ArticleSensors (Basel, Switzerland)2026
Unlocking Roadside Carbon Sequestration Potential: Machine Learning Estimation of AGB in Highway Vegetation Belts Using GF-2 High-Resolution Imagery.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Aboveground biomass (AGB) is a key indicator of vegetation productivity and terrestrial carbon stocks; therefore, robust AGB estimation is critical for assessing ecosystem services and carbon cycle research. Previous studies have largely focused on forest and cropland ecosystems. In contrast, roadside vegetation along highways and other linear transport corridors remains comparatively underexplored despite its potentially important role as a carbon sink. Here, we integrate field-measured AGB samples with GF-2 high-resolution satellite imagery to evaluate the suitability of multiple remote-sensing predictors and machine-learning algorithms for estimating AGB in highway roadside vegetation. Six remote-sensing variables were used as predictors, including four vegetation indices (Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Enhanced Vegetation Index (EVI), and Modified Soil-Adjusted Vegetation Index (MSAVI) and two-band ratios (B342 and B12/34). Five regression models-multiple linear regression (MLR), partial least squares regression (PLSR), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost)-were developed and systematically compared under both single-variable and multi-variable scenarios. Model performance was evaluated using five-fold cross-validation, with the coefficient of determination (R
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