HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023
Abstract The Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is essential for assessing vegetation’s photosynthetic efficiency and ecosystem energy balance. While the MODIS FPAR product provides valuable global data, its reliability is compromised by noise, particularly under poor ob...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04391-4 |
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| author | Kai Yan Xinpei Yu Jinxiu Liu Jingrui Wang Xiuzhi Chen Jiabin Pu Marie Weiss Ranga B. Myneni |
| author_facet | Kai Yan Xinpei Yu Jinxiu Liu Jingrui Wang Xiuzhi Chen Jiabin Pu Marie Weiss Ranga B. Myneni |
| author_sort | Kai Yan |
| collection | DOAJ |
| description | Abstract The Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is essential for assessing vegetation’s photosynthetic efficiency and ecosystem energy balance. While the MODIS FPAR product provides valuable global data, its reliability is compromised by noise, particularly under poor observation conditions like cloud cover. To solve this problem, we developed the Spatio-Temporal Information Composition Algorithm (STICA), which enhances MODIS FPAR by integrating quality control, spatio-temporal correlations, and original FPAR values, resulting in the High-Quality FPAR (HiQ-FPAR) product. HiQ-FPAR shows superior accuracy compared to MODIS FPAR and Sensor-Independent FPAR (SI-FPAR), with RMSE values of 0.130, 0.154, and 0.146, respectively, and R² values of 0.722, 0.630, and 0.717. Additionally, HiQ-FPAR exhibits smoother time series in 52.1% of global areas, compared to 44.2% for MODIS. Available on Google Earth Engine and Zenodo, the HiQ-FPAR dataset offers 500 m and 5 km resolution at an 8-day interval from 2000 to 2023, supporting a wide range of FPAR applications. |
| format | Article |
| id | doaj-art-42ddd5e3e4d64fa8906620e6ba9416fc |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-42ddd5e3e4d64fa8906620e6ba9416fc2025-01-19T12:09:49ZengNature PortfolioScientific Data2052-44632025-01-0112111710.1038/s41597-025-04391-4HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023Kai Yan0Xinpei Yu1Jinxiu Liu2Jingrui Wang3Xiuzhi Chen4Jiabin Pu5Marie Weiss6Ranga B. Myneni7Innovation Research Center of Satellite Application (IRCSA), Faculty of Geographical Science, Beijing Normal UniversitySchool of Information Engineering, China University of GeosciencesSchool of Information Engineering, China University of GeosciencesGuangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen UniversityGuangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen UniversityDepartment of Earth and Environment, Boston UniversityInstitute National de la Recherche Agronomique, Université d’Avignon et des Pays du Vaucluse (INRA-UAPV), 228 Route de l’AérodromeDepartment of Earth and Environment, Boston UniversityAbstract The Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is essential for assessing vegetation’s photosynthetic efficiency and ecosystem energy balance. While the MODIS FPAR product provides valuable global data, its reliability is compromised by noise, particularly under poor observation conditions like cloud cover. To solve this problem, we developed the Spatio-Temporal Information Composition Algorithm (STICA), which enhances MODIS FPAR by integrating quality control, spatio-temporal correlations, and original FPAR values, resulting in the High-Quality FPAR (HiQ-FPAR) product. HiQ-FPAR shows superior accuracy compared to MODIS FPAR and Sensor-Independent FPAR (SI-FPAR), with RMSE values of 0.130, 0.154, and 0.146, respectively, and R² values of 0.722, 0.630, and 0.717. Additionally, HiQ-FPAR exhibits smoother time series in 52.1% of global areas, compared to 44.2% for MODIS. Available on Google Earth Engine and Zenodo, the HiQ-FPAR dataset offers 500 m and 5 km resolution at an 8-day interval from 2000 to 2023, supporting a wide range of FPAR applications.https://doi.org/10.1038/s41597-025-04391-4 |
| spellingShingle | Kai Yan Xinpei Yu Jinxiu Liu Jingrui Wang Xiuzhi Chen Jiabin Pu Marie Weiss Ranga B. Myneni HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023 Scientific Data |
| title | HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023 |
| title_full | HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023 |
| title_fullStr | HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023 |
| title_full_unstemmed | HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023 |
| title_short | HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023 |
| title_sort | hiq fpar a high quality and value added modis global fpar product from 2000 to 2023 |
| url | https://doi.org/10.1038/s41597-025-04391-4 |
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