A Data-Driven Method for Direct Estimation of Global 8-Day 500-m Ecosystem Water Use Efficiency
文献类型：期刊论文
| 作者 | Huang, Lingxiao1,5; Sun, Yifei1,5; Yao, Na2; Liu, Meng3,4 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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| 出版日期 | 2024-12-01 |
| 卷号 | 62页码:4417310 |
| 关键词 | MODIS Predictive models Land surface Training Poles and towers Forests Water Remote sensing Maximum likelihood estimation Grasslands Ecosystem water use efficiency (WUE) evapotranspiration (ET) gross primary production (GPP) machine learning (ML) remote sensing (RS) |
| DOI | 10.1109/TGRS.2024.3501411 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Accurately quantifying ecosystem water use efficiency (WUE) is essential for advancing our understanding of carbon and water exchanges between the land surface and atmosphere. Routinely, WUE is estimated by first predicting gross primary production (GPP) and evapotranspiration (ET) and then calculating WUE as the ratio of GPP to ET. However, this approach can lead to amplified errors in WUE estimates due to uncertainties in GPP and ET predictions. Here, we proposed a novel random forest (RF)-based WUE estimation model, referred to as the DRF model, which directly predicts WUE as the targeted variable to improve WUE estimation. The DRF model was trained using a combination of remote sensing (RS), meteorological reanalysis, and digital elevation model (DEM) datasets, along with in situ WUE observations at 261 global flux tower sites from the FLUXNET2015 and AmeriFlux FLUXNET datasets. Moreover, the DRF model was intercompared with the routine WUE estimation method using the RF model (the IRF model) as well as the widely used Moderate-Resolution Imaging Spectroradiometer (MODIS) and Penman-Monteith-Leuning version 2 (PMLv2) products in WUE estimation. Our results demonstrated that the DRF model well-reproduced 8-day in situ WUE, with the root-mean-square error (RMSE) of 1.07 g C kg(-1) H2O, the coefficient of determination ( R-2 ) of 0.59, and the mean bias error (Bias) of 0.00 g C kg(-1) H2O, and showed significant improvement over the IRF model with the RMSE of 1.20 g C kg(-1) H2O, R-2 of 0.50, and Bias of -0.09 g C kg(-1) H2O. Moreover, the DRF model considerably outperformed the MODIS product (RMSE =1.93 g C kg(-1) H2O, R-2=0.01 , and Bias =-0.49 g C kg(-1) H2O) and the PMLv2 product (RMSE =1.70 g C kg(-1) H2O, R-2=0.22 , and Bias =0.25 g C kg-1 H2O). Finally, the DRF model better captured seasonal fluctuations of in situ WUE than the other three models/products. Our study indicates that the DRF model is a promising alternative to routine WUE estimates in future studies. |
| WOS关键词 | EVAPOTRANSPIRATION ; MODIS |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001377336000029 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210499]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Liu, Meng |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Minist Agr & Rural Affairs, Acad Agr Planning & Engn, Inst Rural Dev & Construct, Beijing 100125, Peoples R China 3.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China 4.Univ Strasbourg, ICube Lab, UMR 7357, CNRS, F-67412 Strasbourg, France 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Lingxiao,Sun, Yifei,Yao, Na,et al. A Data-Driven Method for Direct Estimation of Global 8-Day 500-m Ecosystem Water Use Efficiency[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:4417310. |
| APA | Huang, Lingxiao,Sun, Yifei,Yao, Na,&Liu, Meng.(2024).A Data-Driven Method for Direct Estimation of Global 8-Day 500-m Ecosystem Water Use Efficiency.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,4417310. |
| MLA | Huang, Lingxiao,et al."A Data-Driven Method for Direct Estimation of Global 8-Day 500-m Ecosystem Water Use Efficiency".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):4417310. |
入库方式： OAI收割
来源：地理科学与资源研究所
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