Sentinel-1和Sentinel-2协同反演地表土壤水分

Sentinel-1 and Sentinel-2 Synergistic Retrieval of Surface Soil Moisture

土壤水分是水文循环、生态环境、气候变化等研究中的关键参数,获取高分辨率长时间序列的土壤水分信息对农业管理、作物生长监测等具有重要的意义,同时也是研究的难点。基于时间序列(2019年至2020年)的Sentinel-1雷达数据和Sentinel-2光学数据,构建了地表土壤水分的雷达与光学数据协同反演模型,即裸土条件下地表土壤水分的变化检测方法,并利用归一化植被指数对植被影响进行校正,实现了青藏高原多年冻土区(五道梁)100 m空间分辨率的土壤水分反演。与地面实际观测的土壤水分进行对比验证,结果表明土壤水分反演结果与地面实测数据的相关系数介于0.672与0.941之间,无偏均方根误差介于0.031 m^(3)/m^(3)与0.073 m^(3)/m^(3)之间,土壤水分变化与区域降水事件和特征密切相关,验证了本文提出的考虑植被物候的变化检测方法在地势平坦、植被稀疏的青藏高原地区具有极高的适用性。

Soil moisture is a key parameter in the study of hydrological cycle,ecological environment,climate change,etc.,The acquisition of high-resolution long time series soil moisture information is of great significance for agricultural management and crop growth monitoring,and remote sensing monitoring is also a difficult problem in research.Based on the Sentinel-1 radar data and Sentinel-2 optical data of the time series(2019—2020),this paper constructs a synergistic retrieval model of surface soil moisture,that is,a method for detecting changes in surface soil moisture under bare soil conditions,And the normalized vegetation index was used to correct the vegetation impact.The proposed method has achieved soil moisture mapping with a spatial resolution of 100 meters in the permafrost region(Wudaoliang)of the Qinghai-Tibet Plateau.The comparison and validation with the in-situ measured soil moisture observed show that the correlation coefficient between the soil moisture estimates and the ground measurements is 0.672≤R≤0.941,and the unbiased root mean square error(ubRMSE)is between 0.031 m^(3)/m^(3) and 0.073 m^(3)/m^(3).Soil moisture changes are closely related to regional precipitation events and characteristics,verifying that the change detection method proposed in this study has high applicability in the flat terrain and sparse vegetation areas on the Qinghai-Tibet Plateau.