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标题: A Spatiotemporal Constrained Machine Learning Method for OCO-2 Solar-Induced Chlorophyll Fluorescence (SIF) Reconstruction

作者: Shen, HF (Shen, Huanfeng); Wang, YC (Wang, Yuchen); Guan, XB (Guan, Xiaobin); Huang, WL (Huang, Wenli); Chen, JJ (Chen, Jiajia); Lin, DK (Lin, Dekun); Gan, WX (Gan, Wenxia)

来源出版物: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 卷: 60 文献号: 4413817 DOI: 10.1109/TGRS.2022.3204885 出版年: 2022

摘要: Solar-induced chlorophyll fluorescence (SIF) is an intuitive and accurate way to measure vegetation photosynthesis. Orbiting Carbon Observatory-2 (OCO-2)-retrieved SIF has shown great potential in estimating terrestrial gross primary production (GPP), but the discontinuous spatial coverage limits its application. Although some researchers have reconstructed OCO-2 SIF data, few have considered the uneven spatial and temporal distribution of the swath-distributed data, which can induce large uncertainties. In this article, we propose a spatiotemporal constrained light gradient boosting machine model (ST-LGBM) to reconstruct a contiguous OCO-2 SIF product (eight days, 0.05 degrees), considering the data distribution characteristics. Two spatial and temporal constraining factors are introduced to utilize the relationships between the swath-distributed OCO-2 samples, combining the geographical regularity and vegetation phenological characteristics. The results indicate that the ST-LGBM method can improve the reconstruction accuracy in the missing data areas ( R-2=0.79 ), with an increment of 0.05 in R-2 . The declined accuracy of the traditional light gradient boosting machine (LightGBM) method in the missing data areas is well alleviated in our results. The real-data comparison with TROPOspheric Monitoring Instrument (TROPOMI) SIF observations also shows that the results of the ST-LGBM method can achieve a much better consistency, in both spatial distribution and temporal variation. The sensitivity analysis also shows that the ST-LGBM can support stable results when using various input combinations or different machine learning models. This approach represents an innovative way to reconstruct a more accurate globally continuous OCO-2 SIF product and also provides references to reconstruct other data with a similar distribution.

作者关键词: Machine learning; Orbiting Carbon Observatory-2 (OCO-2); solar-induced chlorophyll fluorescence (SIF); spatiotemporal constraint

地址: [Shen, Huanfeng; Wang, Yuchen; Guan, Xiaobin; Huang, Wenli; Chen, Jiajia; Lin, Dekun] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Shen, Huanfeng] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China.

[Gan, Wenxia] Wuhan Inst Technol, Sch Civil Engn & Architecture, Wuhan 430205, Peoples R China.

通讯作者地址: Guan, XB (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: shenhf@whu.edu.cn; wangyuchencyh1@whu.edu.cn; guanxb@whu.edu.cn; wenli.huang@whu.edu.cn; evechen@whu.edu.cn; lindekun@whu.edu.cn; charlottegan@whu.edu.cn

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