Monitoring soil moisture by remote sensing plays a significant role in the dynamic characterization and management of surface heat balance, water evapotranspiration and soil moisture in agricultural production. In order to verify the applicability of GF1 data in the rapid acquisition of agricultural parameters in Jianghan Plain, this study simulated, compared and validated the effectiveness of soil moisture inversion. All of the data were Sampled in the vegetation area of Qianjiang City on March 8, 2017, GF1 WFV image and measured soil moisture data were used to retrieve the Perpendicular Drought Index(PDI), the Modified Perpendicular Drought Index(MPDI) and the Vegetation Adjusted Perpendicular Drought Index(VAPDI). The results showed as follows: firstly, determinate coefficients of correlation analysis on PDI, MPDI, VAPDI and measured soil moisture were 0.649, 0.802 and 0.821 respectively. All of the models met the accuracy requirements of inversion in the accuracy evaluation of soil moisture. Both of them indicated that using GF1 WFV image is feasible for soil moisture inversion in large scale in Jianghan Plain. Secondly, in the area of moderate vegetation coverage, MPDI and VAPDI had higher inversion accuracy than PDI. To a certain extent, they overcome the influence of mixed pixels on soil moisture spectral information. While in the area of high vegetation coverage, VAPDI modified by Perpendicular Vegetation Index(PVI) were not susceptible to vegetation saturation and thus had higher inversion accuracy. Thirdly, spatial heterogeneity of soil moisture retrieved by three models were similar. However, MPDI and VAPDI were more sensitive to the change of soil moisture, which reflected the actual soil moisture level that covered by different vegetation. The results can provide practical reference for dynamic monitoring of surface soil moisture in large scale in Jianghan Plain.

Key words：GF1; soil moisture; PDI; MPDI; VAPDI; Jianghan Plain