COMPARATIVE PERFORMANCE OF SENTINEL-2 VEGETATION INDICES FOR ESTIMATING ABOVEGROUND BIOMASS AND CARBON STOCK IN FOREST AREAS

Chaiphat PLYBOUR , Yannawut UTTARUK , Tanutdech ROTJANAKUSOL , Jumpol ITSARAWISUT , Thanakrizt PEEBKHUNTHOD, Chunchai JUNTASEN, Natcha LAOSUWAN, Teerawong LAOSUWAN

DOI: 10.21163/GT_2027.221.13

ABSTRACT: This study evaluated the performance of Sentinel-2 vegetation indices for estimating aboveground biomass (AGB) and carbon stock in the Mahasarakham University Forest area, northeastern Thailand. Field data were collected from six 40 × 40 m sample plots, comprising 1,127 trees representing 61 species. AGB was estimated using allometric equations and converted to carbon stock. Six Sentinel-2 vegetation indices—the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index 2 (MSAVI2), Ratio Vegetation Index (RVI), Difference Vegetation Index (DVI), and Green Ratio Vegetation Index (GRVI)—were transformed into fractional vegetation cover (FC) and related to field-derived AGB using exponential regression models. A consistent comparative framework was applied to determine how vegetation-index selection affects model fit, prediction error, and agreement with field-derived carbon stock. Among the six indices, MSAVI2 showed the strongest relationship with field-derived AGB (R² = 0.839) and estimated a total carbon stock of 1,752.866 tC, closest to the field-derived estimate of 1,807.721 tC, with a difference of 3.03%. In contrast, SAVI produced the lowest prediction error (RMSE = 0.901 t ha⁻¹; RMSE% = 1.339%). These results reveal that the best-performing vegetation index depends on the evaluation criterion, as the index with the strongest model fit does not necessarily produce the lowest prediction error. By directly comparing six Sentinel-2 vegetation indices using the same field dataset and modeling framework, this study provides a consistent basis for vegetation-index selection and supports practical applications of Sentinel-2 for forest AGB and carbon stock assessment.

Keywords: Aboveground biomass; Carbon stock; Sentinel-2; Vegetation indices; Fractional vegetation cover; Remote sensing.

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