Coherent and Incoherent Change Detection for Soil Moisture Retrieval From Sentinel-1 Data

This study proposes a hybrid incoherent-coherent change detection (CD) approach to retrieve surface soil moisture (SSM) from Sentinel-1 data. It combines time-series observations of synthetic aperture radar (SAR) backscatter and interferometric closure phase to deliver a method that does not require external calibration. A proof-of-concept assessment based on synthetic and experimental data is presented. Sentinel-1 and in situ data over a study site in Southern Italy during the Winter-Spring season 2017 that covered both bare and vegetated soil conditions have been acquired and analyzed. For bare soils, results indicate good performance, that is, Pearson correlation approximate to 0.8 and root mean square error (RMSE) approximate to 0.05 m(3)/m(3). Conversely, over vegetated surfaces, poor results are found.

Automated summary

This study proposes a hybrid incoherent-coherent change detection approach to retrieve surface soil moisture from Sentinel-1 data. It combines time-series observations of SAR backscatter and interferometric closure phase without external calibration. The results show good performance for bare soils and poor results for vegetated surfaces.