Articles | Volume 16, issue 10
https://doi.org/10.5194/essd-16-4389-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-4389-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
01 Oct 2024
Data description article |
| 01 Oct 2024
| 01 Oct 2024
Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation index (NDVI) with MODIS NDVI
Fenner School of Environment & Society, Australian National University, Canberra, ACT, Australia
Sami W. Rifai
School of Biological Sciences, The University of Adelaide, Adelaide, SA, Australia
Luigi J. Renzullo
Hydrology Science, Bureau of Meteorology, Canberra, ACT, Australia
Albert I. J. M. Van Dijk
Fenner School of Environment & Society, Australian National University, Canberra, ACT, Australia
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Cited articles
Beck, H. E., McVicar, T. R., van Dijk, A. I., Schellekens, J., de Jeu, R. A., and Bruijnzeel, L. A.: Global evaluation of four AVHRR–NDVI data sets: Intercomparison and assessment against Landsat imagery, Remote Sens. Environ., 115, 2547–2563, 2011.
Beringer, J., Moore, C. E., Cleverly, J., Campbell, D. I., Cleugh, H., De Kauwe, M. G., Kirschbaum, M. U., Griebel, A., Grover, S., and Huete, A.: Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network, Glob. Change Biol., 28, 3489–3514, 2022.
Bessenbacher, V., Seneviratne, S. I., and Gudmundsson, L.: CLIMFILL v0.9: a framework for intelligently gap filling Earth observations, Geosci. Model Dev., 15, 4569–4596, https://doi.org/10.5194/gmd-15-4569-2022, 2022.
Broich, M., Huete, A., Tulbure, M. G., Ma, X., Xin, Q., Paget, M., Restrepo-Coupe, N., Davies, K., Devadas, R., and Held, A.: Land surface phenological response to decadal climate variability across Australia using satellite remote sensing, Biogeosciences, 11, 5181–5198, https://doi.org/10.5194/bg-11-5181-2014, 2014.
Burton, C.: cbur24/AusENDVI: First release for publication, Zenodo [code], https://doi.org/10.5281/zenodo.13831836, 2024.
Burton, C., Rifai, S., Renzullo, L., and Van Dijk, A.: AusENDVI: A long-term NDVI dataset for Australia (0.2.0), Zenodo [data set], https://doi.org/10.5281/zenodo.10802703, 2024.
Byrne, G., Broomhall, M., Walsh, A. J., Thankappan, M., Hay, E., Li, F., McAtee, B., Garcia, R., Anstee, J., and Kerrisk, G.: Validating Digital Earth Australia NBART for the Landsat 9 Underfly of Landsat 8, Remote Sens., 16, 1233, https://doi.org/10.3390/rs16071233, 2024.
Canadell, J. G., Meyer, C., Cook, G. D., Dowdy, A., Briggs, P. R., Knauer, J., Pepler, A., and Haverd, V.: Multi-decadal increase of forest burned area in Australia is linked to climate change, Nat. Commun., 12, 6921, https://doi.org/10.1038/s41467-021-27225-4, 2021.
Cawley, G. C. and Talbot, N. L.: On over-fitting in model selection and subsequent selection bias in performance evaluation, J. Mach. Learn. Res., 11, 2079–2107, 2010.
Chambers, L. E., Altwegg, R., Barbraud, C., Barnard, P., Beaumont, L. J., Crawford, R. J., Durant, J. M., Hughes, L., Keatley, M. R., and Low, M.: Phenological changes in the southern hemisphere, PloS one, 8, e75514, https://doi.org/10.1371/journal.pone.0075514, 2013.
Chen, B.: Comparison of the Two Most Common Phenology Algorithms Imbedded in Land Surface Models, J. Geophys. Res.-Atmos., 127, e2022JD037167, https://doi.org/10.1029/2022JD037167, 2022.
Cortés, J., Mahecha, M. D., Reichstein, M., Myneni, R. B., Chen, C., and Brenning, A.: Where are global vegetation greening and browning trends significant?, Geophys. Res. Lett., 48, e2020GL091496, https://doi.org/10.1029/2020GL091496, 2021.
Donohue, R. J., McVICAR, T. R., and Roderick, M. L.: Climate-related trends in Australian vegetation cover as inferred from satellite observations, 1981–2006, Glob. Change Biol., 15, 1025–1039, 2009.
Donohue, R. J., Roderick, M. L., McVicar, T. R., and Farquhar, G. D.: Impact of CO2 fertilization on maximum foliage cover across the globe's warm, arid environments, Geophys. Res. Lett., 40, 3031–3035, 2013.
Dusenge, M. E., Duarte, A. G., and Way, D. A.: Plant carbon metabolism and climate change: elevated CO2 and temperature impacts on photosynthesis, photorespiration and respiration, New Phytologist, 221, 32–49, 2019.
Fensholt, R. and Proud, S. R.: Evaluation of earth observation based global long term vegetation trends – Comparing GIMMS and MODIS global NDVI time series, Remote Sens. Environ., 119, 131–147, 2012.
Franch, B., Vermote, E. F., Roger, J.-C., Murphy, E., Becker-Reshef, I., Justice, C., Claverie, M., Nagol, J., Csiszar, I., and Meyer, D.: A 30+ year AVHRR land surface reflectance climate data record and its application to wheat yield monitoring, Remote Sens., 9, 296, https://doi.org/10.3390/rs9030296, 2017.
Frankenberg, C., Yin, Y., Byrne, B., He, L., and Gentine, P.: Comment on “Recent global decline of CO2 fertilization effects on vegetation photosynthesis”, Science, 373, eabg2947, https://doi.org/10.1126/science.abg2947, 2021.
Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., and Furrer, R.: Predicting missing values in spatio-temporal remote sensing data, IEEE T. Geosci. Remote, 56, 2841–2853, 2018.
Giglio, L. and Roy, D.: On the outstanding need for a long-term, multi-decadal, validated and quality assessed record of global burned area: Caution in the use of Advanced Very High Resolution Radiometer data, Sci. Remote Sens., 2, 100007, https://doi.org/10.1016/j.srs.2020.100007, 2020.
Gorman, A. and McGregor, J.: Some considerations for using AVHRR data in climatological studies: II. Instrument performance, Remote Sens., 15, 549–565, 1994.
Haverd, V., Raupach, M. R., Briggs, P. R., Canadell, J. G., Isaac, P., Pickett-Heaps, C., Roxburgh, S. H., van Gorsel, E., Viscarra Rossel, R. A., and Wang, Z.: Multiple observation types reduce uncertainty in Australia's terrestrial carbon and water cycles, Biogeosciences, 10, 2011–2040, https://doi.org/10.5194/bg-10-2011-2013, 2013.
Head, L., Adams, M., McGregor, H. V., and Toole, S.: Climate change and Australia, Wiley Interdisciplinary Reviews: Climate Change, 5, 175–197, 2014.
Hoffmann, A. A., Rymer, P. D., Byrne, M., Ruthrof, K. X., Whinam, J., McGeoch, M., Bergstrom, D. M., Guerin, G. R., Sparrow, B., and Joseph, L.: Impacts of recent climate change on terrestrial flora and fauna: Some emerging Australian examples, Austral Ecology, 44, 3–27, 2019.
Hope, P. K., Drosdowsky, W., and Nicholls, N.: Shifts in the synoptic systems influencing southwest Western Australia, Clim. Dynam., 26, 751–764, 2006.
Hopper, S. D. and Gioia, P.: The southwest Australian floristic region: evolution and conservation of a global hot spot of biodiversity, Annu. Rev. Ecol. Evol. Syst., 35, 623–650, 2004.
Huang, Z., Zhou, L., and Chi, Y.: Spring phenology rather than climate dominates the trends in peak of growing season in the Northern Hemisphere, Glob. Change Biol., 29, 4543–4555, 2023.
Hughes, L.: Climate change and Australia: key vulnerable regions, Reg. Environ. Change, 11, 189–195, 2011.
Hutchison, M., Kesteven, J., and Xu, T.: ANUClimate collection [data set], https://dapds00.nci.org.au/thredds/catalogs/gh70/catalog.html, 2014.
Krause, C., Dunn, B., Bishop-Taylor, R., Adams, C., Burton, C., Alger, M., Chua, S., Phillips, C., Newey, V., Kouzoubov, K., Leith, A., Ayers, D., and Hicks, A.: DEA Notebooks contributors, Digital Earth Australia notebooks and tools repository, Geoscience Australia, Canberra [code], https://doi.org/10.26186/145234, 2021.
Li, F., Jupp, D. L., Reddy, S., Lymburner, L., Mueller, N., Tan, P., and Islam, A.: An evaluation of the use of atmospheric and BRDF correction to standardize Landsat data, IEEE J. Sel. Top. Appl. Earth Obs., 3, 257–270, 2010.
Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022, Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, 2023a.
Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2) (V1.2), Zenodo [data set], https://doi.org/10.5281/zenodo.8253971, 2023b.
Liao, Z., Van Dijk, A. I., He, B., Larraondo, P. R., and Scarth, P. F.: Woody vegetation cover, height and biomass at 25-m resolution across Australia derived from multiple site, airborne and satellite observations, Int. J. Appl. Earth Obs., 93, 102209, https://doi.org/10.1016/j.jag.2020.102209, 2020.
Liu, Y., Wu, C., Wang, X., Jassal, R. S., and Gonsamo, A.: Impacts of global change on peak vegetation growth and its timing in terrestrial ecosystems of the continental US, Global Planet. Change, 207, 103657, https://doi.org/10.1016/j.gloplacha.2021.103657, 2021.
Lundberg, S.: A unified approach to interpreting model predictions, arXiv [preprint], arXiv:1705.07874, 2017.
Ma, X., Huete, A., Yu, Q., Coupe, N. R., Davies, K., Broich, M., Ratana, P., Beringer, J., Hutley, L. B., and Cleverly, J.: Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect, Remote Sens. Environ., 139, 97–115, 2013.
Ma, X., Huete, A., Moran, S., Ponce-Campos, G., and Eamus, D.: Abrupt shifts in phenology and vegetation productivity under climate extremes, J. Geophys. Res.-Biogeo., 120, 2036–2052, 2015.
Ma, X., Huete, A., and Tran, N. N.: Interaction of seasonal sun-angle and savanna phenology observed and modelled using MODIS, Remote Sens., 11, 1398, https://doi.org/10.3390/rs11121398, 2019.
Meyer, H. and Pebesma, E.: Machine learning-based global maps of ecological variables and the challenge of assessing them, Nat. Commun., 13, 1–4, 2022.
Moore, C. E., Brown, T., Keenan, T. F., Duursma, R. A., van Dijk, A. I. J. M., Beringer, J., Culvenor, D., Evans, B., Huete, A., Hutley, L. B., Maier, S., Restrepo-Coupe, N., Sonnentag, O., Specht, A., Taylor, J. R., van Gorsel, E., and Liddell, M. J.: Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography, Biogeosciences, 13, 5085–5102, https://doi.org/10.5194/bg-13-5085-2016, 2016.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A., and Kent, J.: Biodiversity hotspots for conservation priorities, Nature, 403, 853–858, 2000.
O'Donnell, J., Gallagher, R. V., Wilson, P. D., Downey, P. O., Hughes, L., and Leishman, M. R.: Invasion hotspots for non-native plants in a ustralia under current and future climates, Glob. Change Biol., 18, 617–629, 2012.
Park, T., Chen, C., Macias-Fauria, M., Tømmervik, H., Choi, S., Winkler, A., Bhatt, U. S., Walker, D. A., Piao, S., and Brovkin, V.: Changes in timing of seasonal peak photosynthetic activity in northern ecosystems, Glob. Change Biol., 25, 2382–2395, 2019.
Peters, J. M., López, R., Nolf, M., Hutley, L. B., Wardlaw, T., Cernusak, L. A., and Choat, B.: Living on the edge: A continental-scale assessment of forest vulnerability to drought, Glob. Change Biol., 27, 3620–3641, 2021.
Piao, S., Liu, Q., Chen, A., Janssens, I. A., Fu, Y., Dai, J., Liu, L., Lian, X., Shen, M., and Zhu, X.: Plant phenology and global climate change: Current progresses and challenges, Glob. Change Biol., 25, 1922–1940, 2019.
Pinzon, J. E. and Tucker, C. J.: A non-stationary 1981–2012 AVHRR NDVI3g time series, Remote Sens., 6, 6929–6960, 2014.
Pitman, A., Narisma, G., Pielke Sr, R., and Holbrook, N.: Impact of land cover change on the climate of southwest Western Australia, J. Geophys. Res.-Atmos., 109, 2004.
Poulter, B., Frank, D., Ciais, P., Myneni, R. B., Andela, N., Bi, J., Broquet, G., Canadell, J. G., Chevallier, F., and Liu, Y. Y.: Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle, Nature, 509, 600–603, 2014.
Privette, J., Fowler, C., Wick, G., Baldwin, D., and Emery, W.: Effects of orbital drift on AVHRR products: normalized difference vegetation index and sea surface temperature, Remote Sens. Environ., 53, 164–177, 1995.
Rifai, S. W., De Kauwe, M. G., Ukkola, A. M., Cernusak, L. A., Meir, P., Medlyn, B. E., and Pitman, A. J.: Thirty-eight years of CO2 fertilization has outpaced growing aridity to drive greening of Australian woody ecosystems, Biogeosciences, 19, 491–515, https://doi.org/10.5194/bg-19-491-2022, 2022.
Schaaf, C. and Wang, Z.: MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted RefDaily L3 Global – 500 m V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A4.006, 2015.
Shen, H., Li, X., Cheng, Q., Zeng, C., Yang, G., Li, H., and Zhang, L.: Missing information reconstruction of remote sensing data: A technical review, IEEE Geosci. Remote Sens. Mag., 3, 61–85, 2015.
Steffen, W., Sims, J., Walcott, J., and Laughlin, G.: Australian agriculture: coping with dangerous climate change, Reg. Environ. Change, 11, 205–214, 2011.
Tian, F., Fensholt, R., Verbesselt, J., Grogan, K., Horion, S., and Wang, Y.: Evaluating temporal consistency of long-term global NDVI datasets for trend analysis, Remote Sens. Environ., 163, 326–340, 2015.
Tian, J. and Luo, X.: Conflicting changes of vegetation greenness interannual variability on half of the Global vegetated surface, Earth's Future, 12, e2023EF004119, https://doi.org/10.1029/2023EF004119, 2024.
Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W., Mahoney, R., Vermote, E. F., and El Saleous, N.: An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data, Int. J. Remote Sens., 26, 4485–4498, 2005.
Ukkola, A. M., Prentice, I. C., Keenan, T. F., Van Dijk, A. I., Viney, N. R., Myneni, R. B., and Bi, J.: Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation, Nat. Clim. Change, 6, 75–78, 2016.
Vermote, E. F., El Saleous, N. Z., and Justice, C. O.: Atmospheric correction of MODIS data in the visible to middle infrared: first results, Remote Sens. Environ., 83, 97–111, 2002.
Wang, S., Zhang, Y., Ju, W., Chen, J. M., Cescatti, A., Sardans, J., Janssens, I. A., Wu, M., Berry, J. A., and Campbell, J. E.: Response to Comments on “Recent global decline of CO2 fertilization effects on vegetation photosynthesis”, Science, 373, eabg7484, https://doi.org/10.1126/science.abg7484, 2021.
Wang, Z., Wang, H., Wang, T., Wang, L., Liu, X., Zheng, K., and Huang, X.: Large discrepancies of global greening: Indication of multi-source remote sensing data, Glob. Ecol. Conserv., 34, e02016, https://doi.org/10.1016/j.gecco.2022.e02016, 2022.
Xie, Q., Moore, C. E., Cleverly, J., Hall, C. C., Ding, Y., Ma, X., Leigh, A., and Huete, A.: Land surface phenology indicators retrieved across diverse ecosystems using a modified threshold algorithm, Ecol. Ind., 147, 110000, https://doi.org/10.1016/j.ecolind.2023.110000, 2023.
Ye, W., van Dijk, A. I., Huete, A., and Yebra, M.: Global trends in vegetation seasonality in the GIMMS NDVI3g and their robustness, Int. J. Appl. Earth Obs., 94, 102238, https://doi.org/10.1016/j.jag.2020.102238, 2021.
Zeng, C., Shen, H., Zhong, M., Zhang, L., and Wu, P.: Reconstructing MODIS LST based on multitemporal classification and robust regression, IEEE Geosci. Remote Sens. Lett., 12, 512–516, 2014.
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 the carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change.
Understanding vegetation response to environmental change requires accurate, long-term data on...
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