Evaluating the CASA model for estimating carbon sequestration in sea buckthorn plantations using multi-temporal...

Sea buckthorn (Hippophae rhamnoides L.)-based ecological engineering is critical for ecological restoration and carbon sequestration enhancement in China's Pisha sandstone region. However, accurate large-scale carbon sequestration quantification remains challenging: complex terrain, limited accessibility, and high spatial heterogeneity impede extensive field surveys. To address this, we developed an integrated framework combining multi-temporal remote sensing inversion and systematic field measurements. Using the Carnegie-Ames-Stanford Approach (CASA) model, we estimated 2013-2023 vegetation net primary productivity (NPP) and derived net ecosystem productivity (NEP) as a proxy for carbon sequestration density. Remote sensing-derived estimates were validated against in situ measurements across plots of varying stand ages (5-10 years) and site types (shady/sunny slopes, gully bottoms). Results showed strong agreement between remote sensing and field-measured carbon storage (R² = 0.79, RMSE = 25.07 tCO₂ ha^-1), confirming the model's reliability for regional carbon sink quantification in these shrublands. Spatially, sea buckthorn stands had higher carbon sequestration density in the study area's eastern versus western portion, with gully-bottom sites showing significantly higher capacity than slope sites (p < 0.05). This study validates the CASA model's effectiveness for sea buckthorn carbon sink monitoring in the fragile Pisha sandstone region, providing a species-specific remote sensing-field integrated pathway for carbon quantification in arid/semi-arid sea buckthorn ecosystems to support China's 'Dual Carbon' strategic goals.