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Abstract
Abstract Normalized difference vegetation index (NDVI) is one of the parameters of vegetation that can be studied by remote sensing of land surface with Sentinel-2 (S-2) satellite image. The NDVI is a nondimensional index that depicts the difference in plant cover reflectivity between visible and near-infrared light and can be used to measure the density of green on a piece of land. On the other hand, the dual-pol radar vegetation index (DpRVI) is one of the indices studied using multispectral synthetic aperture radar (SAR) images. Researchers have identified that SAR images are highly sensitive to identify the buildup of biomass from leaf vegetative growth to the flowering stage. Vegetation biophysical characteristics such as the leaf area index (LAI), vegetation water content, and biomass are frequently used as essential system parameters in remote sensing data assimilation for agricultural production models. In the current study, we have used LAI as a system parameter. The findings of the study revealed that the optical data (NDVI) showed a high correlation (up to 0.712) with LAI and a low root-mean-square error (0.0296) compared to microwave data with 0.4523 root-mean-square error. The NDVI, LAI, and DpRVI mean values all decreased between 2019 and 2020. While the DpRVI continued to decline between 2020 and 2021, the NDVI and LAI saw an increase over the same period, which was likely caused by an increase in the study area’s average annual rainfall and the cautious stance of the Red Global (RSG) project on sustainability.
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