Li Zhibin, Zhao Lin, Wang Lingxiao, Liu Guangyue, Du Erji, Zou Defu, Hu Guojie, Xing Zanpin, Wang Chong, Liu Shibo, Xiao Minxuan, Yin Luhui, Wang Yiwei
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
Cryosphere Research Station on the Qinghai‒Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China.
Sci Data. 2025 Mar 24;12(1):497. doi: 10.1038/s41597-025-04831-1.
The characteristics of the spatial distribution of surface soil moisture (SM) on the Qinghai‒Tibet Plateau (QTP) on a fine scale are unclear due to the lack of high-spatial-resolution SM datasets. To improve this situation, we first supplemented 659 SM datasets in areas on the QTP containing sparse monitoring stations from 2021-2022 and integrated published SM datasets. Based on Sentinel-1&2 and measured SM data, we developed an SM retrieval algorithm for the ascending and descending orbits. Then, 100-m-resolution SM spatial data were generated for the thawing season of 2017-2023 in the SAR signal-applicable area on the QTP. As validated by the measured data, the correlation coefficients of the retrieval results for the ascending and descending orbits were 0.72 and 0.69, respectively, and the bias reached 0.07 m³/m³ and an RMSE of 0.07 m³/m³ for both. These SM datasets exhibit notable promise for improving our understanding and analysis of the ecology and hydrology of different environments on the QTP.
由于缺乏高空间分辨率的土壤湿度(SM)数据集,青藏高原(QTP)精细尺度下表层土壤湿度的空间分布特征尚不清楚。为改善这种情况,我们首先补充了2021 - 2022年QTP上监测站点稀疏地区的659个SM数据集,并整合了已发表的SM数据集。基于哨兵1号和2号以及实测的SM数据,我们开发了一种用于升轨和降轨的SM反演算法。然后,生成了QTP上SAR信号适用区域2017 - 2023年解冻季节100米分辨率的SM空间数据。经实测数据验证,升轨和降轨反演结果的相关系数分别为0.72和0.69,偏差均达到0.07立方米/立方米,均方根误差为0.07立方米/立方米。这些SM数据集在改善我们对QTP不同环境的生态和水文的理解与分析方面显示出显著的前景。