Digital Mapping of Soil pH and Electrical Conductivity Using Geostatistics and Machine Learning

Nalabolu Vandana *

Department of Soil Science and Agricultural Chemistry, College of Agriculture, PJTSAU, Rajendranagar, Hyderabad-500030, Telangana, India.

G. Janaki Rama Suresh

Soil and Land Resource Assessment Division, National Remote Sensing Centre, Balanagar, Hyderabad-500037, Telangana, India.

Tarik Mitran

Soil and Land Resource Assessment Division, National Remote Sensing Centre, Balanagar, Hyderabad-500037, Telangana, India.

S. G. Mahadevappa

Department of Agronomy, Agroclimatic Research Centre, ARI, Rajendranagar, Hyderabad-500030, Telangana, India.

*Author to whom correspondence should be addressed.


This study investigates the spatial variability of soil pH and electrical conductivity (EC) in Suryapet district of Southern Telangana Zone through various digital soil mapping approaches. The 202 surface (0-15cm) soil samples were collected and analysed for pH and EC. The analysed data was further divided into calibration set and validation set in the ratio of 75:25. The geostatistical techniques like Ordinary Kriging, Inverse Distance Weighting (IDW) and Regression Kriging and data mining technique like random forest technique were used to predict the spatial distribution of pH and EC (dSm-1) over the study area. The accuracy of these methods was assessed using validation data set by calculating RMSE, ME and R2 values. The results showed that among all the approaches, random forest (RF) technique performed better with lower RMSE, ME and higher R2 values for spatial prediction of soil pH (RMSE=0.014, ME=0.28 and R2=0.81) and EC (RMSE=0.134, ME=0.022 and R2=0.73). The RF predicted maps show that the pH of soils varied from neutral (6.5-7.5) to slightly alkaline (7.5-8.5) and the soils of Suryapet district were considered as non-saline (EC: 0-2 dSm-1). The findings of the current study shows that among digital soil mapping techniques, random forest model can be an effective tool for assessing spatial variability of soil pH and EC for further studies.

Keywords: Ordinary kriging, inverse distance weighting, regression kriging, random forest technique, soil pH and EC

How to Cite

Vandana , N., Suresh, G. J. R., Mitran, T., & Mahadevappa, S. G. (2024). Digital Mapping of Soil pH and Electrical Conductivity Using Geostatistics and Machine Learning . International Journal of Environment and Climate Change, 14(2), 273–286.


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