Fusion of Sentinel-1 and Sentinel-2 Imagery for Land Salinity Mapping: A Case Study in Da'an, Jilin Province

Abstract

The degradation of salinized land in western Jilin Province and its negative impact on the local and global environment have led to increasing interest in salinization restoration. Satellite remote sensing technology may provide up-to-date information on large-scale land salinization, and provide support for land protection and monitoring planning. In this case, the fusion of optical and radar remote sensing data may be precious because most of the land salinization degradation is located in areas with high cloud cover, which limiting the use of optical data. Radar data can “see-through” clouds, but the experience so far shows that it can't distinguish some types of land cover very well. At present, there are many optical and radar data fusion methods, but there is rare information about how to mapping land salinity using data fusion. To evaluate data fusion's potential, we applied classification method to sentinel-1 and sentinel-2 images for mapping the land salinity cover in Da'an City, Western Jilin Province. Based on our research results, we provide a beneficial method to help people involved in salinized land restoration to utilize the potential of big data.