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

OAR@ICRISAT

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Relation http://oar.icrisat.org/11974/
 
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 K
Dixit, D
 
Subject GIS Techniques/Remote Sensing
Soil Fertility
Soil Science
 
Description International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) is
implementing ‘Odisha Bhoochetana’, an agricultural development project in Angul and
Balangir districts in India. Under this project, soil health improvement activity was
initiated by collecting soil samples from selected villages of the districts. Soil
information before sowing helps farmers not only to choose a crop but also in planning
crop nutritional inputs. Soil sampling, collection, and analysis is a costly and laborintensive
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 Sentinel1, Sentinel 2, and
Landsat satellite-derived indices; Data from Sentinel 1, Sentinel 2, and Landsat
satellite missions were used to generate indices and as proxies in a statistical model to
estimate soil pH. Step-wise multiple regression, Artificial Neural networks (ANN) and
Random forest (RF) regression, and Class-wise random forest were used to develop
predictive models for soil pH. Step-wise multiple regression, ANN, and RF regression
are single class models while class-wise RF models are an integration of RF-Acidic,
RF-Alkaline, and RF- Neutral models (based on soil pH). The step-wise regression
model retained the bands and indices that were highly correlated with soil pH. Spectral
regions that were retained in the step-wise regression 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 with p-value
 
Publisher Springer
 
Date 2022-03
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Identifier http://oar.icrisat.org/11974/1/detectingsoil.pdf
Gogumalla, P and Rupavatharam, S and Datta, A and Khopade, R and Choudhari, P and Dhulipala, R K and Dixit, D (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). pp. 1-20. ISSN 0255-660X