Estimation of Soil Organic Carbon Content of Grassland in West Songnen Plain Using Machine Learning Algorithms and Sentinel-1/2 Data

Haoming Li, Jingyao Xia, Yadi Yang, Yansu Bo and Xiaoyan Li ()
Additional contact information
Haoming Li: College of Earth Sciences, Jilin University, Changchun 130061, China
Jingyao Xia: College of Earth Sciences, Jilin University, Changchun 130061, China
Yadi Yang: College of Earth Sciences, Jilin University, Changchun 130061, China
Yansu Bo: College of Earth Sciences, Jilin University, Changchun 130061, China
Xiaoyan Li: College of Earth Sciences, Jilin University, Changchun 130061, China

Agriculture, 2025, vol. 15, issue 15, 1-19

Abstract: Based on multi-source data, including synthetic aperture radar (Sentinel-1, S1) and optical satellite images (Sentinel-2, S2), topographic data, and climate data, this study explored the performance and feasibility of different variable combinations in predicting SOC using three machine learning models. We designed the three models based on 244 samples from the study area, using 70% of the samples for the training set and 30% for the testing set. Nine experiments were conducted under three variable scenarios to select the optimal model. We used this optimal model to achieve high-precision predictions of SOC content. Our results indicated that both S1 and S2 data are significant for SOC prediction, and the use of multi-sensor data yielded more accurate results than single-sensor data. The RF model based on the integration of S1, S2, topography, and climate data achieved the highest prediction accuracy. In terms of variable importance, the S2 data exhibited the highest contribution to SOC prediction (31.03%). The SOC contents within the study region varied between 4.16 g/kg and 29.19 g/kg, showing a clear spatial trend of higher concentrations in the east than in the west. Overall, the proposed model showed strong performance in estimating grassland SOC and offered valuable scientific guidance for grassland conservation in the western Songnen Plain.

Keywords: machine learning; Sentinel-1/2; soil organic carbon; grassland; West Songnen Plain (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/15/1640/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/15/1640/ (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:jagris:v:15:y:2025:i:15:p:1640-:d:1712731

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

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