Abstract
This paper discusses the potential of L-band Advanced Land Observing Satellite-2 (ALOS-2) and C-band Sentinel-1 radar data for retrieving soil parameters over cereal fields. For this purpose, multi-incidence, multi-polarization and dual-frequency satellite data were acquired simultaneously with in situ measurements collected over a semiarid area, the Merguellil Plain (central Tunisia). The L- and C-band signal sensitivity to soil roughness, moisture and vegetation was investigated. High correlation coefficients were observed between the radar signals and soil roughness values for all processed multi-configurations of ALOS-2 and Sentinel-1 data. The sensitivity of SAR (Synthetic Aperture Radar) data to soil moisture was investigated for three classes of the normalized difference vegetation index (NDVI) (low vegetation cover, medium cover and dense cover), illustrating a decreasing sensitivity with increasing NDVI values. The highest sensitivity to soil moisture under the dense cover class is observed in L-band data. For various vegetation properties (leaf area index (LAI), height of vegetation cover (H) and vegetation water content (VWC)), a strong correlation is observed with the ALOS-2 radar signals (in HH(Horizontal-Horizontal) and HV(Horizontal-Vertical) polarizations). Different empirical models that link radar signals (in the L- and C-bands) to soil moisture and roughness parameters, as well as the semi-empirical Dubois modified model (Dubois-B) and the modified integral equation model (IEM-B), over bare soils are proposed for all polarizations. The results reveal that IEM-B performed a better accuracy comparing to Dubois-B. This analysis is also proposed for covered surfaces using different options provided by the water cloud model (WCM) (with and without the soil–vegetation interaction scattering term) coupled with the best accuracy bare soil backscattering models: IEM-B for co-polarization and empirical models for the entire dataset. Based on the validated backscattering models, different options of coupled models are tested for soil moisture inversion. The integration of a soil–vegetation interaction component in the WCM illustrates a considerable contribution to soil moisture precision in the HV polarization mode in the L-band frequency and a neglected effect on C-band data inversion.
Highlights
IntroductionSoil moisture is a key parameter for various processes in the continental water cycle
Soil moisture is a key parameter for various processes in the continental water cycle.It plays an essential role in the separation of precipitation between runoff, infiltration and evaporation from surfaces
This work aims to contribute to a better understanding of the potential of L-band compared to Cband radar data in terms of sensitivities to soil and vegetation properties, as well as their inversion for retrieving soil moisture
Summary
Soil moisture is a key parameter for various processes in the continental water cycle. It plays an essential role in the separation of precipitation between runoff, infiltration and evaporation from surfaces. It is a key parameter in water resource management, especially in the estimation of irrigation needs and monitoring of flood events [1,2,3,4,5,6]. Over the last thirty years, remote sensing has shown a high potential to retrieve soil moisture. In the L-band, satellites such as Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP)
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