TY  - EJOU
AU  - Zeyada, Ahmed M. 
AU  - Al-Gaadi, Khalid A. 
AU  - Tola, ElKamil 
AU  - Madugundu, Rangaswamy 
AU  - Alameen, Ahmed A. 

TI  - Sentinel-2 Satellite Imagery Application to Monitor Soil Salinity and Calcium Carbonate Contents in Agricultural Fields
T2  - Phyton-International Journal of Experimental Botany

PY  - 2023
VL  - 92
IS  - 5
SN  - 1851-5657

AB  - The estuary tides affect groundwater dynamics; these areas are susceptible to waterlogging and salinity issues. A
study was conducted on two fields with a total area of 60 hectares under a center pivot irrigation system that
works with solar energy and belong to a commercial farm located in Northern Sudan. To monitor soil salinity
and calcium carbonate in the area and stop future degradation of soil resources, easy, non-intrusive, and practical
procedures are required. The objective of this study was to use remote sensing-determined Sentinel-2 satellite
imagery using various soil indices to develop prediction models for the estimation of soil electrical conductivity
(EC) and soil calcium carbonate (CaCO<sub>3</sub>). Geo-referenced soil samples were collected from 72 locations and analyzed in the laboratory for soil EC and CaCO<sub>3</sub>. The electrical conductivity of the soil saturation paste extract was
represented by average values in soil dataset samples from two fields collected from the topsoil layer (0 to 15 cm)
characteristic of the local salinity gradient. The various soil indices, used in this study, were calculated from the
Sentinel-2 satellite imagery. The prediction was determined using the root mean square error (RMSE) and cross
validation was done using coefficient of determination. The results of regression analysis showed linear relationships with significant correlation between the EC analyzed in laboratory and the salinity index-2 “SI2” (Model-1:
R<sup>2</sup> = 0.59, <i>p</i> = 0.00019 and root mean square error (RMSE = 1.32%) and the bare soil index “BSI” (Model-2:
R<sup>2</sup> = 0.63, <i>p</i> = 0.00012 and RMSE = 6.42%). Model-1 demonstrated the best model for predicting soil EC, and
validation R<sup>2</sup> and RMSE values of 0.48% and 1.32%, respectively. The regression analysis results for soil CaCO<sub>3</sub> 
determination showed linear relationships with data obtained in laboratory and the bare soil index “BSI” (Model-
3: R<sup>2</sup> = 0. 45, <i>p</i> = 0.00021 and RMSE = 1.29%) and the bare soil index “BSI” & Normalized difference salinity
index “NDSI” (Model-4: R<sup>2</sup> = 0.53, <i>p</i> = 0.00015 and RMSE = 1.55%). The validation confirmed the Model-3 results
for prediction of soil CaCO<sub>3</sub> with R<sup>2</sup> and RMSE values of 0.478% and 1.29%, respectively. Future soil monitoring
programs might consider the use of remote sensing data for assessing soil salinity and CaCO<sub>3</sub> using soil indices
results generated from satellite image (i.e., Sentinel-2).
KW  - Electrical conductivity; modeling; soil indices; remote sensing; prediction

DO  - 10.32604/phyton.2023.027267