Soil Organic Carbon Mapping Using Multi-Frequency SAR Data and Machine Learning Algorithms

Bellam, P K and Gumma, M K and Bhogapurapu, N and Keesara, V R (2025) Soil Organic Carbon Mapping Using Multi-Frequency SAR Data and Machine Learning Algorithms. Land (TSI), 14 (11). pp. 1-12. ISSN 2073-445X

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Abstract

Soil organic carbon (SOC) is a critical component of soil health, influencing soil structure, soil water retention capacity, and nutrient cycling while playing a key role in the global carbon cycle. Accurate SOC estimation over croplands is essential for sustainable land management and climate change mitigation. This study explores a novel approach to SOC estimation using multi-frequency synthetic aperture radar (SAR) data, specifically Sentinel-1 and ALOS-2/PALSAR-2 imagery, combined with advanced machine learning techniques for cropland SOC estimation. Diverse agricultural practices, with major crop types such as rice (Oryza sativa), finger millet (Eleusine coracana), Niger (Guizotia abyssinica), maize (Zea mays), and vegetable cultivation, characterize the study region. By integrating C-band (Sentinel-1) and L-band (ALOS-2/PALSAR-2) SAR data with key polarimetric features such as the C2 matrix, entropy, and degree of polarization, this study enhances SOC estimation. These parameters help distinguish variations in soil moisture, texture, and mineral composition, reducing their confounding effects on SOC estimation. An ensemble model incorporating Random Forest (RF) and neural networks (NNs) was developed to capture the complex relationships between SAR data and SOC. The NN component effectively models complex non-linear relationships, while the RF model helps prevent overfitting. The proposed model achieved a correlation coefficient (r) of 0.64 and a root mean square error (RMSE) of 0.18, demonstrating its predictive capability. In summary, our results offer an efficient approach for enhanced SOC mapping in diverse agricultural landscapes, with ongoing work targeting challenges in data availability to facilitate large-scale SOC mapping.

Item Type:	Article
Divisions:	Global Research Program - Resilient Farm and Food Systems
CRP:	UNSPECIFIED
Uncontrolled Keywords:	SOC, SAR, Sentinel-1, ALOS-2, machine learning, ensemble model
Subjects:	Others > GIS Techniques/Remote Sensing Others > Soil
Depositing User:	Mr Nagaraju T
Date Deposited:	10 Mar 2026 03:27
Last Modified:	10 Mar 2026 03:27
URI:	http://oar.icrisat.org/id/eprint/13528
Official URL:	https://www.mdpi.com/2073-445X/14/11/2105
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	The authors are grateful to CRAL, ICRISAT Patanchervu, for providing soil sample data. We would like to thank NITW, ICRISAT, for providing research facilities.
Links:	Google Scholar

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