Ten, widely-used vegetation indices (VIs), based on mathematical combinations of narrow-band optical reflectance measurements in the visible/near infrared wavelength range were evaluated for their ability to discriminate leaves of 1 month old wheat plants infected with yellow (stripe), leaf and stem rust. Narrow band indices representing changes in non-chlorophyll pigment concentration and the ratio of nonchlorophyll to chlorophyll pigments proved more reliable in discriminating rust infected leaves from healthy plant tissue. Yellow rust produced the strongest response in all the calculated indices when compared to healthy leaves. No single index was capable of discriminating all three rust species from each other. However the sequential application of the Anthocyanin Reflectance Index to separate healthy, yellow and mixed stem rust/leaf rust classes followed by the Transformed Chlorophyll Absorption and Reflectance Index to separate leaf and stem rust classes would provide for the required species discrimination under laboratory conditions and thus could form the basis of rust species discrimination in wheat under field conditions.

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.

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