Inversion of soil moisture based on improved change detection method using Sentinel-1A and MODIS time-series data

Soil moisture is an essential soil parameter affecting energy transfer and surface heat exchange. Accurately acquiring surface soil moisture is important for guiding agricultural production, understanding the global water cycle, and studying climate change. This paper conducted an inversion study of soil moisture in the Shandian River Basin based on the change detection method (CD) using Sentinel-1A and MODIS time series data. It was found that the traditional CD has a mismatch problem between the amount of change in soil moisture and the amount of change in radar backscattering coefficient, which reduces the accuracy of soil moisture inversion. This paper proposes an improved CD that corrects for soil moisture variations and radar backscattering coefficient variations using the normalized difference vegetation index (NDVI) piecewise function. Compared with the traditional CD and the Alpha model, the improved CD proposed in this paper has better applicability in the vegetation cover area and improves the accuracy of the soil moisture inversion, with a Pearson correlation coefficient of 0.813, a coefficient of determination of 0.645, and a root mean square error (RMSE) of 0.046 cm³ /cm³ . The improved CD does not require complex parameters and has the potential for large-scale application. Using the improved CD to invert soil moisture, the inversion of soil moisture value can capture the changing trend of measured soil moisture value in a time series.