Assessing and improving the high uncertainty of global gross primary productivity products based on deep learning under extreme climatic conditions.

Gross Primary Productivity (GPP) is a crucial indicator of the carbon fixed by plants through photosynthesis, playing a vital role in understanding and managing ecological and environmental processes. However, global warming, characterized by elevated temperatures, water shortage, and increased drought stress, has significantly impacted GPP. Various GPP products based on different algorithms and input data have been developed, but their performance under extreme climatic conditions remains unverified. This study evaluated the consistency and accuracy of eight global GPP products from 2003 to 2014 using flux towers data. The results show that GPP products performed well under overall conditions, with an average correlation coefficient (R) of 0.604, and Penman-Monteith-Leuning-version-2 (PMLv2) showed the best performance (R = 0.664). However, under extreme climatic conditions like high temperature, high vapor pressure deficit (VPD), and drought, the accuracy significantly dropped (R = 0.3), with Global-dataset-of-solar-induced-chlorophyll-fluorescence (GOSIF) being the most affected. Accuracy was lower in croplands (CRO) and grasslands (GRA). To enhance accuracy under extreme climatic conditions, GPP products were used as inputs to a Convolutional Neural Network (CNN) based on ECMWF-Reanalysis-5th-Generation (ERA5) meteorological data and compared with random forests (RF). Four GPP products significantly contributed to the model, with a cumulative contribution of 80.3 %. Under extreme climatic conditions, CNN significantly improved the estimation accuracy of GPP and outperformed RF. The optimal values for R and the root mean square error (RMSE) were 0.905 (increase by at least 201.7 %) and 7.708 gC m 8d (decrease by at least 50.7 %). The model also performed well at 20 independent validation sites (R = 0.783). This study offers a method to improve GPP estimation under extreme climatic conditions, unrestricted by time and space.