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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #357157

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

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

item QUI, J. - Sun Yat-Sen University
item WAGNER, W. - Vienna University Of Technology
item ZHAO, T. - Chinese Academy Of Sciences
item Crow, Wade

Submitted to: International Journal of Applied Earth Observation and Geoinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/13/2019
Publication Date: 4/15/2019
Citation: Qui, J., Wagner, W., Zhao, T., Crow, W.T. 2019. Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing. International Journal of Applied Earth Observation and Geoinformation. 80:47-57.

Interpretive Summary: High-resolution (<100-m) estimates of surface soil moisture benefit a wide range of agricultural applications including: irrigation scheduling, yield forecasting and fertilizer application. Such estimates are potentially available from satellite-based synthetic aperture radar (SAR) sensors. However, isolating the SAR signal originating from the soil surface (from the canopy backscatter component) requires access to a reliable estimate of above-ground vegetation biomass. This paper compares several different methods for obtaining such estimates and discusses the impact of each method on the quality of resulting satellite-based soil moisture estimates. It provides specific advice on the optimal approach for inverting SAR measurements into reliable soil moisture estimates within agricultural landscapes. Results from this paper will eventually be used to improve the reliability of high-resolution soil moisture products generated from satellite SAR observations for agricultural monitoring.

Technical Abstract: The recent launch of the Sentinel-1A 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 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) and produced by two different satellite sensors (Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat) are selected to examine their impact on the parameterization of vegetation opacity, and subsequently, 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 discernable difference, except for forested sites (likely due to their complex canopy geometry). Consequently, the soil moisture retrieval accuracies show no noticeable sensitivity to the choice of a particular VI. Generally, meadow and grassland sites with little 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 the soil at about 0.7 [-] in NDVI-based models, and 0.5 [-] 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.