Simulation of NDVI Imagery Using Generative Adversarial Network and Sentinel-1 C-SAR Data

The Normalized Difference Vegetation Index (NDVI) is effective for expressing vegetation status and quantified vegetation attributes. However, optical remote sensing imagery is limited by cloud contamination. On the other hand, synthetic aperture radar (SAR) can work under all weather conditions and overcome this disadvantage of optical remote sensing while it is difficult to recognize the land cover types visually due to the mechanisms of SAR imaging and the speckle noise. In this study, the image-to-image translation methods (pix2pix and CycleGAN) were used to convert Sentinel-1 C-SAR images into Sentinel-2 NDVI images. The results show that the combination of CycleGAN and VH polarization data works well during the growing season of beetroots and the simulated NDVI values were significantly correlated with the real NDVI values.