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

Pavan Kumar Bellam, Murali Krishna Gumma (), Narayanarao Bhogapurapu and Venkata Reddy Keesara
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Pavan Kumar Bellam: Geospatial and Big Data Sciences, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad 502324, India
Murali Krishna Gumma: Geospatial and Big Data Sciences, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad 502324, India
Narayanarao Bhogapurapu: Microwave Remote Sensing Laboratory, University of Massachusetts, Amherst, MA 01003, USA
Venkata Reddy Keesara: Department of Civil Engineering, National Institute of Technology, Warangal 506004, India

Land, 2025, vol. 14, issue 11, 1-12

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.

Keywords: SOC; SAR; Sentinel-1; ALOS-2; machine learning; ensemble model (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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