<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Detecting Soil pH from Open Source Remote Sensing Data: A Case Study of Angul and Balangir districts, Odisha State"^^ . "International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) is\r\nimplementing ‘Odisha Bhoochetana’, an agricultural development project in Angul and\r\nBalangir districts in India. Under this project, soil health improvement activity was\r\ninitiated by collecting soil samples from selected villages of the districts. Soil\r\ninformation before sowing helps farmers not only to choose a crop but also in planning\r\ncrop nutritional inputs. Soil sampling, collection, and analysis is a costly and laborintensive\r\nactivity that cannot cover the entire farmlands, hence it was conceived to use\r\nhigh-speed open-source platforms like Google Earth Engine in this research to\r\nestimate soil characteristics remotely using high-resolution open-source satellite data.\r\nThe objective of this research was to estimate soil pH from Sentinel1, Sentinel 2, and\r\nLandsat satellite-derived indices; Data from Sentinel 1, Sentinel 2, and Landsat\r\nsatellite missions were used to generate indices and as proxies in a statistical model to\r\nestimate soil pH. Step-wise multiple regression, Artificial Neural networks (ANN) and\r\nRandom forest (RF) regression, and Class-wise random forest were used to develop\r\npredictive models for soil pH. Step-wise multiple regression, ANN, and RF regression\r\nare single class models while class-wise RF models are an integration of RF-Acidic,\r\nRF-Alkaline, and RF- Neutral models (based on soil pH). The step-wise regression\r\nmodel retained the bands and indices that were highly correlated with soil pH. Spectral\r\nregions that were retained in the step-wise regression are B2, B11, Brightness Index,\r\nSalinity Index 2, Salinity Index 5 of Sentinel 2 data; VH/VV index of Sentinel 1 and\r\nTIR1 (thermal infrared band1) Landsat with p-value <0.001. Amongst the four statistical\r\nmodels developed, the class-wise RF model performed better than other models with a\r\ncumulative R 2 and RMSE of 0.78 and 0.35 respectively. The better performance of class-wise RF models over single class models can be attributed to different spectral\r\ncharacteristics of different soil pH groups. Though neural networks performed better\r\nthan the stepwise multiple regression model, they are limited to a regression while the\r\nrandom forest model was capable of regression and classification. The large tracts of\r\nacidic soils (datasets) in the study area contributed to the training of the model\r\naccordingly leading to neutral and alkaline soils that were misclassified hindering the\r\nsingle class model performance. However, the class-wise RF model was able to\r\naddress this issue with different models for different soil pH classes dramatically\r\nimproving prediction. Our results show that the spectral bands and indices can be used\r\nas proxies to soil pH with individual classes of acidic, neutral, and alkaline soils. This\r\nstudy 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."^^ . "2022-03" . . . "Springer"^^ . . . "Journal of the Indian Society of Remote Sensing (TSI)"^^ . . . "0255660X" . . . . . . . . . . . . . . . . . . . . . . . . . "R K"^^ . "Dhulipala"^^ . "R K Dhulipala"^^ . . "P"^^ . "Choudhari"^^ . "P Choudhari"^^ . . "P"^^ . "Gogumalla"^^ . "P Gogumalla"^^ . . "R"^^ . "Khopade"^^ . "R Khopade"^^ . . "A"^^ . "Datta"^^ . "A Datta"^^ . . "D"^^ . "Dixit"^^ . "D Dixit"^^ . . "S"^^ . "Rupavatharam"^^ . "S Rupavatharam"^^ . . . . . . "Detecting Soil pH from Open Source Remote Sensing Data: A Case Study of Angul and Balangir districts, Odisha State (PDF)"^^ . . . . . "detectingsoil.pdf"^^ . . . "Detecting Soil pH from Open Source Remote Sensing Data: A Case Study of Angul and Balangir districts, Odisha State (Other)"^^ . . . . . . "indexcodes.txt"^^ . . "HTML Summary of #11974 \n\nDetecting Soil pH from Open Source Remote Sensing Data: A Case Study of Angul and Balangir districts, Odisha State\n\n" . "text/html" . . . "GIS Techniques/Remote Sensing"@en . . . "Soil Fertility"@en . . . "Soil Science"@en . .