<![CDATA[Climate change increases the importance of aboveground biomass information as an indicator of carbon stocks and a basis for urban vegetation management. This study aims to determine the best model for estimating aboveground biomass using Sentinel-2 imagery-based vegetation indices and to map biomass distribution in Banjarbaru City. The study uses a quantitative approach with remote sensing-based spatial analysis. The study population is all woody vegetation in Banjarbaru City, while the sample consists of 45 plots determined using stratified random sampling. The research instruments include Diameter at Breast Height (DHT), tree height, Global Positioning System (GPS), and Sentinel-2B Level-2A imagery. Data analysis was carried out through biomass calculations using allometric equations, Pearson correlation analysis, linear, exponential, polynomial, and power regressions, regression assumption testing, model validation using SA, SR, RMSE, bias, and chi-square, and spatial mapping. The results showed that the Linear SAVI model was the best model with the equation B = -287.341 + 517.644 SAVI and an R² value of 0.905. In conclusion, the Linear SAVI model is able to provide accurate and representative biomass estimates, while the application of masking improves the quality of biomass mapping, thereby supporting carbon inventory and green open space planning.]]>
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