ALOS-2 and Sentinel-1 SAR data sensitivity analysis to surface soil moisture over bare and vegetated agricultural fields


COMPUTERS AND ELECTRONICS IN AGRICULTURE, vol.171, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 171
  • Publication Date: 2020
  • Doi Number: 10.1016/j.compag.2020.105303
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, BIOSIS, CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Food Science & Technology Abstracts, Geobase, INSPEC, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Hacettepe University Affiliated: Yes


Surface Soil Moisture (SSM) is one of the important variables that help to understand the physical process between the land and the atmosphere. Thus, temporal and spatial distribution of SSM is crucial for a sustainable environment. Radar remote sensing, especially Synthetic Aperture Radar (SAR), is an important technology for the estimation of spatio-temporal SSM. The aim of this study is to examine the potential of L-band ALOS-2 and C-band Sentinel-1 SAR data for SSM estimation over bare and vegetated agricultural fields in a dry season (no irrigation). Field campaigns were carried out simultaneous with ALOS-2 acquisitions on 22 April 2016 (bare surfaces) and 12 June 2016 (vegetation covered surfaces). In addition, in-situ SSM, Plant Coverage (PC) and surface roughness were measured in these campaigns. The sensitivity of ALOS-2 and Sentinel-1 polarizations was investigated for bare and vegetation covered surfaces. For bare soil surfaces, the highest sensitivity was obtained from HH polarization of ALOS-2 (R-2 = 0.36) and VV polarization of Sentinel-1 (R-2 = 0.74). The results demonstrated that the potential of Sentinel-1 for SSM estimation was higher than ALOS-2 over bare soil surfaces. Three models, namely Dubois semi-empirical model and Multiple Linear Regression (MLR) analysis for bare soil surfaces, and Water Cloud Model (WCM) for vegetated surfaces, were regarded for SSM estimation. Dubois model did not respond well for both data sets. Considering the MLR analyses, the testing results showed that Sentinel-1 (R-2 = 0.73 and RMSE = 1.76 vol %) presented better accuracy results than ALOS-2 (R-2 = 0.57 and RMSE = 2.29 vol%). WCM was effective in eliminating the vegetation backscattering effect and the inversion of WCM presented satisfactory results in estimating SSM with both ALOS-2 and Sentinel-1 data. Furthermore, L-band ALOS-2 provided slightly better results than C-band Sentinel-1 in WCM analysis.