Estimating NDVI from SAR Images Using DNN

Abstract

The Normalized Difference Vegetation Index (NDVI) is an important factor to be considered in vegetation tracking and analysis, which can be easily derived from multispectral (MS) images. However, the limitation imposed by the atmospheric conditions makes the calculation of this index difficult. Because of the clouds, only a limited number of multispectral bands can capture the land appropriately. Furthermore, the multispectral sensors are dependent on the sunlight, which makes the acquisition of data more limited. These limitations do not hinder other types of Earth Observation (EO) data, like the scenes captured by the Synthetic Aperture Radar (SAR). However, SAR images cannot be used in NDVI calculation. In this article, we propose a deep learning (DL) based method for NDVI estimation from SAR data. Using a database with corresponding MS and SAR patches, we calculate the NDVI for each sample, then use a convolutional neural network (CNN) for predicting the NDVI of SAR images. This simple method leads to a precision of 70% in NDVI estimation from SAR images.