Sentinel-2 Satellite Imagery Application to Monitor Soil Salinity and Calcium Carbonate Contents in Agricultural Fields

作     者：Ahmed M.Zeyada Khalid A.Al-Gaadi ElKamil Tola Rangaswamy Madugundu Ahmed A.Alameen 

作者机构：Department of Agricultural EngineeringCollege of Food and Agriculture SciencesKing Saud UniversityRiyadh11451Saudi Arabia Precision Agriculture Research ChairDeanship of Scientific ResearchKing Saud UniversityRiyadh11451Saudi Arabia 

出 版 物：《Phyton-International Journal of Experimental Botany》 (国际实验植物学杂志（英文）)

年 卷 期：2023年第92卷第5期

页      面：1603-1620页

核心收录：

学科分类：0710[理学-生物学] 09[农学] 0903[农学-农业资源与环境] 090301[农学-土壤学] 

基　　金：Deanship of Scientific Research  King Saud University 

主　　题：Electrical conductivity modeling soil indices remote sensing prediction 

摘      要：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 *** monitor soil salinity and calcium carbonate in the area and stop future degradation of soil resources,easy,non-intrusive,and practical procedures are *** 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_(3)).Geo-referenced soil samples were collected from 72 locations and analyzed in the laboratory for soil EC and CaCO_(3).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 *** various soil indices,used in this study,were calculated from the Sentinel-2 satellite *** prediction was determined using the root mean square error(RMSE)and cross validation was done using coefficient of *** 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^(2)=0.59,p=0.00019 and root mean square error(RMSE=1.32%)and the bare soil index“BSI(Model-2:R^(2)=0.63,p=0.00012 and RMSE=6.42%).Model-1 demonstrated the best model for predicting soil EC,and validation R^(2)and RMSE values of 0.48%and 1.32%,*** regression analysis results for soil CaCO_(3)determination showed linear relationships with data obtained in laboratory and the bare soil index“BSI(Model-3:R^(2)=0.45,p=0.00021 and RMSE=1.29%)and the bare soil index“BSI&Normalized difference salinity index“NDSI(Model-4:R^(2)=0.53,