisTrad residuals in urban areas. The improved resampling technique is based on a regression relationship between the residuals of the temperature estimation and the impervious percentage index. Validation of the improved DisTrad, the original DisTrad, and the uniformly disaggregated MODIS land surface temperature images (UniTrad) are performed by comparative analysis with a time-coincident Landsat 7 ETM+ thermal image. Statistical results indicate that the improved DisTrad method shows a higher correlation (R2 = 0.48) with the observed temperatures than the original DisTrad (R2 = 0.43) and a lower mean absolute error (MAE = 1.88 °C) than the original DisTrad (MAE = 2.07 °C). It is concluded that the improved DisTrad method has a stronger capability to downscale land surface temperatures in urban areas than the original DisTrad. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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Open AccessArticle Local Deep Hashing Matching of Aerial Images Based on Relative Distance and Absolute Distance Constraints
Remote Sens. 2017, 9(12), 1244; doi:10.3390/rs9121244
Received: 21 September 2017 / Revised: 24 November 2017 / Accepted: 29 November 2017 / Published: 1 December 2017
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Abstract
Aerial images have features of high resolution, complex background, and usually require large amounts of calculation, however, most algorithms used in matching of aerial images adopt the shallow hand-crafted features expressed as floating-point descriptors (e.g., SIFT (Scale-invariant Feature Transform), SURF (Speeded Up Robust
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Aerial images have features of high resolution, complex background, and usually require large amounts of calculation, however, most algorithms used in matching of aerial images adopt the shallow hand-crafted features expressed as floating-point descriptors (e.g., SIFT (Scale-invariant Feature Transform), SURF (Speeded Up Robust Features)), which may suffer from poor matching speed and are not well represented in the literature. Here, we propose a novel Local Deep Hashing Matching (LDHM) method for matching of aerial images with large size and with lower complexity or fast matching speed. The basic idea of the proposed algorithm is to utilize the deep network model in the local area of the aerial images, and study the local features, as well as the hash function of the images. Firstly, according to the course overlap rate of aerial images, the algorithm extracts the local areas for matching to avoid the processing of redundant information. Secondly, a triplet network structure is proposed to mine the deep features of the patches of the local image, and the learned features are imported to the hash layer, thus obtaining the representation of a binary hash code. Thirdly, the constraints of the positive samples to the absolute distance are added on the basis of the triplet loss, a new objective function is constructed to optimize the parameters of the network and enhance the discriminating capabilities of image patch features. Finally, the obtained deep hash code of each image patch is used for the similarity comparison of the image patches in the Hamming space to complete the matching of aerial images. The proposed LDHM algorithm evaluates the UltraCam-D dataset and a set of actual aerial images, simulation result demonstrates that it may significantly outperform the state-of-the-art algorithm in terms of the efficiency and performance. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Barest Pixel Composite for Agricultural Areas Using Landsat Time Series
Remote Sens. 2017, 9(12), 1245; doi:10.3390/rs9121245
Received: 27 September 2017 / Revised: 21 November 2017 / Accepted: 27 November 2017 / Published: 1 December 2017
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Abstract
Many soil remote sensing applications rely on narrow-band observations to exploit molecular absorption features. However, broadband sensors are invaluable for soil surveying, agriculture, land management and mineral exploration, amongst others. These sensors provide denser time series compared to high-resolution airborne imaging spectrometers and
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Many soil remote sensing applications rely on narrow-band observations to exploit molecular absorption features. However, broadband sensors are invaluable for soil surveying, agriculture, land management and mineral exploration, amongst others. These sensors provide denser time series compared to high-resolution airborne imaging spectrometers and hold the potential of increasing the observable bare-soil area at the cost of spectral detail. The wealth of data coming along with these applications can be handled using cloud-based processing platforms such as Earth Engine. We present a method for identifying the least-vegetated observation, or so called barest pixel, in a dense time series between January 1985 and March 2017, based on Landsat 5, 7 and 8 observations. We derived a Barest Pixel Composite and Bare Soil Composite for the agricultural area of the Swiss Plateau. We analysed the available data over time and concluded that about five years of Landsat data were needed for a full-coverage composite (90% of the maximum bare soil area). Using the Swiss harmonised soil data, we derived soil properties (sand, silt, clay, and soil organic matter percentages) and discuss the contribution of these soil property maps to existing conventional and digital soil maps. Both products demonstrate the substantial potential of Landsat time series for digital soil mapping, as well as for land management applications and policy making. Full article
(This article belongs to the Special Issue Dense Image Time Series Analysis for Ecosystem Monitoring)
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Open AccessArticle Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI
Remote Sens. 2017, 9(12), 1246; doi:10.3390/rs9121246
Received: 1 November 2017 / Revised: 27 November 2017 / Accepted: 29 November 2017 / Published: 1 December 2017
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Abstract
The Ocean and Land Color Imager (OLCI) on the Sentinel-3A satellite, which was launched by the European Space Agency in 2016, is a new-generation water color sensor with a spatial resolution of 300 m and 21 bands in the range of 400–1020 nm.
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The Ocean and Land Color Imager (OLCI) on the Sentinel-3A satellite, which was launched by the European Space Agency in 2016, is a new-generation water color sensor with a spatial resolution of 300 m and 21 bands in the range of 400–1020 nm. The OLCI is important to the expansion of remote sensing monitoring of inland waters using water color satellite data. In this study, we developed a dual band ratio algorithm for the downwelling diffuse attenuation coefficient at 490 nm (Kd(490)) for the waters of Lake Taihu, a large shallow lake in China, based on data measured during seven surveys conducted between 2008 and 2017 in combination with Sentinel-3A-OLCI data. The results show that: (1) Compared to the available Kd(490) estimation algorithms, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm developed in this study had a higher estimation accuracy (N = 26, coefficient of determination (R2) = 0.81, root-mean-square error (RMSE) = 0.99 m−1 and mean absolute percentage error (MAPE) = 19.55%) and validation accuracy (N = 14, R2 = 0.83, RMSE = 1.06 m−1 and MAPE = 27.30%), making it more suitable for turbid inland waters; (2) A comparison of the OLCI Kd(490) product and a similar Moderate Resolution Imaging Spectroradiometer (MODIS) product reveals a high consistency between the OLCI and MODIS products in terms of the spatial distribution of Kd(490). However, the OLCI product has a smoother spatial distribution and finer textural characteristics than the MODIS product and contains notably higher-quality data; (3) The Kd(490) values for Lake Taihu exhibit notable spatial and temporal variations. Kd(490) is higher in seasons with relatively high wind speeds and in open waters that are prone to wind- and wave-induced sediment resuspension. Finally, the Sentinel-3A-OLCI has a higher spatial resolution and is equipped with a relatively wide dynamic range of spectral bands suitable for inland waters. The Sentinel-3B satellite will be launched soon and, together with the Sentinel-3A satellite, will form a two-satellite network with the ability to make observations twice every three days. This satellite network will have a wider range of application and play an important role in the monitoring of inland waters with complex optical properties. Full article
(This article belongs to the Special Issue Remote Sensing of Floodpath Lakes and Wetlands)
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Open AccessArticle Estimating Land Surface Temperature from Feng Yun-3C/MERSI Data Using a New Land Surface Emissivity Scheme
Remote Sens. 2017, 9(12), 1247; doi:10.3390/rs9121247
Received: 2 November 2017 / Revised: 29 November 2017 / Accepted: 29 November 2017 / Published: 1 December 2017
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Abstract
Land surface temperature (LST) is a key parameter for a wide number of applications, including hydrology, meteorology and surface energy balance. In this study, we first proposed a new land surface emissivity (LSE) scheme, including a lookup table-based method to determine the vegetated
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Land surface temperature (LST) is a key parameter for a wide number of applications, including hydrology, meteorology and surface energy balance. In this study, we first proposed a new land surface emissivity (LSE) scheme, including a lookup table-based method to determine the vegetated surface emissivity and an empirical method to derive the bare soil emissivity from the Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product. Then, the Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis data and the Feng Yun-3C/Medium Resolution Spectral Imager (FY-3C/MERSI) precipitable water vapor product were used to correct the atmospheric effects. After resolving the land surface emissivity and atmospheric effects, the LST was derived in a straightforward manner from the FY-3C/MERSI data by the radiative transfer equation algorithm and the generalized single-channel algorithm. The mean difference between the derived LSE and field-measured LSE over seven stations is approximately 0.002. Validation of the LST retrieved with the LSE determined by the new scheme can achieve an acceptable accuracy. The absolute biases are less than 1 K and the STDs (RMSEs) are less than 1.95 K (2.2 K) for both the 1000 m and 250 m spatial resolutions. The LST accuracy is superior to that retrieved with the LSE determined by the commonly used Normalized Difference Vegetation Index (NDVI) threshold method. Thus, the new emissivity scheme can be used to improve the accuracy of the LSE and further the LST for sensors with broad spectral ranges such as FY-3C/MERSI. Full article
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Open AccessArticle On-Orbit Radiometric Calibration for a Space-Borne Multi-Camera Mosaic Imaging Sensor
Remote Sens. 2017, 9(12), 1248; doi:10.3390/rs9121248
Received: 3 November 2017 / Revised: 26 November 2017 / Accepted: 28 November 2017 / Published: 1 December 2017
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Abstract
As the core and foundational technology, on-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. As for the space-borne multi-camera mosaic imaging sensor, however, the currently available on-orbit radiometric calibration method cannot carry out the integrated
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As the core and foundational technology, on-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. As for the space-borne multi-camera mosaic imaging sensor, however, the currently available on-orbit radiometric calibration method cannot carry out the integrated processing of on-orbit absolute radiometric calibration and relative radiometric correction simultaneously between cameras, influencing the accuracy of quantitative applications. Therefore, taking the GaoFen-1 (GF-1) wide-field-of-view (WFV) sensor as an example in this research, an innovative on-orbit radiometric calibration method is proposed to overcome this bottleneck. Firstly, according to the principle of the cross-calibration approach, we retrieve valid MODIS and GF-1 WFV image pairs over the Dunhuang radiometric calibration sites (DRCS) in China by using a set of criteria and extract the radiometric control points (RCPs) connecting in both images. Secondly, the DEM-aided block adjustment of the rational function model is applied to eliminate the geometrical misalignment of GF-1 WFV images at the same orbit. Then, the average digital numbers of spectral and spatial homogeneous surfaces are calculated and chosen as the radiometric tie points (RTPs) extracted from the overlapping region of the adjacent WFV cameras. Thirdly, the radiometric block adjustment (RBA) algorithm is introduced into on-orbit radiometric calibration of the space-borne multi-camera mosaic imaging sensor. Finally, the radiometric calibration coefficients are solved by the least square method. The validation results indicate that our proposed method can acquire high absolute radiometric calibration accuracy and achieve relative radiometric correction between cameras. Compared with the results using the cross-calibration method to calibrate each WFV camera independently, the advantages of RBA are presented. In addition, the uncertainties caused by RCPs’ distribution are discussed, which is beneficial to further optimize the calibration program. Full article
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Open AccessArticle Matching Multi-Source Optical Satellite Imagery Exploiting a Multi-Stage Approach
Remote Sens. 2017, 9(12), 1249; doi:10.3390/rs9121249
Received: 22 October 2017 / Revised: 29 November 2017 / Accepted: 30 November 2017 / Published: 1 December 2017
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Abstract
Geometric distortions and intensity differences always exist in multi-source optical satellite imagery, seriously reducing the similarity between images, making it difficult to obtain adequate, accurate, stable, and well-distributed matches for image registration. With the goal of solving these problems, an effective image matching
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Geometric distortions and intensity differences always exist in multi-source optical satellite imagery, seriously reducing the similarity between images, making it difficult to obtain adequate, accurate, stable, and well-distributed matches for image registration. With the goal of solving these problems, an effective image matching method is presented in this study for multi-source optical satellite imagery. The proposed method includes three steps: feature extraction, initial matching, and matching propagation. Firstly, a uniform robust scale invariant feature transform (UR-SIFT) detector was used to extract adequate and well-distributed feature points. Secondly, initial matching was conducted based on the Euclidean distance to obtain a few correct matches and the initial projective transformation between the image pair. Finally, two matching strategies were used to propagate matches and produce more reliable matching results. By using the geometric relationship between the image pair, geometric correspondence matching found more matches than the initial UR-SIFT feature points. Further probability relaxation matching propagated some new matches around the initial UR-SIFT feature points. Comprehensive experiments on Chinese ZY3 and GaoFen (GF) satellite images revealed that the proposed algorithm performs well in terms of the number of correct matches, correct matching rate, spatial distribution, and matching accuracy, compared to the standard UR-SIFT and triangulation-based propagation method. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems
Remote Sens. 2017, 9(12), 1250; doi:10.3390/rs9121250
Received: 6 October 2017 / Revised: 20 November 2017 / Accepted: 23 November 2017 / Published: 2 December 2017
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Abstract
Phenotyping morphological traits of blueberry bushes in the field is important for selecting genotypes that are easily harvested by mechanical harvesters. Morphological data can also be used to assess the effects of crop treatments such as plant growth regulators, fertilizers, and environmental conditions.
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Phenotyping morphological traits of blueberry bushes in the field is important for selecting genotypes that are easily harvested by mechanical harvesters. Morphological data can also be used to assess the effects of crop treatments such as plant growth regulators, fertilizers, and environmental conditions. This paper investigates the feasibility and accuracy of an inexpensive unmanned aerial system in determining the morphological characteristics of blueberry bushes. Color images collected by a quadcopter are processed into three-dimensional point clouds via structure from motion algorithms. Bush height, extents, canopy area, and volume, in addition to crown diameter and width, are derived and referenced to ground truth. In an experimental farm, twenty-five bushes were imaged by a quadcopter. Height and width dimensions achieved a mean absolute error of 9.85 cm before and 5.82 cm after systematic under-estimation correction. Strong correlation was found between manual and image derived bush volumes and their traditional growth indices. Hedgerows of three Southern Highbush varieties were imaged at a commercial farm to extract five morphological features (base angle, blockiness, crown percent height, crown ratio, and vegetation ratio) associated with cultivation and machine harvestability. The bushes were found to be partially separable by multivariate analysis. The methodology developed from this study is not only valuable for plant breeders to screen genotypes with bush morphological traits that are suitable for machine harvest, but can also aid producers in crop management such as pruning and plot layout organization. Full article
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Open AccessArticle Comparison and Evaluation of the TES and ANEM Algorithms for Land Surface Temperature and Emissivity Separation over the Area of Valencia, Spain
Remote Sens. 2017, 9(12), 1251; doi:10.3390/rs9121251
Received: 10 October 2017 / Revised: 28 November 2017 / Accepted: 30 November 2017 / Published: 2 December 2017
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Abstract
Land Surface temperature (LST) is a key magnitude for numerous studies, especially for climatology and assessment of energy fluxes between surface and atmosphere. Retrieval of accurate LST requires a good characterization of surface emissivity. Both quantities are coupled in a single radiance measurement;
[...] Read more.
Land Surface temperature (LST) is a key magnitude for numerous studies, especially for climatology and assessment of energy fluxes between surface and atmosphere. Retrieval of accurate LST requires a good characterization of surface emissivity. Both quantities are coupled in a single radiance measurement; for this reason, for N spectral bands available in a remote sensor, there will always be N + 1 unknowns. To solve the indeterminacy, temperature-emissivity separation methods have been proposed, among which the Temperature Emissivity Separation (TES) algorithm is one of the most widely used. The Adjusted Normalized Emissivity Method (ANEM) was proposed as a modification of the Normalized Emissivity Method (NEM) algorithm by adjusting the initial emissivity guess using an estimation provided by the Vegetation Cover Method (VCM). In this work, both methods were applied to a set of five ASTER scenes over the area of Valencia, Spain, which were recalibrated and atmospherically corrected using local radiosoundings and ground measurements. These scenes were compared to the ASTER temperature and emissivity standard products (AST08 and AST05, respectively). The comparison to reference measurements showed a better agreement of ANEM LST in low spectral contrast surfaces, with biases of +0.4 K, +0.8 K for TES and +1.4 K for the AST08 product in a rice crop site. For sea surface temperature, bias was −0.1 K for ANEM, +0.3 K for TES and +1.3 K for the AST08 product. The larger differences of the AST08 product could be ascribed mainly to the atmospheric correction based on NCEP profiles in contrast to the local correction used in TES and ANEM and to a lesser extent the Maximum-Minimum Difference (MMD) empirical relationship used by TES. In terms of emissivity, ANEM obtained biases up to ±0.007 (positive over vegetation and negative over water), while TES biases were up to −0.015. The AST05 product showed differences up to −0.050, although for high contrast areas, such as sand surfaces, it showed better accuracy than both TES and ANEM. A comparison between TES and ANEM on four different classes within the scene showed a systematic difference between both algorithms, which was more pronounced for low spectral contrast surfaces. Therefore, ANEM improves the accuracy at low spectral contrast surfaces, while obtaining similar results to TES at higher spectral contrast surfaces, such as urban areas. The combination of both methods could provide a procedure benefiting from the strengths shown by each of them. Full article
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Open AccessArticle An Analysis of Ku-Band Profiling Radar Observations of Boreal Forest
Remote Sens. 2017, 9(12), 1252; doi:10.3390/rs9121252
Received: 20 September 2017 / Revised: 21 November 2017 / Accepted: 30 November 2017 / Published: 2 December 2017
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Abstract
Radar sensors have the potential to retrieve vertical forest structure measurements thanks to their capability to penetrate into the foliage. However, studies are needed in order to understand better the interaction of radar beams with the canopy. The most commonly used radar technique
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Radar sensors have the potential to retrieve vertical forest structure measurements thanks to their capability to penetrate into the foliage. However, studies are needed in order to understand better the interaction of radar beams with the canopy. The most commonly used radar technique for estimating forest parameters operates from spacecraft at different wavelength (X-, C-, and L-band). In order to assist in the interpretation of satellite data for forest applications, and as a possible complementary technique to Lidar (Light detection and ranging), the Finnish Geospatial Research Institute has developed the first helicopter-borne profiling radar system operating in Ku-band, called Tomoradar, which is able to provide a vertical canopy profile. The study focuses on the analyses of Ku-band profiling radar waveforms and the backscatter signal of boreal forest, supported by simultaneously acquired Lidar measurements, in order to detect ground and canopy profiles and quantify the ground echo ratio under different canopy coverage and the backscatter signal variation through the vegetation. The Tomoradar data was acquired simultaneously with a lightweight Velodyne VLP-16 Lidar system in October 2016 over a boreal forest located in Evo in southern Finland. Additionally, highly accurate Riegl VQ-480 Lidar data, acquired in 2014, was used as a ground reference for both lightweight systems. We analysed the co- and cross-polarized (HH and HV) Tomoradar backscatter signals of a 600 m long profile. It is found that the Ku-band Tomoradar penetrates the canopy to a similar extent as the Velodyne Lidar, i.e., the distribution of backscatter signals through the vegetation follows the vegetation density. Moreover, the ground backscatter signal strength and ground echo ratio depend strongly on the presence of gaps in the canopy. By comparing the elevation of the corresponding canopy and ground Tomoradar signal peaks with the Velodyne Lidar data, the Tomoradar ground elevation accuracy is on average −0.03 m and −0.20 m for the cross- and co-polarization, respectively, whereas the bias of the canopy elevation is, on average, −0.58 m and 1.35 m for the cross- and co-polarization, respectively. With respect to the ground height data derived from the Lidar measurements of 2014, the Tomoradar ground profile reveals, on average, higher accuracy (i.e., 0.00 m (σ = 0.41 m) and 0.04 m (σ = 0.37 m) for the co-and cross-polarizations, respectively) than the Velodyne system (−0.37 m with σ = 0.25 m). Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data
Remote Sens. 2017, 9(12), 1253; doi:10.3390/rs9121253
Received: 12 October 2017 / Revised: 24 November 2017 / Accepted: 28 November 2017 / Published: 2 December 2017
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Abstract
This paper report experiences from the processing and mosaicking of 518 TanDEM-X image pairs covering the entirety of Sweden, with two single map products of above-ground biomass (AGB) and forest stem volume (VOL), both with 10 m resolution. The main objective was to
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This paper report experiences from the processing and mosaicking of 518 TanDEM-X image pairs covering the entirety of Sweden, with two single map products of above-ground biomass (AGB) and forest stem volume (VOL), both with 10 m resolution. The main objective was to explore the possibilities and overcome the challenges related to forest mapping extending a large number of adjacent satellite scenes. Hence, numerous examples are presented to illustrate challenges and possible solutions. To derive the forest maps, the observables backscatter, interferometric phase height and interferometric coherence, obtained from TanDEM-X, were evaluated using empirical robust linear regression models with reference data extracted from 2288 national forest inventory plots with a 10 m radius. The interferometric phase height was the single most important observable, to predict AGB and VOL. The mosaics were evaluated on different datasets with field-inventoried stands across Sweden. The root mean square error (RMSE) was about 21%–25% (27–30 tons/ha and 52–65 m3/ha) at the stand level. It was noted that the most influencing factors on the observables in this study were local temperature and geolocation errors that were challenging to robustly compensate against. Because of this variability at the scene-level, determinations of AGB and VOL for single stands are recommended to be used with care, as an equivalent accuracy is difficult to achieve for all different scenes, with varying acquisition conditions. Still, for the evaluated stands, the mosaics were of sufficient accuracy to be used for forest management at the stand level. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Annual Seasonality Extraction Using the Cubic Spline Function and Decadal Trend in Temporal Daytime MODIS LST Data
Remote Sens. 2017, 9(12), 1254; doi:10.3390/rs9121254
Received: 27 October 2017 / Revised: 25 November 2017 / Accepted: 29 November 2017 / Published: 2 December 2017
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Abstract
Examining climate-related satellite data that strongly relate to seasonal phenomena requires appropriate methods for detecting the seasonality to accommodate different temporal resolutions, high signal variability and consecutive missing values in the data series. Detection of satellite-based Land Surface Temperature (LST) seasonality is essential
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Examining climate-related satellite data that strongly relate to seasonal phenomena requires appropriate methods for detecting the seasonality to accommodate different temporal resolutions, high signal variability and consecutive missing values in the data series. Detection of satellite-based Land Surface Temperature (LST) seasonality is essential and challenging due to missing data and noise in time series data, particularly in tropical regions with heavy cloud cover and rainy seasons. We used a semi-parametric approach, involving the cubic spline function with the annual periodic boundary condition and weighted least square (WLS) regression, to extract annual LST seasonal pattern without attempting to estimate the missing values. The time series from daytime Aqua eight-day MODIS LST located on Phuket Island, southern Thailand, was selected for seasonal extraction modelling across three different land cover types. The spline-based technique with appropriate number and placement of knots produces an acceptable seasonal pattern of surface temperature time series that reflects the actual local season and weather. Finally, the approach was applied to the morning and afternoon MODIS LST datasets (MOD11A2 and MYD11A2) to demonstrate its application on seasonally-adjusted long-term LST time series. The surface temperature trend in both space and time was examined to reveal the overall 10-year period trend of LST in the study area. The result of decadal trend analysis shows that various Land Use and Land Cover (LULC) types have increasing, but variable surface temperature trends. Full article
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Open AccessArticle Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification
Remote Sens. 2017, 9(12), 1255; doi:10.3390/rs9121255
Received: 28 September 2017 / Revised: 23 November 2017 / Accepted: 30 November 2017 / Published: 2 December 2017
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Abstract
Although sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order
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Although sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Land Use Classification: A Surface Energy Balance and Vegetation Index Application to Map and Monitor Irrigated Lands
Remote Sens. 2017, 9(12), 1256; doi:10.3390/rs9121256
Received: 22 September 2017 / Revised: 15 November 2017 / Accepted: 26 November 2017 / Published: 5 December 2017
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Abstract
Irrigated agriculture consumes the largest share of available fresh water, and awareness of the spatial distribution and application rates is paramount to a functional and sustainable communal consumptive water use. This remote sensing study leverages surface energy balance fluxes and vegetation indices to
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Irrigated agriculture consumes the largest share of available fresh water, and awareness of the spatial distribution and application rates is paramount to a functional and sustainable communal consumptive water use. This remote sensing study leverages surface energy balance fluxes and vegetation indices to classify and map the spatial distribution of irrigated and non-irrigated croplands. The purpose is to introduce a classification scheme applicable across a wide variation in regional climate and inter-growing seasonal precipitation. The rationale for climate and inter-growing seasonal adaptability is founded in the derivation and calibration of the scheme based on the wettest growing season. Therefore, the scheme becomes a more efficient classifier during normal and dry growing seasons. Using empirical distribution functions, two indices are derived from evapotranspiration fluxes and vegetation indices to contrast and classify irrigated croplands from non-irrigated. The synergy of the two indices increases the classification proficiency by adding another classifying layer which re-characterizes misclassified croplands by the base index. The scheme was applied to a region with wide climate variation and to multiple years of growing seasons. The results presented, in cross validation with groundtruth, show an accurate and consistent approach to classify irrigation with overall accuracy of 92.1%, applicable from humid to semi-arid climate, and from dry to normal and wet growing seasons. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Measuring Vegetation Height in Linear Disturbances in the Boreal Forest with UAV Photogrammetry
Remote Sens. 2017, 9(12), 1257; doi:10.3390/rs9121257
Received: 16 October 2017 / Revised: 28 November 2017 / Accepted: 29 November 2017 / Published: 3 December 2017
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Abstract
Monitoring vegetation recovery typically requires ground measurements of vegetation height, which is labor-intensive and time-consuming. Recently, unmanned aerial vehicles (UAVs) have shown great promise for characterizing vegetation in a cost-efficient way, but the literature on specific methods and cost savings is scant. In
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Monitoring vegetation recovery typically requires ground measurements of vegetation height, which is labor-intensive and time-consuming. Recently, unmanned aerial vehicles (UAVs) have shown great promise for characterizing vegetation in a cost-efficient way, but the literature on specific methods and cost savings is scant. In this study, we surveyed vegetation height on seismic lines in Alberta’s Boreal Forest using a point-intercept sampling strategy, and compared them to height estimates derived from UAV-based photogrammetric point clouds. In order to derive UAV-based vegetation height, we tested three different approaches to estimate terrain elevation: (1) UAV_RTK, where photogrammetric point clouds were normalized using terrain measurements obtained from a real-time kinematic global navigation satellite system (RTK GNSS) surveys; (2) UAV_LiDAR, where photogrammetric data were normalized using pre-existing LiDAR (Light Detection and Ranging) data; and (3) UAV_UAV, where UAV photogrammetry data were used alone. Comparisons were done at two scales: point level (n = 1743) and site level (n = 30). The point-level root-mean-square errors (RMSEs) of UAV_RTK, UAV_LiDAR, and UAV_UAV were 28 cm, 31 cm, and 30 cm, respectively. The site-level RMSEs were 11 cm, 15 cm, and 8 cm, respectively. At the aggregated site level, we found that UAV photogrammetry could replace traditional field-based vegetation surveys of mean vegetation height across the range of conditions assessed in this study, with an RMSE less than 10 cm. Cost analysis indicates that using UAV-based point clouds is more cost-effective than traditional field vegetation surveys. Full article
(This article belongs to the Special Issue Remote Sensing from Unmanned Aerial Vehicles (UAVs))
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Open AccessArticle Grassland Phenology Response to Drought in the Canadian Prairies
Remote Sens. 2017, 9(12), 1258; doi:10.3390/rs9121258
Received: 25 August 2017 / Revised: 15 November 2017 / Accepted: 27 November 2017 / Published: 4 December 2017
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Abstract
Drought is a significant climatic disturbance in grasslands, yet the impact drought caused by global warming has on grassland phenology is still unclear. Our research investigates the long-term variability of grassland phenology in relation to drought in the Canadian prairies from 1982 to
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Drought is a significant climatic disturbance in grasslands, yet the impact drought caused by global warming has on grassland phenology is still unclear. Our research investigates the long-term variability of grassland phenology in relation to drought in the Canadian prairies from 1982 to 2014. Based on the start of growing season (SOG) and the end of growing season (EOG) derived from Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g datasets, we found that grasslands demonstrated complex phenology trends over our study period. We retrieved the drought conditions of the prairie ecozone at multiple time scales from the 1- to 12-month Standardized Precipitation Evapotranspiration Index (SPEI). We evaluated the correlations between the detrended time series of phenological metrics and SPEIs through Pearson correlation analysis and identified the dominant drought where the maximum correlations were found for each ecozone and each phenological metric. The dominant drought over preceding months account for 14–33% and 26–44% of the year-to-year variability of SOG and EOG, respectively, and fewer water deficits would favor an earlier SOG and delayed EOG. The drought-induced shifts in SOG and EOG were determined based on the correlation between the dominant drought and the year-to-year variability using ordinary least square (OLS) method. Our research also quantifies the correlation between precipitation and the evolution of the dominant droughts and the drought-induced shifts in grassland phenology. Every millimeter (mm) increase in precipitation accumulated over the dominant periods would cause SOG to occur 0.06–0.21 days earlier, and EOG to occur 0.23–0.45 days later. Our research reveals a complex phenology response in relation to drought in the Canadian prairie grasslands and demonstrates that drought is a significant factor in the timing of both SOG and EOG. Thus, it is necessary to include drought-related climatic variables when predicting grassland phenology response to climate change and variability. Full article
(This article belongs to the Special Issue Land Surface Phenology)
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Open AccessArticle Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning
Remote Sens. 2017, 9(12), 1259; doi:10.3390/rs9121259
Received: 31 October 2017 / Revised: 27 November 2017 / Accepted: 28 November 2017 / Published: 4 December 2017
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Abstract
This study aimed at evaluating the synergistic use of Sentinel-1 and Sentinel-2 data combined with the Support Vector Machines (SVMs) machine learning classifier for mapping land use and land cover (LULC) with emphasis on wetlands. In this context, the added value of spectral
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This study aimed at evaluating the synergistic use of Sentinel-1 and Sentinel-2 data combined with the Support Vector Machines (SVMs) machine learning classifier for mapping land use and land cover (LULC) with emphasis on wetlands. In this context, the added value of spectral information derived from the Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Grey Level Co-occurrence Matrix (GLCM) to the classification accuracy was also evaluated. As a case study, the National Park of Koronia and Volvi Lakes (NPKV) located in Greece was selected. LULC accuracy assessment was based on the computation of the classification error statistics and kappa coefficient. Findings of our study exemplified the appropriateness of the spatial and spectral resolution of Sentinel data in obtaining a rapid and cost-effective LULC cartography, and for wetlands in particular. The most accurate classification results were obtained when the additional spectral information was included to assist the classification implementation, increasing overall accuracy from 90.83% to 93.85% and kappa from 0.894 to 0.928. A post-classification correction (PCC) using knowledge-based logic rules further improved the overall accuracy to 94.82% and kappa to 0.936. This study provides further supporting evidence on the suitability of the Sentinels 1 and 2 data for improving our ability to map a complex area containing wetland and non-wetland LULC classes. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)
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Open AccessArticle Multipath Interferences in Ground-Based Radar Data: A Case Study
Remote Sens. 2017, 9(12), 1260; doi:10.3390/rs9121260
Received: 24 October 2017 / Revised: 30 November 2017 / Accepted: 2 December 2017 / Published: 5 December 2017
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Abstract
Multipath interference can occur in ground-based radar data acquired with systems with a large antenna beam width in elevation in an upward looking geometry, where the observation area and the radar are separated by a reflective surface. Radiation reflected at this surface forms
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Multipath interference can occur in ground-based radar data acquired with systems with a large antenna beam width in elevation in an upward looking geometry, where the observation area and the radar are separated by a reflective surface. Radiation reflected at this surface forms a coherent overlay with the direct image of the observation area and appears as a fringe-like pattern in the data. This deteriorates the phase and intensity data and therefore can pose a considerable disadvantage to many ground-based radar measurement campaigns. This poses a problem for physical parameter retrieval from backscatter intensity and polarimetric data, absolute and relative calibration on corner reflectors, the generation of digital elevation models from interferograms and in the case of a variable reflective surface, differential interferometry. The main parameters controlling the interference pattern are the vertical distance between the radar antennas and the reflective surface, and the reflectivity of this surface. We used datasets acquired in two different locations under changing conditions as well as a model to constrain and fully understand the phenomenon. To avoid data deterioration in test sites prone to multipath interference, we tested a shielding of the antennas preventing the radar waves from illuminating the reflective surface. In our experiment, this strongly reduced but did not completely prevent the interference. We therefore recommend avoiding measurement geometries prone to multipath interferences. Full article
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Open AccessArticle Ocean Wind and Wave Measurements Using X-Band Marine Radar: A Comprehensive Review
Remote Sens. 2017, 9(12), 1261; doi:10.3390/rs9121261
Received: 13 November 2017 / Revised: 1 December 2017 / Accepted: 2 December 2017 / Published: 5 December 2017
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Abstract
Ocean wind and wave parameters can be measured by in-situ sensors such as anemometers and buoys. Since the 1980s, X-band marine radar has evolved as one of the remote sensing instruments for such purposes since its sea surface images contain considerable wind and
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Ocean wind and wave parameters can be measured by in-situ sensors such as anemometers and buoys. Since the 1980s, X-band marine radar has evolved as one of the remote sensing instruments for such purposes since its sea surface images contain considerable wind and wave information. The maturity and accuracy of X-band marine radar wind and wave measurements have already enabled relevant commercial products to be used in real-world applications. The goal of this paper is to provide a comprehensive review of the state of the art algorithms for ocean wind and wave information extraction from X-band marine radar data. Wind measurements are mainly based on the dependence of radar image intensities on wind direction and speed. Wave parameters can be obtained from radar-derived wave spectra or radar image textures for non-coherent radar and from surface radial velocity for coherent radar. In this review, the principles of the methodologies are described, the performances are compared, and the pros and cons are discussed. Specifically, recent developments for wind and wave measurements are highlighted. These include the mitigation of rain effects on wind measurements and wave height estimation without external calibrations. Finally, remaining challenges and future trends are discussed. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle A Novel Pixel-Level Image Matching Method for Mars Express HRSC Linear Pushbroom Imagery Using Approximate Orthophotos
Remote Sens. 2017, 9(12), 1262; doi:10.3390/rs9121262
Received: 25 October 2017 / Revised: 1 December 2017 / Accepted: 2 December 2017 / Published: 5 December 2017
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Abstract
Mars topographic data, such as digital orthophoto maps (DOMs) and digital elevation models (DEMs) are essential to planetary science and exploration missions. The main objective of our study is to generate a higher resolution DEM using the Mars Express (MEX) High Resolution Stereo
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Mars topographic data, such as digital orthophoto maps (DOMs) and digital elevation models (DEMs) are essential to planetary science and exploration missions. The main objective of our study is to generate a higher resolution DEM using the Mars Express (MEX) High Resolution Stereo Camera (HRSC). This paper presents a novel pixel-level image matching method for HRSC linear pushbroom imagery. We suggest that image matching firstly be carried out on the approximate orthophotos. Then, the matched points are converted to the original images for forward intersection. The proposed method adopts some practical strategies such as hierarchical image matching and normalized cross-correlation (NCC). The characteristic strategies are: (1) the generation of a DEM and a DOM at each pyramid level; (2) the use of the generated DEM at the current pyramid level as reference data to generate approximate orthophotos at the next pyramid level; and (3) the use of the ground point coordinates of orthophotos to estimate the approximate positions of conjugate points. Hence, the refined DEM is used in the image rectification process, and pixel coordinate displacements of conjugate points on the approximate orthophotos will become smaller and smaller. Four experimental datasets acquired by the HRSC were used to verify the proposed method. The generated DEM was compared with the HRSC Level-4 DEM product. Experimental results demonstrate that an accurate and precise Mars DEM can be generated with the proposed method. The approximate positions of the conjugate points can be estimated with an accuracy of three pixels at the original image resolution level. Though slight systematic errors of about two pixels were observed, the generated DEM results show good consistency with the HRSC Level-4 DEM. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities
Remote Sens. 2017, 9(12), 1263; doi:10.3390/rs9121263
Received: 12 October 2017 / Revised: 22 November 2017 / Accepted: 1 December 2017 / Published: 6 December 2017
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Abstract
The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60°N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information.
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The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60°N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 ∆TB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 ∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg·m−2, IWP > 0.24 kg·m−2 over land, and SIC > 57%, TPW > 5.1 kg·m−2 over sea). The complex combined 166 ∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms. Full article
(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)
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Open AccessArticle Mapping Plastic-Mulched Farmland with C-Band Full Polarization SAR Remote Sensing Data
Remote Sens. 2017, 9(12), 1264; doi:10.3390/rs9121264
Received: 26 September 2017 / Revised: 3 December 2017 / Accepted: 4 December 2017 / Published: 6 December 2017
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Abstract
Plastic mulching is an important technology in agricultural production both in China and the rest of the world. In spite of its benefit of increasing crop yields, the booming expansion of the plastic mulching area has been changing the landscape patterns and affecting
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Plastic mulching is an important technology in agricultural production both in China and the rest of the world. In spite of its benefit of increasing crop yields, the booming expansion of the plastic mulching area has been changing the landscape patterns and affecting the environment. Accurate and effective mapping of Plastic-Mulched Farmland (PMF) can provide useful information for leveraging its advantages and disadvantages. However, mapping the PMF with remote sensing is still challenging owing to its varying spectral characteristics with the crop growth and geographic spatial division. In this paper, we investigated the potential of Radarsat-2 data for mapping PMF. We obtained the backscattering intensity of different polarizations and multiple polarimetric decomposition descriptors. These remotely-sensed information was used as input features for Random Forest (RF) and Support Vector Machine (SVM) classifiers. The results indicated that the features from Radarsat-2 data have great potential for mapping PMF. The overall accuracies of PMF mapping with Radarsat-2 data were close to 75%. Although the classification accuracy with the back-scattering intensity information alone was relatively lower owing to the inherent speckle noise in SAR data, it has been improved significantly by introducing the polarimetric decomposition descriptors. The accuracy was nearly 75%. In addition, the features derived from the Entropy/Anisotropy/Alpha (H/A/Alpha) polarimetric decomposition, such as Alpha, entropy, and so on, made a greater contribution to PMF mapping than the Freeman decomposition, Krogager decomposition and the Yamaguchi4 decomposition. The performances of different classifiers were also compared. In this study, the RF classifier performed better than the SVM classifier. However, it is expected that the classification accuracy of PMF with SAR remote sensing data can be improved by combining SAR remote sensing data with optical remote sensing data. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle A 30-Year Assessment of Phytoplankton Blooms in Erhai Lake Using Landsat Imagery: 1987 to 2016
Remote Sens. 2017, 9(12), 1265; doi:10.3390/rs9121265
Received: 11 October 2017 / Revised: 4 December 2017 / Accepted: 5 December 2017 / Published: 6 December 2017
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Abstract
Long-term information of phytoplankton bloom is critical for assessing the processes driving blooms in lakes. A three-decade survey of the phytoplankton blooms was completed for Erhai Lake from 1987 to 2016 with Landsat imagery. A modified three-band model using Landsat broad bands is
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Long-term information of phytoplankton bloom is critical for assessing the processes driving blooms in lakes. A three-decade survey of the phytoplankton blooms was completed for Erhai Lake from 1987 to 2016 with Landsat imagery. A modified three-band model using Landsat broad bands is developed by comparing reflectance data from Landsat imagery to two field datasets. The model is applied to the archived imagery (1987–2016) to predict chlorophyll-a (Chl-a). Predicted ln(Chl-a) and observed ln(Chl-a) measurements are significantly correlated (R2 = 0.70; RMSE = 0.13 ug/L). Bloom maps are generated by identifying Landsat pixels that have Chl-a concentrations larger than 20 ug/L as bloom area. Bloom extent and magnitude are estimated. Our study reveals that algal blooms first occurred in 1996 with a bloom area of 150 km2. Bloom occurred frequently from 2002 to 2016, with extreme blooms in 2003, 2013 and 2016. Algal blooms were mostly distributed in the northern and southern part of the lake. The proposed method uses one model for all Landsat images for Erhai Lake and can predict past blooms and extend the record to early years when field data is not available. The bloom extent and magnitude produced in this study can be used as the basis for the understanding of the processes that control the bloom outbreak. Full article
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Open AccessArticle Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega-Cities
Remote Sens. 2017, 9(12), 1266; doi:10.3390/rs9121266
Received: 31 August 2017 / Revised: 26 November 2017 / Accepted: 4 December 2017 / Published: 7 December 2017
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Abstract
Urban green spaces can yield considerable health benefits to urban residents. Assessing these health benefits is a key step for managing urban green spaces for human health and wellbeing in cities. In this study, we assessed the change of health benefits generated by
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Urban green spaces can yield considerable health benefits to urban residents. Assessing these health benefits is a key step for managing urban green spaces for human health and wellbeing in cities. In this study, we assessed the change of health benefits generated by urban green spaces in 28 megacities worldwide between 2005 and 2015 by using availability and accessibility as proxy indicators. We first mapped land covers of 28 megacities using 10,823 scenes of Landsat images and a random forest classifier running on Google Earth Engine. We then calculated the availability and accessibility of urban green spaces using the land cover maps and gridded population data. The results showed that the mean availability of urban green spaces in these megacities increased from 27.63% in 2005 to 31.74% in 2015. The mean accessibility of urban green spaces increased from 65.76% in 2005 to 72.86% in 2015. The increased availability and accessibility of urban green spaces in megacities have brought more health benefits to their residents. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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Open AccessArticle Estimating Diurnal Courses of Gross Primary Production for Maize: A Comparison of Sun-Induced Chlorophyll Fluorescence, Light-Use Efficiency and Process-Based Models
Remote Sens. 2017, 9(12), 1267; doi:10.3390/rs9121267
Received: 2 October 2017 / Revised: 27 November 2017 / Accepted: 5 December 2017 / Published: 7 December 2017
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Abstract
Accurately quantifying gross primary production (GPP) is of vital importance to understanding the global carbon cycle. Light-use efficiency (LUE) models and process-based models have been widely used to estimate GPP at different spatial and temporal scales. However, large uncertainties remain in quantifying GPP,
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Accurately quantifying gross primary production (GPP) is of vital importance to understanding the global carbon cycle. Light-use efficiency (LUE) models and process-based models have been widely used to estimate GPP at different spatial and temporal scales. However, large uncertainties remain in quantifying GPP, especially for croplands. Recently, remote measurements of solar-induced chlorophyll fluorescence (SIF) have provided a new perspective to assess actual levels of plant photosynthesis. In the presented study, we evaluated the performance of three approaches, including the LUE-based multi-source data synergized quantitative (MuSyQ) GPP algorithm, the process-based boreal ecosystem productivity simulator (BEPS) model, and the SIF-based statistical model, in estimating the diurnal courses of GPP at a maize site in Zhangye, China. A field campaign was conducted to acquire synchronous far-red SIF (SIF760) observations and flux tower-based GPP measurements. Our results showed that both SIF760 and GPP were linearly correlated with APAR, and the SIF760-GPP relationship was adequately characterized using a linear function. The evaluation of the modeled GPP against the GPP measured from the tower demonstrated that all three approaches provided reasonable estimates, with R2 values of 0.702, 0.867, and 0.667 and RMSE values of 0.247, 0.153, and 0.236 mg m−2 s−1 for the MuSyQ-GPP, BEPS and SIF models, respectively. This study indicated that the BEPS model simulated the GPP best due to its efficiency in describing the underlying physiological processes of sunlit and shaded leaves. The MuSyQ-GPP model was limited by its simplification of some critical ecological processes and its weakness in characterizing the contribution of shaded leaves. The SIF760-based model demonstrated a relatively limited accuracy but showed its potential in modeling GPP without dependency on climate inputs in short-term studies. Full article
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Open AccessArticle An Evaluation of Satellite Estimates of Solar Surface Irradiance Using Ground Observations in San Antonio, Texas, USA
Remote Sens. 2017, 9(12), 1268; doi:10.3390/rs9121268
Received: 27 October 2017 / Revised: 23 November 2017 / Accepted: 4 December 2017 / Published: 7 December 2017
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Abstract
Estimates of solar irradiance at the earth’s surface from satellite observations are useful for planning both the deployment of distributed photovoltaic systems and their integration into electricity grids. In order to use surface solar irradiance from satellites for these purposes, validation of its
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Estimates of solar irradiance at the earth’s surface from satellite observations are useful for planning both the deployment of distributed photovoltaic systems and their integration into electricity grids. In order to use surface solar irradiance from satellites for these purposes, validation of its accuracy against ground observations is needed. In this study, satellite estimates of surface solar irradiance from Geostationary Operational Environmental Satellite (GOES) are compared with ground observations at two sites, namely the main campus of the University of Texas at San Antonio (UTSA) and the Alamo Solar Farm of San Antonio (ASF). The comparisons are done mostly on an hourly timescale, under different cloud conditions classified by cloud types and cloud layers, and at different solar zenith angle intervals. It is found that satellite estimates and ground observations of surface solar irradiance are significantly correlated (p < 0.05) under all sky conditions (r: 0.80 and 0.87 on an hourly timescale and 0.94 and 0.91 on a daily timescale, respectively for the UTSA and ASF sites); on the hourly timescale, the correlations are 0.77 and 0.86 under clear-sky conditions, and 0.74 and 0.84 under cloudy conditions, respectively for the UTSA and ASF sites, and mostly >0.60 under different cloud types and layers for both sites. The correlations under cloudy-sky conditions are mostly stronger than those under clear-sky conditions at different solar zenith angles. The correlation coefficients are mostly the smallest with solar zenith angle in the range of 75–90° under all sky, clear-sky and cloudy-sky conditions. At the ASF site, the overall bias of GOES surface solar irradiance is small (+1.77 Wm−2) under all sky while relatively larger under clear-sky (−22.29 Wm−2) and cloudy-sky (+40.31 Wm−2) conditions. The overall good agreement of the satellite estimates with the ground observations underscores the usefulness of the GOES surface solar irradiance estimates for solar energy studies in the San Antonio area. Full article
(This article belongs to the Section Land Surface Fluxes)
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Open AccessCommunication Evaluation and Comparison of Long-Term MODIS C5.1 and C6 Products against AERONET Observations over China
Remote Sens. 2017, 9(12), 1269; doi:10.3390/rs9121269
Received: 26 October 2017 / Revised: 23 November 2017 / Accepted: 30 November 2017 / Published: 7 December 2017
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Abstract
MODIS (MODerate Resolution Imaging Spectroradiometer) aerosol products are the most widely used satellite retrieved aerosol optic depth (AOD) products, which compensate for the spatial lack of ground-based sun photometer observations. The newly released Collection 6 (C6) aerosol products have some improvements compared to
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MODIS (MODerate Resolution Imaging Spectroradiometer) aerosol products are the most widely used satellite retrieved aerosol optic depth (AOD) products, which compensate for the spatial lack of ground-based sun photometer observations. The newly released Collection 6 (C6) aerosol products have some improvements compared to the Collection 5.1 (C5.1) products with optimized algorithms and newly revised upstream products. Additionally, a three-kilometer resolution AOD product was added in the C6 product. In this study, the accuracies and regional applicability of long-term (2001–2015) different MODIS C5.1 and C6 aerosol products in China were evaluated against the 16 AERONET (Aerosol Robotic Network) observations with observations over more than three years. The overall analysis indicates that the C6 DT (Dark Target) 10 km products slightly improved the retrieval accuracies, with about 3% more data falling within the Expected Error (EE) envelope. However, for Deep Blue (DB) products, the C6 algorithm significantly improved the accuracy over all of China, and increased the successful retrieval number by extending retrieval coverages. Regional analysis demonstrated that the C6 DT 10 km product did not perform well in East China, with only 33.5% of retrievals falling within the EE envelope. For the DB product, the C6 algorithm significantly increased the number successfully retrieved, and was more accurate in all four regions in China. The validation of the DT 3 km product suggests large differences existed between the Terra and Aqua results. The accuracy of the Aqua DT 3 km product is obviously higher than that of the Terra DT 3 km product. The results of the study suggest that proper AOD products need to be considered when evaluating aerosol loading situations in different regions in China. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data
Remote Sens. 2017, 9(12), 1270; doi:10.3390/rs9121270
Received: 31 October 2017 / Revised: 27 November 2017 / Accepted: 4 December 2017 / Published: 7 December 2017
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Abstract
Sea ice monitoring and classification is one of the main applications of Synthetic Aperture Radar (SAR) remote sensing. C-band SAR imagery is regarded as an optimal choice for sea ice applications; however, other SAR frequencies has not been extensively assessed. In this study,
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Sea ice monitoring and classification is one of the main applications of Synthetic Aperture Radar (SAR) remote sensing. C-band SAR imagery is regarded as an optimal choice for sea ice applications; however, other SAR frequencies has not been extensively assessed. In this study, we evaluate the potential of fully polarimetric L-band SAR imagery for monitoring and classifying sea ice during dry winter conditions compared to fully polarimetric C-band SAR. Twelve polarimetric SAR parameters are derived using sets of C- and L-band SAR imagery and the capabilities of the derived parameters for the discrimination between First Year Ice (FYI) and Old Ice (OI), which is considered to be a mixture of Second Year Ice (SYI) and Multiyear Ice (MYI), are investigated. Feature vectors of effective C- and L-band polarimetric parameters are extracted and used for sea ice classification. Results indicate that C-band SAR provides high classification accuracy (98.99%) of FYI and OI in comparison to the obtained accuracy using L-band SAR (82.17% and 81.85%), as expected. However, L-band SAR was found to classify only the MYI floes as OI, while merging both FYI and SYI into one separate class. This comes in contrary to C-band SAR, which classifies as OI both MYI and SYI. This indicates a new potential for discriminating SYI from MYI by combining C- and L-band SAR in dry ice winter conditions. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data
Remote Sens. 2017, 9(12), 1271; doi:10.3390/rs9121271
Received: 8 September 2017 / Revised: 1 December 2017 / Accepted: 4 December 2017 / Published: 7 December 2017
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Abstract
Many time-series smoothing methods can be used for reducing noise and extracting plant phenological parameters from remotely-sensed data, but there is still no conclusive evidence in favor of one method over others. Here we use moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation
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Many time-series smoothing methods can be used for reducing noise and extracting plant phenological parameters from remotely-sensed data, but there is still no conclusive evidence in favor of one method over others. Here we use moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) to investigate five smoothing methods: Savitzky-Golay fitting (SG), locally weighted regression scatterplot smoothing (LO), spline smoothing (SP), asymmetric Gaussian function fitting (AG), and double logistic function fitting (DL). We use ground tower measured NDVI (10 sites) and gross primary productivity (GPP, 4 sites) to evaluate the smoothed satellite-derived NDVI time-series, and elevation data to evaluate phenology parameters derived from smoothed NDVI. The results indicate that all smoothing methods can reduce noise and improve signal quality, but that no single method always performs better than others. Overall, the local filtering methods (SG and LO) can generate very accurate results if smoothing parameters are optimally calibrated. If local calibration cannot be performed, cross validation is a way to automatically determine the smoothing parameter. However, this method may in some cases generate poor fits, and when calibration is not possible the function fitting methods (AG and DL) provide the most robust description of the seasonal dynamics. Full article
(This article belongs to the Special Issue Land Surface Phenology)
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Open AccessArticle Analysis of Suspended Particulate Matter and Its Drivers in Sahelian Ponds and Lakes by Remote Sensing (Landsat and MODIS): Gourma Region, Mali
Remote Sens. 2017, 9(12), 1272; doi:10.3390/rs9121272
Received: 1 September 2017 / Revised: 29 November 2017 / Accepted: 29 November 2017 / Published: 7 December 2017
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Abstract
The Sahelian region is characterized by significant variations in precipitation, impacting water quantity and quality. Suspended particulate matter (SPM) dynamics has a significant impact on inland water ecology and water resource management. In-situ data in this region are scarce and, consequently, the environmental
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The Sahelian region is characterized by significant variations in precipitation, impacting water quantity and quality. Suspended particulate matter (SPM) dynamics has a significant impact on inland water ecology and water resource management. In-situ data in this region are scarce and, consequently, the environmental factors triggering SPM variability are yet to be understood. This study addresses these issues using remote sensing optical data. Turbidity and SPM of the Agoufou Lake in Sahelian Mali were measured from October 2014 to present, providing a large range of `values (SPM ranging from 106 to 4178 mg/L). These data are compared to satellite reflectance from Landsat (ETM+, OLI) and MODIS (MOD09GQ, MYD09GQ). For each of these sensors, a spectral band in the near infrared region is found to be well suited to retrieve turbidity and SPM, up to very high values (R2 = 0.70) seldom addressed by remote sensing studies. The satellite estimates are then employed to assess the SPM dynamics in the main lakes and ponds of the Gourma region and its links to environmental and anthropogenic factors. The main SPM seasonal peak is observed in the rainy season (June to September) in relation to precipitation and sediment transport. A second important peak occurs during the dry season, highlighting the importance of resuspension mechanisms in maintaining high values of SPM. Three different periods are observed: first, a relatively low winds period in the early dry season, when SPM decreases rapidly due to deposition; then, a period of wind-driven resuspension in January‒March; and lastly, an SPM deposition period in April–May, when the monsoon replaces the winter trade wind. Overall, a significant increase of 27% in SPM values is observed between 2000 and 2016 in the Agoufou Lake. The significant spatio-temporal variability in SPM revealed by this study highlights the importance of high resolution optical sensors for continuous monitoring of water quality in these poorly instrumented regions. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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Open AccessArticle Improved Atmospheric Modelling of the Oasis-Desert System in Central Asia Using WRF with Actual Satellite Products
Remote Sens. 2017, 9(12), 1273; doi:10.3390/rs9121273
Received: 25 September 2017 / Revised: 30 November 2017 / Accepted: 4 December 2017 / Published: 7 December 2017
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Abstract
Because of the use of outdated terrestrial datasets, regional climate models (RCMs) have a limited ability to accurately simulate weather and climate conditions over heterogeneous oasis-desert systems, especially near large mountains. Using actual terrestrial datasets from satellite products for RCMs is the only
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Because of the use of outdated terrestrial datasets, regional climate models (RCMs) have a limited ability to accurately simulate weather and climate conditions over heterogeneous oasis-desert systems, especially near large mountains. Using actual terrestrial datasets from satellite products for RCMs is the only possible solution to the limitation; however, it is impractical for long-period simulations due to the limited satellite products available before 2000 and the extremely time- and labor-consuming processes involved. In this study, we used the Weather Research and Forecasting (WRF) model with observed estimates of land use (LU), albedo, Leaf Area Index (LAI), and green Vegetation Fraction (VF) datasets from satellite products to examine which terrestrial datasets have a great impact on simulating water and heat conditions over heterogeneous oasis-desert systems in the northern Tianshan Mountains. Five simulations were conducted for 1–31 July in both 2010 and 2012. The decrease in the root mean squared error and increase in the coefficient of determination for the 2 m temperature (T2), humidity (RH), latent heat flux (LE), and wind speed (WS) suggest that these datasets improve the performance of WRF in both years; in particular, oasis effects are more realistically simulated. Using actual satellite-derived fractional vegetation coverage data has a much greater effect on the simulation of T2, RH, and LE than the other parameters, resulting in mean error correction values of 62%, 87%, and 92%, respectively. LU data is the primary parameter because it strongly influences other secondary land surface parameters, such as LAI and albedo. We conclude that actual LU and VF data should be used in the WRF for both weather and climate simulations. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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