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Sentinel-2 Images for Effective Mapping of Soil Salinity in Agricultural Fields


Affiliations
1 Precision Agriculture Research Chair, Deanship of Scientific Research, King Saud University, Riyadh 11451, Saudi Arabia
2 Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia
 

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.

Keywords

Agricultural Lands, Multiple Linear Regression, Satellite Data Simplified Brightness Index, Soil Salinity.
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  • Sentinel-2 Images for Effective Mapping of Soil Salinity in Agricultural Fields

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Authors

Khalid A. Al-Gaadi
Precision Agriculture Research Chair, Deanship of Scientific Research, King Saud University, Riyadh 11451, Saudi Arabia
ElKamil Tola
Precision Agriculture Research Chair, Deanship of Scientific Research, King Saud University, Riyadh 11451, Saudi Arabia
Rangaswamy Madugundu
Precision Agriculture Research Chair, Deanship of Scientific Research, King Saud University, Riyadh 11451, Saudi Arabia
Ronnel B. Fulleros
Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia

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.

Keywords


Agricultural Lands, Multiple Linear Regression, Satellite Data Simplified Brightness Index, Soil Salinity.

References





DOI: https://doi.org/10.18520/cs%2Fv121%2Fi3%2F384-390