OAR@ICRISATDetecting Soil pH from Open-Source Remote Sensing Data: A Case Study of Angul and Balangir Districts, Odisha StatePGogumallaauthorSRupavatharamauthorADattaauthorRKhopadeauthorPChoudhariauthorRDhulipalaauthorSDixitauthorSoil sampling, collection, and analysis are a costly and labor-intensive activity that cannot cover the entire farmlands; hence, it was conceived to use high-speed open-source platforms like Google Earth Engine in this research to estimate soil characteristics remotely using high-resolution open-source satellite data. The objective of this research was to estimate soil pH from Sentinel-1, Sentinel-2, and Landsat-8 satellite-derived indices; data from Sentinel-1, Sentinel-2, and Landsat-8 satellite missions were used to generate indices and as proxies in a statistical model to estimate soil pH. Step-wise multiple regression (SWMR), artificial neural networks (ANN), and random forest (RF) regression were used to develop predictive models for soil pH, SWMR, ANN, and RF regression models. The SWMR greedy method of variable selection was used to select the appropriate independent variables that were highly correlated with soil pH. Variables that were retained in the SWMR are B2, B11, Brightness index, Salinity index 2, Salinity index 5 of Sentinel-2 data; VH/VV index of Sentinel 1 and TIR1 (thermal infrared band1) Landsat-8 with p-value\0.05. Among the four statistical models developed, the class-wise RF model performed better than other models with a cumulative correlation coefficient of 0.87 and RMSE of 0.35. The better performance of class-wise RF models can be attributed to different spectral characteristics of different soil pH groups. More than 70% of the soils in Angul and Balangir districts are acidic soils, and therefore, the training of the dataset was affected by that leading to misclassification of neutral and alkaline soils hindering the performance of single class models. Our results showed that the spectral bands and indices can be used as proxies to soil pH with individual classes of acidic, neutral, and alkaline soils. This study has shown the potential in using big data analytics to predict soil pH leading to the accurate mapping of soils and help in decision support.Remote SensingSoilSoil Science2022-02SpringerArticle

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