Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

Abstract

Abstract. Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative spatial information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually recurring patterns. However, precipitation-driven nonannual phenology of arid and semi-arid systems (i.e., drylands) received much less attention, despite the fact that they cover more than 30 % of the global land surface. Here, we focused on Australia, a continent with one of the most variable rainfall climates in the world and vast areas of dryland systems, where a detailed phenological investigation and a characterization of the relationship between phenology and climate variability are missing.To fill this knowledge gap, we developed an algorithm to characterize phenological cycles, and analyzed geographic and climate-driven variability in phenology from 2000 to 2013, which included extreme drought and wet years. We linked derived phenological metrics to rainfall and the Southern Oscillation Index (SOI). We conducted a continentwide investigation and a more detailed investigation over the Murray-Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia.Results showed high inter-and intra-annual variability in phenological cycles across Australia. The peak of phenological cycles occurred not only during the austral summer, but also at any time of the year, and their timing varied by more than a month in the interior of the continent. The magnitude of the phenological cycle peak and the integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over northeastern Australia and within the MDB, predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of vegetation productivity) showed positive anomalies of more than 2 standard deviations over most of eastern Australia in 2009-2010, which coincided with the transition from the El Niño-induced decadal droughts to flooding caused by La Niña.Published by Copernicus Publications on behalf of the European Geosciences Union.

Land surface albedo and vegetation feedbacks enhanced the millennium drought in south-east Australia

“…The 2002 drought occurs It should be noted that, in the Murray Basin during the peak drought years, an entire phenological cycle is skipped and replaced by a steady decline in vegetation. This ability for vegetation to skip phenological cycles during drought years is an adaptation found commonly in Australian arid or semi-arid zones (Broich et al, 2014). Here, we see that even temperature zone species are able to do this to an extent in extreme drought years.…”

Woody plant cover estimation in drylands from Earth Observation based seasonal metrics

“…Seasonal metrics can be highly dynamic (Broich et al, 2014). In the interest of developing a robust and stable relationship between ground observations and satellite metrics, both ground and satellite data were averaged over the period of acquisition prior to extraction of the pixel values.…”

Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography

“…In addition, vegetation optical depth and biomass can be derived from radar or passive microwave remote sensing (Andela et al, 2013;Liu et al, 2007Liu et al, , 2011Liu et al, , 2013. Such remotely sensed vegetation phenology proxies can be analysed to estimate temporal changes in the vegetation characteristics on the landscape scale and contribute toward the development of satellite phenology products, such as the MODerate resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics product (Ganguly et al, 2010) and the Australian satellite land surface phenology product (Broich et al, 2015).…”