Reconstructing Optical Imagery from Microwave Data: A Neural Regression-Based Framework

To monitor the vegetation condition during extreme weather events, this article suggests an approach for mimicking vegetation indices, notably the Normalized Difference Vegetation Index (NDVI), using microwave data. The main goal is to generate simulated NDVI images from microwave data to improve temporal availability and fill the data gaps in the time series when optical data is unavailable. A neural network regression model was developed, using SAR data as input features to predict optical data. The network architecture included an input layer, followed by a dense layer with 32 ReLU-activated nodes, and an output layer with a single node. The model was trained using the Adam optimizer and mean squared error (MSE) as the loss function. Initial results show an $R^{2}$ of 0.62. Pearson’s correlation coefficient is $\mathbf{- 0. 7 9}$ and 0.019 for red vs VV and NIR vs VH bands, respectively.