Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation
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Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

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  • Journal Title:
    Remote Sensing
  • Personal Author:
  • NOAA Program & Office:
    NOS (National Ocean Service)
  • Description:
    Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.
  • Keywords:
  • Source:
    Remote Sensing, 13(3), 438
  • DOI:
  • ISSN:
    2072-4292
  • Format:
    PDF
  • Publisher:
    MDPI AG
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA16NOS4780208
  • License:
  • Rights Information:
    CC BY
  • Compliance:
    Library
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