Harmonizing Fengyun-3D MERSI-II with MODIS NDVI for a Global Climate Data Record.
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| Title: | Harmonizing Fengyun-3D MERSI-II with MODIS NDVI for a Global Climate Data Record. |
|---|---|
| Authors: | Wang, Yanjiao1 (AUTHOR), Xiao, Fengjin1,2 (AUTHOR) xiaofj@cma.gov.cn, Wu, Linrong2,3 (AUTHOR), Wang, Feng1,3 (AUTHOR) |
| Source: | Remote Sensing. Sep2026, Vol. 18 Issue 18, p3075. 20p. |
| Subjects: | Calibration, Normalized difference vegetation index, MODIS (Spectroradiometer), Radiative transfer |
| Geographic Terms: | China |
| Abstract: | Highlights: What are the main findings? This study develops a full-chain technical framework that integrates cross-calibration and atmospheric correction, successfully reducing systematic biases between FY-3D MERSI-II and MODIS NDVI products, with RMSE decreased by more than 25.9% and the correlation coefficient improved by about 5%. This work provides a transferable technical solution for the operational integration of Chinese Fengyun satellite data with international reference datasets (MODIS/VIIRS), supporting the continuity of global long-term vegetation climate data records beyond the MODIS era. What are the implications of the main findings? The harmonized FY-3D NDVI product shows consistent performance across diverse vegetation types and climatic zones globally, with mean biases confined within ±0.1 over most land areas, confirming the robustness of the proposed method. The framework's successful implementation in an operational environment demonstrates its practical value for integrating China's meteorological satellite missions into global change research. The development of temporally consistent long-term normalized difference vegetation index (NDVI)climate data records is essential for global change and ecosystem research. The Medium Resolution Spectral Imager-II (MERSI-II) sensor aboard China's Fengyun-3D (FY-3D) satellite shares similar spectral characteristics with Moderate Resolution Imaging Spectroradiometer (MODIS), offering potential for synergistic applications. However, systematic biases arising from differences in sensor design, radiometric calibration, and atmospheric correction hinder their direct combination. This study established a full-chain framework that integrated cross-calibration of surface reflectance using quasi-synchronous FY-3D/MODIS observations and a MERSI-II-specific atmospheric correction scheme based on the 6S radiative transfer model. After correction, the FY-3D NDVI shows substantially improved consistency with MODIS, achieving a reduction in root mean square error of over 25.9%, an increase in correlation coefficient of approximately 5%, and a decrease in mean absolute error of about 40%. Spatial biases are within ±0.1 over most global land areas, with robust performance across vegetation types and climate zones. Based on this technical framework, a fused FY-3D and MODIS NDVI climate data record was established, which has been operationalized at the Beijing Climate Center for global vegetation monitoring. This work provides a transferable framework for integrating Chinese Fengyun satellite data with international datasets like MODIS/VIIRS. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 197381198 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Harmonizing Fengyun-3D MERSI-II with MODIS NDVI for a Global Climate Data Record. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yanjiao%22">Wang, Yanjiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiao%2C+Fengjin%22">Xiao, Fengjin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> xiaofj@cma.gov.cn</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Linrong%22">Wu, Linrong</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Feng%22">Wang, Feng</searchLink><relatesTo>1,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Sep2026, Vol. 18 Issue 18, p3075. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22MODIS+%28Spectroradiometer%29%22">MODIS (Spectroradiometer)</searchLink><br /><searchLink fieldCode="DE" term="%22Radiative+transfer%22">Radiative transfer</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? This study develops a full-chain technical framework that integrates cross-calibration and atmospheric correction, successfully reducing systematic biases between FY-3D MERSI-II and MODIS NDVI products, with RMSE decreased by more than 25.9% and the correlation coefficient improved by about 5%. This work provides a transferable technical solution for the operational integration of Chinese Fengyun satellite data with international reference datasets (MODIS/VIIRS), supporting the continuity of global long-term vegetation climate data records beyond the MODIS era. What are the implications of the main findings? The harmonized FY-3D NDVI product shows consistent performance across diverse vegetation types and climatic zones globally, with mean biases confined within ±0.1 over most land areas, confirming the robustness of the proposed method. The framework's successful implementation in an operational environment demonstrates its practical value for integrating China's meteorological satellite missions into global change research. The development of temporally consistent long-term normalized difference vegetation index (NDVI)climate data records is essential for global change and ecosystem research. The Medium Resolution Spectral Imager-II (MERSI-II) sensor aboard China's Fengyun-3D (FY-3D) satellite shares similar spectral characteristics with Moderate Resolution Imaging Spectroradiometer (MODIS), offering potential for synergistic applications. However, systematic biases arising from differences in sensor design, radiometric calibration, and atmospheric correction hinder their direct combination. This study established a full-chain framework that integrated cross-calibration of surface reflectance using quasi-synchronous FY-3D/MODIS observations and a MERSI-II-specific atmospheric correction scheme based on the 6S radiative transfer model. After correction, the FY-3D NDVI shows substantially improved consistency with MODIS, achieving a reduction in root mean square error of over 25.9%, an increase in correlation coefficient of approximately 5%, and a decrease in mean absolute error of about 40%. Spatial biases are within ±0.1 over most global land areas, with robust performance across vegetation types and climate zones. Based on this technical framework, a fused FY-3D and MODIS NDVI climate data record was established, which has been operationalized at the Beijing Climate Center for global vegetation monitoring. This work provides a transferable framework for integrating Chinese Fengyun satellite data with international datasets like MODIS/VIIRS. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18183075 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 3075 Subjects: – SubjectFull: Calibration Type: general – SubjectFull: Normalized difference vegetation index Type: general – SubjectFull: MODIS (Spectroradiometer) Type: general – SubjectFull: Radiative transfer Type: general – SubjectFull: China Type: general Titles: – TitleFull: Harmonizing Fengyun-3D MERSI-II with MODIS NDVI for a Global Climate Data Record. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Yanjiao – PersonEntity: Name: NameFull: Xiao, Fengjin – PersonEntity: Name: NameFull: Wu, Linrong – PersonEntity: Name: NameFull: Wang, Feng IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 18 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
