Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Ni Huang¹  Li Wang¹  Zheng Niu¹  Yuelin Zhang¹  Shuai Gao¹ 

[1] State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences and Beijing Normal University, Beijing, China;

关键词: FLUXNET;    net ecosystem exchange (NEE);    random forest (RF);    remote sensing;   

DOI  :  10.1109/JSTARS.2021.3114190

来源: DOAJ

【 摘 要 】

Despite considerable progress in scaling carbon fluxes from eddy covariance sites to globe, significant uncertainties still exist when estimating the global net ecosystem exchange (NEE). In this study, the site-level NEE was estimated from FLUXNET, a global network of eddy covariance towers, using a random forest (RF) model based on remote sensing products and precipitation data. The plant function type (PFT) had the highest relative explanatory power in predicting the global site-level NEE. However, within PFTs, water-related variables (i.e., the total precipitation, remotely sensed evapotranspiration, land surface water index, and the difference between daytime and nighttime land surface temperature) and soil respiration (R_s) were strong predictors of NEE variability. Cross-validation analyses revealed the good performance of RF in predicting the spatiotemporal variability of monthly NEE at 168 global FLUXNET sites, with R² of 0.72 and RMSE of 0.96 g·C·m⁻²·day⁻¹. The performance was also good when predicting across-site (R² = 0.75) and seasonal patterns (R² = 0.92) over the 58 sites with available data being longer than two years and the 12-month value being present for each year. The RF-estimated NEE showed better relationships with the tower-measured NEE than a global NEE product from FLUXCOM across all PFTs. The difference between the RF-estimated NEE and FLUXCOM NEE was likely linked to the different predictor sets, such as those with more water-related variables and R_s. This study indicates the importance of considering the influence of water-related variables and R_s in the estimation of NEE at the global scale.

【 授权许可】

Unknown