A novel re-compositing approach to create continuous and consistent cross-sensor/cross-production global NDVI datasets

The longest Normalized Difference Vegetation Index (NDVI) time series produced from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) has ended in 2017. At some point in the near future, all AVHRR sensors will be retired. To maintain continui...

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Bibliographic Details
Published in:International journal of remote sensing 2021-08, Vol.42 (16), p.6023-6047
Main Authors: Yang, Wenze, Kogan, Felix, Guo, Wei, Chen, Yong
Format: Article
Language:English
Subjects:
Advanced Very High Resolution Radiometer
Climate
Climate and vegetation
Climatic indexes
Climatology
Continuity
Datasets
Imaging radiometers
Infrared imaging
Infrared radiometers
Meteorological data
Normalized difference vegetative index
Radiometers
Radiometry
Sensors
Vegetation
Vegetation index
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Summary:The longest Normalized Difference Vegetation Index (NDVI) time series produced from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) has ended in 2017. At some point in the near future, all AVHRR sensors will be retired. To maintain continuity and consistency of this global data set, it is imperative to extend NDVI from other sensors, especially the operational Visible Infrared Imaging Radiometer Suite (VIIRS), which planned to maintain continuity at least through 2038. NDVI could be de-composited into two components: (1) the multi-year climatology and (2) the vegetation condition index (VCI). The former contains climate information and a majority of sensor noise, and the latter contains weather information and residual sensor noise. With the assumption that VCI from different sensors are similar, we re-composited the cross-sensor/cross-production NDVI with original VCI and the cross-sensor/cross-production climatology, and compared various cross-converted datasets with the three base NDVI datasets: two NDVI productions derived from AVHRR observation and another from VIIRS observation. As a result, the re-composited NDVI agrees well with the target base NDVI spatially and temporally, with an accuracy of 0.02 NDVI unit at a global scale. The comparison with several regression approaches distinguish the superiority of the new re-compositing approach.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2021.1934597