Unlocking carbon sequestration in mango orchards with machine learning
A data-driven approach using sentinel-2 imagery
image: Machine learning technique for carbon sequestration estimation of mango orchards area using Sentinel-2 Data
Credit: Gitsada Panumonwatee, Sittichai Choosumrong*, Savent Pampasit, Rudklow Premprasit, Tatsuya Nemoto and Venkatesh Raghavan
The study, titled "Machine Learning Technique for Carbon Sequestration Estimation of Mango Orchards Area Using Sentinel-2 Data," is led by Prof. Sittichai Choosumrong from the Department of Natural Resources and Environment at Naresuan University in Phitsanulok Province, Thailand, and Prof. Tatsuya Nemoto from the Graduate School of Science at Osaka Metropolitan University in Osaka, Japan. This research offers a detailed analysis of how machine learning can enhance our understanding of carbon sequestration in agricultural landscapes.
Accurate estimation of carbon sequestration in agricultural areas is crucial for understanding their role in climate change mitigation. Professors Sittichai Choosumrong and Tatsuya Nemoto are at the forefront of this research, leveraging Sentinel-2 data and machine learning algorithms to provide precise estimates of carbon sequestration in mango orchards. Their work highlights the potential of combining remote sensing with advanced analytics to address environmental challenges.
Imagine a study that uses cutting-edge machine learning techniques to analyze high-resolution satellite imagery for environmental insights. This is precisely what Professors Sittichai Choosumrong and Tatsuya Nemoto have achieved. By applying the Random Forest algorithm to Sentinel-2 data, their team has developed a robust method for estimating carbon sequestration in mango orchards. Their approach not only provides accurate data but also offers a scalable solution for monitoring carbon sequestration in agricultural areas globally.
This pioneering research conducted at Naresuan University and Osaka Metropolitan University reveals several key insights:
- Accurate Carbon Sequestration Estimates: The study demonstrates that machine learning techniques can provide highly accurate estimates of carbon sequestration in mango orchards.
- Remote Sensing Applications: The research highlights the effectiveness of using Sentinel-2 data for detailed environmental monitoring, offering a powerful tool for researchers and policymakers.
- Scalability and Practicality: The findings suggest that this method can be applied to other agricultural areas, providing a practical solution for large-scale carbon sequestration estimation.
Looking ahead, Professors Sittichai Choosumrong and Tatsuya Nemoto plan to further explore the long-term applications of this method in various agricultural settings. Their work promises to provide valuable insights for environmental scientists and policymakers seeking to enhance carbon sequestration efforts.
By providing a comprehensive analysis of machine learning techniques applied to Sentinel-2 data, Professors Sittichai Choosumrong and Tatsuya Nemoto are contributing to global efforts to monitor and manage carbon sequestration. Their work underscores the importance of advanced analytics in addressing environmental challenges.
Stay tuned for more updates on this pioneering research from Naresuan University in Phitsanulok Province, Thailand, and Osaka Metropolitan University in Osaka, Japan. Professors Sittichai Choosumrong and Tatsuya Nemoto and their team are leading the way in exploring innovative solutions for environmental monitoring. Their work is a testament to the power of scientific inquiry and the potential of machine learning to drive progress in environmental management. Together, we can develop effective strategies to enhance carbon sequestration and promote sustainable practices.
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Citation: Panumonwatee, G., Choosumrong, S., Pampasit, S. et al. Machine learning technique for carbon sequestration estimation of mango orchards area using Sentinel-2 Data. Carbon Res. 4, 33 (2025). https://doi.org/10.1007/s44246-025-00201-z
Title: Machine learning technique for carbon sequestration estimation of mango orchards area using Sentinel-2 Data
Keywords: Carbon sequestration; Random Forest; Remote sensing; Vegetation indices
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Media Contact:
Wushuang Li
liwushuang@syau.edu.cn
About Carbon Research
The journal Carbon Research is an international multidisciplinary platform for communicating advances in fundamental and applied research on natural and engineered carbonaceous materials that are associated with ecological and environmental functions, energy generation, and global change. It is a fully Open Access (OA) journal and the Article Publishing Charges (APC) are waived until Dec 31, 2025. It is dedicated to serving as an innovative, efficient and professional platform for researchers in the field of carbon functions around the world to deliver findings from this rapidly expanding field of science. The journal is currently indexed by Scopus and Ei Compendex, and as of June 2025, the dynamic CiteScore value is 15.4.