Digital Mapping of Soil Organic Carbon Using Machine Learning Algorithms in the Upper Brahmaputra Valley of Northeastern India

Amit Kumar (), Pravash Chandra Moharana, Roomesh Kumar Jena, Sandeep Kumar Malyan, Gulshan Kumar Sharma, Ram Kishor Fagodiya, Aftab Ahmad Shabnam, Dharmendra Kumar Jigyasu, Kasthala Mary Vijaya Kumari and Subramanian Gandhi Doss
Additional contact information
Amit Kumar: Central Muga Eri Research and Training Institute, Lahdoigarh, Jorhat 785700, Assam, India
Pravash Chandra Moharana: ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur 440033, Maharashtra, India
Roomesh Kumar Jena: ICAR-Indian Institute of Water Management, Bhubaneswar 751023, Odisha, India
Sandeep Kumar Malyan: Department of Environmental Studies, Dyal Singh Evening College, University of Delhi, New Delhi 110003, India
Gulshan Kumar Sharma: ICAR-Indian Institute of Soil and Water Conservation, Research Centre, Kota 324002, Rajasthan, India
Ram Kishor Fagodiya: ICAR-Central Soil Salinity Research Institute, Karnal 132001, Haryana, India
Aftab Ahmad Shabnam: Central Muga Eri Research and Training Institute, Lahdoigarh, Jorhat 785700, Assam, India
Dharmendra Kumar Jigyasu: Central Muga Eri Research and Training Institute, Lahdoigarh, Jorhat 785700, Assam, India
Kasthala Mary Vijaya Kumari: Central Muga Eri Research and Training Institute, Lahdoigarh, Jorhat 785700, Assam, India
Subramanian Gandhi Doss: Central Sericultural Research and Training Institute, Mysuru 570008, Karnataka, India

Land, 2023, vol. 12, issue 10, 1-17

Abstract: Soil Organic Carbon (SOC) is a crucial indicator of ecosystem health and soil quality. Machine learning (ML) models that predict soil quality based on environmental parameters are becoming more prevalent. However, studies have yet to examine how well each ML technique performs when predicting and mapping SOC, particularly at high spatial resolutions. Model predictors include topographic variables generated from SRTM DEM; vegetation and soil indices derived from Landsat satellite images predict SOC for the Lakhimpur district of the upper Brahmaputra Valley of Assam, India. Four ML models, Random Forest (RF), Cubist, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), were utilized to predict SOC for the top layer of soil (0–15 cm) at a 30 m resolution. The results showed that the descriptive statistics of the calibration and validation sets were close enough to the total set data and calibration dataset, representing the complete samples. The measured SOC content varied from 0.10 to 1.85%. The RF model’s performance was optimal in the calibration and validation sets (R 2 c = 0.966, RMSE c = 0.159%, R 2 v = 0.418, RMSE v = 0.377%). The SVM model, on the other hand, had the next-lowest accuracy, explaining 47% of the variation (R 2 c = 0.471, RMSEc = 0.293, R 2 v = 0.081, RMSEv = 0.452), while the Cubist model fared the poorest in both the calibration and validation sets. The most-critical variable in the RF model for predicting SOC was elevation, followed by MAT and MRVBF. The essential variables for the Cubist model were slope, TRI, MAT, and Band4. AP and LS were the most-essential factors in the XGBoost and SVM models. The predicted OC ranged from 0.44 to 1.35%, 0.031 to 1.61%, 0.035 to 1.71%, and 0.47 to 1.36% in the RF, Cubist, XGBoost, and SVM models, respectively. Compared with different ML models, RF was optimal (high accuracy and low uncertainty) for predicting SOC in the investigated region. According to the present modeling results, SOC may be determined simply and accurately. In general, the high-resolution maps might be helpful for decision-makers, stakeholders, and applicants in sericultural management practices towards precision sericulture.

Keywords: environmental covariates; predictive mapping; random forest; sericulture soil; digital SOC map (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2073-445X/12/10/1841/pdf (application/pdf)
https://www.mdpi.com/2073-445X/12/10/1841/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:10:p:1841-:d:1248554

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().