The normalized difference vegetation index (NDVI), introduced in the 1970s, has powerful applications in land management, food security, and physical models. Acquiring NDVI in both high spatial and temporal resolutions is preferable for these applications. however, the temporal and spatial resolution has a trade-off, making it difficult to use satellite images. To solve this issue, a lot of researchers proposed different architectures of convolutional neural networks (CNN) capable of estimating high-resolution NDVI. One of these studies, titled ‘Downscaling of MODIS NDVI by Using a Convolutional Neural Network-Based Model with Higher Resolution SAR Data’, proposed to estimate 10-m high-resolution NDVI from MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m resolution NDVI by using Sentinel-1 10-m resolution synthetic aperture radar (SAR) data. The best model MSE was equal to 0.0362 on validation datasets and 0.0348 on test datasets.