Remote Sensing

31 pages, 10856 KiB

Open AccessEditor’s ChoiceArticle

Estimation of Changes of Forest Structural Attributes at Three Different Spatial Aggregation Levels in Northern California using Multitemporal LiDAR

by Francisco Mauro, Martin Ritchie, Brian Wing, Bryce Frank, Vicente Monleon, Hailemariam Temesgen and Andrew Hudak

Remote Sens. 2019, 11(8), 923; https://doi.org/10.3390/rs11080923 - 16 Apr 2019

Cited by 29 | Viewed by 4312

Abstract

Accurate estimates of growth and structural changes are key for forest management tasks such as determination of optimal rotation times, optimal rotation times, site indices and for identifying areas experiencing difficulties to regenerate. Estimation of structural changes, especially for biomass, is also key [...] Read more.

Accurate estimates of growth and structural changes are key for forest management tasks such as determination of optimal rotation times, optimal rotation times, site indices and for identifying areas experiencing difficulties to regenerate. Estimation of structural changes, especially for biomass, is also key to quantify greenhouse gas (GHG) emissions/sequestration. We compared two different modeling strategies to estimate changes in V, BA and B, at three different spatial aggregation levels using auxiliary information from two light detection and ranging (LiDAR) flights. The study area is Blacks Mountains Experimental Forest, a ponderosa pine dominated forest in Northern California for which two LiDAR acquisitions separated by six years were available. Analyzed strategies consisted of (1) directly modeling the observed changes as a function of the LiDAR auxiliary information (

δ

-modeling method) and (2) modeling V, BA and B at two different points in time, including a term to account for the temporal correlation, and then computing the changes as the difference between the predicted values of V, BA and B for time two and time one. We analyzed predictions and measures of uncertainty at three different level of aggregation (i.e., pixels, stands or compartments and the entire study area). Results showed that changes were very weakly correlated with the LiDAR auxiliary information. Both modeling alternatives provided similar results with a better performance of the

δ

-modeling for the entire study area; however, this method also showed some inconsistencies and seemed to be very prone to extrapolation problems. The

y

-modeling method, which seems to be less prone to extrapolation problems, allows obtaining more outputs that are flexible and can outperform the

δ

-modeling method at the stand level. The weak correlation between changes in structural attributes and LiDAR auxiliary information indicates that pixel-level maps have very large uncertainties and estimation of change clearly requires some degree of spatial aggregation; additionally, in similar environments, it might be necessary to increase the time lapse between LiDAR acquisitions to obtain reliable estimates of change. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Global Forest Monitoring)

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29 pages, 12489 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of the Potential of Convolutional Neural Networks and Random Forests for Multi-Class Segmentation of Sentinel-2 Imagery

by Vasileios Syrris, Paul Hasenohr, Blagoj Delipetrev, Alexander Kotsev, Pieter Kempeneers and Pierre Soille

Remote Sens. 2019, 11(8), 907; https://doi.org/10.3390/rs11080907 - 14 Apr 2019

Cited by 29 | Viewed by 8272

Abstract

Motivated by the increasing availability of open and free Earth observation data through the Copernicus Sentinel missions, this study investigates the capacity of advanced computational models to automatically generate thematic layers, which in turn contribute to and facilitate the creation of land cover [...] Read more.

Motivated by the increasing availability of open and free Earth observation data through the Copernicus Sentinel missions, this study investigates the capacity of advanced computational models to automatically generate thematic layers, which in turn contribute to and facilitate the creation of land cover products. In concrete terms, we assess the practical and computational aspects of multi-class Sentinel-2 image segmentation based on a convolutional neural network and random forest approaches. The annotated learning set derives from data that is made available as result of the implementation of European Union’s INSPIRE Directive. Since this network of data sets remains incomplete in regard to some geographic areas, another objective of this work was to provide consistent and reproducible ways for machine-driven mapping of these gaps and a potential update of the existing ones. Finally, the performance analysis identifies the most important hyper-parameters, and provides hints on the models’ deployment and their transferability. Full article

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29 pages, 8885 KiB

Open AccessEditor’s ChoiceArticle

Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique

by Ya-Lun S. Tsai, Andreas Dietz, Natascha Oppelt and Claudia Kuenzer

Remote Sens. 2019, 11(8), 895; https://doi.org/10.3390/rs11080895 - 12 Apr 2019

Cited by 53 | Viewed by 11529

Abstract

Traditional studies on mapping wet snow cover extent (SCE) often feature limitations, especially in vegetated and mountainous areas. The aim of this study is to propose a new total and wet SCE mapping strategy based on freely accessible spaceborne synthetic aperture radar (SAR) [...] Read more.

Traditional studies on mapping wet snow cover extent (SCE) often feature limitations, especially in vegetated and mountainous areas. The aim of this study is to propose a new total and wet SCE mapping strategy based on freely accessible spaceborne synthetic aperture radar (SAR) data. The approach is transferable on a global scale as well as for different land cover types (including densely vegetated forest and agricultural regions), and is based on the use of backscattering coefficient, interferometric SAR coherence, and polarimetric parameters. Furthermore, four topographical factors were included in the simple tuning of random forest-based land cover type-dependent classification strategy. Results showed the classification accuracy was above 0.75, with an F-measure higher than 0.70, in all five selected regions of interest located around globally distributed mountain ranges. Whilst excluding forest-type land cover classes, the accuracy and F-measure increases to 0.80 and 0.75. In cross-location model set, the accuracy can also be maintained at 0.80 with non-forest accuracy up to 0.85. It has been found that the elevation and polarimetric parameters are the most critical factors, and that the quality of land cover information would also affect the subsequent mapping reliability. In conclusion, through comprehensive validation using optical satellite and in-situ data, our land cover-dependent total SCE mapping approach has been confirmed to be robustly applicable, and the holistic SCE map for different months were eventually derived. Full article

(This article belongs to the Section Environmental Remote Sensing)

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47 pages, 11531 KiB

Open AccessEditor’s ChoiceReview

Retrieving Sea Level and Freeboard in the Arctic: A Review of Current Radar Altimetry Methodologies and Future Perspectives

by Graham D. Quartly, Eero Rinne, Marcello Passaro, Ole B. Andersen, Salvatore Dinardo, Sara Fleury, Amandine Guillot, Stefan Hendricks, Andrey A. Kurekin, Felix L. Müller, Robert Ricker, Henriette Skourup and Michel Tsamados

Remote Sens. 2019, 11(7), 881; https://doi.org/10.3390/rs11070881 - 11 Apr 2019

Cited by 47 | Viewed by 11468

Abstract

Spaceborne radar altimeters record echo waveforms over all Earth surfaces, but their interpretation and quantitative exploitation over the Arctic Ocean is particularly challenging. Radar returns may be from all ocean, all sea ice, or a mixture of the two, so the first task [...] Read more.

Spaceborne radar altimeters record echo waveforms over all Earth surfaces, but their interpretation and quantitative exploitation over the Arctic Ocean is particularly challenging. Radar returns may be from all ocean, all sea ice, or a mixture of the two, so the first task is the determination of which surface and then an interpretation of the signal to give range. Subsequently, corrections have to be applied for various surface and atmospheric effects before making a comparison with a reference level. This paper discusses the drivers for improved altimetry in the Arctic and then reviews the various approaches that have been used to achieve the initial classification and subsequent retracking over these diverse surfaces, showing examples from both LRM (low resolution mode) and SAR (synthetic aperture radar) altimeters. The review then discusses the issues concerning corrections, including the choices between using other remote-sensing measurements and using those from models or climatology. The paper finishes with some perspectives on future developments, incorporating secondary frequency, interferometric SAR and opportunities for fusion with measurements from laser altimetry or from the SMOS salinity sensor, and provides a full list of relevant abbreviations. Full article

(This article belongs to the Special Issue Advances in Satellite Altimetry and Its Application)

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20 pages, 8482 KiB

Open AccessEditor’s ChoiceArticle

Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results

by Meisam Amani, Sahel Mahdavi, Majid Afshar, Brian Brisco, Weimin Huang, Sayyed Mohammad Javad Mirzadeh, Lori White, Sarah Banks, Joshua Montgomery and Christopher Hopkinson

Remote Sens. 2019, 11(7), 842; https://doi.org/10.3390/rs11070842 - 8 Apr 2019

Cited by 180 | Viewed by 21060

Abstract

Although wetlands provide valuable services to humans and the environment and cover a large portion of Canada, there is currently no Canada-wide wetland inventory based on the specifications defined by the Canadian Wetland Classification System (CWCS). The most practical approach for creating the [...] Read more.

Although wetlands provide valuable services to humans and the environment and cover a large portion of Canada, there is currently no Canada-wide wetland inventory based on the specifications defined by the Canadian Wetland Classification System (CWCS). The most practical approach for creating the Canadian Wetland Inventory (CWI) is to develop a remote sensing method feasible for large areas with the potential to be updated within certain time intervals to monitor dynamic wetland landscapes. Thus, this study aimed to create the first Canada-wide wetland inventory using Landsat-8 imagery and innovative image processing techniques available within Google Earth Engine (GEE). For this purpose, a large amount of field samples and approximately 30,000 Landsat-8 surface reflectance images were initially processed using several advanced algorithms within GEE. Then, the random forest (RF) algorithm was applied to classify the entire country. The final step was an original CWI map considering the five wetland classes defined by the CWCS (i.e., bog, fen, marsh, swamp, and shallow water) and providing updated and comprehensive information regarding the location and spatial extent of wetlands in Canada. The map had reasonable accuracy in terms of both visual and statistical analyses considering the large area of country that was classified (9.985 million km²). The overall classification accuracy and the average producer and user accuracies for wetland classes exclusively were 71%, 66%, and 63%, respectively. Additionally, based on the final classification map, it was estimated that 36% of Canada is covered by wetlands. Full article

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23 pages, 7356 KiB

Open AccessEditor’s ChoiceArticle

Downscaling GRACE TWSA Data into High-Resolution Groundwater Level Anomaly Using Machine Learning-Based Models in a Glacial Aquifer System

by Wondwosen M. Seyoum, Dongjae Kwon and Adam M. Milewski

Remote Sens. 2019, 11(7), 824; https://doi.org/10.3390/rs11070824 - 5 Apr 2019

Cited by 107 | Viewed by 11405

Abstract

With continued threat from climate change and human impacts, high-resolution and continuous hydrologic data accessibility has a paramount importance for predicting trends and availability of water resources. This study presents a novel machine learning (ML)-based downscaling algorithm that produces a high spatial resolution [...] Read more.

With continued threat from climate change and human impacts, high-resolution and continuous hydrologic data accessibility has a paramount importance for predicting trends and availability of water resources. This study presents a novel machine learning (ML)-based downscaling algorithm that produces a high spatial resolution groundwater level anomaly (GWLA) from the Gravity Recovery and Climate Experiment (GRACE) data by utilizing the relationship between Terrestrial Water Storage Anomaly (TWSA) from GRACE and other land surface and hydro-climatic variables (e.g., vegetation coverage, land surface temperature, precipitation, streamflow, and in-situ groundwater level data). The predicted downscaled GWLA data were tested using monthly in-situ groundwater level observations. Of the 32 groundwater monitoring wells available in the study site, 21 wells were used to develop the ML-based downscaling model, while the remaining 11 wells were used to assess the performance of the ML-based downscaling model. The test results showed that the model satisfactorily reproduces the spatial and temporal variation of the GWLA in the area, with acceptable correlation coefficient and Nash-Sutcliffe Efficiency values of ~0.76 and ~0.45, respectively. GRACE TWSA was the most influential predictor variable in the models, followed by stream discharge and soil moisture storage. Though model limitations and uncertainty could exist due to high spatial heterogeneity of the geologic materials and omission of human impact (e.g., abstraction), the significance of the result is undeniable, particularly in areas where in-situ well measurements are sparse. Full article

(This article belongs to the Special Issue Remote Sensing by Satellite Gravimetry)

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19 pages, 2475 KiB

Open AccessEditor’s ChoiceArticle

Metrics of Lidar-Derived 3D Vegetation Structure Reveal Contrasting Effects of Horizontal and Vertical Forest Heterogeneity on Bird Species Richness

by Luis Carrasco, Xingli Giam, Monica Papeş and Kimberly S. Sheldon

Remote Sens. 2019, 11(7), 743; https://doi.org/10.3390/rs11070743 - 27 Mar 2019

Cited by 63 | Viewed by 12678

Abstract

The structural heterogeneity of vegetation is a key factor for explaining animal diversity patterns at a local scale. Improvements in airborne light detection and ranging (lidar) technologies have enabled researchers to study forest 3D structure with increasing accuracy. Most structure–animal diversity work has [...] Read more.

The structural heterogeneity of vegetation is a key factor for explaining animal diversity patterns at a local scale. Improvements in airborne light detection and ranging (lidar) technologies have enabled researchers to study forest 3D structure with increasing accuracy. Most structure–animal diversity work has focused on structural metrics derived from lidar returns from canopy and terrain features. Here, we built new lidar structural metrics based on the Leaf Area Density (LAD) at each vegetation height layer, and used these metrics to study how different aspects of forest structural heterogeneity explain variation in bird species richness. Our goals were to test: (1) whether LAD-based metrics better explained bird species richness compared to metrics based on the top of the canopy; and (2) if different aspects of structural heterogeneity had diverse effects on bird richness. We used discrete lidar data together with 61 breeding landbird points provided by the National Ecological Observatory Network at five forest sites of the eastern US. We used the lidar metrics as predictors of bird species richness and analyzed the shape of the response curves against each predictor. Metrics based on LAD measurements had better explanatory power (43% of variance explained) than those based on the variation of canopy heights (32% of variance explained). Dividing the forest plots into smaller grids allowed us to study the within-plot horizontal variation of the vertical heterogeneity, as well as to analyze how the vegetation density is horizontally distributed at each height layer. Bird species richness increased with horizontal heterogeneity, while vertical heterogeneity had negative effects, contrary to previous research. The increasing capabilities of lidar will allow researchers to characterize forest structure with higher detail. Our findings highlight the need for structure–animal diversity studies to incorporate metrics that are able to capture different aspects of forest 3D heterogeneity. Full article

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20 pages, 16859 KiB

Open AccessEditor’s ChoiceArticle

Operational Large-Scale Segmentation of Imagery Based on Iterative Elimination

by James D. Shepherd, Pete Bunting and John R. Dymond

Remote Sens. 2019, 11(6), 658; https://doi.org/10.3390/rs11060658 - 18 Mar 2019

Cited by 43 | Viewed by 9577

Abstract

Image classification and interpretation are greatly aided through the use of image segmentation. Within the field of environmental remote sensing, image segmentation aims to identify regions of unique or dominant ground cover from their attributes such as spectral signature, texture and context. However, [...] Read more.

Image classification and interpretation are greatly aided through the use of image segmentation. Within the field of environmental remote sensing, image segmentation aims to identify regions of unique or dominant ground cover from their attributes such as spectral signature, texture and context. However, many approaches are not scalable for national mapping programmes due to limits in the size of images that can be processed. Therefore, we present a scalable segmentation algorithm, which is seeded using k-means and provides support for a minimum mapping unit through an innovative iterative elimination process. The algorithm has also been demonstrated for the segmentation of time series datasets capturing both the intra-image variation and change regions. The quality of the segmentation results was assessed by comparison with reference segments along with statistics on the inter- and intra-segment spectral variation. The technique is computationally scalable and is being actively used within the national land cover mapping programme for New Zealand. Additionally, 30-m continental mosaics of Landsat and ALOS-PALSAR have been segmented for Australia in support of national forest height and cover mapping. The algorithm has also been made freely available within the open source Remote Sensing and GIS software Library (RSGISLib). Full article

(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)

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20 pages, 4019 KiB

Open AccessEditor’s ChoiceArticle

Preliminary Assessment of Turbidity and Chlorophyll Impact on Bathymetry Derived from Sentinel-2A and Sentinel-3A Satellites in South Florida

by Isabel Caballero, Richard P. Stumpf and Andrew Meredith

Remote Sens. 2019, 11(6), 645; https://doi.org/10.3390/rs11060645 - 16 Mar 2019

Cited by 101 | Viewed by 9971

Abstract

Evaluation of the impact of turbidity on satellite-derived bathymetry (SDB) is a crucial step for selecting optimal scenes and for addressing the limitations of SDB. This study examines the relatively high-resolution MultiSpectral instrument (MSI) onboard Sentinel-2A (10–20–60 m) and the moderate-resolution Ocean and [...] Read more.

Evaluation of the impact of turbidity on satellite-derived bathymetry (SDB) is a crucial step for selecting optimal scenes and for addressing the limitations of SDB. This study examines the relatively high-resolution MultiSpectral instrument (MSI) onboard Sentinel-2A (10–20–60 m) and the moderate-resolution Ocean and Land Color instrument (OLCI) onboard Sentinel-3A (300 m) for generating bathymetric maps through a conventional ratio transform model in environments with some turbidity in South Florida. Both sensors incorporate additional spectral bands in the red-edge near infrared (NIR) region, allowing turbidity detection in optically shallow waters. The ratio model only requires two calibration parameters for vertical referencing using available chart data, whereas independent lidar surveys are used for validation and error analysis. The MSI retrieves bathymetry at 10 m with errors of 0.58 m at depths ranging between 0–18 m (limit of lidar survey) in West Palm Beach and of 0.22 m at depths ranging between 0–5 m in Key West, in conditions with low turbidity. In addition, this research presents an assessment of the SDB depth limit caused by turbidity as determined with the reflectance of the red-edge bands at 709 nm (OLCI) and 704 nm (MSI) and a standard ocean color chlorophyll concentration. OLCI and MSI results are comparable, indicating the potential of the two optical missions as interchangeable sensors that can help determine the selection of the optimal scenes for SDB mapping. OLCI can provide temporal data to identify water quality characteristics and general SDB patterns. The relationship of turbidity with depth detection may help to enhance the operational use of SDB over environments with varying water transparency conditions, particularly in remote and inaccessible regions of the world. Full article

(This article belongs to the Special Issue Satellite Derived Bathymetry)

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18 pages, 8504 KiB

Open AccessEditor’s ChoiceArticle

A Method for Landsat and Sentinel 2 (HLS) BRDF Normalization

by Belen Franch, Eric Vermote, Sergii Skakun, Jean-Claude Roger, Jeffrey Masek, Junchang Ju, Jose Luis Villaescusa-Nadal and Andres Santamaria-Artigas

Remote Sens. 2019, 11(6), 632; https://doi.org/10.3390/rs11060632 - 15 Mar 2019

Cited by 46 | Viewed by 9938

Abstract

The Harmonized Landsat/Sentinel-2 (HLS) project aims to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2 remote sensing satellites. These satellites’ sampling characteristics provide nearly constant observation geometry and low illumination variation through the scene. However, the [...] Read more.

The Harmonized Landsat/Sentinel-2 (HLS) project aims to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2 remote sensing satellites. These satellites’ sampling characteristics provide nearly constant observation geometry and low illumination variation through the scene. However, the illumination variation throughout the year impacts the surface reflectance by producing higher values for low solar zenith angles and lower reflectance for large zenith angles. In this work, we present a model to derive the bidirectional reflectance distribution function (BRDF) normalization and apply it to the HLS product at 30 m spatial resolution. It is based on the BRDF parameters estimated from the MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product (M{O,Y}D09) at 1 km spatial resolution using the VJB method (Vermote et al., 2009). Unsupervised classification (segmentation) of HLS images is used to disaggregate the BRDF parameters to the HLS spatial resolution and to build a BRDF parameters database at HLS scale. We first test the proposed BRDF normalization for different solar zenith angles over two homogeneous sites, in particular one desert and one Peruvian Amazon forest. The proposed method reduces both the correlation with the solar zenith angle and the coefficient of variation (CV) of the reflectance time series in the red and near infrared bands to 4% in forest and keeps a low CV of 3% to 4% for the deserts. Additionally, we assess the impact of the view zenith angle (VZA) in an area of the Brazilian Amazon forest close to the equator, where impact of the angular variation is stronger because it occurs in the principal plane. The directional reflectance shows a strong dependency with the VZA. The current HLS BRDF correction reduces this dependency but still shows an under-correction, especially in the near infrared, while the proposed method shows no dependency with the view angles. We also evaluate the BRDF parameters using field surface albedo measurements as a reference over seven different sites of the US surface radiation budget observing network (SURFRAD) and five sites of the Australian OzFlux network. Full article

(This article belongs to the Special Issue Remotely Sensed Albedo)

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14 pages, 794 KiB

Open AccessEditor’s ChoiceArticle

Water Vapor Calibration: Using a Raman Lidar and Radiosoundings to Obtain Highly Resolved Water Vapor Profiles

by Birte Solveig Kulla and Christoph Ritter

Remote Sens. 2019, 11(6), 616; https://doi.org/10.3390/rs11060616 - 13 Mar 2019

Cited by 9 | Viewed by 5214

Abstract

We revised the calibration of a water vapor Raman lidar by co-located radiosoundings for a site in the high European Arctic. For this purpose, we defined robust criteria for a valid calibration. One of these criteria is the logarithm of the water vapor [...] Read more.

We revised the calibration of a water vapor Raman lidar by co-located radiosoundings for a site in the high European Arctic. For this purpose, we defined robust criteria for a valid calibration. One of these criteria is the logarithm of the water vapor mixing ratio between the sonde and the lidar. With an error analysis, we showed that for our site correlations smaller than 0.95 could be explained neither by noise in the lidar nor by wrong assumptions concerning the aerosol or Rayleigh extinction. However, highly variable correlation coefficients between sonde and consecutive lidar profiles were found, suggesting that small scale variability of the humidity was our largest source of error. Therefore, not all co-located radiosoundings are useful for lidar calibration. As we assumed these changes to be non-systematic, averaging over several independent measurements increased the calibration’s quality. The calibration of the water vapor measurements from the lidar for individual profiles varied by less than ±5%. The seasonal median, used for calibration in this study, was stable and reliable (confidence ±1% for the season with most calibration profiles). Thus, the water vapor mixing ratio profiles from the Koldewey Aerosol Raman Lidar (KARL) are very accurate. They show high temporal variability up to 4 km altitude and, therefore, provide additional, independent information to the radiosonde. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

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23 pages, 9069 KiB

Open AccessEditor’s ChoiceArticle

Flood Inundation Mapping of the Sparsely Gauged Large-Scale Brahmaputra Basin Using Remote Sensing Products

by Biswa Bhattacharya, Maurizio Mazzoleni and Reyne Ugay

Remote Sens. 2019, 11(5), 501; https://doi.org/10.3390/rs11050501 - 1 Mar 2019

Cited by 31 | Viewed by 8411

Abstract

Sustainable water management is one of the important priorities set out in the Sustainable Development Goals (SDGs) of the United Nations, which calls for efficient use of natural resources. Efficient water management nowadays depends a lot upon simulation models. However, the availability of [...] Read more.

Sustainable water management is one of the important priorities set out in the Sustainable Development Goals (SDGs) of the United Nations, which calls for efficient use of natural resources. Efficient water management nowadays depends a lot upon simulation models. However, the availability of limited hydro-meteorological data together with limited data sharing practices prohibits simulation modelling and consequently efficient flood risk management of sparsely gauged basins. Advances in remote sensing has significantly contributed to carrying out hydrological studies in ungauged or sparsely gauged basins. In particular, the global datasets of remote sensing observations (e.g., rainfall, evaporation, temperature, land use, terrain, etc.) allow to develop hydrological and hydraulic models of sparsely gauged catchments. In this research, we have considered large scale hydrological and hydraulic modelling, using freely available global datasets, of the sparsely gauged trans-boundary Brahmaputra basin, which has an enormous potential in terms of agriculture, hydropower, water supplies and other utilities. A semi-distributed conceptual hydrological model was developed using HEC-HMS (Hydrologic Modelling System from Hydrologic Engineering Centre). Rainfall estimates from Tropical Rainfall Measuring Mission (TRMM) was compared with limited gauge data and used in the simulation. The Nash Sutcliffe coefficient of the model with the uncorrected rainfall data in calibration and validation were 0.75 and 0.61 respectively whereas the similar values with the corrected rainfall data were 0.81 and 0.74. The output of the hydrological model was used as a boundary condition and lateral inflow to the hydraulic model. Modelling results obtained using uncorrected and corrected remotely sensed products of rainfall were compared with the discharge values at the basin outlet (Bahadurabad) and with altimetry data from Jason-2 satellite. The simulated flood inundation maps of the lower part of the Brahmaputra basin showed reasonably good match in terms of the probability of detection, success ratio and critical success index. Overall, this study demonstrated that reliable and robust results can be obtained in both hydrological and hydraulic modelling using remote sensing data as the only input to large scale and sparsely gauged basins. Full article

(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)

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30 pages, 22248 KiB

Open AccessEditor’s ChoiceArticle

30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine

by Tengfei Long, Zhaoming Zhang, Guojin He, Weili Jiao, Chao Tang, Bingfang Wu, Xiaomei Zhang, Guizhou Wang and Ranyu Yin

Remote Sens. 2019, 11(5), 489; https://doi.org/10.3390/rs11050489 - 27 Feb 2019

Cited by 182 | Viewed by 18422

Abstract

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In [...] Read more.

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation (

R^{2} = 0.74

) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively. Full article

(This article belongs to the Special Issue New Trends in Forest Fire Research Incorporating Big Data and Climate Change Modeling)

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17 pages, 9897 KiB

Open AccessEditor’s ChoiceArticle

L-Band UAVSAR Tomographic Imaging in Dense Forests: Gabon Forests

by Ibrahim El Moussawi, Dinh Ho Tong Minh, Nicolas Baghdadi, Chadi Abdallah, Jalal Jomaah, Olivier Strauss and Marco Lavalle

Remote Sens. 2019, 11(5), 475; https://doi.org/10.3390/rs11050475 - 26 Feb 2019

Cited by 17 | Viewed by 5553

Abstract

Developing and enhancing strategies to characterize actual forests structure is a timely challenge, particularly for tropical forests. P-band synthetic aperture radar (SAR) tomography (TomoSAR) has previously been demonstrated as a powerful tool for characterizing the 3-D vertical structure of tropical forests, and its [...] Read more.

Developing and enhancing strategies to characterize actual forests structure is a timely challenge, particularly for tropical forests. P-band synthetic aperture radar (SAR) tomography (TomoSAR) has previously been demonstrated as a powerful tool for characterizing the 3-D vertical structure of tropical forests, and its capability and potential to retrieve tropical forest structure has been discussed and assessed. On the other hand, the abilities of L-band TomoSAR are still in the early stages of development. Here, we aim to provide a better understanding of L-band TomoSAR capabilities for retrieving the 3-D structure of tropical forests and estimating the top height in dense forests. We carried out tomographic analysis using L-band UAVSAR data from the AfriSAR campaign conducted over Gabon Lopé Park in February 2016. First, it was found that L-band TomoSAR was able to penetrate into and through the canopy down to the ground, and thus the canopy and ground layers were detected correctly. The resulting TomoSAR vertical profiles were validated with a digital terrain model and canopy height model extracted from small-footprint Lidar (SFL) data. Second, there was a strong correlation between the L-band Capon beam forming profile in HH and HV polarizations with Land Vegetation Ice Sensor (LVIS) Level 1B waveform Lidar over different kinds of forest in Gabon Lopé National Park. Finally, forest top height from the L-band data was estimated and validated with SFL data, resulting in a root mean square error of 3 m and coefficient of determination of 0.92. The results demonstrate that L-band TomoSAR is capable of characterizing 3-D structure of tropical forests. Full article

(This article belongs to the Section Forest Remote Sensing)

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31 pages, 5333 KiB

Open AccessEditor’s ChoiceArticle

Multiple Remotely Sensed Lines of Evidence for a Depleting Seasonal Snowpack in the Near East

by Yeliz A. Yılmaz, Kristoffer Aalstad and Omer L. Sen

Remote Sens. 2019, 11(5), 483; https://doi.org/10.3390/rs11050483 - 26 Feb 2019

Cited by 20 | Viewed by 6053

Abstract

The snow-fed river basins of the Near East region are facing an urgent threat in the form of declining water resources. In this study, we analyzed several remote sensing products (optical, passive microwave, and gravimetric) and outputs of a meteorological reanalysis data set [...] Read more.

The snow-fed river basins of the Near East region are facing an urgent threat in the form of declining water resources. In this study, we analyzed several remote sensing products (optical, passive microwave, and gravimetric) and outputs of a meteorological reanalysis data set to understand the relationship between the terrestrial water storage anomalies and the mountain snowpack. The results from different satellite retrievals show a clear signal of a depletion of both water storage and the seasonal snowpack in four basins in the region. We find a strong reduction in terrestrial water storage over the Gravity Recovery and Climate Experiment (GRACE) observational period, particularly over the higher elevations. Snow-cover duration estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) products point towards negative and significant trends up to one month per decade in the current era. These numbers are a clear indicator of the partial disappearance of the seasonal snow-cover in the region which has been projected to occur by the end of the century. The spatial patterns of changes in the snow-cover duration are positively correlated with both GRACE terrestrial water storage decline and peak snow water equivalent (SWE) depletion from the ERA5 reanalysis. Possible drivers of the snowpack depletion are a significant reduction in the snowfall ratio and an earlier snowmelt. A continued depletion of the montane snowpack in the Near East paints a bleak picture for future water availability in this water-stressed region. Full article

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16 pages, 2424 KiB

Open AccessEditor’s ChoiceTechnical Note

Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science

by Céline Boisvenue and Joanne C. White

Remote Sens. 2019, 11(4), 463; https://doi.org/10.3390/rs11040463 - 23 Feb 2019

Cited by 28 | Viewed by 6860

Abstract

Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key research priorities for remote sensing science. However, estimating and understanding forest carbon and its spatiotemporal variations requires diverse knowledge from [...] Read more.

Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key research priorities for remote sensing science. However, estimating and understanding forest carbon and its spatiotemporal variations requires diverse knowledge from multiple research domains, none of which currently offer a complete understanding of forest carbon dynamics. New large-area forest information products derived from remotely sensed data provide unprecedented spatial and temporal information about our forests, which is information that is currently underutilized in forest carbon models. Our goal in this communication is to articulate the information needs of next-generation forest carbon models in order to enable the remote sensing community to realize the best and most useful application of its science, and perhaps also inspire increased collaboration across these research fields. While remote sensing science currently provides important contributions to large-scale forest carbon models, more coordinated efforts to integrate remotely sensed data into carbon models can aid in alleviating some of the main limitations of these models; namely, low sample sizes and poor spatial representation of field data, incomplete population sampling (i.e., managed forests exclusively), and an inadequate understanding of the processes that influence forest carbon accumulation and fluxes across spatiotemporal scales. By articulating the information needs of next-generation forest carbon models, we hope to bridge the knowledge gap between remote sensing experts and forest carbon modelers, and enable advances in large-area forest carbon modeling that will ultimately improve estimates of carbon stocks and fluxes. Full article

(This article belongs to the Section Forest Remote Sensing)

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19 pages, 7598 KiB

Open AccessEditor’s ChoiceArticle

Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring

by Alexey V. Egorov, David P. Roy, Hankui K. Zhang, Zhongbin Li, Lin Yan and Haiyan Huang

Remote Sens. 2019, 11(4), 447; https://doi.org/10.3390/rs11040447 - 21 Feb 2019

Cited by 55 | Viewed by 8682

Abstract

The Landsat Analysis Ready Data (ARD) are designed to make the U.S. Landsat archive straightforward to use. In this paper, the availability of the Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) ARD over the conterminous [...] Read more.

The Landsat Analysis Ready Data (ARD) are designed to make the U.S. Landsat archive straightforward to use. In this paper, the availability of the Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) ARD over the conterminous United States (CONUS) are quantified for a 36-year period (1 January 1982 to 31 December 2017). Complex patterns of ARD availability occur due to the satellite orbit and sensor geometry, cloud, sensor acquisition and health issues and because of changing relative orientation of the ARD tiles with respect to the Landsat orbit paths. Quantitative per-pixel and summary ARD tile results are reported. Within the CONUS, the average annual number of non-cloudy observations in each 150 × 150 km ARD tile varies from 0.53 to 16.80 (Landsat 4 TM), 11.08 to 22.83 (Landsat 5 TM), 9.73 to 21.72 (Landsat 7 ETM+) and 14.23 to 30.07 (all three sensors). The annual number was most frequently only 2 to 4 Landsat 4 TM observations (36% of the CONUS tiles), increasing to 14 to 16 Landsat 5 TM observations (26% of tiles), 12 to 14 Landsat 7 ETM+ observations (31% of tiles) and 18 to 20 observations (23% of tiles) when considering all three sensors. The most frequently observed ARD tiles were in the arid south-west and in certain mountain rain shadow regions and the least observed tiles were in the north-east, around the Great Lakes and along parts of the north-west coast. The quality of time series algorithm results is expected to be reduced at ARD tiles with low reported availability. The smallest annual number of cloud-free observations for the Landsat 5 TM are over ARD tile h28v04 (northern New York state), for Landsat 7 ETM+ are over tile h25v07 (Ohio and Pennsylvania) and for Landsat 4 TM are over tile h22v08 (northern Indiana). The greatest annual number of cloud-free observations for the Landsat 5 TM and 7 ETM+ ARD are over southern California ARD tile h04v11 and for the Landsat 4 TM are over southern Arizona tile h06v13. The reported results likely overestimate the number of good surface observations because shadows and cirrus clouds were not considered. Implications of the findings for terrestrial monitoring and future ARD research are discussed. Full article

(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)

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15 pages, 4333 KiB

Open AccessEditor’s ChoiceArticle

Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates

by Brianna R. Pagán, Wouter H. Maes, Pierre Gentine, Brecht Martens and Diego G. Miralles

Remote Sens. 2019, 11(4), 413; https://doi.org/10.3390/rs11040413 - 18 Feb 2019

Cited by 47 | Viewed by 10508

Abstract

The opening and closing of plant stomata regulates the global water, carbon and energy cycles. Biophysical feedbacks on climate are highly dependent on transpiration, which is mediated by vegetation phenology and plant responses to stress conditions. Here, we explore the potential of satellite [...] Read more.

The opening and closing of plant stomata regulates the global water, carbon and energy cycles. Biophysical feedbacks on climate are highly dependent on transpiration, which is mediated by vegetation phenology and plant responses to stress conditions. Here, we explore the potential of satellite observations of solar-induced chlorophyll fluorescence (SIF)—normalized by photosynthetically-active radiation (PAR)—to diagnose the ratio of transpiration to potential evaporation (‘transpiration efficiency’, τ). This potential is validated at 25 eddy-covariance sites from seven biomes worldwide. The skill of the state-of-the-art land surface models (LSMs) from the eartH2Observe project to estimate τ is also contrasted against eddy-covariance data. Despite its relatively coarse (0.5°) resolution, SIF/PAR estimates, based on data from the Global Ozone Monitoring Experiment 2 (GOME-2) and the Clouds and Earth’s Radiant Energy System (CERES), correlate to the in situ τ significantly (average inter-site correlation of 0.59), with higher correlations during growing seasons (0.64) compared to decaying periods (0.53). In addition, the skill to diagnose the variability of in situ τ demonstrated by all LSMs is on average lower, indicating the potential of SIF data to constrain the formulations of transpiration in global models via, e.g., data assimilation. Overall, SIF/PAR estimates successfully capture the effect of phenological changes and environmental stress on natural ecosystem transpiration, adequately reflecting the timing of this variability without complex parameterizations. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing of Terrestrial Evaporation)

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19 pages, 4559 KiB

Open AccessEditor’s ChoiceArticle

Impact of Soil Moisture Data Characteristics on the Sensitivity to Crop Yields Under Drought and Excess Moisture Conditions

by Catherine Champagne, Jenelle White, Aaron Berg, Stephane Belair and Marco Carrera

Remote Sens. 2019, 11(4), 372; https://doi.org/10.3390/rs11040372 - 13 Feb 2019

Cited by 29 | Viewed by 7773

Abstract

Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling [...] Read more.

Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling and the development of satellite technology to indirectly measure surface soil moisture has led to the emergence of a number of national and global soil moisture data sets that can provide insight into the dynamics of agricultural drought. Droughts are often defined by normal conditions for a given time and place; as a result, data sets used to quantify drought need a representative baseline of conditions in order to accurately establish a normal. This presents a challenge when working with earth observation data sets which often have very short baselines for a single instrument. This study assessed three soil moisture data sets: a surface satellite soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission operating since 2010; a blended surface satellite soil moisture data set from the European Space Agency Climate Change Initiative (ESA-CCI) that has a long history and a surface and root zone soil moisture data set from the Canadian Meteorology Centre (CMC)’s Regional Deterministic Prediction System (RDPS). An iterative chi-squared statistical routine was used to evaluate each data set’s sensitivity to canola yields in Saskatchewan, Canada. The surface soil moisture from all three data sets showed a similar temporal trend related to crop yields, showing a negative impact on canola yields when soil moisture exceeded a threshold in May and June. The strength and timing of this relationship varied with the accuracy and statistical properties of the data set, with the SMOS data set showing the strongest relationship (peak X² = 170 for Day of Year 145), followed by the ESA-CCI (peak X² = 89 on Day of Year 129) and then the RDPS (peak X² = 65 on Day of Year 129). Using short baseline soil moisture data sets can produce consistent results compared to using a longer data set, but the characteristics of the years used for the baseline are important. Soil moisture baselines of 18–20 years or more are needed to reliably estimate the relationship between high soil moisture and high yielding years. For the relationship between low soil moisture and low yielding years, a shorter baseline can be used, with reliable results obtained when 10–15 years of data are available, but with reasonably consistent results obtained with as few as 7 years of data. This suggests that the negative impacts of drought on agriculture may be reliably estimated with a relatively short baseline of data. Full article

(This article belongs to the Special Issue Remote Sensing of Drought Monitoring)

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21 pages, 4484 KiB

Open AccessEditor’s ChoiceArticle

Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time

by Zhi Zhang, Dagang Wang, Guiling Wang, Jianxiu Qiu and Weilin Liao

Remote Sens. 2019, 11(3), 368; https://doi.org/10.3390/rs11030368 - 12 Feb 2019

Cited by 16 | Viewed by 6172

Abstract

Satellite-based precipitation products have been widely used in a variety of fields. However, near real time products still contain substantial biases compared with the ground data. Recent studies showed that surface soil moisture can be utilized in improving rainfall estimation as it reflects [...] Read more.

Satellite-based precipitation products have been widely used in a variety of fields. However, near real time products still contain substantial biases compared with the ground data. Recent studies showed that surface soil moisture can be utilized in improving rainfall estimation as it reflects recent precipitation. In this study, soil moisture data from Soil Moisture Active Passive (SMAP) satellite and observation-based fitting are used to correct near real time satellite-based precipitation product Global Precipitation Measurement (GPM) in mainland China. The particle filter is adopted to assimilate the SMAP soil moisture into a simple hydrological model, the antecedent precipitation index (API) model; three fitting methods—i.e., linear, nonlinear, and cumulative distribution function (CDF) fitting corrections—both separately and in combination with the SMAP soil moisture data, are then used to correct GPM. The results show that the soil moisture-based correction significantly reduces the root mean square error (RMSE) and mean absolute errors (BIAS) of the original GPM product in most areas of China. The median RMSE value for daily precipitation over China is decreased by approximately 18% from 5.25 mm/day for the GPM estimates to 4.32 mm/day for the soil moisture corrected estimates, and the median BIAS value is decreased by approximately 13% from 2.03 mm/day to 1.76 mm/day. The fitting correction method alone also improves GPM, although to a lesser extent. The best performance is found when the SMAP soil moisture assimilation is combined with the linear fitting of observed precipitation, with a median RMSE of 4.00 mm/day and a BIAS of 1.69 mm/day. Despite significant reductions to the biases of the satellite precipitation product, none of these methods is effective in improving the correlation between the satellite product and observational reference. Leaf area index and the frequency of the SMAP overpasses are among the potential factors influencing the correction effect. This study highlights that combining soil moisture and historical precipitation information can effectively improve satellite-based precipitation products in near real time. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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16 pages, 3294 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data

by Tianfang Xu, Jillian M. Deines, Anthony D. Kendall, Bruno Basso and David W. Hyndman

Remote Sens. 2019, 11(3), 370; https://doi.org/10.3390/rs11030370 - 12 Feb 2019

Cited by 31 | Viewed by 5816

Abstract

High-resolution mapping of irrigated fields is needed to better estimate water and nutrient fluxes in the landscape, food production, and local to regional climate. However, this remains a challenge in humid to subhumid regions, where irrigation has been expanding into what was largely [...] Read more.

High-resolution mapping of irrigated fields is needed to better estimate water and nutrient fluxes in the landscape, food production, and local to regional climate. However, this remains a challenge in humid to subhumid regions, where irrigation has been expanding into what was largely rainfed agriculture due to trends in climate, crop prices, technologies and practices. One such region is southwestern Michigan, USA, where groundwater is the main source of irrigation water for row crops (primarily corn and soybeans). Remote sensing of irrigated areas can be difficult in these regions as rainfed areas have similar characteristics. We present methods to address this challenge and enhance the contrast between neighboring rainfed and irrigated areas, including weather-sensitive scene selection, applying recently developed composite indices and calculating spatial anomalies. We create annual, 30m-resolution maps of irrigated corn and soybeans for southwestern Michigan from 2001 to 2016 using a machine learning method (random forest). The irrigation maps reasonably capture the spatial and temporal pattern of irrigation, with accuracies that exceed available products. Analysis of the irrigation maps showed that the irrigated area in southwestern Michigan tripled in the last 16 years. We also discuss the remaining challenges for irrigation mapping in humid to subhumid areas. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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16 pages, 1420 KiB

Open AccessEditor’s ChoiceArticle

Maritime Vessel Classification to Monitor Fisheries with SAR: Demonstration in the North Sea

by Boris Snapir, Toby W. Waine and Lauren Biermann

Remote Sens. 2019, 11(3), 353; https://doi.org/10.3390/rs11030353 - 11 Feb 2019

Cited by 42 | Viewed by 7979

Abstract

Integration of methods based on satellite remote sensing into current maritime monitoring strategies could help tackle the problem of global overfishing. Operational software is now available to perform vessel detection on satellite imagery, but research on vessel classification has mainly focused on bulk [...] Read more.

Integration of methods based on satellite remote sensing into current maritime monitoring strategies could help tackle the problem of global overfishing. Operational software is now available to perform vessel detection on satellite imagery, but research on vessel classification has mainly focused on bulk carriers, container ships, and oil tankers, using high-resolution commercial Synthetic Aperture Radar (SAR) imagery. Here, we present a method based on Random Forest (RF) to distinguish fishing and non-fishing vessels, and apply it to an area in the North Sea. The RF classifier takes as input the vessel’s length, longitude, and latitude, its distance to the nearest shore, and the time of the measurement (am or pm). The classifier is trained and tested on data from the Automatic Identification System (AIS). The overall classification accuracy is 91%, but the precision for the fishing class is only 58% because of specific regions in the study area where activities of fishing and non-fishing vessels overlap. We then apply the classifier to a collection of vessel detections obtained by applying the Search for Unidentified Maritime Objects (SUMO) vessel detector to the 2017 Sentinel-1 SAR images of the North Sea. The trend in our monthly fishing-vessel count agrees with data from Global Fishing Watch on fishing-vessel presence. These initial results suggest that our approach could help monitor intensification or reduction of fishing activity, which is critical in the context of the global overfishing problem. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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30 pages, 30402 KiB

Open AccessEditor’s ChoiceArticle

PS-InSAR Analysis of Sentinel-1 Data for Detecting Ground Motion in Temperate Oceanic Climate Zones: A Case Study in the Republic of Ireland

by Simone Fiaschi, Eoghan P. Holohan, Michael Sheehy and Mario Floris

Remote Sens. 2019, 11(3), 348; https://doi.org/10.3390/rs11030348 - 10 Feb 2019

Cited by 32 | Viewed by 10480

Abstract

Regions of temperate oceanic climate have historically represented a challenge for the application of satellite-based multi-temporal SAR interferometry. The landscapes of such regions are commonly characterized by extensive, seasonally-variable vegetation coverage that can cause low temporal coherence and limit the detection capabilities of [...] Read more.

Regions of temperate oceanic climate have historically represented a challenge for the application of satellite-based multi-temporal SAR interferometry. The landscapes of such regions are commonly characterized by extensive, seasonally-variable vegetation coverage that can cause low temporal coherence and limit the detection capabilities of SAR imagery as acquired, for instance, by previous ERS-1/2 and ENVISAT missions. In this work, we exploited the enhanced resolution in space and time of the recently deployed Sentinel-1A/B SAR satellites to detect and monitor ground motions occurring in two study areas in the Republic of Ireland. The first, is a ~1800 km² area spanning the upland karst of the Clare Burren and the adjacent mantled lowland karst of east Galway. The second, is an area of 100 km² in Co. Meath spanning an active mine site. The available datasets, consisting of more than 100 images acquired in both ascending and descending orbits from April 2015 to March 2018, were processed by using the Permanent Scatterer approach. The obtained results highlight the presence of small-scale ground motions in both urban and natural environments with displacement rates along the satellite line of sight up to −17 mm/year. Localized subsidence was detected in recently built areas, along the infrastructure (both roads and railways), and over the mine site, while zones of subsidence, uplift, or both, have been recorded in a number of peatland areas. Furthermore, several measured target points indicate the presence of unstable areas along the coastline. Many of the detected movements were previously unknown. These results demonstrate the feasibility of adopting multi-temporal interferometry based on Sentinel-1 data for the detection and monitoring of mm-scale ground movements even over small areas (<100 m²) in environments influenced by temperate oceanic climate. Full article

(This article belongs to the Special Issue SAR for Natural Hazard )

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29 pages, 17064 KiB

Open AccessEditor’s ChoiceArticle

UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras

by Vasit Sagan, Maitiniyazi Maimaitijiang, Paheding Sidike, Kevin Eblimit, Kyle T. Peterson, Sean Hartling, Flavio Esposito, Kapil Khanal, Maria Newcomb, Duke Pauli, Rick Ward, Felix Fritschi, Nadia Shakoor and Todd Mockler

Remote Sens. 2019, 11(3), 330; https://doi.org/10.3390/rs11030330 - 7 Feb 2019

Cited by 211 | Viewed by 28437

Abstract

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are [...] Read more.

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market that are available at a low cost. However, their efficacy and accuracy in various applications has not been tested. In this study, three commercially available UAV thermal cameras, including ICI 8640 P-series (Infrared Cameras Inc., USA), FLIR Vue Pro R 640 (FLIR Systems, USA), and thermoMap (senseFly, Switzerland) have been tested and evaluated for their potential for forest monitoring, vegetation stress detection, and plant phenotyping. Mounted on multi-rotor or fixed wing systems, these cameras were simultaneously flown over different experimental sites located in St. Louis, Missouri (forest environment), Columbia, Missouri (plant stress detection and phenotyping), and Maricopa, Arizona (high throughput phenotyping). Thermal imagery was calibrated using procedures that utilize a blackbody, handheld thermal spot imager, ground thermal targets, emissivity and atmospheric correction. A suite of statistical analyses, including analysis of variance (ANOVA), correlation analysis between camera temperature and plant biophysical and biochemical traits, and heritability were utilized in order to examine the sensitivity and utility of the cameras against selected plant phenotypic traits and in the detection of plant water stress. In addition, in reference to quantitative assessment of image quality from different thermal cameras, a non-reference image quality evaluator, which primarily measures image focus that is based on the spatial relationship of pixels in different scales, was developed. Our results show that (1) UAV-based thermal imaging is a viable tool in precision agriculture and (2) the three examined cameras are comparable in terms of their efficacy for plant phenotyping. Overall, accuracy, when compared against field measured ground temperature and estimating power of plant biophysical and biochemical traits, the ICI 8640 P-series performed better than the other two cameras, followed by FLIR Vue Pro R 640 and thermoMap cameras. Our results demonstrated that all three UAV thermal cameras provide useful temperature data for precision agriculture and plant phenotying, with ICI 8640 P-series presenting the best results among the three systems. Cost wise, FLIR Vue Pro R 640 is more affordable than the other two cameras, providing a less expensive option for a wide range of applications. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing of Land Surface Variables)

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20 pages, 7591 KiB

Open AccessEditor’s ChoiceArticle

A Self-Calibrated Non-Parametric Time Series Analysis Approach for Assessing Insect Defoliation of Broad-Leaved Deciduous Nothofagus pumilio Forests

by Roberto O. Chávez, Ronald Rocco, Álvaro G. Gutiérrez, Marcelo Dörner and Sergio A. Estay

Remote Sens. 2019, 11(2), 204; https://doi.org/10.3390/rs11020204 - 21 Jan 2019

Cited by 29 | Viewed by 6559

Abstract

Folivorous insects cause some of the most ecologically and economically important disturbances in forests worldwide. For this reason, several approaches have been developed to exploit the temporal richness of available satellite time series data to detect and quantify insect forest defoliation. Current approaches [...] Read more.

Folivorous insects cause some of the most ecologically and economically important disturbances in forests worldwide. For this reason, several approaches have been developed to exploit the temporal richness of available satellite time series data to detect and quantify insect forest defoliation. Current approaches rely on parametric functions to describe the natural annual phenological cycle of the forest, from which anomalies are calculated and used to assess defoliation. Quantification of the natural variability of the annual phenological baseline is limited in parametric approaches, which is critical to evaluating whether an observed anomaly is “true” defoliation or only part of the natural forest variability. We present here a fully self-calibrated, non-parametric approach to reconstruct the annual phenological baseline along with its confidence intervals using the historical frequency of a vegetation index (VI) density, accounting for the natural forest phenological variability. This baseline is used to calculate per pixel (1) a VI anomaly per date and (2) an anomaly probability flag indicating its probability of being a “true” anomaly. Our method can be self-calibrated when applied to deciduous forests, where the winter VI values are used as the leafless reference to calculate the VI loss (%). We tested our approach with dense time series from the MODIS enhanced vegetation index (EVI) to detect and map a massive outbreak of the native Ormiscodes amphimone caterpillars which occurred in 2015–2016 in Chilean Patagonia. By applying the anomaly probability band, we filtered out all pixels with a probability <0.9 of being “true” defoliation. Our method enabled a robust spatiotemporal assessment of the O. amphimone outbreak, showing severe defoliation (60–80% and >80%) over an area of 15,387 ha of Nothofagus pumilio forests in only 40 days (322 ha/day in average) with a total of 17,850 ha by the end of the summer. Our approach is useful for the further study of the apparent increasing frequency of insect outbreaks due to warming trends in Patagonian forests; its generality means it can be applied in deciduous broad-leaved forests elsewhere. Full article

(This article belongs to the Special Issue Dense Image Time Series Analysis for Ecosystem Monitoring)

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30 pages, 43448 KiB

Open AccessEditor’s ChoiceArticle

Adaptive Framework for the Delineation of Homogeneous Forest Areas Based on LiDAR Points

by Moritz Bruggisser, Markus Hollaus, Di Wang and Norbert Pfeifer

Remote Sens. 2019, 11(2), 189; https://doi.org/10.3390/rs11020189 - 18 Jan 2019

Cited by 9 | Viewed by 5606

Abstract

We propose a flexible framework for automated forest patch delineations that exploits a set of canopy structure features computed from airborne laser scanning (ALS) point clouds. The approach is based on an iterative subdivision of the point cloud using k-means clustering followed by [...] Read more.

We propose a flexible framework for automated forest patch delineations that exploits a set of canopy structure features computed from airborne laser scanning (ALS) point clouds. The approach is based on an iterative subdivision of the point cloud using k-means clustering followed by an iterative merging step to tackle oversegmentation. The framework can be adapted for different applications by selecting relevant input features that best measure the intended homogeneity. In our study, the performance of the segmentation framework was tested for the delineation of forest patches with a homogeneous canopy height structure on the one hand and with similar water cycle conditions on the other. For the latter delineation, canopy components that impact interception and evapotranspiration were used, and the delineation was mainly driven by leaf area, tree functional type, and foliage density. The framework was further tested on two scenes covering a variety of forest conditions and topographies. We demonstrate that the delineated patches capture well the spatial distributions of relevant canopy features that are used for defining the homogeneity. The consistencies range from

R^{2} = 0.84

to

R^{2} = 0.86

and from

R^{2} = 0.80

to

R^{2} = 0.91

for the most relevant features in the delineation of patches with similar height structure and water cycle conditions, respectively. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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22 pages, 8259 KiB

Open AccessEditor’s ChoiceArticle

A Structural Classification of Australian Vegetation Using ICESat/GLAS, ALOS PALSAR, and Landsat Sensor Data

by Peter Scarth, John Armston, Richard Lucas and Peter Bunting

Remote Sens. 2019, 11(2), 147; https://doi.org/10.3390/rs11020147 - 14 Jan 2019

Cited by 42 | Viewed by 13385

Abstract

Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference [...] Read more.

Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference map of Australian forest and woodland structure (height and cover), with this generated by integrating Landsat Thematic Mapper (TM) and Enhanced TM, Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and Ice, Cloud, and land Elevation (ICESat),and Geoscience Laser Altimeter System (GLAS) data. ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC) were used to segment and classify the Australian landscape. Then, from intersecting ICESat waveform data, vertical foliage profiles and height metrics (e.g., 95% percentile height, mean height and the height to maximum vegetation density) were extracted for each of the classes generated. Within each class, and for selected areas, the variability in ICESat profiles was found to be similar with differences between segments of the same class attributed largely to clearance or disturbance events. ICESat metrics and profiles were then assigned to all remaining segments across Australia with the same class allocation. Validation against airborne LiDAR for a range of forest structural types indicated a high degree of correspondence in estimated height measures. On this basis, a map of vegetation height was generated at a national level and was combined with estimates of cover to produce a revised structural classification based on the scheme of the Australian National Vegetation Information System (NVIS). The benefits of integrating the three datasets for segmenting and classifying the landscape and retrieving biophysical attributes was highlighted with this leading the way for future mapping using ALOS-2 PALSAR-2, Landsat/Sentinel-2, Global Ecosystem Dynamics Investigation (GEDI), and ICESat-2 LiDAR data. The ability to map across large areas provides considerable benefits for quantifying carbon dynamics and informing on biodiversity metrics. Full article

(This article belongs to the Special Issue The Kyoto and Carbon Initiative—Environmental Applications by ALOS-2 PALSAR-2)

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16 pages, 88312 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Bathymetry of Northwest Greenland Using “Ocean Melting Greenland” (OMG) High-Resolution Airborne Gravity and Other Data

by Lu An, Eric Rignot, Romain Millan, Kirsty Tinto and Josh Willis

Remote Sens. 2019, 11(2), 131; https://doi.org/10.3390/rs11020131 - 11 Jan 2019

Cited by 27 | Viewed by 8433

Abstract

Marine-terminating glaciers dominate the evolution of the Greenland Ice Sheet (GrIS) and its contribution to sea-level rise. Widespread glacier acceleration has been linked to the warming of ocean waters around the periphery of Greenland but a lack of information on the bathymetry of [...] Read more.

Marine-terminating glaciers dominate the evolution of the Greenland Ice Sheet (GrIS) and its contribution to sea-level rise. Widespread glacier acceleration has been linked to the warming of ocean waters around the periphery of Greenland but a lack of information on the bathymetry of the continental shelf and glacial fjords has limited our ability to understand how subsurface, warm, salty ocean waters of Atlantic origin (AW) reach the glaciers and melt them from below. Here, we employ high-resolution, airborne gravity data (AIRGrav) in combination with multibeam echo sounding (MBES) data, to infer the bathymetry of the coastal areas of Northwest Greenland for NASA’s Ocean Melting Greenland (OMG) mission. High-resolution, AIRGrav data acquired on a 2 km spacing, 150 m ground clearance, with 1.5 mGal crossover error, is inverted in three dimensions to map the bathymetry. To constrain the inversion away from MBES data, we compare two methods: one based on the Direct Current (DC) shift of the gravity field (absolute minus observed gravity) and another based on the density of the bedrock. We evaluate and compare the two methods in areas with complete MBES coverage. We find the lowest standard error in bed elevation (±60 m) using the DC shift method. When applied to the entire coast of Northwest Greenland, the three-dimensional inversion reveals a complex network of connected sea bed channels, not known previously, that provide natural and varied pathways for AW to reach the glaciers across the continental shelf. The study demonstrates that the gravity approach offers an efficient and practical alternative to extensive ship mapping in ice-filled waters to obtain information critical to understanding and modeling ice-ocean interaction along ice sheet margins. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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19 pages, 6121 KiB

Open AccessEditor’s ChoiceArticle

Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs

by Senlin Guan, Koichiro Fukami, Hitoshi Matsunaka, Midori Okami, Ryo Tanaka, Hiroshi Nakano, Tetsufumi Sakai, Keiko Nakano, Hideki Ohdan and Kimiyasu Takahashi

Remote Sens. 2019, 11(2), 112; https://doi.org/10.3390/rs11020112 - 9 Jan 2019

Cited by 132 | Viewed by 16051

Abstract

The aim of this study was to use small unmanned aerial vehicles (UAVs) for determining high-resolution normalized difference vegetation index (NDVI) values. Subsequently, these results were used to assess their correlations with fertilizer application levels and the yields of rice and wheat crops. [...] Read more.

The aim of this study was to use small unmanned aerial vehicles (UAVs) for determining high-resolution normalized difference vegetation index (NDVI) values. Subsequently, these results were used to assess their correlations with fertilizer application levels and the yields of rice and wheat crops. For multispectral sensing, we flew two types of small UAVs (DJI Phantom 4 and DJI Phantom 4 Pro)—each equipped with a compact multispectral sensor (Parrot Sequoia). The information collected was composed of numerous RGB orthomosaic images as well as reflectance maps with spatial resolution greater than a ground sampling distance of 10.5 cm. From 223 UAV flight campaigns over 120 fields with a total area coverage of 77.48 ha, we determined that the highest efficiency for the UAV-based remote sensing measurement was approximately 19.8 ha per 10 min while flying 100 m above ground level. During image processing, we developed and used a batch image alignment algorithm—a program written in Python language–to calculate the NDVI values in experimental plots or fields in a batch of NDVI index maps. The color NDVI distribution maps of wide rice fields identified differences in stages of ripening and lodging-injury areas, which accorded with practical crop growth status from aboveground observation. For direct-seeded rice, variation in the grain yield was most closely related to that in the NDVI at the early reproductive and late ripening stages. For wheat, the NDVI values were highly correlated with the yield (

R^{2}

= 0.601–0.809) from the middle reproductive to the early ripening stages. Furthermore, using the NDVI values, it was possible to differentiate the levels of fertilizer application for both rice and wheat. These results indicate that the small UAV-derived NDVI values are effective for predicting yield and detecting fertilizer application levels during rice and wheat production. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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19 pages, 5028 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Ground Surface Models Derived from Unmanned Aerial Systems with Digital Aerial Photogrammetry in a Disturbed Conifer Forest

by Alexander Graham, Nicholas C. Coops, Michael Wilcox and Andrew Plowright

Remote Sens. 2019, 11(1), 84; https://doi.org/10.3390/rs11010084 - 4 Jan 2019

Cited by 39 | Viewed by 7661

Abstract

Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in [...] Read more.

Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in areas of dense vegetation. This study investigates the terrain modeling potential of UAS-DAP methods within a temperate conifer forest in British Columbia, Canada. UAS-acquired images were photogrammetrically processed to produce high-resolution DAP point clouds. To evaluate the terrain modeling ability of DAP, first, a sensitivity analysis was conducted to estimate optimal parameters of three ground-point classification algorithms designed for airborne laser scanning (ALS). Algorithms tested include progressive triangulated irregular network (TIN) densification (PTD), hierarchical robust interpolation (HRI) and simple progressive morphological filtering (SMRF). Points were classified as ground from the ALS and served as ground-truth data to which UAS-DAP derived DEMs were compared. The proportion of area with root mean square error (RMSE) <1.5 m were 56.5%, 51.6% and 52.3% for the PTD, HRI and SMRF methods respectively. To assess the influence of terrain slope and canopy cover, error values of DAP-DEMs produced using optimal parameters were compared to stratified classes of canopy cover and slope generated from ALS point clouds. Results indicate that canopy cover was approximately three times more influential on RMSE than terrain slope. Full article

(This article belongs to the Special Issue Aerial and Near-Field Remote Sensing Developments in Forestry)

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13 pages, 1121 KiB

Open AccessEditor’s ChoiceTechnical Note

Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study

by Yara Mohajerani, Michael Wood, Isabella Velicogna and Eric Rignot

Remote Sens. 2019, 11(1), 74; https://doi.org/10.3390/rs11010074 - 3 Jan 2019

Cited by 68 | Viewed by 10404

Abstract

The continuous and precise mapping of glacier calving fronts is essential for monitoring and understanding rapid glacier changes in Antarctica and Greenland, which have the potential for significant sea level rise within the current century. This effort has been mostly restricted to the [...] Read more.

The continuous and precise mapping of glacier calving fronts is essential for monitoring and understanding rapid glacier changes in Antarctica and Greenland, which have the potential for significant sea level rise within the current century. This effort has been mostly restricted to the slow and painstaking manual digitalization of the calving front positions in thousands of satellite imagery products. Here, we have developed a machine learning toolkit to automatically detect glacier calving front margins in satellite imagery. The toolkit is based on semantic image segmentation using Convolutional Neural Networks (CNN) with a modified U-Net architecture to isolate the calving fronts from satellite images after having been trained with a dataset of images and their corresponding manually-determined calving fronts. As a case study we train our neural network on a varied set of Landsat images with lowered resolutions from Jakobshavn, Sverdrup, and Kangerlussuaq glaciers, Greenland and test the results on images from Helheim glacier, Greenland to evaluate the performance of the approach. The neural network is able to identify the calving front in new images with a mean deviation of 96.3 m from the true fronts, equivalent to 1.97 pixels on average, while the corresponding error for manually-determined fronts on the same resolution images is 92.5 m (1.89 pixels). We find that the trained neural network significantly outperforms common edge detection techniques, and can be used to continuously map out calving-ice fronts with a variety of data products. Full article

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31 pages, 16313 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Multi-Platform Hydrometeorological Analysis of the Flash Flood Event of 15 November 2017 in Attica, Greece

by George Varlas, Marios N. Anagnostou, Christos Spyrou, Anastasios Papadopoulos, John Kalogiros, Angeliki Mentzafou, Silas Michaelides, Evangelos Baltas, Efthimios Karymbalis and Petros Katsafados

Remote Sens. 2019, 11(1), 45; https://doi.org/10.3390/rs11010045 - 28 Dec 2018

Cited by 70 | Viewed by 8920

Abstract

Urban areas often experience high precipitation rates and heights associated with flash flood events. Atmospheric and hydrological models in combination with remote-sensing and surface observations are used to analyze these phenomena. This study aims to conduct a hydrometeorological analysis of a flash flood [...] Read more.

Urban areas often experience high precipitation rates and heights associated with flash flood events. Atmospheric and hydrological models in combination with remote-sensing and surface observations are used to analyze these phenomena. This study aims to conduct a hydrometeorological analysis of a flash flood event that took place in the sub-urban area of Mandra, western Attica, Greece, using remote-sensing observations and the Chemical Hydrological Atmospheric Ocean Wave System (CHAOS) modeling system that includes the Advanced Weather Research Forecasting (WRF-ARW) model and the hydrological model (WRF-Hydro). The flash flood was caused by a severe storm during the morning of 15 November 2017 around Mandra area resulting in extensive damages and 24 fatalities. The X-band dual-polarization (XPOL) weather radar of the National Observatory of Athens (NOA) observed precipitation rates reaching 140 mm/h in the core of the storm. CHAOS simulation unveils the persistent orographic convergence of humid southeasterly airflow over Pateras mountain as the dominant parameter for the evolution of the storm. WRF-Hydro simulated the flood using three different precipitation estimations as forcing data, obtained from the CHAOS simulation (CHAOS-hydro), the XPOL weather radar (XPOL-hydro) and the Global Precipitation Measurement (GMP)/Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite dataset (GPM/IMERG-hydro). The findings indicate that GPM/IMERG-hydro underestimated the flood magnitude. On the other hand, XPOL-hydro simulation resulted to discharge about 115 m³/s and water level exceeding 3 m in Soures and Agia Aikaterini streams, which finally inundated. CHAOS-hydro estimated approximately the half water level and even lower discharge compared to XPOL-hydro simulation. Comparing site-detailed post-surveys of flood extent, XPOL-hydro is characterized by overestimation while CHAOS-hydro and GPM/IMERG-hydro present underestimation. However, CHAOS-hydro shows enough skill to simulate the flooded areas despite the forecast inaccuracies of numerical weather prediction. Overall, the simulation results demonstrate the potential benefit of using high-resolution observations from a X-band dual-polarization radar as an additional forcing component in model precipitation simulations. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation)

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25 pages, 9096 KiB

Open AccessEditor’s ChoiceArticle

On the Synergistic Use of Optical and SAR Time-Series Satellite Data for Small Mammal Disease Host Mapping

by Christopher Marston and Patrick Giraudoux

Remote Sens. 2019, 11(1), 39; https://doi.org/10.3390/rs11010039 - 27 Dec 2018

Cited by 9 | Viewed by 5394

Abstract

(1) Background: Echinococcus multilocularis (Em), a highly pathogenic parasitic tapeworm, is responsible for a significant burden of human disease. In this study, optical and time-series Synthetic Aperture Radar (SAR) data is used synergistically to model key land cover characteristics driving the spatial distributions [...] Read more.

(1) Background: Echinococcus multilocularis (Em), a highly pathogenic parasitic tapeworm, is responsible for a significant burden of human disease. In this study, optical and time-series Synthetic Aperture Radar (SAR) data is used synergistically to model key land cover characteristics driving the spatial distributions of two small mammal intermediate host species, Ellobius tancrei and Microtus gregalis, which facilitate Em transmission in a highly endemic area of Kyrgyzstan. (2) Methods: A series of land cover maps are derived from (a) single-date Landsat Operational Land Imager (OLI) imagery, (b) time-series Sentinel-1 SAR data, and (c) Landsat OLI and time-series Sentinel-1 SAR data in combination. Small mammal distributions are analyzed in relation to the surrounding land cover class coverage using random forests, before being applied predictively over broader areas. A comparison of models derived from the three land cover maps are made, assessing their potential for use in cloud-prone areas. (3) Results: Classification accuracies demonstrated the combined OLI-SAR classification to be of highest accuracy, with the single-date OLI and time-series SAR derived classifications of equivalent quality. Random forest analysis identified statistically significant positive relationships between E. tancrei density and agricultural land, and between M. gregalis density and water and bushes. Predictive application of random forest models identified hotspots of high relative density of E. tancrei and M. gregalis across the broader study area. (4) Conclusions: This offers valuable information to improve the targeting of limited-resource disease control activities to disrupt disease transmission in this area. Time-series SAR derived land cover maps are shown to be of equivalent quality to those generated from single-date optical imagery, which enables application of these methods in cloud-affected areas where, previously, this was not possible due to the sparsity of cloud-free optical imagery. Full article

(This article belongs to the Special Issue Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems)

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24 pages, 5184 KiB

Open AccessEditor’s ChoiceArticle

Identification of Dust Sources in a Saharan Dust Hot-Spot and Their Implementation in a Dust-Emission Model

by Stefanie Feuerstein and Kerstin Schepanski

Remote Sens. 2019, 11(1), 4; https://doi.org/10.3390/rs11010004 - 20 Dec 2018

Cited by 26 | Viewed by 7954

Abstract

Although mineral dust plays a key role in the Earth’s climate system and in climate and weather prediction, models still have difficulties in predicting the amount and distribution of mineral dust in the atmosphere. One reason for this is the limited understanding of [...] Read more.

Although mineral dust plays a key role in the Earth’s climate system and in climate and weather prediction, models still have difficulties in predicting the amount and distribution of mineral dust in the atmosphere. One reason for this is the limited understanding of the distribution of dust sources and their behavior with respect to their spatiotemporal variability in activity. For a better estimation of the atmospheric dust load, this paper presents an approach to localize dust sources and thereby estimate the sediment supply for a study area centered on the Aïr Massif in Niger with a north–south extent of 16

^{\circ}

–22

^{\circ}

N and an east–west extent of 4

^{\circ}

–12

^{\circ}

E. This approach uses optical Sentinel-2 data at visible and near infrared wavelengths together with HydroSHEDS flow accumulation data to localize ephemeral riverbeds. Visible channels from Sentinel-2 data are used to detect sand sheets and dunes. The identified sediment supply map was compared to the dust source activation frequency derived from the analysis of Desert-Dust-RGB imagery from the Meteosat Second Generation series of satellites. This comparison reveals the strong connection between dust activity, prevailing meteorology and sediment supply. In a second step, the sediment supply information was implemented in a dust-emission model. The simulated emission flux shows how much the model results benefit from the updated sediment supply information in localizing the main dust sources and in retrieving the seasonality of dust activity from these sources. The described approach to characterize dust sources can be implemented in other regional model studies, or even globally, and can thereby help to get a more accurate picture of dust source distribution and a more realistic estimation of the atmospheric dust load. Full article

(This article belongs to the Special Issue Remote Sensing in Support of Aeolian Research)

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18 pages, 4020 KiB

Open AccessEditor’s ChoiceArticle

Sea Ice Albedo from MISR and MODIS: Production, Validation, and Trend Analysis

by Said Kharbouche and Jan-Peter Muller

Remote Sens. 2019, 11(1), 9; https://doi.org/10.3390/rs11010009 - 20 Dec 2018

Cited by 10 | Viewed by 6528

Abstract

The Multi-angle Imaging SpectroRadiometer (MISR) sensor onboard the Terra satellite provides high accuracy albedo products. MISR deploys nine cameras each at different view angles, which allow a near-simultaneous angular sampling of the surface anisotropy. This is particularly important to measure the near-instantaneous albedo [...] Read more.

The Multi-angle Imaging SpectroRadiometer (MISR) sensor onboard the Terra satellite provides high accuracy albedo products. MISR deploys nine cameras each at different view angles, which allow a near-simultaneous angular sampling of the surface anisotropy. This is particularly important to measure the near-instantaneous albedo of dynamic surface features such as clouds or sea ice. However, MISR’s cloud mask over snow or sea ice is not yet sufficiently robust because MISR’s spectral bands are only located in the visible and the near infrared. To overcome this obstacle, we performed data fusion using a specially processed MISR sea ice albedo product (that was generated at Langley Research Center using Rayleigh correction) combining this with a cloud mask of a sea ice mask product, MOD29, which is derived from the MODerate Resolution Imaging Spectroradiometer (MODIS), which is also, like MISR, onboard the Terra satellite. The accuracy of the MOD29 cloud mask has been assessed as >90% due to the fact that MODIS has a much larger number of spectral bands and covers a much wider range of the solar spectrum. Four daily sea ice products have been created, each with a different averaging time window (24 h, 7 days, 15 days, 31 days). For each time window, the number of samples, mean and standard deviation of MISR cloud-free sea ice albedo is calculated. These products are publicly available on a predefined polar stereographic grid at three spatial resolutions (1 km, 5 km, 25 km). The time span of the generated sea ice albedo covers the months between March and September of each year from 2000 to 2016 inclusive. In addition to data production, an evaluation of the accuracy of sea ice albedo was performed through a comparison with a dataset generated from a tower based albedometer from NOAA/ESRL/GMD/GRAD. This comparison confirms the high accuracy and stability of MISR’s sea ice albedo since its launch in February 2000. We also performed an evaluation of the day-of-year trend of sea ice albedo between 2000 and 2016, which confirm the reduction of sea ice shortwave albedo with an order of 0.4–1%, depending on the day of year and the length of observed time window. Full article

(This article belongs to the Special Issue MISR)

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72 pages, 23053 KiB

Open AccessEditor’s ChoiceReview

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

by Gianpaolo Balsamo, Anna Agusti-Panareda, Clement Albergel, Gabriele Arduini, Anton Beljaars, Jean Bidlot, Eleanor Blyth, Nicolas Bousserez, Souhail Boussetta, Andy Brown, Roberto Buizza, Carlo Buontempo, Frédéric Chevallier, Margarita Choulga, Hannah Cloke, Meghan F. Cronin, Mohamed Dahoui, Patricia De Rosnay, Paul A. Dirmeyer, Matthias Drusch, Emanuel Dutra, Michael B. Ek, Pierre Gentine, Helene Hewitt, Sarah P.E. Keeley, Yann Kerr, Sujay Kumar, Cristina Lupu, Jean-François Mahfouf, Joe McNorton, Susanne Mecklenburg, Kristian Mogensen, Joaquín Muñoz-Sabater, Rene Orth, Florence Rabier, Rolf Reichle, Ben Ruston, Florian Pappenberger, Irina Sandu, Sonia I. Seneviratne, Steffen Tietsche, Isabel F. Trigo, Remko Uijlenhoet, Nils Wedi, R. Iestyn Woolway and Xubin Zeng Show full author list Hide full author list

Remote Sens. 2018, 10(12), 2038; https://doi.org/10.3390/rs10122038 - 14 Dec 2018

Cited by 128 | Viewed by 23914

Abstract

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for [...] Read more.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort. Full article

(This article belongs to the Special Issue Advancing Earth Surface Representation via Enhanced Use of Earth Observations in Monitoring and Forecasting Applications)

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22 pages, 6781 KiB

Open AccessEditor’s ChoiceArticle

Monitoring Crop Evapotranspiration and Crop Coefficients over an Almond and Pistachio Orchard Throughout Remote Sensing

by Joaquim Bellvert, Karine Adeline, Shahar Baram, Lars Pierce, Blake L. Sanden and David R. Smart

Remote Sens. 2018, 10(12), 2001; https://doi.org/10.3390/rs10122001 - 10 Dec 2018

Cited by 66 | Viewed by 10356

Abstract

In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods [...] Read more.

In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods that provide information about the temporal and spatial variability of crop water requirements, which allow farmers to make irrigation decisions at field scale. This study focuses on estimating the actual evapotranspiration and crop coefficients of an almond and pistachio orchard located in Central Valley (California) during an entire growing season by combining a simple crop evapotranspiration model with remote sensing data. A dataset of the vegetation index NDVI derived from Landsat-8 was used to facilitate the estimation of the basal crop coefficient (K_cb), or potential crop water use. The soil water evaporation coefficient (K_e) was measured from microlysimeters. The water stress coefficient (K_s) was derived from airborne remotely sensed canopy thermal-based methods, using seasonal regressions between the crop water stress index (CWSI) and stem water potential (Ψ_stem). These regressions were statistically-significant for both crops, indicating clear seasonal differences in pistachios, but not in almonds. In almonds, the estimated maximum K_cb values ranged between 1.05 to 0.90, while for pistachios, it ranged between 0.89 to 0.80. The model indicated a difference of 97 mm in transpiration over the season between both crops. Soil evaporation accounted for an average of 16% and 13% of the total actual evapotranspiration for almonds and pistachios, respectively. Verification of the model-based daily crop evapotranspiration estimates was done using eddy-covariance and surface renewal data collected in the same orchards, yielding an R² ≥ 0.7 and average root mean square errors (RMSE) of 0.74 and 0.91 mm·day⁻¹ for almond and pistachio, respectively. It is concluded that the combination of crop evapotranspiration models with remotely-sensed data is helpful for upscaling irrigation information from plant to field scale and thus may be used by farmers for making day-to-day irrigation management decisions. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Water Management)

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24 pages, 9269 KiB

Open AccessEditor’s ChoiceArticle

GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station

by Constantin-Octavian Andrei, Sonja Lahtinen, Maaria Nordman, Jyri Näränen, Hannu Koivula, Markku Poutanen and Juha Hyyppä

Remote Sens. 2018, 10(12), 1937; https://doi.org/10.3390/rs10121937 - 1 Dec 2018

Cited by 12 | Viewed by 11160

Abstract

Continuous Global Positioning System (GPS) observations have been logged at the Finnish Antarctic research station (Aboa) since February 2003. The station is located in Dronning Maud Land, East Antarctica. Almost 5000 daily observation files have been archived based on yearly scientific expeditions. These [...] Read more.

Continuous Global Positioning System (GPS) observations have been logged at the Finnish Antarctic research station (Aboa) since February 2003. The station is located in Dronning Maud Land, East Antarctica. Almost 5000 daily observation files have been archived based on yearly scientific expeditions. These files have not been fully analysed until now. This study reports for the first time on the consistent and homogeneous data processing and analysis of the 15-year long time series. Daily coordinates are obtained using Precise Point Positioning (PPP) processing based on two approaches. The first approach is based on the Kalman filter and uses the RTKLIB open source library to produce daily solutions by unconventionally running the filter in the forward and backward direction. The second approach uses APPS web service and is based on GIPSY scientific processing engine. The two approaches show an excellent agreement with less than 3 mm rms error horizontally and 6 mm rms error vertically. The derived position time series is analysed in terms of trend, periodicity and noise characteristics. The noise of the time series was found to be power-law noise model with spectral index closer to flicker noise. In addition, several periodic signals were found at 5, 14, 183 and 362 days. Furthermore, most of the horizontal movement was found to be in the North direction at a rate of 11.23 ± 0.09 mm/y, whereas the rate in the East direction was estimated to be 1.46 ± 0.05 mm/y. Lastly, the 15-year long time series revealed a movement upwards at a rate of 0.79 ± 0.35 mm/y. Despite being an unattended station, Aboa provides one of the most continuous and longest GPS time series in Antarctica. Therefore, we believe that this research increases the awareness of local geophysical phenomena in a less reported area of the Antarctic continent. Full article

(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))

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17 pages, 2890 KiB

Open AccessEditor’s ChoiceArticle

Deforestation and Forest Degradation Due to Gold Mining in the Peruvian Amazon: A 34-Year Perspective

by Jorge Caballero Espejo, Max Messinger, Francisco Román-Dañobeytia, Cesar Ascorra, Luis E. Fernandez and Miles Silman

Remote Sens. 2018, 10(12), 1903; https://doi.org/10.3390/rs10121903 - 29 Nov 2018

Cited by 223 | Viewed by 33459

Abstract

While deforestation rates decline globally they are rising in the Western Amazon. Artisanal-scale gold mining (ASGM) is a large cause of this deforestation and brings with it extensive environmental, social, governance, and public health impacts, including large carbon emissions and mercury pollution. Underlying [...] Read more.

While deforestation rates decline globally they are rising in the Western Amazon. Artisanal-scale gold mining (ASGM) is a large cause of this deforestation and brings with it extensive environmental, social, governance, and public health impacts, including large carbon emissions and mercury pollution. Underlying ASGM is a broad network of factors that influence its growth, distribution, and practices such as poverty, flows of legal and illegal capital, conflicting governance, and global economic trends. Despite its central role in land use and land cover change in the Western Amazon and the severity of its social and environmental impacts, it is relatively poorly studied. While ASGM in Southeastern Peru has been quantified previously, doing so is difficult due to the heterogeneous nature of the resulting landscape. Using a novel approach to classify mining that relies on a fusion of CLASlite and the Global Forest Change dataset, two Landsat-based deforestation detection tools, we sought to quantify ASGM-caused deforestation in the period 1984–2017 in the southern Peruvian Amazon and examine trends in the geography, methods, and impacts of ASGM across that time. We identify nearly 100,000 ha of deforestation due to ASGM in the 34-year study period, an increase of 21% compared to previous estimates. Further, we find that 10% of that deforestation occurred in 2017, the highest annual amount of deforestation in the study period, with 53% occurring since 2011. Finally, we demonstrate that not all mining is created equal by examining key patterns and changes in ASGM activity and techniques through time and space. We discuss their connections with, and impacts on, socio-economic factors, such as land tenure, infrastructure, international markets, governance efforts, and social and environmental impacts. Full article

(This article belongs to the Section Forest Remote Sensing)

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18 pages, 7117 KiB

Open AccessFeature PaperEditor’s ChoiceTechnical Note

Quantitative Assessment of Desertification in an Arid Oasis Using Remote Sensing Data and Spectral Index Techniques

by Atman Ait Lamqadem, Hafid Saber and Biswajeet Pradhan

Remote Sens. 2018, 10(12), 1862; https://doi.org/10.3390/rs10121862 - 22 Nov 2018

Cited by 59 | Viewed by 7824

Abstract

Desertification is an environmental problem worldwide. Remote sensing data and technique offer substantial information for mapping and assessment of desertification. Desertification is one of the most serious forms of environmental threat in Morocco, especially in the oases in the south-eastern part of the [...] Read more.

Desertification is an environmental problem worldwide. Remote sensing data and technique offer substantial information for mapping and assessment of desertification. Desertification is one of the most serious forms of environmental threat in Morocco, especially in the oases in the south-eastern part of the country. This study aims to map the degree of desertification in middle Draa Valley in 2017 using a Sentinel-2 MSI (multispectral instrument) image. Firstly, three indices, namely, tasselled cap brightness (TCB), greenness (TCG) and wetness (TCW) were extracted using the tasselled cap transformation method. Secondly, other indices, such as normalized difference vegetation index (NDVI) and albedo, were retrieved. Thirdly, a linear regression analysis was performed on NDVI–albedo, TCG–TCB and TCW–TCB combinations. Results showed a higher correlation between TCW and TCB (r = −0.812) than with that of the NDVI–albedo (r = −0.50). On the basis of this analysis, a desertification degree index was developed using the TCW–TCB feature space classification. A map of desertification grades was elaborated and divided into five classes, namely, nondesertification, low, moderate, severe and extreme levels. Results indicated that only 6.20% of the study area falls under the nondesertification grade, whereas 26.92% and 32.85% fall under the severe and extreme grades, respectively. The employed method was useful for the quantitative assessment of desertification with an overall accuracy of 93.07%. This method is simple, robust, powerful, and easy to use for the management and protection of the fragile arid and semiarid lands. Full article

(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)

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22 pages, 3337 KiB

Open AccessEditor’s ChoiceArticle

CubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture

by Bruno Aragon, Rasmus Houborg, Kevin Tu, Joshua B. Fisher and Matthew McCabe

Remote Sens. 2018, 10(12), 1867; https://doi.org/10.3390/rs10121867 - 22 Nov 2018

Cited by 76 | Viewed by 13049

Abstract

Remote sensing based estimation of evapotranspiration (ET) provides a direct accounting of the crop water use. However, the use of satellite data has generally required that a compromise between spatial and temporal resolution is made, i.e., one could obtain low spatial resolution data [...] Read more.

Remote sensing based estimation of evapotranspiration (ET) provides a direct accounting of the crop water use. However, the use of satellite data has generally required that a compromise between spatial and temporal resolution is made, i.e., one could obtain low spatial resolution data regularly, or high spatial resolution occasionally. As a consequence, this spatiotemporal trade-off has tended to limit the impact of remote sensing for precision agricultural applications. With the recent emergence of constellations of small CubeSat-based satellite systems, these constraints are rapidly being removed, such that daily 3 m resolution optical data are now a reality for earth observation. Such advances provide an opportunity to develop new earth system monitoring and assessment tools. In this manuscript we evaluate the capacity of CubeSats to advance the estimation of ET via application of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) retrieval model. To take advantage of the high-spatiotemporal resolution afforded by these systems, we have integrated a CubeSat derived leaf area index as a forcing variable into PT-JPL, as well as modified key biophysical model parameters. We evaluate model performance over an irrigated farmland in Saudi Arabia using observations from an eddy covariance tower. Crop water use retrievals were also compared against measured irrigation from an in-line flow meter installed within a center-pivot system. To leverage the high spatial resolution of the CubeSat imagery, PT-JPL retrievals were integrated over the source area of the eddy covariance footprint, to allow an equivalent intercomparison. Apart from offering new precision agricultural insights into farm operations and management, the 3 m resolution ET retrievals were shown to explain 86% of the observed variability and provide a relative RMSE of 32.9% for irrigated maize, comparable to previously reported satellite-based retrievals. An observed underestimation was diagnosed as a possible misrepresentation of the local surface moisture status, highlighting the challenge of high-resolution modeling applications for precision agriculture and informing future research directions. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing of Terrestrial Evaporation)

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24 pages, 6510 KiB

Open AccessEditor’s ChoiceArticle

Object-Based Image Analysis for Sago Palm Classification: The Most Important Features from High-Resolution Satellite Imagery

by Sarip Hidayat, Masayuki MATSUOKA, Sumbangan Baja and Dorothea Agnes Rampisela

Remote Sens. 2018, 10(8), 1319; https://doi.org/10.3390/rs10081319 - 20 Aug 2018

Cited by 20 | Viewed by 9166

Abstract

Sago palm (Metroxylon sagu) is a palm tree species originating in Indonesia. In the future, this starch-producing tree will play an important role in food security and biodiversity. Local governments have begun to emphasize the sustainable development of sago palm plantations; therefore, they [...] Read more.

Sago palm (Metroxylon sagu) is a palm tree species originating in Indonesia. In the future, this starch-producing tree will play an important role in food security and biodiversity. Local governments have begun to emphasize the sustainable development of sago palm plantations; therefore, they require near-real-time geospatial information on palm stands. We developed a semi-automated classification scheme for mapping sago palm using machine learning within an object-based image analysis framework with Pleiades-1A imagery. In addition to spectral information, arithmetic, geometric, and textural features were employed to enhance the classification accuracy. Recursive feature elimination was applied to samples to rank the importance of 26 input features. A support vector machine (SVM) was used to perform classifications and resulted in the highest overall accuracy of 85.00% after inclusion of the eight most important features, including three spectral features, three arithmetic features, and two textural features. The SVM classifier showed normal fitting up to the eighth most important feature. According to the McNemar test results, using the top seven to 14 features provided a better classification accuracy. The significance of this research is the revelation of the most important features in recognizing sago palm among other similar tree species. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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21 pages, 3472 KiB

Open AccessEditor’s ChoiceArticle

Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China

by Jueying Bai, Qian Cui, Deqing Chen, Haiwei Yu, Xudong Mao, Lingkui Meng and Yang Cai

Remote Sens. 2018, 10(8), 1302; https://doi.org/10.3390/rs10081302 - 18 Aug 2018

Cited by 44 | Viewed by 7888

Abstract

China is frequently subjected to local and regional drought disasters, and thus, drought monitoring is vital. Drought assessments based on available surface soil moisture (SM) can account for soil water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with [...] Read more.

China is frequently subjected to local and regional drought disasters, and thus, drought monitoring is vital. Drought assessments based on available surface soil moisture (SM) can account for soil water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with a high temporal resolution. At present, the evaluation of Soil Moisture Active Passive (SMAP) SM products is inadequate, and L-band microwave data have not been applied to agricultural drought monitoring throughout China. In this study, first, we provide a pivotal evaluation of the SMAP L3 radiometer-derived SM product using in situ observation data throughout China, to assist in subsequent drought assessment, and then the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) is compared with the atmospheric water deficit (AWD) and vegetation health index (VHI). It is found that the SMAP can obtain SM with relatively high accuracy and the SWDI-SMAP has a good overall performance on drought monitoring. Relatively good performance of SWDI-SMAP is shown, except in some mountain regions; the SWDI-SMAP generally performs better in the north than in the south for less dry bias, although better performance of SMAP SM based on the R is shown in the south than in the north; differences between the SWDI-SMAP and VHI are mainly shown in areas without vegetation or those containing drought-resistant plants. In summary, the SWDI-SMAP shows great application potential in drought monitoring. Full article

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18 pages, 1632 KiB

Open AccessEditor’s ChoiceArticle

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

by Reza Attarzadeh, Jalal Amini, Claudia Notarnicola and Felix Greifeneder

Remote Sens. 2018, 10(8), 1285; https://doi.org/10.3390/rs10081285 - 15 Aug 2018

Cited by 87 | Viewed by 8535

Abstract

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation [...] Read more.

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R²), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map. Full article

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21 pages, 3932 KiB

Open AccessEditor’s ChoiceArticle

SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

by Jean-François Rysman, Giulia Panegrossi, Paolo Sanò, Anna Cinzia Marra, Stefano Dietrich, Lisa Milani and Mark S. Kulie

Remote Sens. 2018, 10(8), 1278; https://doi.org/10.3390/rs10081278 - 14 Aug 2018

Cited by 41 | Viewed by 6317

Abstract

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of [...] Read more.

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10⁻³ kg·m⁻²) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m⁻². Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation)

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15 pages, 3175 KiB

Open AccessEditor’s ChoiceArticle

Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield

by Kensuke Kawamura, Hiroshi Ikeura, Sengthong Phongchanmaixay and Phanthasin Khanthavong

Remote Sens. 2018, 10(8), 1249; https://doi.org/10.3390/rs10081249 - 8 Aug 2018

Cited by 40 | Viewed by 15908

Abstract

Canopy hyperspectral (HS) sensing is a promising tool for estimating rice (Oryza sativa L.) yield. However, the timing of HS measurements is crucial for assessing grain yield prior to harvest because rice growth stages strongly influence the sensitivity to different wavelengths and [...] Read more.

Canopy hyperspectral (HS) sensing is a promising tool for estimating rice (Oryza sativa L.) yield. However, the timing of HS measurements is crucial for assessing grain yield prior to harvest because rice growth stages strongly influence the sensitivity to different wavelengths and the evaluation performance. To clarify the optimum growth stage for HS sensing-based yield assessments, the grain yield of paddy fields during the reproductive phase to the ripening phase was evaluated from field HS data in conjunction with iterative stepwise elimination partial least squares (ISE-PLS) regression. The field experiments involved three different transplanting dates (12 July, 26 July, and 9 August) in 2017 for six cultivars with three replicates (n = 3 × 6 × 3 = 54). Field HS measurements were performed on 2 October 2017, during the panicle initiation, booting, and ripening growth stages. The predictive accuracy of ISE-PLS was compared with that of the standard full-spectrum PLS (FS-PLS) via coefficient of determination (R²) values and root mean squared errors of cross-validation (RMSECV), and the robustness was evaluated by the residual predictive deviation (RPD). Compared with the FS-PLS models, the ISE-PLS models exhibited higher R² values and lower RMSECV values for all data sets. Overall, the highest R² values and the lowest RMSECV values were obtained from the ISE-PLS model at the booting stage (R² = 0.873, RMSECV = 22.903); the RPD was >2.4. Selected HS wavebands in the ISE-PLS model were identified in the red-edge (710–740 nm) and near-infrared (830 nm) regions. Overall, these results suggest that the booting stage might be the best time for in-season rice grain assessment and that rice yield could be evaluated accurately from the HS sensing data via the ISE-PLS model. Full article

(This article belongs to the Special Issue Remote Sensing in Support of Transforming Smallholder Agriculture)

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19 pages, 7268 KiB

Open AccessEditor’s ChoiceArticle

Assisting Flood Disaster Response with Earth Observation Data and Products: A Critical Assessment

by Guy J-P. Schumann, G. Robert Brakenridge, Albert J. Kettner, Rashid Kashif and Emily Niebuhr

Remote Sens. 2018, 10(8), 1230; https://doi.org/10.3390/rs10081230 - 6 Aug 2018

Cited by 135 | Viewed by 15215

Abstract

Floods are among the top-ranking natural disasters in terms of annual cost in insured and uninsured losses. Since high-impact events often cover spatial scales that are beyond traditional regional monitoring operations, remote sensing, in particular from satellites, presents an attractive approach. Since the [...] Read more.

Floods are among the top-ranking natural disasters in terms of annual cost in insured and uninsured losses. Since high-impact events often cover spatial scales that are beyond traditional regional monitoring operations, remote sensing, in particular from satellites, presents an attractive approach. Since the 1970s, there have been many studies in the scientific literature about mapping and monitoring of floods using data from various sensors onboard different satellites. The field has now matured and hence there is a general consensus among space agencies, numerous organizations, scientists, and end-users to strengthen the support that satellite missions can offer, particularly in assisting flood disaster response activities. This has stimulated more research in this area, and significant progress has been achieved in recent years in fostering our understanding of the ways in which remote sensing can support flood monitoring and assist emergency response activities. This paper reviews the products and services that currently exist to deliver actionable information about an ongoing flood disaster to emergency response operations. It also critically discusses requirements, challenges and perspectives for improving operational assistance during flood disaster using satellite remote sensing products. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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22 pages, 7683 KiB

Open AccessEditor’s ChoiceArticle

Accuracy Assessment of GlobeLand30 2010 Land Cover over China Based on Geographically and Categorically Stratified Validation Sample Data

by Yu Wang, Jingxiong Zhang, Di Liu, Wenjing Yang and Wangle Zhang

Remote Sens. 2018, 10(8), 1213; https://doi.org/10.3390/rs10081213 - 2 Aug 2018

Cited by 39 | Viewed by 5825

Abstract

Land cover information is vital for research and applications concerning natural resources and environmental modeling. Accuracy assessment is an important dimension in use and production of land cover information. GlobeLand30 is a relatively new global land cover information product with a fine spatial [...] Read more.

Land cover information is vital for research and applications concerning natural resources and environmental modeling. Accuracy assessment is an important dimension in use and production of land cover information. GlobeLand30 is a relatively new global land cover information product with a fine spatial resolution of 30 m and is potentially useful for many applications. This paper describes the methods for and results from the first country-wide and statistically based accuracy assessment of GlobeLand30 2010 land cover dataset over China. For this, a total of 8400 validation sample pixels were collected based on a sampling design featuring two levels of stratification (ten geographical regions, each with nine or eight land-cover classes). Validation sample data with reference class labels were acquired from visual interpretation based on Google Earth high-resolution satellite images. Error matrices for individual regions and entire China were estimated properly based on the sampling design adopted, with the former aggregated to get the latter through suitable weighting. Results were obtained, with agreement at a sample pixel defined both as a match between the map (class) label and either the primary or alternate reference label therein and, more strictly, as a match between the map label and the primary reference label only. Based on the former definition of agreement, the overall accuracy of GlobeLand30 2010 land cover for China was assessed to be 84.2%. User’s accuracy and producer’s accuracy were both greater than 80% for cultivated land, forest, permanent snow and ice, and bareland, with user’s accuracy for water bodies estimated 94.2% (82.1% for wetland, 79.8% for artificial surface) and producer’s accuracy for grassland estimated 89.0%. These indicate that GlobeLand30 2010 depicts land cover circa 2010 in China quite accurately, although estimates of accuracy indicators based on the latter definition of agreement were lower as expected with an estimated national overall accuracy of 81.0%. Regional and class variations in accuracy were revealed and examined in the light of their associations with land cover distributions and patterns. Implications for use and production of GlobeLand30 land cover information were discussed, so were commonality and lack of it between GlobeLand30 and other fine-resolution land cover products. Full article

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19 pages, 1799 KiB

Open AccessEditor’s ChoiceArticle

Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting

by Jacopo Acquarelli, Elena Marchiori, Lutgarde M.C. Buydens, Thanh Tran and Twan Van Laarhoven

Remote Sens. 2018, 10(7), 1156; https://doi.org/10.3390/rs10071156 - 21 Jul 2018

Cited by 34 | Viewed by 7830

Abstract

Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of the increased risk of overfitting [...] Read more.

Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of the increased risk of overfitting when training a classifier. In this paper, we show that in this setting, a convolutional neural network with a single hidden layer can achieve state-of-the-art performance when three tricks are used: a spectral-locality-aware regularization term and smoothing- and label-based data augmentation. The shallow network architecture prevents overfitting in the presence of many features and few training samples. The locality-aware regularization forces neighboring wavelengths to have similar contributions to the features generated during training. The new data augmentation procedure favors the selection of pixels in smaller classes, which is beneficial for skewed class label distributions. The accuracy of the proposed method is assessed on five publicly available hyperspectral images, where it achieves state-of-the-art results. As other spectral-spatial classification methods, we use the entire image (labeled and unlabeled pixels) to infer the class of its unlabeled pixels. To investigate the positive bias induced by the use of the entire image, we propose a new learning setting where unlabeled pixels are not used for building the classifier. Results show the beneficial effect of the proposed tricks also in this setting and substantiate the advantages of using labeled and unlabeled pixels from the image for hyperspectral image classification. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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23 pages, 6896 KiB

Open AccessEditor’s ChoiceArticle

Aboveground Forest Biomass Estimation Combining L- and P-Band SAR Acquisitions

by Michael Schlund and Malcolm W. J. Davidson

Remote Sens. 2018, 10(7), 1151; https://doi.org/10.3390/rs10071151 - 20 Jul 2018

Cited by 44 | Viewed by 9365

Abstract

While considerable research has focused on using either L-band or P-band SAR (Synthetic Aperture Radar) on their own for forest biomass retrieval, the use of the two bands simultaneously to improve forest biomass retrieval remains less explored. In this paper, we make use [...] Read more.

While considerable research has focused on using either L-band or P-band SAR (Synthetic Aperture Radar) on their own for forest biomass retrieval, the use of the two bands simultaneously to improve forest biomass retrieval remains less explored. In this paper, we make use of L- and P-band airborne SAR and in situ data measured in the field together with laser scanning data acquired over one hemi-boreal (Remningstorp) and one boreal (Krycklan) forest study area in Sweden. We fit statistical models to different combinations of topographic-corrected SAR backscatter and forest heights estimated from PolInSAR for the biomass estimation, and evaluate retrieval performance in terms of

R^{2}

and using 10-fold cross-validation. The study shows that specific combinations of radar observables from L- and P-band lead to biomass predictions that are more accurate in comparison with single-band retrievals. The correlations and accuracies between the combinations of SAR features and aboveground biomass are consistent across the two study areas, whereas the retrieval performance varied for individual bands. P-band-based retrievals were more accurate than L-band for the hemi-boreal Remningstorp site and less accurate than L-band for the boreal Krycklan site. The aboveground biomass levels as well as the ground topography differ between the two sites. The results suggest that P-band is more sensitive to higher biomass and L-band to lower biomass forests. The forest height from PolInSAR improved the results at L-band in the higher biomass substantially, whereas no improvement was observed at P-band in both study areas. These results are relevant in the context of combining information over boreal forests from future low-frequency SAR missions such as the European Space Agency (ESA) BIOMASS mission, which will operate at P-band, and future L-band missions planned by several space agencies. Full article

(This article belongs to the Section Forest Remote Sensing)

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