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Detecting Soil pH from Open-Source Remote Sensing Data: A Case Study of Angul and Balangir Districts, Odisha State

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Relation http://oar.icrisat.org/11985/
https://doi.org/10.1007/s12524-022-01524-9
doi:10.1007/s12524-022-01524-9
 
Title Detecting Soil pH from Open-Source Remote Sensing Data: A Case Study of Angul and Balangir Districts, Odisha State
 
Creator Gogumalla, P
Rupavatharam, S
Datta, A
Khopade, R
Choudhari, P
Dhulipala, R
Dixit, S
 
Subject Remote Sensing
Soil
Soil Science
 
Description Soil 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.
 
Publisher Springer
 
Date 2022-02
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Identifier http://oar.icrisat.org/11985/1/Gogumalla_et_al-2022-Journal_of_the_Indian_Society_of_Remote_Sensing.pdf
Gogumalla, P and Rupavatharam, S and Datta, A and Khopade, R and Choudhari, P and Dhulipala, R and Dixit, S (2022) Detecting Soil pH from Open-Source Remote Sensing Data: A Case Study of Angul and Balangir Districts, Odisha State. Journal of the Indian Society of Remote Sensing (TSI). ISSN 0255-660X