Abstract
Surface soil moisture is an essential parameter for the hydrological modelling of the watershed. Field techniques adopted for soil moisture measurements are laborious and time-consuming. In a watershed, the soil moisture varies both spatially and temporally. Making continuous on-field observations simultaneously at several locations is practically impossible for large watersheds. Soil moisture estimation using remote sensing techniques is considered a viable alternative. In this study, it is proposed to apply a modified water cloud model for surface soil moisture (SSM) retrieval over partially vegetated regions. This method combines the microwave data obtained from Sentinel-I and optical data obtained from Landsat Operational Land Image (OLI) for SSM estimation. It is necessary to remove the effect of vegetation moisture content for better SSM estimation. Therefore, the index derived from the Landsat OLI spectral bands is applied to build a model for the vegetation water content estimation. A modified water cloud model (MWCM) is developed by integrating the vegetation index with the original water cloud model. In this study, the backscatter coefficients measured using the C-band (5.405 GHz) synthetic aperture radar (SAR) sensor onboard the Sentinel-IA satellite has been used. The developed MWCM has been used to prepare multi-temporal SSM maps.
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Krishnankutty, A., Sathish Kumar, D. (2022). Surface Soil Moisture Retrieval Over Partially Vegetated Areas from the Remote Sensing Data Using a Modified Water Cloud Model. In: Dikshit, A.K., Narasimhan, B., Kumar, B., Patel, A.K. (eds) Innovative Trends in Hydrological and Environmental Systems. Lecture Notes in Civil Engineering, vol 234. Springer, Singapore. https://doi.org/10.1007/978-981-19-0304-5_39
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DOI: https://doi.org/10.1007/978-981-19-0304-5_39
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