  The Light Use Efficiency model, epitomized by the CASA model, is extensively
applied in the quantitative estimation of vegetation Net Primary Productivity.
However, the classic CASA model is marked by significant complexity: the
estimation of environmental stress parameters, in particular, necessitates
multi-source observation data, adding to the complexity and uncertainty of the
model's operation. Additionally, the saturation effect of the Normalized
Difference Vegetation Index (NDVI), a key variable in the CASA model, weakened
the accuracy of CASA's NPP predictions in densely vegetated areas. To address
these limitations, this study introduces the Exponential-CASA (EXP-CASA) model.
The EXP-CASA model effectively improves the CASA model by using novel functions
for estimating the fraction of absorbed photosynthetically active radiation
(FPAR) and environmental stress, by utilizing long-term observational data from
FLUXNET and MODIS surface reflectance data. In a comparative analysis of NPP
estimation accuracy among four different NPP products, EXP-CASA ($R^2 = 0.68,
RMSE= 1.1gC\cdot m^{-2} \cdot d^{-1}$) outperforms others, followed by
GLASS-NPP, and lastly MODIS-NPP and classic CASA. Additionally, this research
assesses the EXP-CASA model's adaptability to various vegetation indices,
evaluates the sensitivity and stability of its parameters over time, and
compares its accuracy against other leading NPP estimation products. The
findings reveal that the EXP-CASA model exhibits strong adaptability to diverse
vegetation indices and stability of model parameters over time series. By
introducing a novel estimation approach that optimizes model construction, the
EXP-CASA model remarkably improves the accuracy of NPP estimations and paves
the way for global-scale, consistent, and continuous assessment of vegetation
NPP.
