摘要 应用气旋全球导航卫星系统（cyclone global navigation satellite system，CYGNSS）的星载反射测量遥感数据和土壤湿度主被动项目（soil moisture active passive，SMAP）发布的土壤湿度产品，对2种星载全球导航卫星系统反射测量（global navigation satellite system reflectometry，GNSS-R）地表土壤湿度反演建模方法进行对比与评价。结果表明，在单个格网上直接对标定的有效反射率和SMAP参考土壤湿度回归建立线性模型，反演得到的地表土壤湿度精度远高于格网统一建模的反演精度。使用1 a的CYGNSS陆地观测数据建立的反演模型已经具有极高的时域稳定性。5个月的测试数据表明，单格网模型反演的地表土壤湿度RMSE可达0.056 cm3/cm3，相关系数达0.9。
Abstract：Application of spaceborne reflectance measurements of remote sensing data from the cyclone global navigation satellite system(CYGNSS) and soil moisture active passive(SMAP) soil moisture products, two methods for surface soil moisture retrieval from spaceborne global navigation satellites system reflectometry (GNSS-R) are compared and evaluated. The results show that, the simple linear regression modeling between calibrated gridded effective reflectivity and reference SMAP soil moisture on each aggregated grid pixel is superior to the integrated modeling over global. An inversion model with significant time-domain stability can be produced with 1 a of the training data. On a five-month testing dataset, the RMSE of the estimated soil moisture from the pixel-by-pixel retrieval model can reach 0.056 cm3/cm3, and the correlation between estimated soil moisture and reference soil moisture is 0.9.
