Monitoring Oasis Cotton Fields Expansion in Arid Zones Using the Google Earth Engine: A Case Study in the Ogan-Kucha River Oasis, Xinjiang, China by Lijing Han, Jianli Ding, Jinjie Wang, Junyong Zhang, Boqiang Xie, Jianping Hao

“…After the cotton fields were mulched, there was a significant increase in spectral reflectance in the visible, red-edge and near-infrared bands, and a decrease in the short-wave infrared band. …”
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UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF by Zeyuan Wang, Zeyuan Wang, Zeyuan Wang, Jianli Ding, Jianli Ding, Jianli Ding, Jiao Tan, Jiao Tan, Jiao Tan, Junhao Liu, Junhao Liu, Junhao Liu, Tingting Zhang, Weijian Cai, Weijian Cai, Weijian Cai, Shanshan Meng, Shanshan Meng, Shanshan Meng

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Automatic Detection of Regional Snow Avalanches with Scattering and Interference of C-band SAR Data by Jinming Yang, Chengzhi Li, Lanhai Li, Jianli Ding, Run Zhang, Tao Han, Yang Liu

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Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms by Boqiang Xie, Jianli Ding, Xiangyu Ge, Xiaohang Li, Lijing Han, Zheng Wang

“…The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R² = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R² by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. …”
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