Study of water cloud model vegetation descriptors in estimating soil moisture in Solani catchment
作者： Kamal Kumar , Hari Prasad Suryanarayana Rao , M. K. Arora
DOI: 10.1002/HYP.10344
关键词:
摘要: Water cloud model (WCM) relates the backscatter coefficient (σo) with soil moisture. The includes due to vegetation (σoveg), and (σosoil). σoveg of WCM depends upon characteristics. present study is aimed investigate effect different descriptors in estimating moisture from WCM. carried out Solani River catchment India. Envisat Advanced Synthetic Aperture Radar (ASAR) images three dates were acquired for study. field data, volumetric upper 0–10 cm layer, texture, surface roughness, leaf area index (LAI), water index, normalized plant content average height corresponding satellite pass collected. Genetic algorithm optimization technique used estimate parameters. use LAI as descriptor results minimum root mean square error (RMSE) 1.77 dB between computed ASAR observed backscatter. Also, least RMSE 4.19%, estimated first campaign, whereas it was 5.64% last campaign which undertaken after 35 days campaign. It concluded that can be treated best studies retrieving microwave remote sensing data. Copyright © 2014 John Wiley & Sons, Ltd.
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