Spatial soil moisture estimation in agro-pastoral transitional zone based on synergistic use of SAR and optical-thermal satellite images

Abstract

Synthetic Aperture Radar (SAR) Sentinel-1 (S1) surface soil moisture (SSM) retrieval is promising but challenging in canopy areas. Additional data from optical-thermal infrared (TIR) sensors (e.g., Moderate Resolution Imaging Spectroradiometer, MODIS) have potential to compensate for SSM monitoring from single S1 product, suggesting their synergistic use for SSM retrieval in vegetated terrains. A physical, straightforward and non-calibration synergy method of SAR S1 and optical-TIR MODIS was proposed in this study to enhance SSM estimation in an agro-pastoral transitional zone. Change detection for S1 backscatter (σ°) and simplified triangle method representing land surface temperature (LST) – normalized difference vegetation index (NDVI) space (Ts-VI) for MODIS, were combined under a synergistic framework regarding vegetation conditions. The synergy method performance was evaluated against in-situ soil moisture sites for different phenological stages. Correlation analysis results indicated that S1 σ° is highly sensitive to SSM in non-growing season, whereas MODIS LST is negatively linked to root-zone soil moisture during summer crop growth as the seasonal vegetation effect that can be explained via the observed trapezoidal Ts-VI shape. For the synergy, S1 is mainly involved in during non-growing period, while major contribution in growing period was from MODIS, which accounted for 90% improvements in correlation (R, 0.29 – 0.55) and 30% for Root-Mean-Square Error (RMSE, 0.093 – 0.065 m3m−3) during a four-month crop growth as compared to single S1 product. For entire period, the synergy method outperformed single S1 over all sites, where the average R and RMSE improved 30% (0.46 – 0.60) and 14% (0.070 – 0.060 m3m−3), respectively, with highest values (R = 0.73 and RMSE = 0.039 m3m−3) observed in a maize site. This synergy suggests a suitability of integrating optical-TIR data to improve SAR SSM estimation in agricultural lands, especially over the crop growth period when water is a major limiting factor.