Monitoring of Time-Series Soil moisture based on advanced DInSAR

Abstract

Understanding soil moisture is essential for earth and environmental sciences especially in geology, hydrology, and meteorology. Remote sensing techniques are widely applied to large-scale monitoring tasks. Among them, DInSAR using multi-temporal spaceborne SAR images is able to derive surface movement up to mm level over an area. One of the factors inducing the movement is variation of soil moisture. Based on this, a semi-empirical approach can be tailored to retrieve the underground water content. However, the derived movement is often contaminated with other irrelevant noise. Besides, a time-series analysis could not be simply implemented without additional fusion and calibration. In this paper, we propose a novel modelling based on advanced DInSAR to solve these problems. The irrelevant noise will be removed as parts of the modelled elements in the DInSAR processing. A forward model on a scene is built by regressing the measured soil moisture on the DInSAR-derived movement series. We tested our approach using Sentinel-1 images in the grasslands of organic soil within State of Brandenburg, Germany. The Pearson correlation coefficients between the measured soil moistures and the DInSAR-derived movements are up to 0.91. The mean square errors of the predicted soil moistures compared with the measurements reach 3.03 % (volumetric water content) at best. Our study shows a promising new concept to develop a global monitoring of soil moisture in the future.