Remote Sensing-Based Mapping of Senescent Leaf C:N Ratio in the Sundarbans Reserved Forest Using Machine Learning Techniques

Research demonstrates that remote sensing data, specifically Landsat TM and Landsat 8 imagery, can accurately map the carbon to nitrogen (C:N) ratio of senescent leaves in mangroves, including the Sundarbans Reserved Forest. The study compared four machine learning models—Stochastic Gradient Boosting, Random Forest, Support Vector Machine, and Partial Least Square Regression—and found that SGB, RF, and SVM outperformed PLSR with statistically significant accuracy (p < 0.05). This breakthrough enables the spatial and temporal monitoring of litter quality, a critical factor in soil decomposition and biogeochemical cycles. The findings highlight the potential for integrating remote sensing techniques into ecosystem modeling to better understand mangrove dynamics.

Rahman, M. M., Zhang, X., Ahmed, I., Iqbal, Z., Zeraatpisheh, M., Kanzaki, M., & Xu, M. (2020). Remote Sensing-Based Mapping of Senescent Leaf C:N Ratio in the Sundarbans Reserved Forest Using Machine Learning Techniques. Remote Sensing, 12(9), 1375. https://doi.org/10.3390/rs12091375