Trait-to-landscape mapping of mangrove leaf organic carbon: a field-calibrated, species-resolved framework integrating Earth observation and machine learning in the western Sundarbans

Mangrove carbon assessments commonly emphasize woody biomass and sediments, whereas species-level variation in leaf carbon traits remains insufficiently quantified at landscape scales. This study has developed a field-calibrated Earth observation–machine-learning (EO-ML) framework to quantify and map area-based leaf organic carbon (LOC a ) across the western Sundarbans, India. Field sampling at 100 georeferenced locations during 2024 represented seven dominant mangrove species. Carbon mass fraction was measured by dry combustion, leaf mass per area was derived from dry mass and projected one-sided leaf area, and LOC a . Landsat-8 spectral indices, canopy and species layers, terrain attributes, and hydroclimatic covariates were integrated through LASSO-based feature selection and comparative ML modeling. Nested five-fold cross-validation identified random forest (RF) and gradient boosting regression (GBR) as the strongest models (R 2 = 0.87 and 0.86, respectively; normalized RMSE = 0.03), substantially outperforming support vector regression (SVR) (R 2 = 0.45). Species-level high-class LOC a ranged from 58.13 g C m −2 in Acanthus ilicifolius to 102.02 g C m −2 in Bruguiera gymnorhiza ; Avicennia officinalis and Aegialitis rotundifolia also exhibited comparatively high values. Spatial predictions revealed species-specific hotspots and marked within-species heterogeneity, while bivariate analysis identified discrete sectors where elevated LOC a coincided with high mangrove richness. Secondary standing-leaf carbon and stoichiometric CO 2 -equivalent products were retained as static, assumption-bound sensitivity representations rather than annual sequestration, total ecosystem carbon, or verified offsets. Research establishes a robust trait-to-landscape approach for species-resolved assessment of mangrove leaf carbon density, providing an important regional baseline for ecological monitoring and future investigations of mangrove carbon dynamics under changing environmental conditions.

Hali tarjima qilinmagan