Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades
Satellite normalized difference vegetation index (NDVI) time series, essential for ecological and environmental applications, is still limited by a trade-off between the spatiotemporal resolution and time coverage despite various global products. The Advanced Very High-Resolution Radiometer (AVHRR)...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-02-01
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| Series: | Big Earth Data |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2024.2448072 |
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| author | Xiaobin Guan Huanfeng Shen Yuchen Wang Dong Chu Xinghua Li Linwei Yue Wei Li Xinxin Liu Liangpei Zhang |
| author_facet | Xiaobin Guan Huanfeng Shen Yuchen Wang Dong Chu Xinghua Li Linwei Yue Wei Li Xinxin Liu Liangpei Zhang |
| author_sort | Xiaobin Guan |
| collection | DOAJ |
| description | Satellite normalized difference vegetation index (NDVI) time series, essential for ecological and environmental applications, is still limited by a trade-off between the spatiotemporal resolution and time coverage despite various global products. The Advanced Very High-Resolution Radiometer (AVHRR) instrument can provide the longest continuous time series since 1982, but with the drawback of coarse spatial resolution and poor data quality. To address this issue, a spatiotemporal fusion-based long-term NDVI product (STFLNDVI) since 1982 was generated in this study at a 1-km spatial resolution with monthly intervals, by fusing with the Moderate Resolution Imaging Spectroradiometer (MODIS) data. A multi-step processing fusion framework, containing temporal filtering, normalization, spatiotemporal fusion, and residual error correction, was employed to combine the superior characteristics of the two products, respectively. Simulated comparison with MODIS data and real-data assessments with true 1 km AVHRR data both confirm the ideal accuracy of the fusion product in spatial distribution and temporal variation, providing stable long-term results similar to MODIS data. We believe that the STFLNDVI product will be of great significance in characterizing the spatial patterns and long-term variations of global vegetation and the historical radiometric calibrations in AVHRR data gaps around the Arctic and instrument differences between MODIS and AVHRR should be further considered in the future. |
| format | Article |
| id | doaj-art-25fc3caf4e13403781992559a55bf37d |
| institution | Kabale University |
| issn | 2096-4471 2574-5417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Big Earth Data |
| spelling | doaj-art-25fc3caf4e13403781992559a55bf37d2025-02-01T13:40:43ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172025-02-0112810.1080/20964471.2024.2448072Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decadesXiaobin Guan0Huanfeng Shen1Yuchen Wang2Dong Chu3Xinghua Li4Linwei Yue5Wei Li6Xinxin Liu7Liangpei Zhang8School of Resource and Environmental Sciences, Wuhan University, Wuhan, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha, ChinaKey Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan, ChinaSatellite normalized difference vegetation index (NDVI) time series, essential for ecological and environmental applications, is still limited by a trade-off between the spatiotemporal resolution and time coverage despite various global products. The Advanced Very High-Resolution Radiometer (AVHRR) instrument can provide the longest continuous time series since 1982, but with the drawback of coarse spatial resolution and poor data quality. To address this issue, a spatiotemporal fusion-based long-term NDVI product (STFLNDVI) since 1982 was generated in this study at a 1-km spatial resolution with monthly intervals, by fusing with the Moderate Resolution Imaging Spectroradiometer (MODIS) data. A multi-step processing fusion framework, containing temporal filtering, normalization, spatiotemporal fusion, and residual error correction, was employed to combine the superior characteristics of the two products, respectively. Simulated comparison with MODIS data and real-data assessments with true 1 km AVHRR data both confirm the ideal accuracy of the fusion product in spatial distribution and temporal variation, providing stable long-term results similar to MODIS data. We believe that the STFLNDVI product will be of great significance in characterizing the spatial patterns and long-term variations of global vegetation and the historical radiometric calibrations in AVHRR data gaps around the Arctic and instrument differences between MODIS and AVHRR should be further considered in the future.https://www.tandfonline.com/doi/10.1080/20964471.2024.2448072NDVIMODISAVHRRspatiotemporal fusionlong-term |
| spellingShingle | Xiaobin Guan Huanfeng Shen Yuchen Wang Dong Chu Xinghua Li Linwei Yue Wei Li Xinxin Liu Liangpei Zhang Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades Big Earth Data NDVI MODIS AVHRR spatiotemporal fusion long-term |
| title | Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades |
| title_full | Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades |
| title_fullStr | Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades |
| title_full_unstemmed | Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades |
| title_short | Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades |
| title_sort | fusing modis and avhrr products to generate a global 1 km continuous ndvi time series covering four decades |
| topic | NDVI MODIS AVHRR spatiotemporal fusion long-term |
| url | https://www.tandfonline.com/doi/10.1080/20964471.2024.2448072 |
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