Preprints
https://doi.org/10.5194/essd-2021-156
https://doi.org/10.5194/essd-2021-156
18 Jun 2021
 | 18 Jun 2021
Status: this preprint has been withdrawn by the authors.

Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades

Xiaobin Guan, Huanfeng Shen, Yuchen Wang, Dong Chu, Xinghua Li, Linwei Yue, Xinxin Liu, and Liangpei Zhang

Abstract. Satellite normalized difference vegetation index (NDVI) time-series data are an essential data source for numerous ecological and environmental applications. Although various long-term global NDVI products have been produced with different characteristics over the past decades, there is still an apparent trade-off between the spatiotemporal resolution and time coverage. The Advanced Very High-Resolution Radiometer (AVHRR) instrument can provide the only continuous time series with the longest time coverage since the early 1980s, but with the drawback of a coarse spatial resolution and poor data quality compared to the observations of later instruments. To address this issue, a spatio-temporal fusion-based long-term NDVI product (STFLNDVI) since 1982 was generated in this study, with a 1-km spatial resolution and a monthly temporal resolution. A multi-step processing fusion framework was employed to combine the superior characteristics of Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR products, respectively. Simulated and real-data assessments both confirm the ideal accuracy of the fusion result with regard to the spatial distribution and temporal variation. Only a few relatively unsatisfactory results are found due to the poor relationship between the original AVHRR and MODIS data. The evaluations also show that the proposed fusion framework can obtain stable results similar to MODIS data in different years and seasons, even when the temporal distance between the fusion data and the reference data is large. We believe that the STFLNDVI product will be of great significance to characterize the spatial patterns and long-term variations of global vegetation. The NDVI product is available at DOI: http://doi.org/10.5281/zenodo.4734593 (Guan et al., 2021).

This preprint has been withdrawn.

Xiaobin Guan, Huanfeng Shen, Yuchen Wang, Dong Chu, Xinghua Li, Linwei Yue, Xinxin Liu, and Liangpei Zhang

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-156', Anonymous Referee #1, 01 Jul 2021
    • AC2: 'Reply on RC1', Xiaobin Guan, 15 Sep 2021
  • RC2: 'Comment on essd-2021-156', Anonymous Referee #2, 21 Jul 2021
    • AC3: 'Reply on RC2', Xiaobin Guan, 15 Sep 2021
  • EC1: 'Comment on essd-2021-156', Bo Zheng, 18 Aug 2021
    • AC1: 'Reply on EC1', Xiaobin Guan, 15 Sep 2021
      • EC2: 'Reply on AC1', Bo Zheng, 27 Sep 2021

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-156', Anonymous Referee #1, 01 Jul 2021
    • AC2: 'Reply on RC1', Xiaobin Guan, 15 Sep 2021
  • RC2: 'Comment on essd-2021-156', Anonymous Referee #2, 21 Jul 2021
    • AC3: 'Reply on RC2', Xiaobin Guan, 15 Sep 2021
  • EC1: 'Comment on essd-2021-156', Bo Zheng, 18 Aug 2021
    • AC1: 'Reply on EC1', Xiaobin Guan, 15 Sep 2021
      • EC2: 'Reply on AC1', Bo Zheng, 27 Sep 2021
Xiaobin Guan, Huanfeng Shen, Yuchen Wang, Dong Chu, Xinghua Li, Linwei Yue, Xinxin Liu, and Liangpei Zhang

Data sets

STFLNDVI: A long-term 1km NDVI time series since 1982 by fusing MODIS and AVHRR products Xiaobin Guan, Huanfeng Shen, Yuchen Wang, Dong Chu, Xinghua Li, Linwei Yue, Xinxin Liu, Liangpei Zhang http://doi.org/10.5281/zenodo.4734593

Xiaobin Guan, Huanfeng Shen, Yuchen Wang, Dong Chu, Xinghua Li, Linwei Yue, Xinxin Liu, and Liangpei Zhang

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Latest update: 13 Apr 2024
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This preprint has been withdrawn.

Short summary
This study generated the first global 1-km continuous NDVI product (STFLNDVI) for 4-decades by fusing multi-source satellite products. Simulated and real-data assessments confirmed the satisfactory and stable accuracy of STFLNDVI regarding spatial details and temporal variations. STFLNDVI is an ideal solution to the trade-off between spatial resolution and time coverage in current NDVI products, which of great significance for long-term regional and global vegetation and climate change studies.
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