基于地表温度日较差-植被覆盖度特征空间的土壤含水量反演方法

土壤含水量是气候、水文和生态等研究的重要参数。地表温度-植被指数特征空间法是遥感监测土壤含水量的常用方法。以欧洲伊比利亚半岛为研究区，使用MSG-SEVIRI （Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager）晴空数据，构建地表温度日较差-植被覆盖度特征空间。在此特征空间上，结合研究区土壤质地数据，建立土壤含水量反演模型反演土壤体积含水量。利用西班牙REMEDHUS （REd de MEDiciòn de la HUmedad del Suelo）土壤含水量观测网络的实测数据对反演结果进行验证，均方根误差均在0.05m³/m³以内，具有较高的精度。与常用的地表温度-植被覆盖度特征空间的结果对比证明，以地表温度日较差替代地表温度，能够减小地表温度反演误差导致的土壤含水量估算误差，从而提高土壤含水量反演精度。

A method for surface soil moisture estimation based on the DTR-FVC space

Soil moisture is a key parameter for the studies of climatology, hydrology, and ecology. The commonly used remotely sensed approach is based on the land surface temperature-vegetation index (LST-VI) space. With the Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) data, the study is conducted over Ibérian Peninsula. In this study, we use the diurnal temperature range (DTR), instead of surface temperature, to compose a diurnal temperature range-fraction vegetation coverage (DTR-FVC) space. Based on the DTR-FVC space, the soil moisture retrieval model is established to estimate soil moisture with the soil texture data. The results are validated by in situ measurements from 19 meteorological stations in Spain, and the root mean square error (RMSE) is about 0.05m³/m³. Compared with the LST-VI space, the DTR-FVC space reduces the retrieval error caused by the uncertainty of instantaneous land surface temperature, and thus the accuracy of soil moisture is improved.