Assessment of Synthetic Aperture Radar (SAR) and Optical Remote Sensing Data in Aboveground Carbon Stock Estimation of Coconut (Cocos nucifera L.) in Butuan City

Evaluating aboveground biomass (AGB) assists foresters and scientists in comprehending and monitoring ecosystem responses and contributions to the global carbon cycle, climate change, and terrestrial carbon accounting.Traditional approaches, such as the destructive method, are the most accurate; however, they are often expensive and inefficient, which favors the remote sensing approach because of its convenience.This study assessed remote sensing datasets, which are Synthetic Aperture Radar (SAR) and Optical Remote Sensing, to gauge the AGB of coconut trees and their carbon storage.Employing Multi-linear Regression with 33 variables led to several AGB models, among which Model 3 stood out for its excellent balance of coefficient of determination (R 2 ) and Root Mean Square Error (RMSE) values.Results estimated that the aboveground carbon stock of coconut trees is 13 megagrams of carbon per hectare (MgC/ha) with an RMSE of 2.345 MgC/ha.These findings' efficiency and accuracy demonstrate that integrating multiple satellite sensors creates more reliable AGB and carbon stock estimations.