Spatiotemporal Modeling of Mangrove Carbon Stock Along Pakistan’s Coast Using Multi-Sensor Sentinel and Landsat Data

This study quantifies coastal mangrove carbon stocks and their interannual variability along the Pakistan coastline by developing a multi-sensor fusion framework integrated with a process-based light use efficiency (LUE) modeling approach. To ensure high-cadence monitoring and overcome persistent cloud cover over the Indus Delta, data from multiple satellite sensors including Landsat 8/9 and Sentinel-2 within Google Earth Engine were utilized. Sentinel-2-derived Normalized Difference Vegetation Index (NDVI) data composited for the January–March period was processed to estimate vegetation productivity. Field-based validation of biomass estimates was conducted using 57 georeferenced sampling points, cross-compared with Sentinel-2 data. Mangrove extent was delineated through land use and land cover (LULC) classification into water bodies, mangroves, mudflats, land parcels, and sand surfaces. The LUE model incorporated environmental stress scalars, including temperature, vapor pressure deficit (VPD), salinity, and photosynthetically active radiation (PAR) to estimate gross primary productivity and derive total biomass, which was subsequently converted into carbon stocks. Results indicate a mean carbon stock of 31.95 Mg C ha−1 (equivalent to 117.3 Mg CO2 ha−1), with significant interannual variation (coefficient of variation = 19.8%). A significant decline in carbon stocks was observed in 2021 (−11.11%; 3.56 Mg C ha−1), corresponding to a reduction in NDVI value (0.55 compared to 0.58 in other years). Spatial analysis revealed substantial heterogeneity in carbon distribution (20.51 to 55.93 Mg C ha−1), primarily influenced by localized salinity gradients and water stress conditions. This study mapped mangrove extent, quantified environmental stress, and estimated carbon stocks across Pakistan’s coast from 2020 to 2024, delivering a spatially resolved, multi-year baseline for coastal carbon assessment and ecosystem monitoring in arid tidal environments.