基于Sentinel-1和MODIS数据反演农田地表土壤水分—以REMEDHUS地区为例

土壤水分是陆地生态系统和水循环的重要状态变量，在植被生长监测、农作物产量评估等研究中均发挥着重要作用。为了消除植被散射的影响，进而实现农田地表土壤水分的高精度反演，以时间序列Sentinel-1影像及MODIS产品为实验数据，基于高级积分方程模型和比值植被模型的耦合模型，通过采用不同光学植被参数和VH交叉极化后向散射系数，分别对农田植被散射贡献进行表征，消除植被散射的影响，进而实现土壤水分的高精度反演。结果表明：当利用VH极化进行参数化植被散射贡献时，标定的耦合模型，虽然可消除对光学植被参数的依赖并较好地模拟Sentinel-1卫星观测，但土壤水分反演结果效果欠理想，相关系数R最大仅为0.54；与VH极化相比，利用光学植被参数表征植被散射贡献时，土壤水分整体反演效果较理想，R最大达到0.79，但光学植被参数反演结果在不同站点存在显著的空间差异性，R介于0.07~0.79之间。因此，在未来研究中可尝试将雷达数据与光学数据协同反演，以期在消除植被散射影响的基础上，实现植被覆盖区域土壤水分的高精度反演及动态变化监测。

Retrieval of Soil Moisture in Agricultural Area based on Sentinel-1 and MODIS Data

Soil moisture is considered as an important state variable of terrestrial ecosystems and water cycle， which plays an important role in many researches， such as vegetation growth monitoring and crop yield evaluation. In order to eliminate the effect of vegetation scattering and achieve high-precision retrieval of farmland soil moisture， time series of Sentinel-1 data and MODIS products were used as experimental data to retrieve soil moisture. The advanced integral equation model was coupled with ratio vegetation model， which is parameterized by four different optical vegetation parameters and VH cross-polarization backscattering coefficient for vegetation scattering contribution， to eliminate the impact of vegetation scattering and then achieve high-precision inversion of soil moisture. The results show that the coupled model can simulate the Sentinel-1 VV-polarized backscattering coefficient but the soil moisture retrieval results are not ideal with the maximum correlation coefficient （R） is 0.54 when VH is used for parameterizing the scattering contribution of vegetation. Different from VH， the overall soil moisture retrieval results is relatively better with a maximum R of 0.79 when four optical vegetation parameters are used. However， there is significant spatial difference at different stations with the results of optical vegetation parameters， with R ranging from 0.07 to 0.79. Therefore， in future results， it is better to combine radar data and optical data for eliminating the scattering contribution of vegetation and realizing the high-precision retrieval of soil moisture and observation of dynamic changes in vegetated areas.