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Global L-band equivalent AI-based vegetation optical depth dataset.

Olya Skulovich1, Xiaojun Li2, Jean-Pierre Wigneron2

  • 1Columbia University, New York, NY, 10027, USA. os2328@columbia.edu.

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|August 28, 2024
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A new AI-based vegetation optical depth dataset, GLAB-VOD, extends vegetation monitoring from 2002-2020. This long-term consistent microwave dataset improves vegetation biomass and canopy height studies.

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Area of Science:

  • Earth Science
  • Remote Sensing
  • Ecology

Background:

  • L-band vegetation optical depth (VOD) is crucial for vegetation monitoring due to minimal saturation.
  • Existing L-band VOD datasets have limited temporal coverage tied to satellite mission start dates.

Purpose of the Study:

  • To create a globally consistent, long-term L-band VOD dataset (GLAB-VOD) by extending existing data.
  • To develop a parallel consistent brightness temperature product (GLAB-TB) for inter-satellite consistency.

Main Methods:

  • Utilized machine learning to expand the SMAP-IB VOD dataset's temporal coverage.
  • Developed GLAB-VOD with 18-day temporal and 25 km spatial resolution (EASE2 grid) from 2002-2020.
  • Created GLAB-TB to ensure VOD product consistency across different microwave satellite periods.

Main Results:

  • The GLAB-VOD dataset provides consistent vegetation optical depth from 2002 to 2020.
  • Demonstrated excellent global spatial correlation with biomass (up to R=0.92) and canopy height (R=0.93).
  • GLAB-VOD outperforms the target SMAP-IB VOD dataset in correlation metrics.

Conclusions:

  • GLAB-VOD offers a temporally consistent, long-term solution for vegetation monitoring.
  • The dataset is suitable for studying global and regional vegetation biomass trends.
  • Applicable to any research requiring long-term consistent vegetation optical depth data.