Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial Ecosystems
The maximum light use efficiency (LUE) (ε<sub>0</sub>) is a key essential parameter of the LUE model, and its accurate estimation is crucial for quantifying gross primary production (GPP) and better understanding the global carbon budget. Currently, a comprehensive understanding of the p...
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MDPI AG
2023-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/16/4002 |
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| author | Yulong Lv Hong Chi Peichen Shi Duan Huang Jialiang Gan Yifan Li Xinyi Gao Yifei Han Cun Chang Jun Wan Feng Ling |
| author_facet | Yulong Lv Hong Chi Peichen Shi Duan Huang Jialiang Gan Yifan Li Xinyi Gao Yifei Han Cun Chang Jun Wan Feng Ling |
| author_sort | Yulong Lv |
| collection | DOAJ |
| description | The maximum light use efficiency (LUE) (ε<sub>0</sub>) is a key essential parameter of the LUE model, and its accurate estimation is crucial for quantifying gross primary production (GPP) and better understanding the global carbon budget. Currently, a comprehensive understanding of the potential of seasonal variations of ε<sub>0</sub> in GPP estimation across different plant functional types (PFTs) is still lacking. In this study, we used a phenology-based strategy for the estimation of ε<sub>0</sub> to find the optimal photosynthetic responses of the parameter in different phenological stages. The start and end of growing season (SOS and EOS) from time series vegetation indices and the camera-derived greenness index were extracted across seven PFT flux sites using the methods of the hybrid generalized additive model (HGAM) and double logistic function (DLF). Optimal extractions of SOS and EOS were evaluated, and the ε<sub>0</sub> was estimated from flux site observations during the optimal phenological stages with the light response equation. Coupled with other obligatory parameters of the LUE model, phenology-based GPP (GPP<sub>phe-based</sub>) was estimated over 21 site-years and compared with vegetation photosynthesis model (VPM)-based GPP (GPP<sub>VPM</sub>) and eddy covariance-measured GPP (GPP<sub>EC</sub>). Generally, GPP<sub>phe-based</sub> basically tracked both the seasonal dynamics and inter-annual variation of GPP<sub>EC</sub> well, especially at forest, cropland, and wetland flux sites. The R<sup>2</sup> between GPP<sub>phe-based</sub> and GPP<sub>EC</sub> was stable between 0.85 and 0.95 in forest ecosystems, between 0.75 and 0.85 in cropland ecosystems, and around 0.9 in wetland ecosystems. Furthermore, we found that GPP<sub>phe-based</sub> was significantly improved compared to GPP<sub>VPM</sub> in cropland, grassland, and wetland ecosystems, implying that phenology-based ε<sub>0</sub> is more appropriate in the GPP estimation of herbaceous plants. In addition, we found that GPP<sub>phe-based</sub> was significantly improved over GPP<sub>VPM</sub> in cropland, grassland, and wetland ecosystems, and the R<sup>2</sup> between GPP<sub>phe-based</sub> and GPP<sub>EC</sub> was improved by up to 0.11 in cropland ecosystems and 0.05 in wetland ecosystems compared to GPP<sub>VPM</sub>, and RMSE was reduced by up to 5.90 and 2.11 g C m<sup>−2</sup> 8 day<sup>−1</sup>, respectively, implying that phenology-based ε<sub>0</sub> in herbaceous plants is more appropriate for GPP estimation. This work highlights the potential of phenology-based ε<sub>0</sub> in understanding the seasonal variation of vegetation photosynthesis and production. |
| first_indexed | 2024-03-10T23:36:21Z |
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| publishDate | 2023-08-01 |
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| spelling | doaj.art-2425d36b7b004a62ae03aee7ee05aa3c2023-11-19T02:53:10ZengMDPI AGRemote Sensing2072-42922023-08-011516400210.3390/rs15164002Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial EcosystemsYulong Lv0Hong Chi1Peichen Shi2Duan Huang3Jialiang Gan4Yifan Li5Xinyi Gao6Yifei Han7Cun Chang8Jun Wan9Feng Ling10School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaKey Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, ChinaKey Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaWuhan Regional Climate Center, Wuhan 430074, ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaThe maximum light use efficiency (LUE) (ε<sub>0</sub>) is a key essential parameter of the LUE model, and its accurate estimation is crucial for quantifying gross primary production (GPP) and better understanding the global carbon budget. Currently, a comprehensive understanding of the potential of seasonal variations of ε<sub>0</sub> in GPP estimation across different plant functional types (PFTs) is still lacking. In this study, we used a phenology-based strategy for the estimation of ε<sub>0</sub> to find the optimal photosynthetic responses of the parameter in different phenological stages. The start and end of growing season (SOS and EOS) from time series vegetation indices and the camera-derived greenness index were extracted across seven PFT flux sites using the methods of the hybrid generalized additive model (HGAM) and double logistic function (DLF). Optimal extractions of SOS and EOS were evaluated, and the ε<sub>0</sub> was estimated from flux site observations during the optimal phenological stages with the light response equation. Coupled with other obligatory parameters of the LUE model, phenology-based GPP (GPP<sub>phe-based</sub>) was estimated over 21 site-years and compared with vegetation photosynthesis model (VPM)-based GPP (GPP<sub>VPM</sub>) and eddy covariance-measured GPP (GPP<sub>EC</sub>). Generally, GPP<sub>phe-based</sub> basically tracked both the seasonal dynamics and inter-annual variation of GPP<sub>EC</sub> well, especially at forest, cropland, and wetland flux sites. The R<sup>2</sup> between GPP<sub>phe-based</sub> and GPP<sub>EC</sub> was stable between 0.85 and 0.95 in forest ecosystems, between 0.75 and 0.85 in cropland ecosystems, and around 0.9 in wetland ecosystems. Furthermore, we found that GPP<sub>phe-based</sub> was significantly improved compared to GPP<sub>VPM</sub> in cropland, grassland, and wetland ecosystems, implying that phenology-based ε<sub>0</sub> is more appropriate in the GPP estimation of herbaceous plants. In addition, we found that GPP<sub>phe-based</sub> was significantly improved over GPP<sub>VPM</sub> in cropland, grassland, and wetland ecosystems, and the R<sup>2</sup> between GPP<sub>phe-based</sub> and GPP<sub>EC</sub> was improved by up to 0.11 in cropland ecosystems and 0.05 in wetland ecosystems compared to GPP<sub>VPM</sub>, and RMSE was reduced by up to 5.90 and 2.11 g C m<sup>−2</sup> 8 day<sup>−1</sup>, respectively, implying that phenology-based ε<sub>0</sub> in herbaceous plants is more appropriate for GPP estimation. This work highlights the potential of phenology-based ε<sub>0</sub> in understanding the seasonal variation of vegetation photosynthesis and production.https://www.mdpi.com/2072-4292/15/16/4002gross primary productionlight use efficiency modelmaximum LUEphenology-basedhybrid generalized additive model |
| spellingShingle | Yulong Lv Hong Chi Peichen Shi Duan Huang Jialiang Gan Yifan Li Xinyi Gao Yifei Han Cun Chang Jun Wan Feng Ling Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial Ecosystems Remote Sensing gross primary production light use efficiency model maximum LUE phenology-based hybrid generalized additive model |
| title | Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial Ecosystems |
| title_full | Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial Ecosystems |
| title_fullStr | Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial Ecosystems |
| title_full_unstemmed | Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial Ecosystems |
| title_short | Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial Ecosystems |
| title_sort | phenology based maximum light use efficiency for modeling gross primary production across typical terrestrial ecosystems |
| topic | gross primary production light use efficiency model maximum LUE phenology-based hybrid generalized additive model |
| url | https://www.mdpi.com/2072-4292/15/16/4002 |
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