We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Sentinel-2 images for effective mapping of soil salinity in agricultural fields.
- Authors
Al-Gaadi, Khalid A.; Tola, ElKamil; Madugundu, Rangaswamy; Fulleros, Ronnel B.
- Abstract
Salinity is a critical feature for the management of agricultural soil, particularly in arid and semi-arid areas. The present study was conducted to develop an effective soil salinity prediction model using Sentinel-2A (S2) satellite data. Initially, the collected soil samples were analysed for soil salinity (ECe). Subsequently, multiple linear regression analysis was carried out between the obtained ECe values and S2 data, for the prediction of soil salinity models. The relationship between ECe and S2 data, including individual bands, band ratios and spectral indices showed moderate to highly significant correlations (R² = 0.43-0.83). A combination of SWIR-1 band and the simplified brightness index was found to be the most appropriate (R² = 0.65; P < 0.001) for prediction of soil salinity. The results of this study demonstrate the ability to obtain reliable estimates of EC using S2 data.
- Subjects
SOIL salinity; SOIL mapping; MULTIPLE regression analysis; SOIL sampling
- Publication
Current Science (00113891), 2021, Vol 121, Issue 3, p384
- ISSN
0011-3891
- Publication type
Article
- DOI
10.18520/cs/v121/i3/384-390