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Using Advanced Very High Resolution Radiometer data spanning 1981–2006 and calibrated for long-term analyses of vegetation dynamics, we examine whether vegetation cover has increased across Australia and whether there has been a differential response of vegetation functional types in response to changes in climatic growing conditions. Trends in vegetation cover are interpreted within Budyko's energy – water limitation framework. Results from an Australia-wide analysis indicate that vegetation cover (as described by the fraction of Photosynthetically Active Radiation absorbed by vegetation; fPAR) has increased, on average, by 0.0007 per year – an increase of ∼8% over the 26 years. The majority of this change is due to a 0.0010 per year increase in persistent fPAR (representing nondeciduous perennial vegetation types; up 21%). In contrast, recurrent fPAR (representing deciduous, annual and ephemeral vegetation types) decreased, on average, by 0.0003 per year (down 7%), the trends of which are highly seasonal. Over the same period, Australian average annual precipitation increased by 1.3 mm yr−2 (up 7%). A site-based analysis using 90 long-term meteorological stations with minimal localized land-cover changes showed that energy-limited sites where total fPAR increased generally experienced decreases in precipitation, and water-limited sites that experienced decreases in cover were almost always associated with decreases in precipitation. Interestingly, where vegetation cover increased at water-limited sites, precipitation trends were variable indicating that this is not the only factor driving vegetation response. As Australia is a generally highly water-limited environment, these findings indicate that the effective availability of water to plants has increased on average over the study period. Results also show that persistent vegetation types have benefited more than recurrent types from recent changes in growing conditions. Regardless of what has been driving these changes, the overall response of vegetation over the past 2–3 decades has resulted in an observable greening of the driest inhabited continent on Earth. [ABSTRACT FROM AUTHOR]

Abstract: Long-term vegetation dynamics associated with climatic changes can be assessed using Advanced Very High Resolution Radiometer (AVHRR) red and near-infrared reflectance data provided that the data have been processed to remove the effects of non-target signal variability, such as atmospheric and sensor calibration effects. Here we present a new method that performs a relative calibration of reflectance data to produce consistent long-term vegetation information. It is based on a simple biological framework that assumes that the position of the vegetation cover triangle is invariant in reflectance space. This assumption is in fact an intrinsic assumption behind the commonly used Normalised Difference Vegetation Index (NDVI) and is violated when the NDVI is calculated from inadequately corrected reflectance data. In this new method, any temporal variability in the position of the cover triangle is removed by geometrically transforming the observed reflectance data such that two features of the triangle—the soil line and the dark point—are stationary in reflectance space. The fraction of Photosynthetically Active Radiation absorbed by vegetation (fPAR; 0.0–0.95) is then calculated, via the NDVI, from calibrated reflectances. This method was tested using two distinct, monthly AVHRR products for Australia: (i) the coarse-resolution, fully calibrated, partially atmospherically corrected PAL data (1981–1994); and (ii) the fine-resolution, fully calibrated, non-atmospherically corrected HRPT data (1992–2004). Results show that, in the 20-month period when the two datasets overlap (1992–1994), the Australia-wide, root mean square difference between the two datasets improved from 0.098 to 0.027 fPAR units. The calibrations have produced two approximately equivalent datasets that can be combined as a single input into time-series analyses. The application of this method is limited to areas that have a wide-enough variety of land-cover types so that the soil line and dark point are evident in the cover triangle in every image of the time-series. Another limitation is that the methodology performs only bulk, relative calibrations and does not remove the absolute effects of observation uncertainties. The simplicity of the method means that the calibration procedure can be easily incorporated into near-real-time operational remote-sensing environments. Vegetation information produced using this invariant-cover-triangle method is expected to be well suited to the analysis of long-term vegetation dynamics and change. [Copyright &y& Elsevier]

There is considerable demand for nationwide grain yield estimation during the cropping season by growers, grain marketers, grain handlers, agricultural businesses, and market brokers. In this paper, we developed a semi-empirical model (Crop-SI) to estimate the yield of the three major crops in the dryland Australian wheatbelt by combining a radiation use efficiency approach with meteorology driven Stress Indices (SI) at critical crop growth stages (e.g., anthesis and grain filling). These crop-specific SI (e.g., drought, heat and cold stress) help explain the impact of high spatial agro-environmental heterogeneity, which lead to substantial improvement in grain yield prediction. Crop-SI explains 87%, 69% and 83% of the observed field-scale grain yield variability with root mean square error of ~0.4, 0.4 and 0.5 t/ha for canola, wheat, and barley, respectively. At the pixel-level, Crop-SI reduces the relative error in grain yield estimation to 34%, 25%, and 20% for canola, wheat, barley, respectively, compared to two benchmark models. By incorporating water- and temperature-driven stresses, Crop-SI's predictive skill in highly variable environments is enhanced. As such, it paves the way for the next generation of agricultural systems models, knowledge products and decision support tools that need to operate at various scales. [ABSTRACT FROM AUTHOR]