Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing

Abstract

The recent launch of the Sentinel-1 A and Sentinel-1B synthetic aperture radar (SAR) satellite constellation has provided high-quality SAR data with fine spatial and temporal sampling characterizations (6˜12 revisit days at 10 m spatial resolution). When combined with high-resolution optical remote sensing, this data can potentially be used for high-resolution soil moisture retrieval over vegetated areas. However, the suitability of different vegetation index (VI) types for the parameterization of vegetation water content in SAR vegetation scattering models requires further investigation. In this study, the widely-used physical-based Advanced Integral Equation Model (AIEM) is coupled with the Water Cloud Model (WCM) for the retrieval of field-scale soil moisture. Three different VIs (NDVI, EVI, and LAI) produced by two different satellite sensors (Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat) are selected to examine their impact on the parameterization of vegetation water content, and subsequently, on soil moisture retrieval accuracy. Results indicate that, despite the different sensitivity of estimated surface roughness parameters to various VIs (i.e., this sensitivity is highest when utilizing MODIS EVI and lowest in the LAI-based model), the optimum roughness parameters derived from each VI exhibit no discernible difference. Consequently, the soil moisture retrieval accuracies show no noticeable sensitivity to the choice of a particular VI. Generally, meadow and grassland sites with small differences in VI-derived roughness parameters exhibit good performance in soil moisture estimation. With respect to the relative components in the coupled model, the vegetative contribution to the scattering signal exceeds that of soil at VI about 0.6∼0.8 [-] in NDVI-based models and 0.4∼0.6 [-] in EVI-based models. This study provides insight into the proper selection of vegetation indices during the use of SAR and optical imagery for the retrieval of high-resolution surface soil moisture.