Year: 2025
Type: article
Abstract: The determination of soil salinization in the Shirvan Plain, considered the main agricultural zone of Azerbaijan, negatively affects the productivity of agricultural crops. Based on 10 m Sentinel-2 images on Google Earth Engine platforms and by examining SI1, green-red band normalized difference vegetation index (GRNDVI), green normalized difference vegetation index (GNDVI), normalized difference vegetation index (NDVI), and difference vegetation index of the environment (DVI), four remote sensing salinity monitoring index models, S1DI1, S1DI2, S1DI3, and S1DI4, were constructed to extract soil salinity information in the Shirvan Plain in combination with the measured electrical conductivity. The results show that the overall classification accuracy of S1DI1 (SI1-GRNDVI), S1DI2 (SI1-GNDVI), S1DI3 (SI1-NDVI), and S1DI4 (SI1-DVI) models for salinity monitoring are 82.35%, 83.10%, 81.96%, and 79.25%, respectively.
Authors: Sahib Shukurov Khudaverdi, Aygun Ismayilova Azer, Ramil Sadigov Ali, Maya Karimova Javanshir, Turkan Hasanova Allahverdi, Gunel Asgarova Farhad
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDGs: Life on land, Zero hunger
Open Access status: diamond
Funder: Baki Dövlət Universiteti
OpenAlex ID: W4414218059