Evaluating Machine Learning Models for Soil Salinity Estimation Using Satellite Imagery

Abstract

Salinity is one of the most critical problems for agricultural lands. Soil salinity should be monitored with fast, economical and accurate data and methods. In this study, soil salinity was estimated using remote sensing data and machine learning algorithms, where fıve different methods were used, and the results were compared. As a study area, Alpu, Turkey has been selected. Within the scope of the study, on-site measurements were made in cultivation areas where there are different agricultural products such as beets, wheat, tomatoes, and corn in the district. The results show that machine learning algorithms and Planetscope images successfully determine soil salinity. Future studies will evaluate the methods by taking samples from different product classes and wet/arid lands.