Improving MODIS Gross Primary Productivity by Bridging Big‐Leaf and Two‐Leaf Light Use Efficiency Models

Abstract

Gross primary productivity (GPP) is an important component of the terrestrial carbon cycle in climate change research. The global GPP product derived using Moderate Resolution Imaging Spectroradiometer (MODIS) data is perhaps the most widely used. Unfortunately, many studies have indicated evident error patterns in the MODIS GPP product. One of the main reasons for this is that the applied big‐leaf (BL) MOD17 model cannot properly handle the variable relative contribution of sunlit and shaded leaves to the total canopy‐level GPP. In this study, we developed a model for correcting the errors in the MODIS GPP product by bridging BL and two‐leaf (TL) light use efficiency (LUE) models (CTL‐MOD17). With the available MODIS GPP product, which considers environmental stress factors, the CTL‐MOD17 model only needs to reuse the two inputs of the leaf area index (LAI) and incoming radiation. The CTL‐MOD17 model was calibrated and validated at 153 global FLUXNET eddy covariance (EC) sites. The results indicate that the modeled GPP obtained with the correction model matches better with the EC GPP than the original MODIS GPP product at different time scales, with an improvement of 0.07 in R2 and a reduction in root‐mean‐square error (RMSE) of 117.08 g C m−2 year−1. The improvements are more significant in the green season when the contribution of shaded leaves is larger. In terms of the global spatial pattern, the obvious underestimation in the regions with high LAI and the overestimation in the low LAI regions of the MODIS GPP product is effectively corrected by the CTL‐MOD17 model. This paper not only bridges the BL and TL LUE models, but also provides a new and simple method to obtain accurate GPP through reusing two inputs used in producing the MODIS GPP product.