The normalized difference vegetation index (NDVI) is essential for monitoring urban green space, forest cover, and crop growth from sowing to harvesting. High-resolution images from Sentinel-2 and Landsat-8 satellites are available nowadays to get field-level phenological information at short intervals. However, cloud cover is one of the biggest hindrances in vegetation monitoring using NDVI, resulting in data gaps. To address this issue, we propose an NDVI gap-filling methodology to generate cloud-free NDVI time series from Sentinel-1 SAR data using the pix2pix generative adversarial network (GAN) model. Pix2pix GANs with generators based on U-Net and ResNet were designed, and the performance of the models was compared for both the VV and VH polarizations. The generalization capability of the models was studied using synthetic aperture radar (SAR) and NDVI image pairs with different vegetation, field sizes, and shapes. Experimental results show that the generated synthetic NDVI images can effectively substitute the cloudy images for gap-filling. Compared to ResNet, the U-Net generator has given the best results with PSNR = 29.447 dB, RMSE = 0.055, and Pearson correlation coefficient ρ = + 0.909 for VH polarization.