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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 R2 values of 0.74 and 0.70, respectively. It is relevant to note that the backscattering coefficient of the vertical-vertical (VV) and vertical-horizontal (VH) polarizations emerged as the variable with the greatest contribution to the models. This finding reinforces the importance of these parameters in the accuracy of estimates. Ultimately, this approach is promising for the creation of time series of NDVI and NDWI in regions that are frequently affected by cloud cover, thus representing a valuable complement to optical sensor data. This integration is particularly valuable for monitoring agricultural crops.
Castro et al. (Tue,) studied this question.