Remote sensing of young leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forests
<p>Determining the large-scale RuBisCO carboxylation maximum rate (<span class="inline-formula"><i>V</i><sub>c,max25</sub></span>) in relation to leaf age is essential for evaluating the photosynthetic capacity of canopy leaves in global forests. Y...
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Copernicus Publications
2025-07-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/3293/2025/essd-17-3293-2025.pdf |
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| author | X. Yang X. Yang X. Yang Q. Sun L. Han J. Tian W. Yuan L. Liu W. Zheng M. Wang Y. Wang Y. Wang X. Chen |
| author_facet | X. Yang X. Yang X. Yang Q. Sun L. Han J. Tian W. Yuan L. Liu W. Zheng M. Wang Y. Wang Y. Wang X. Chen |
| author_sort | X. Yang |
| collection | DOAJ |
| description | <p>Determining the large-scale RuBisCO carboxylation maximum rate (<span class="inline-formula"><i>V</i><sub>c,max25</sub></span>) in relation to leaf age is essential for evaluating the photosynthetic capacity of canopy leaves in global forests. Young leaves (<span class="inline-formula">≤</span> 180 <span class="inline-formula">d</span>), which exhibit higher <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> compared to old leaves (<span class="inline-formula">></span> 180 <span class="inline-formula">d</span>), are key to controlling the seasonality of leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forests (TEFs). Nevertheless, quantifying the leaf photosynthetic capacities of different ages across TEFs remains challenging, especially when considering continuous temporal variations at continental scales. In this study, we propose a novel methodology that leverages neighborhood pixel analysis with nonlinear least-squares optimization to derive the <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> of young leaves at 0.25° spatial resolution. This approach utilizes satellite-based solar-induced chlorophyll fluorescence (SIF) products spanning the period from 2001 to 2018, which were reconstructed using both TROPOMI (Tropospheric Monitoring Instrument) SIF and MODIS reflectance data (RTSIF). Validations against in situ observations demonstrate that the newly developed <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> products accurately capture the seasonality of young leaves in South America and subtropical Asia, with correlation coefficients of 0.84, 0.66, and 0.95, respectively. The <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> of the young leaves simulated from the RTSIF-derived gross primary production (GPP) is effectively correlated (<span class="inline-formula"><i>R</i></span> <span class="inline-formula">></span> 0.51) with that dissolved from the global Orbiting Carbon Observatory-2 (OCO-2)-based SIF (GOSIF) GPP. Furthermore, the gridded <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> dataset for young leaves successfully detects the green-up regions during the dry seasons in the tropics. Overall, this study presents the first satellite-based <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> dataset specifically targeting photosynthetically efficient young leaves, providing valuable insights for modeling large-scale photosynthetic dynamics and carbon cycles in TEFs. Herein, we provide the <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> time series derived from RTSIF GPP as the primary dataset, supplemented by GOSIF-derived and FLUXCOM products. These <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> products are available at <a href="https://doi.org/10.5281/zenodo.14807414">https://doi.org/10.5281/zenodo.14807414</a> (Yang et al., 2025).</p> |
| format | Article |
| id | doaj-art-81398c0e310e4f19acd41c3341dd80fb |
| institution | Kabale University |
| issn | 1866-3508 1866-3516 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj-art-81398c0e310e4f19acd41c3341dd80fb2025-08-20T03:28:50ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162025-07-01173293331410.5194/essd-17-3293-2025Remote sensing of young leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forestsX. Yang0X. Yang1X. Yang2Q. Sun3L. Han4J. Tian5W. Yuan6L. Liu7W. Zheng8M. Wang9Y. Wang10Y. Wang11X. Chen12Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, ChinaGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 101408, ChinaGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaCollege of Urban and Environmental Sciences, School of Urban Planning and Design, Peking University, Beijing 100871, ChinaLaboratoire des Sciences du Climat et de l'Environnement, IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaGuangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 101408, ChinaGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China<p>Determining the large-scale RuBisCO carboxylation maximum rate (<span class="inline-formula"><i>V</i><sub>c,max25</sub></span>) in relation to leaf age is essential for evaluating the photosynthetic capacity of canopy leaves in global forests. Young leaves (<span class="inline-formula">≤</span> 180 <span class="inline-formula">d</span>), which exhibit higher <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> compared to old leaves (<span class="inline-formula">></span> 180 <span class="inline-formula">d</span>), are key to controlling the seasonality of leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forests (TEFs). Nevertheless, quantifying the leaf photosynthetic capacities of different ages across TEFs remains challenging, especially when considering continuous temporal variations at continental scales. In this study, we propose a novel methodology that leverages neighborhood pixel analysis with nonlinear least-squares optimization to derive the <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> of young leaves at 0.25° spatial resolution. This approach utilizes satellite-based solar-induced chlorophyll fluorescence (SIF) products spanning the period from 2001 to 2018, which were reconstructed using both TROPOMI (Tropospheric Monitoring Instrument) SIF and MODIS reflectance data (RTSIF). Validations against in situ observations demonstrate that the newly developed <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> products accurately capture the seasonality of young leaves in South America and subtropical Asia, with correlation coefficients of 0.84, 0.66, and 0.95, respectively. The <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> of the young leaves simulated from the RTSIF-derived gross primary production (GPP) is effectively correlated (<span class="inline-formula"><i>R</i></span> <span class="inline-formula">></span> 0.51) with that dissolved from the global Orbiting Carbon Observatory-2 (OCO-2)-based SIF (GOSIF) GPP. Furthermore, the gridded <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> dataset for young leaves successfully detects the green-up regions during the dry seasons in the tropics. Overall, this study presents the first satellite-based <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> dataset specifically targeting photosynthetically efficient young leaves, providing valuable insights for modeling large-scale photosynthetic dynamics and carbon cycles in TEFs. Herein, we provide the <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> time series derived from RTSIF GPP as the primary dataset, supplemented by GOSIF-derived and FLUXCOM products. These <span class="inline-formula"><i>V</i><sub>c,max25</sub></span> products are available at <a href="https://doi.org/10.5281/zenodo.14807414">https://doi.org/10.5281/zenodo.14807414</a> (Yang et al., 2025).</p>https://essd.copernicus.org/articles/17/3293/2025/essd-17-3293-2025.pdf |
| spellingShingle | X. Yang X. Yang X. Yang Q. Sun L. Han J. Tian W. Yuan L. Liu W. Zheng M. Wang Y. Wang Y. Wang X. Chen Remote sensing of young leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forests Earth System Science Data |
| title | Remote sensing of young leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forests |
| title_full | Remote sensing of young leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forests |
| title_fullStr | Remote sensing of young leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forests |
| title_full_unstemmed | Remote sensing of young leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forests |
| title_short | Remote sensing of young leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forests |
| title_sort | remote sensing of young leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forests |
| url | https://essd.copernicus.org/articles/17/3293/2025/essd-17-3293-2025.pdf |
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