Affiliation:
1. School of Information Engineering China University of Geosciences Beijing China
2. International Institute for Earth System Science Nanjing University Nanjing China
3. Zhejiang Carbon Neutral Innovation Institute Zhejiang University of Technology Hangzhou China
4. State Key Laboratory of Hydrology‐Water Resources and Hydraulic Engineering College of Hydrology and Water Resources Hohai University Nanjing China
5. State Key Laboratory of Remote Sensing Science Jointly Sponsored By Beijing Normal University and Aerospace Information Research Institute Chinese Academy of Sciences Beijing China
6. School of Geography and Tourism Anhui Normal University Wuhu China
Abstract
AbstractUpscaling flux tower measurements based on machine learning (ML) algorithms is an essential approach for large‐scale net ecosystem CO2 exchange (NEE) estimation, but existing ML upscaling methods face some challenges, particularly in capturing NEE interannual variations (IAVs) that may relate to lagged effects. With the capacity to characterize temporal memory effects, the Long Short‐Term Memory (LSTM) networks are expected to help solve this problem. Here we explored the potential of LSTM for predicting NEE across various ecosystems using flux tower data over 82 sites in North America. The LSTM model with differentiated plant function types (PFTs) demonstrates the capability to explain 79.19% (R2 = 0.79) of the monthly variations in NEE within the testing set, with RMSE and Mean Absolute Error values of 0.89 and 0.57 g C m−2 d−1 respectively (r = 0.89, p < 0.001). Moreover, the LSTM model performed robustly in predicting cross‐site variability, with 67.19% of the sites that can be predicted by both LSTM models with and without distinguished PFTs showing improved predictive ability. Most importantly, the IAV of predicted NEE highly correlated with that in flux observations (r = 0.81, p < 0.001), clearly outperforming that by the random forest model (r = −0.21, p = 0.011). Among all nine PFTs, solar‐induced chlorophyll fluorescence, downward shortwave radiation, and leaf area index are the most important variables for explaining NEE variations, collectively accounting for approximately 54.01% in total. This study highlights the great potential of LSTM for improving carbon flux upscaling with multi‐source remote sensing data.
Funder
National Natural Science Foundation of China
Publisher
American Geophysical Union (AGU)