Semantic segmentation of soil salinity using in-situ EC measurements and deep learning based U-NET architecture

Soil salinity may occur naturally through pedogenetic processes or may result from abiotic factors resulting from human activities such as irrigation with poor quality water, lack of drainage and land development. The infertility of agricultural lands due to soil salinity brings many economic and social problems on a local and global scale. In this study, the salinity problem in the Harran Plain, one of the largest agricultural areas in Turkey, was investigated using deep learning-based U-NET algorithm. Different combinations of Normalized Difference Salinity Index (NDSI), Salinity Index I (SI), Salinity Index II (SII), and Normalized Difference Vegetation Index (NDVI) indices integrated into the RapidEye multispectral image to increase the segmentation accuracy. The most successful result (93.78% overall accuracy) was achieved when the algorithm was trained with 300 iterations and only the SII index was added to the original image. The same images were also segmented by the SVM method, and the U-NET deep learning architecture was able to detect soil salinity about 20-32% more accurately than SVM for three different 5-band test images. This study demonstrates that the delineation and mapping of soil salinity can be done automatically thanks to a deep learning (DL) network with greater accuracy, less time and effort, and without requiring continuous in-situ Electrical Conductivity (EC) measurements and repetitive supervised learning for each new satellite image.

Automated summary

Soil salinity, caused by natural processes or human activities, brings economic and social problems. This study used deep learning-based U-NET algorithm and multispectral images to investigate soil salinity in the Harran Plain, Turkey. The results show that the deep learning architecture achieved higher accuracy in detecting soil salinity compared to the SVM method.