State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
Department of Geography, Ghent University, Ghent, 9000, Belgium.
Sci Data. 2023 Sep 7;10(1):587. doi: 10.1038/s41597-023-02473-9.
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), 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.
为了准确了解陆地生态系统的碳-水循环，需要基于稀疏且不均匀分布的涡度协方差通量站来模拟更广泛分布的气象站的碳-水通量。我们建立了一个新的框架，包括机器学习、决定系数 (R)、欧几里得距离和遥感 (RS)，以使用随机森林模型或/和 RS 来模拟欧亚气象站的日净生态系统二氧化碳交换 (NEE) 和水通量 (WF)。分别为 3774 个和 4427 个气象站生成了具有 RS 信息的日 NEE 和 WF 数据集（NEE-RS 和 WF-RS），用于 2002-2020 年；以及没有 RS 信息的日 NEE 和 WF 数据集（NEE-WRS 和 WF-WRS），用于 1983-2018 年的 4667 个和 6763 个气象站。对于每个气象站，碳-水通量都满足准确性要求并且具有准观测特性。这四个碳-水通量数据集有很大潜力来改进对生态系统碳-水动力学的评估。
