Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods

Abstract

Land use and land cover maps are vital sources of information for many applications. Recently, using high-resolution and open-access satellite images has become a preferred method for mapping land cover, especially over large areas. This study was designed to map the land cover and agricultural fields of a large area using Sentinel-1A synthetic aperture radar (SAR) images. Seven machine-learning methods were employed for image analyses. The Random Forest classifier algorithm outperformed the other machine-learning methods in the training step; thus, we selected it for further use and tuned its parameters. After several image processing steps, we classified the final image into 23 land cover classes and achieved an overall accuracy of 42% for all classes, and 57% for agricultural fields. This research note highlights some characteristics and advantages of using Sentinel-1A images and provides novel methods for nation-wide large-area mapping applications.