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Land cover land use (LCLU) products provide essential information for numerous environmental and human studies. Here, we assess the accuracy of eleven global and regional products over the conterminous U.S. using 25,000 high-confidence randomly distributed samples. Results show that in general, the National Land Cover Database (NLCD) and the Land Change Monitoring, Assessment and Projection (LCMAP) outperform other multi-class products, both in terms of higher individual class accuracy and with accuracy variability across classes. More specifically, F1 accuracy comparisons between the best performing USGS and non-USGS products indicate: (i) similar performance for the water class, (ii) USGS product outperformance in the developed (+1.3%), grass/shrub (+3.2%) and tree cover (+4.2%) classes, and (iii) non-USGS product (WorldCover) gains in the cropland (+5.1%) class. The NLCD and LCMAP also outperformed specialized single-class products, such as the Hansen Global Forest Change, the Cropland Data Layer and the Global Artificial Impervious Areas, while offering comparable results to the Global Surface Water Dynamics product. Spatial visualizations also allowed accuracy comparisons across different geographic areas. In general, the NLCD and LCMAP have disagreements mainly in the middle and southeastern part of conterminous U.S. while Esri, WorldCover and Dynamic World have most errors in the western U.S. Comparisons were also undertaken on a subset of the reference data, called spatial edge samples, that identifies samples surrounded by neighboring samples of different class labels, thus excluding easy-to-classify homogenous areas. There, the WorldCover product offers higher accuracies for the highly dynamic grass/shrub (+4.4%) and cropland (+8.1%) classes when compared to the NLCD and LCMAP products. An important conclusion while looking at these challenging samples is that except for the tree class (78%), the best performing products per class range in accuracy between 55% and 70%, which suggests that there is substantial room for improvement. [ABSTRACT FROM AUTHOR]

Farmland is a crucial resource for the survival and evolution of humans. The accurate evaluation of farmland production capacity (FPC) is of great significance for planting structure optimization, the improvement of low-yield farmland and sustainable utilization. The objective of this study is to quantitatively evaluate the FPC at the county scale using time series remote sensing (RS) images. Taking winter wheat as a benchmark crop, the relations between annual yield and the Normalized Difference Vegetation Index (NDVI) were established by a multiple linear regression algorithm. The mean and standard deviations (SD) of the multi-year yield of winter wheat were used to evaluate FPC and its instability using the farmland parcels as the basic unit. The results show that the estimation model for annual winter wheat yield performed best in 2011. The R2 of the modeling sample was 0.93, and the RMSE of the testing sample was 368.1 kg/ha. The FPC grades in the south and north of the study area were relatively high with a good stability, while those in the center were low with poor stability. There was a certain correlation between FPC and soil organic matter (SOM), and the correlation coefficient was 0.525 (p < 0.01). In this study, taking the farmland parcel as a basic unit instead of a pixel, long time series of multi-source RS images with medium resolution were used to monitor the per unit yield of benchmark crops and then evaluate the FPC. This can provide a method for the rapid evaluation of FPC at the county scale. [ABSTRACT FROM AUTHOR]

Chinese pine has tremendous applications in many fields. Mapping the distribution of Chinese pine is of great importance for government decision-making and forest management. In order to extract Chinese pine on a large scale, efficient algorithms and open remote-sensing datasets are needed. It is widely believed that machine learning algorithms and medium-resolution remote-sensing datasets can work well for this purpose. Unfortunately, their performance for Chinese pine extraction has remained unclear until now. Therefore, this study aims to explore the ability of the different machine learning algorithms and open remote-sensing datasets for Chinese pine extraction over large areas on Google Earth Engine (GEE). So, based on the combination of three typical machine learning algorithms, namely deep neural network (DNN), support vector machine (SVM), random forest (RF), and three open medium-resolution remote-sensing datasets, namely Sentinel-2, Gaofen-1, and Landsat-8 OLI, 27 models are constructed and GEE, with its powerful computing ability, is used. The main findings are as follows: (1) DNN has the highest accuracy for Chinese pine extraction, followed by SVM and RF; DNN is more sensitive to spatial geometric information, while SVM and RF algorithms are more sensitive to spectral information. (2) Spectral indexes are helpful for improving the extraction accuracy of Chinese pine. The extraction accuracy by using Gaofen-1 dataset increases 7.6% after adding spectral indexes, while the accuracies by using Sentinel-2 and Landsat-8 datasets increase 1.8% and 1.9% after adding spectral indexes, respectively. (3) The extraction accuracy by using DNN and Sentinel-2 dataset with spectral indexes is the highest, with an overall accuracy of 94.4%. (4) The area of Chinese pine is 153.73 km2, accounting for 5.06% of the administrative area of Karaqin Banner, and it is convenient to extract Chinese pine on a large scale by using GEE. [ABSTRACT FROM AUTHOR]

Associating a radar scatterer to a physical object is crucial for the correct interpretation of interferometric synthetic aperture radar measurements. Yet, especially for medium-resolution imagery, this is notoriously difficult and dependent on the accurate 3-D positioning of the scatterers. Here, we investigate the 3-D positioning capabilities of ENVISAT medium-resolution data. We find that the data are perturbed by range-and-epoch-dependent timing errors and calibration offsets. Calibration offsets are estimated to be about 1.58 m in azimuth and 2.84 m in range and should be added to ASAR products to improve geometric calibration. The timing errors involve a bistatic offset, atmospheric path delay, solid earth tides, and local oscillator drift. This way, we achieve an unbiased positioning capability in 2-D, while in 3-D, a scatterer was located at a distance of 28 cm from the true location. 3-D precision is now expressed as an error ellipsoid in local coordinates. Using the Bhattacharyya metric, we associate radar scatterers to real-world objects. Interpreting deformation of individual infrastructure is shown to be feasible for this type of medium-resolution data. [ABSTRACT FROM AUTHOR]

In change detection (CD) of medium-resolution remote sensing images, the threshold and clustering methods are two kinds of the most popular ones. It is found that the threshold method of the expectation maximum (EM) algorithm usually generates a CD map including many false alarms but almost detecting all changes, and the fuzzy local information c-means algorithm (FLICM) obtains a homogeneous CD map but with some missed detections. Therefore, we aim to design a framework to improve CD results by fusing the advantages of threshold and clustering methods. Experimental results indicate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Monitoring surface albedo at medium-to-fine resolution (<100 m) has become increasingly important for medium-to-fine scale applications and coarse-resolution data evaluation. This paper presents a method for estimating surface albedo directly using top-of-atmosphere reflectance. This is the first attempt to derive surface albedo for both snow-free and snow-covered conditions from medium-resolution data with a single approach. We applied this method to the multispectral data from the wide-swath Chinese HuanJing (HJ) satellites at a spatial resolution of 30 m to demonstrate the feasibility of this data for surface albedo monitoring over rapidly changing surfaces. Validation against ground measurements shows that the method is capable of accurately estimating surface albedo over both snow-free and snow-covered surfaces with an overall root mean square error (RMSE) of 0.030 and r-square (R2) of 0.947. The comparison between HJ albedo estimates and the Moderate Resolution Imaging Spectral Radiometer (MODIS) albedo product suggests that the HJ data and proposed algorithm can generate robust albedo estimates over various land cover types with an RMSE of 0.011-0.014. The accuracy of HJ albedo estimation improves with the increase in view zenith angles, which further demonstrates the unique advantage of wide-swath satellite data in albedo estimation. [ABSTRACT FROM AUTHOR]

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It is well known that there are geometric distortions in synthetic aperture radar (SAR) images when the terrain undulates. Layover is the most common one, which brings challenges to the application of SAR remote sensing. This study proposes a novel detection method that is mainly aimed at the layover caused by mountains and can be performed with only medium-resolution SAR images and no other auxiliary data. The detection includes the following four stages: initial processing, difference image calculation and rough and fine layover detection. Initial processing mainly obtains the potential layover areas, which are mixed with the built-up areas after classification. Additionally, according to the analysis of the backscatter coefficient (BC) of various ground objects with different polarization images, the layover areas are detected step-by-step from the mixed areas, in which the region-based FCM segmentation algorithm and spatial relationship criteria are used. Taking the Danjiangkou Reservoir area as the study area, the relevant experiments with Sentinel-1A SAR images were conducted. The quantitative analysis of detection results adopted the figure of merit (FoM), and the highest accuracy was up to 87.6% of one selected validation region. Experiments in the South Taihang area also showed the satisfactory effect of layover detection, and the values of FoM were all above 85%. These results show that the proposed method can do well in the layover detection caused by mountains. Its simplicity and effectiveness are helpful in removing the influence of layover on SAR image applications to a certain extent and improving the development of SAR remote sensing technology. [ABSTRACT FROM AUTHOR]

Time series of vegetation biophysical variables (leaf area index (LAI), fraction canopy cover (FCOVER), fraction of absorbed photosynthetically active radiation (FAPAR), canopy chlorophyll content (CCC), and canopy water content (CWC)) were estimated from interpolated Sentinel-2 (S2-LIKE) surface reflectance images, for an agricultural region located in central Canada, using the Simplified Level 2 Product Prototype Processor (SL2P). S2-LIKE surface reflectance data were generated by blending clear-sky Sentinel-2 Multispectral Imager (S2-MSI) images with daily BRDF-adjusted Moderate Resolution Imaging Spectrometer images using the Prediction Smooth Reflectance Fusion Model (PSFRM), and validated using thirteen independent S2-MSI images (RMSE ≤ 6%). The uncertainty of S2-LIKE surface reflectance data increases with the time delay between the prediction date and the closest S2-MSI image used for training PSFRM. Vegetation biophysical variables from S2-LIKE products are validated qualitatively and quantitatively by comparison to the corresponding vegetation biophysical variables from S2-MSI products (RMSE = 0.55 for LAI, ~10% for FCOVER and FAPAR, and 0.13 g/m2 for CCC and 0.16 kg/m2 for CWC). Uncertainties of vegetation biophysical variables derived from S2-LIKE products are almost linearly related to the uncertainty of the input reflectance data. When compared to the in situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 field campaign, uncertainties of LAI (0.83) and FCOVER (13.73%) estimates from S2-LIKE products were slightly larger than uncertainties of LAI (0.57) and FCOVER (11.80%) estimates from S2-MSI products. However, equal uncertainties (0.32 kg/m2) were obtained for CWC estimates using SL2P with either S2-LIKE or S2-MSI input data. [ABSTRACT FROM AUTHOR]

We developed a land cover and land use mapping framework specifically designed for agricultural systems of the Sudan-Sahel region. The mapping approach extracts information from inter- and intra-annual vegetation dynamics from dense stacks of Landsat 8 images. We applied this framework to create a 30 m spatial resolution land use map with a focus on agricultural landscapes of northern Nigeria for 2015. This map provides up-to-date information with a higher level of spatial and thematic detail resulting in a more precise characterization of agriculture in the region. The map reveals that agriculture is the main land use in the region. Arable land represents on average 52.5% of the area, higher than the reported national average for Nigeria (38.4%). Irrigated agriculture covers nearly 2.2% of the total area, reaching nearly 20% of the cultivated land when traditional floodplain agriculture systems are included, above the reported national average (0.63%). There is significant variability in land use within the region. Cultivated land in the northern section can reach values higher than 75%, most land suitable for agriculture is already under cultivation and there is limited land for future agricultural expansion. Marginal lands, not suitable for permanent agriculture, can reach 30% of the land at lower altitudes in the northeast and northwest. In contrast, the southern section presents lower land use intensity that results in a complex landscape that intertwines areas farms and larger patches of natural vegetation. This map improves the spatial detail of existing sources of LCLU information for the region and provides updated information of the current status of its agricultural landscapes. This study demonstrates the feasibility of multi temporal medium resolution remote sensing data to provide detailed and up-to-date information about agricultural systems in arid and sub arid landscapes of the Sahel region. [ABSTRACT FROM AUTHOR]