Jie Liu

TL;DR: It is concluded that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy via UAV hyperspectral imagery on a regional scale and might improve management and conservation strategies for agroecosystem systems in arid regions.

TL;DR: Wang et al. as mentioned in this paper proposed a modified Sentinel 1A water index based on the relationship between normalized difference water index (NDWI) imageries and S1A data, and the results showed that the classification results were acceptable with the optimal overall accuracy of 99.94% and kappa coefficient of 0.9971.

TL;DR: This research provided a promising data mining approach for UAV-based hyperspectral imaging data by building the best estimation model for agricultural SMC in arid regions using the eXtreme Gradient Boost algorithm.

TL;DR: In this article, the applicability of multiangle implementation of atmospheric correction (MAIAC) AOD in Central Asia was comprehensively analyzed, and the variation in AOD over the last 20 years was analyzed by the information entropy method.

TL;DR: The land use types affected by anthropogenic activities were smaller contributors to regional aerosols than barren lands affected by climate factors, which advances the understanding of relationship between aerosols and land use/cover and facilitates land use decision making.