Enhancing terrestrial net primary productivity estimation with EXP-CASA: A novel light use efficiency model approach

Abstract

The Light Use Efficiency (LUE) model, epitomized by the Carnegie-Ames-Stanford Approach (CASA) model, is extensively applied in the quantitative estimation and analysis of vegetation Net Primary Productivity (NPP). However, the classic CASA model is marked by significant complexity: the estimation of environmental stress, in particular, necessitates multi-source observation data and model parameters, 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, weakens 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 with RMSE decreasing by 37.5% by using novel functions for estimating the fraction of absorbed photosynthetically active radiation (FPAR) and environmental stress, utilizing long-term observational data from FLUXNET and MODIS surface reflectance data. In a comparative analysis of NPP estimation accuracy, EXP-CASA ($R^2=0.68$, $RMSE=1.1\mathrm{gC}\cdot m^{-2}\cdot d^{-1}$) performs better than the NPP product from GLASS ($R^2=0.61$, $RMSE=1.2\mathrm{gC}\cdot m^{-2}\cdot d^{-1}$). 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 across different seasons, latitudinal zones, ecological types, and temporal sequences. The findings reveal that the EXP-CASA model exhibits strong adaptability to diverse vegetation indices and stability of model parameters over time series. Importantly, EXP-CASA displays superior sensitivity to NPP anomalies at flux sites and more accurately simulates short-term NPP fluctuations than GLASS-NPP and captures periodic trends. By introducing a novel estimation approach that optimizes model construction, the EXP-CASA model remarkably improves the accuracy of NPP estimations, paving the way for global-scale, consistent, and continuous assessment of vegetation NPP. It presents an effective approach for evaluating the saturation effect of vegetation indices and the influence of category independence on NPP estimation.