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

doi:10.7523/j.issn.2095-6134.2018.06.008

Abstract

Abstract: 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.

Key words: soil moisture, MSG data, DTR, FVC, triangle space

CLC Number:

TP722.5

RU Chen, DUAN Sibo, JIANG Xiaoguang, LENG Pei, GAO Maofang, HUO Hongyuan, LI Zhaoliang. A method for surface soil moisture estimation based on the DTR-FVC space[J]. , 2018, 35(6): 771-781.

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