Estimating above-ground biomass and carbon stock of a date palm (Phoenix dactylifera L.) tree using Sentinel 2 imagery in Afambo district, Northern Ethiopia

Accurate estimation of forest aboveground biomass (AGB) and carbon stocks is crucial for staining forest management and mitigating climate change, supporting REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests and the conservation and enhancement of forest carbon stocks) processes. The main objective of this study was to estimate the aboveground biomass and carbon stock of a date palm tree using Sentinel-2 imagery in Afambo District, Northern Ethiopia. Five variables, two vegetation indices, i.e., IRICI and NDVI, and three biophysical variables, i.e., FVC, FPAR, and LAI, were extracted from Sentinel-2 imagery and used to construct the AGB prediction model. Forest stand parameters, such as DBH and tree height, were collected on July 15, 2022, at 20×20 m within 88 plots and used to determine in situ AGB using an allometric equation. The correlations between the AGB measured in each plot and the variables extracted from the Sentinel-2 images were assessed using Pearson correlation analysis and were used to estimate AGB. A strong correlation was observed between the field AGB and predictor variables, with a coefficient of determntion (R²) ranging from 0.47 to 0.89. Among the overall variables, vegetation indices were strongly correlated (R² = 0.84−0.89) with aboveground biomass. Relatively good correlations (R² = 0.47 −0.57) were recorded between the 2 sentinel-extracted biophysical variables and AGB. The model has a coefficient of determination value of 0.79, and overall, the Sentinel 2 variables performed better in estimating AGB. Integrating field data with remote sensing methods increases the accuracy of estimating forest AGB and carbon stocks.