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dc:title "HTML Summary of #11985 \n\nDetecting Soil pH from Open-Source Remote Sensing Data: A Case Study of Angul and Balangir Districts, Odisha State\n\n";
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bibo:abstract "Soil sampling, collection, and analysis are a costly and labor-intensive activity that cannot cover the entire farmlands;\r\nhence, it was conceived to use high-speed open-source platforms like Google Earth Engine in this research to estimate soil\r\ncharacteristics remotely using high-resolution open-source satellite data. The objective of this research was to estimate soil\r\npH from Sentinel-1, Sentinel-2, and Landsat-8 satellite-derived indices; data from Sentinel-1, Sentinel-2, and Landsat-8\r\nsatellite missions were used to generate indices and as proxies in a statistical model to estimate soil pH. Step-wise multiple\r\nregression (SWMR), artificial neural networks (ANN), and random forest (RF) regression were used to develop predictive\r\nmodels for soil pH, SWMR, ANN, and RF regression models. The SWMR greedy method of variable selection was used to\r\nselect the appropriate independent variables that were highly correlated with soil pH. Variables that were retained in the\r\nSWMR are B2, B11, Brightness index, Salinity index 2, Salinity index 5 of Sentinel-2 data; VH/VV index of Sentinel 1 and\r\nTIR1 (thermal infrared band1) Landsat-8 with p-value\\0.05. Among the four statistical models developed, the class-wise\r\nRF model performed better than other models with a cumulative correlation coefficient of 0.87 and RMSE of 0.35. The\r\nbetter performance of class-wise RF models can be attributed to different spectral characteristics of different soil pH\r\ngroups. More than 70% of the soils in Angul and Balangir districts are acidic soils, and therefore, the training of the dataset\r\nwas affected by that leading to misclassification of neutral and alkaline soils hindering the performance of single class\r\nmodels. Our results showed that the spectral bands and indices can be used as proxies to soil pH with individual classes of\r\nacidic, neutral, and alkaline soils. This study has shown the potential in using big data analytics to predict soil pH leading to\r\nthe accurate mapping of soils and help in decision support."^^xsd:string;
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dct:date "2022-02";
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skos:prefLabel "Remote Sensing"@en .
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skos:prefLabel "Soil"@en .
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skos:prefLabel "Soil Science"@en .
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foaf:name "S Dixit"^^xsd:string;
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foaf:familyName "Dhulipala"^^xsd:string;
foaf:givenName "R"^^xsd:string;
foaf:name "R Dhulipala"^^xsd:string;
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foaf:familyName "Gogumalla"^^xsd:string;
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foaf:familyName "Datta"^^xsd:string;
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bibo:issn "0255660X";
foaf:name "Journal of the Indian Society of Remote Sensing (TSI)"^^xsd:string;
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