Preprints
https://doi.org/10.5194/essd-2024-89
https://doi.org/10.5194/essd-2024-89
09 Apr 2024
 | 09 Apr 2024
Status: this preprint is currently under review for the journal ESSD.

Enhancing Long-Term Vegetation Monitoring in Australia: A New Approach for Harmonising and Gap-Filling AVHRR and MODIS NDVI

Chad A. Burton, Sami W. Rifai, Luigi J. Renzullo, and Albert I. J. M. Van Dijk

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing global AVHRR NDVI products for their suitability for long-term vegetation monitoring in Australia. Comparisons with MODIS NDVI highlight significant deficiencies, particularly over densely vegetated regions. Moreover, all the assessed products failed to adequately reproduce inter-annual variability in the pre-MODIS era as indicated by Landsat NDVI anomalies. To address these limitations, we propose a new approach to calibrating and harmonising NOAA’s Climate Data Record AVHRR NDVI to MODIS MCD43A4 NDVI for Australia using a gradient-boosting decision tree ensemble method. Two versions of the datasets are developed, one incorporating climate data in the predictors (‘AusENDVI-clim’: Australian Empirical NDVI-climate) and another independent of climate data (‘AusENDVI-noclim’). These datasets, spanning 1982–2013 at a spatial resolution of 0.05°, exhibit strong correlation and low relative errors compared to MODIS NDVI, accurately reproducing seasonal cycles over densely vegetated regions. Furthermore, they closely replicate the interannual variability in vegetation condition in the pre-MODIS era. A reliable method for gap-filling the AusENDVI record is also developed that leverages climate, atmospheric CO2 concentration, and woody cover fraction predictors. The resulting synthetic NDVI dataset shows excellent agreement with observations. Finally, we provide a complete 41-year dataset where gap filled AusENDVI from January 1982 to February 2000 is seamlessly joined with MODIS NDVI from March 2000 to December 2022. Analysing 40-year per-pixel trends in Australia’s annual maximum NDVI revealed increasing values across most of the continent. Moreover, shifts in the timing of annual peak NDVI are identified, underscoring the dataset's potential to address crucial questions regarding changing vegetation phenology and its drivers. The AusENDVI dataset can be used for studying Australia's changing vegetation dynamics and downstream impacts on terrestrial carbon and water cycles, and provides a reliable foundation for further research into the drivers of vegetation change. AusENDVI is open access and available at https://doi.org/10.5281/zenodo.10802704 (Burton, 2024).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Chad A. Burton, Sami W. Rifai, Luigi J. Renzullo, and Albert I. J. M. Van Dijk

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-89', Anonymous Referee #1, 17 Apr 2024
    • AC1: 'Reply on RC1', Chad Burton, 17 May 2024
  • RC2: 'Comment on essd-2024-89', Anonymous Referee #2, 05 May 2024
    • AC2: 'Reply on RC2', Chad Burton, 17 May 2024
  • RC3: 'Comment on essd-2024-89', Anonymous Referee #3, 07 May 2024
    • AC3: 'Reply on RC3', Chad Burton, 17 May 2024
Chad A. Burton, Sami W. Rifai, Luigi J. Renzullo, and Albert I. J. M. Van Dijk

Data sets

AusENDVI: A long-term NDVI dataset for Australia Chad Burton et al. https://doi.org/10.5281/zenodo.10802704

Model code and software

AusENDVI: A long-term NDVI dataset for Australia Chad Burton https://github.com/cbur24/AusENDVI

Chad A. Burton, Sami W. Rifai, Luigi J. Renzullo, and Albert I. J. M. Van Dijk

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Short summary
Understanding vegetation response to environmental change requires accurate, long-term data on vegetation condition (VC). We evaluated existing satellite VC datasets over Australia and found them lacking so we developed a new VC dataset for Australia, “AusENDVI”. It can be used for studying Australia's changing vegetation dynamics and downstream impacts on carbon and water cycles, and provides a reliable foundation for further research into the drivers of vegetation change.
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