This study was based on analysis of Sentinel-1 (SAR) data to estimate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) during the period 2019 to 2020 in a region with a range of different land uses. The methodology adopted involved the construction of four regression models: linear regression (LR), support vector machine (SVM), random forest (RF), and artificial neural network (ANN). These models aimed to determine vegetation indices based on Sentinel-1 backscattering data, which were used as independent variables. As dependent variables, the NDVI and NDWI obtained via Sentinel-2 data were used. The implementation of the models included the application of cross-validation with an analysis of performance metrics to identify the most effective model. The results revealed that, based on the post-hoc test, the SVM model presented the best performance in the estimation of NDVI and NDWI, with mean |
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Synthetic aperture radar
Data modeling
Vegetation
Performance modeling
Backscatter
Artificial neural networks
Lawrencium