Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
Résumé
Abstract
Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R
2
), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002–2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983–2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.
Auteurs, date et publication :
Auteurs Mingjuan Xie , Xiaofei Ma , Yuangang Wang , Chaofan Li , Haiyang Shi , Xiuliang Yuan , Olaf Hellwich , Chunbo Chen , Wenqiang Zhang , Chen Zhang , Qing Ling , Ruixiang Gao , Yu Zhang , Friday Uchenna Ochege , Amaury Frankl , Philippe De Maeyer , Nina Buchmann , Iris Feigenwinter , Jørgen E. Olesen , Radoslaw Juszczak
Publication : Scientific Data
Date : 2023
Volume : 10
Issue : 1
Pages : 587