"11974","11","archive","3170",,,"disk0/00/01/19/74","2022-03-20 09:38:40","2022-03-20 09:38:40","2022-03-20 09:38:40","article",,,"show",,,,"","","","","","","","","","",,,,"Gogumalla","P","","","","","","Female","Gogumalla","P","","","","",,,,,"","",,,,,"","","ICRISAT (Patancheru)","India","Detecting Soil pH from Open Source Remote Sensing Data: A Case Study of Angul and Balangir districts, Odisha State","pub","S100","GRP_RFFS","","public",,,"soil pH, GEE, Sentinel, Landsat-8, ANN, Random forest, Odisha",,"The authors want to acknowledge the grants from the Department of Agriculture, Odisha state
to undertake Bhoochetana project by ICRISAT. We are also grateful to all the participating of
farmers, departmental staff, NGOs and University students of University of Agriculture and
Technology, Odisha.","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 <0.001. Amongst the four statistical
models developed, the class-wise RF model performed better than other models with a
cumulative 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
characteristics of different soil pH groups. Though neural networks performed better
than the stepwise multiple regression model, they are limited to a regression while the
random forest model was capable of regression and classification. The large tracts of
acidic soils (datasets) in the study area contributed to the training of the model
accordingly leading to neutral and alkaline soils that were misclassified hindering the
single class model performance. However, the class-wise RF model was able to
address this issue with different models for different soil pH classes dramatically
improving prediction. Our results show 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.","2022-03","published",,"Journal of the Indian Society of Remote Sensing (TSI)",,,"Springer",,"1-20",,,,,,,,,,,"TRUE",,"0255-660X",,,,,,"",,"https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Detecting+Soil+pH+from+Open+Source+Remote+Sensing+Data%3A+A+Case+Study+of+Angul+and+Balangir+districts%2C+Odisha+State&btnG=","pub",,"","",,,,,,"",,,,,,,"",,,,,"",,,,,"","",,,,,"","",,,,,
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