380 / 2026-03-15 14:55:55

Unveiling the accuracy of global GPP products in data-scarce mountain ecosystems of Southwest China

Gross primary production (GPP) Breathing earth system simulator (BESS) Solar-induced chlorophyll fluorescence (SIF) Moderate resolution imaging spectroradiometer (MODIS) Vegetation photosynthesis model (VPM)

主题1 天气、气候、全球变化、行星大气 > 专题1.1 地球系统模式与模拟

Accurate estimation of terrestrial gross primary production (GPP) in ecologically and topographically complex

regions remains a major challenge. This study evaluated four global GPP products—BESS, GOSIF, MOD17, and

VPM—against eddy covariance (EC) observations from 11 flux towers in Southwest China. The region spans

diverse vegetation types and climatic gradients, making it an ideal testbed for satellite-based GPP assessment.

Site-level evaluation showed that GOSIF had the strongest correlation with GPP-EC (R2 > 0.61) but consistently

overestimated GPP, especially during summer. BESS achieved the lowest RMSE (2.02 g C/m2/d) and better

captured interannual variation, including drought impacts. MOD17 and VPM tended to underestimate summer

peaks, particularly in evergreen broadleaf forests. Seasonal analysis revealed that all four products performed

better in the non-growing season, with BESS showing the highest growing-season accuracy (R2 = 0.39, RMSE =

2.44 g C/m2/d). GOSIF notably overestimated seasonal totals, especially in shrubland and grassland ecosystems.

Spatially, mean annual GPP estimates varied by over 30 % among products, with GOSIF being the highest

(1497.32 g C/m2) and BESS being the lowest (1109.73 g C/m2). The vegetation-type analysis revealed the

highest level of agreement in deciduous broadleaf forests, while the greatest discrepancies were observed in open

shrublands. Our findings highlight the strengths and limitations of current GPP products in a mountainous,

heterogeneous region. Incorporating physiological indicators such as solar-induced chlorophyll fluorescence

(SIF), improving model parameterization of phenology, and accounting for local environmental stressors are

essential for enhancing GPP estimation in Southwest China and similar landscapes.