Allergic diseases, including respiratory conditions of allergic rhinitis (hay fever) and asthma, affect up to 500 million people worldwide. Grass pollen are one major source of aeroallergens globally. Pollen forecast methods are generally site-based and rely on empirical meteorological relationships and/or the use of labour-intensive pollen collection traps that are restricted to sparse sampling locations. The spatial and temporal dynamics of the grass pollen sources themselves, however, have received less attention. Here we utilised a consistent set of MODIS satellite measures of grass cover and seasonal greenness (EVI) over five contrasting urban environments, located in Northern (France) and Southern Hemispheres (Australia), to evaluate their utility for predicting airborne grass pollen concentrations. Strongly seasonal and pronounced pollinating periods, synchronous with satellite measures of grass cover greenness, were found at the higher latitude temperate sites in France (46–50° N. Lat.), with peak pollen activity lagging peak greenness, on average by 2–3weeks. In contrast, the Australian sites (34–38° S. Lat.) displayed pollinating periods that were less synchronous with satellite greenness measures as peak pollen concentrations lagged peak greenness by as much as 4 to 7weeks. The Australian sites exhibited much higher spatial and inter-annual variations compared to the French sites and at the Sydney site, broader and multiple peaks in both pollen concentrations and greenness data coincided with flowering of more diverse grasses including subtropical species. Utilising generalised additive models (GAMs) we found the satellite greenness data of grass cover areas explained 80–90% of airborne grass pollen concentrations across the three French sites (p<0.001) and accounted for 34 to 76% of grass pollen variations over the two sites in Australia (p<0.05). Our results demonstrate the potential of satellite sensing to augment forecast models of grass pollen aerobiology as a tool to reduce the health and socioeconomic burden of pollen-sensitive allergic diseases.

Recently evolved satellite-based sun-induced fluorescence (SIF) spectroscopy is considered as a direct measure of photosynthetic activity of vegetation. We have used monthly averages of satellite-based SIF retrievals for three agricultural year cycles, i.e. May to April for each of the three years, viz. 2007-08, 2008-09 and 2009-10 to assess comparative performance of SIF and normalized difference vegetation index (NDVI) for predicting net primary productivity (NPP) over the Indo-Gangetic Plains, India. Results show that SIF values for C4 crop-dominated districts were higher than C3 crop-dominated districts during summer and low during winter for all three years. SIF explained more or less above 70% of variance in NPP. The variance explained by integrated NDVI ranged from 60% to 67%. Thus the present study has shown the potential of SIF data for improved modelling of agricultural productivity at a regional scale.

The rainfed cropland belt in Australia is of great importance to the world grain market but has the highest climate variability of all such regions globally. However, the spatial-temporal impacts of climate variability on crops during different crop growth stages across broadacre farming systems are largely unknown. This study aims to quantify the contributions of climate and Land Surface Temperature (LST) variations to the variability of the Enhanced Vegetation Index (EVI) by using remote sensing methods. The datasets were analyzed at an 8-day time-scale across the rainfed cropland of eastern Australia. First, we found that EVI values were more variable during the crop reproductive growth stages than at any other crop life stage within a calendar year, but nevertheless had the highest correlation with crop grain yield (t ha(-1)). Second, climate factors and LST during the crop reproductive growth stages showed the largest variability and followed a typical east-west gradient of rainfall and a north-south temperature gradient across the study area during the crop growing season. Last, we identified two critical 8-day periods, beginning on day of the year (DoY) 257 and 289, as the key 'windows' of crop growth variation that arose from the variability in climate and LST. Our results show that the sum of the variability of the climate components within these two 8-day 'windows' explained > 88% of the variability in the EVI, with LST being the dominant factor. This study offers a fresh understanding of the spatial-temporal climate-crop relationships in rainfed cropland and can serve as an early warning system for agricultural adaptation in broadacre rainfed cropping practices in Australia and worldwide.

This study examines the spatio-temporal trends obtained from decade long (Jan 2003-Dec 2014) satellite observational data of Atmospheric Infrared Sounder (AIRS) and Measurements of Pollution in the Troposphere (MOPITT) on carbon monoxide (CO) concentration over the Indo-Gangetic Plains (IGP) region. The time sequence plots of columnar CO levels over the western, central and eastern IGP regions reveal marked seasonal behaviour, with lowest CO levels occurring during the monsoon months and the highest CO levels occurring during the pre-monsoon period. A negative correlation between CO levels and rainfall is observed. CO vertical profiles show relatively high values in the upper troposphere at similar to 200 hPa level during the monsoon months, thus suggesting the role of convective transport and advection in addition to washout behind the decreased CO levels during this period. MOPITT and AIRS observations show a decreasing trend of 9.6 Chi 10(15) and 1.5 Chi 10(16) molecules cm(-2) yr(-1), respectively, in columnar CO levels over the IGP region. The results show the existence of a spatial gradient in CO from the eastern (higher levels) to western IGP region (lower levels). Data from the Census of India on the number of households using various cooking fuels in the IGP region shows the prevalence of biomass-fuel (i.e. firewood, crop residue, cowdung etc.) use over the eastern and central IGP regions and that of liquefied petroleum gas over the western IGP region. CO emission estimates from cooking activity over the three IGP regions are found to be in the order east > central > west, which support the existence of the spatial gradient in CO from eastern to the western IGP region. Our results support the intervention of present Indian government on limiting the use of biomass-fuels in domestic cooking to achieve the benefits in terms of the better air quality, household health and regional/global climate change mitigation. (C) 2017 Elsevier Ltd. All rights reserved.

Diabetic retinopathy (DR) is a common retinal complication associated with diabetes. It is a major cause of blindness in middle as well as older age groups. Therefore early detection through regular screening and timely intervention will be highly beneficial in effectively controlling the progress of the disease. Since the ratio of people afflicted with the disease to the number of eye specialist who can screen these patients is very high, there is a need of automated diagnostic system for diabetic retinopathy changes in the eye so that only diseased persons can be referred to the specialist for further intervention and treatment.

Wheat is the most important grain crop in Australia, which plays a significant role in world grain-trading market. However, climate warming, water shortage, as well as more frequent extreme weather events (e.g., heatwaves, droughts and floods), under pressure of food demand, would pose great risks to all aspects of wheat production worldwide, especially in Australia with high climate variability. This study aggregated multi-source observational data by using meteorological statistics, in-situ investigation data and the MODIS Enhanced Vegetation Index (EVI) product to explore and examine the correlation between climate variability and spatial-temporal patterns of wheat phenology metrics and productivity. The results from tests over 370 wheat trial sites showed: 1) narrower and earlier sowing and harvesting windows occurred in a drought year (2006) compared with a normal year (2005). Differences in sowing and harvesting window lengths were 9 and 5 days, respectively; 2) different weather patterns in each agro-climatic zone were followed by different remotely sensed crop EVI seasonality profiles. Crop growth was least affected by climate variability in agro-climate region E2, which is located in the south part of study area. This study reveals new information on cropland-climate relationships across the wheat belt in NSW in a changing climate.

Arid wetlands are important for biodiversity conservation, but sensitive and vulnerable to climate variability and hydroclimatic events. Amplification of the water cycle, including the increasing frequency and severity of droughts and wet extremes, is expected to alter spatial and temporal hydrological patterns in arid wetlands globally, with potential threats to ecosystem services and their functioning. Despite these pressing challenges, the ecohydrological interactions and resilience of arid wetlands to highly variable water regimes over long time periods remain largely unknown. Recent broad-scale drought and floods over Australia provide unique opportunities to improve our understanding of arid wetland ecosystem responses to hydroclimatic extremes. Here we analysed the ecohydrological dynamics of the Coongie Lakes arid wetland in central Australia, one of the world's largest Ramsar-designated wetlands, using more than two decades (1988-2011) of vegetation and floodwater extent retrievals derived from Landsat satellite observations. To explore the impacts of large-scale hydrological fluctuations on the arid wetland, we further coupled Landsat measurements with Total Water Storage Anomaly (TWSA) data obtained from the Gravity Recovery and Climate Experiment (GRACE) satellites. Pronounced seasonal and inter-annual variabilities of flood and vegetation activities were observed over the wetland, with variations in vegetation growth extent highly correlated with flood extent (r = 0.64, p < 0.05) that ranged from nearly zero to 3456 km(2). We reported the hydrological dynamics and associated ecosystem responses to be largely driven by the two phases (El Nino and La Nina) of the El Nino-Southern Oscillation (ENSO) ocean-atmosphere system. Changes in flood and vegetation extent were better explained by GRACE-TWSA (r = 0.8, lag = 0 month) than rainfall (r = 034, lag = 3 months) over the water source area, demonstrating that TWS is a valuable hydrological indicator for complex dryland river systems. The protracted Millennium Drought from 2001 to 2009 resulted in long-term absence of major flood events, which substantially suppressed wetland vegetation growth. However, the 2010-11 La Nina induced flooding events led to an exceptionally large resurgence of vegetation, with a mean vegetation growth extent anomaly exceeding the historical average (1988-2011) by more than 1.5 standard deviations, suggesting a significant resilience of arid wetland ecosystems to climate variability. This study showed the ecological functioning of arid wetlands is particularly sensitive to large-scale hydrological fluctuations and extreme drought conditions, and vulnerable to future altered water regimes due to climate change. The methods developed herein can be applied to arid wetlands located in other dryland river systems across the globe. (C) 2016 Elsevier B.V. All rights reserved.

Crop extent and frequency maps are an important input to inform the debate around land value and competitive land uses, in particular between cropping and mining in the case of Queensland, Australia. Such spatial datasets are useful for supporting decisions on natural resource management, planning and policy. For the major broadacre cropping regions of Queensland, Australia, the complete Landsat Time Series (LTS) archive from 1987 to 2015 was used in a multi-temporal mapping approach, where spatial, spectral and temporal information were combined in multiple crop-modelling steps, supported by training data sampled across space and time for the classes Crop and No-Crop. Temporal information within summer and winter growing seasons were summarised for each year, and combined with various vegetation indices and band ratios computed from a pixel-based mid-season spectral synthetic image. All available temporal information was spatially aggregated to the scale of image segments in the mid-season synthetic image for each growing season and used to train a number of different predictive models for a Crop and No-Crop classification. Validation revealed that the predictive accuracy varied by growing season and region and a random forest classifier performed best, with κ = 0.88 to 0.91 for the summer growing season and κ = 0.91 to 0.97 for the winter growing season, and are thus suitable for mapping current and historic cropping activity.

Most cities have become net sources of heat with well-documented examples of anthropogenic climate modification in urban areas driving the Urban Heat Island (UHI) effect. This is defined as having higher temperatures (air /surface) in the built environment of cities compared with the surrounding and in rural areas. In this study we integrated remotely sensed satellite data to map and monitor the UHI effect over the Sydney region in Australia. Terra/Aqua MODIS Land Surface Temperature (LST) time series data for 2003 to 2015 were analysed to determine the spatio-temporal dynamics of UHI intensity. Land cover data from the MODIS were used to delineate the urban, rural and water class for the Sydney region. The UHI intensities were extracted from LST images by normalising rural LST patterns for each date. A Gaussian approximation was then applied in order to quantify spatial extent, centre and magnitude of UHI intensities. The temporal analysis on seasonal and interannual variations of UHI, revealed maximum intensities in daytime periods, particularly during the summer season. The daytime UHI intensity in Sydney could be as large as 7 - 8 °C in summer days. However, relatively weak UHI intensities were observed in night-time periods during all seasons. It was observed from the time series data that there were slight non-significant increasing trend in daytime UHI magnitudes for Sydney. However, pixel based UHI intensities at dense urban suburbs showed significant increasing trends for daytime and no defined trend for night time observations. To better characterise the locational nature of UHI, Landsat-8 land surface temperature data were analysed for summer period. Landsat data were helpful in extracting the information on hot spots in urban area more precisely. Satellite data of MODIS and Landsat provided efficient results for monitoring, mapping and characterizing UHI patterns over time and space. Future work involves classification of urban areas using thermal and vegetation index combined analyses in order to better understand the drivers of the UHI.

Australia experienced one of the worst droughts in history during the early 21st-century (termed the ‘big dry’), exerting negative impacts on food production and water supply, with increased forest die-back and bushfires across large areas. Following the ‘big dry’, one of the largest La Niña events in the past century, in conjunction with an extreme positive excursion of the Southern Annular Mode (SAM), resulted in dramatic increased precipitation from 2010 to 2011 (termed the ‘big wet’), causing widespread flooding and a recorded sea level drop. Despite these extreme hydroclimatic impacts, the spatial partitioning and temporal evolution of total water storage across Australia remains unknown. In this study we investigated the spatial-temporal impacts of the recent ‘big dry’ and ‘big wet’ events on Australia's water storage dynamics using the total water storage anomaly (TWSA) data derived from the Gravity Recovery and Climate Experiment (GRACE) satellites.Results showed widespread, continental-scale decreases in TWS during the ‘big dry’, resulting in a net loss of 3.89±0.47cm (299km3) total water, while the ‘big wet’ induced a sharp increase in TWS, equivalent to 11.68±0.52cm (898km3) of water, or three times the total water loss during the ‘big dry’. We found highly variable continental patterns in water resources, involving differences in the direction, magnitude, and duration of TWS responses to drought and wet periods. These responses clustered into three distinct geographic zones that correlated well with the influences from multiple large-scale climate modes. Specifically, a persistent increasing trend in TWS was recorded over northern and northeastern Australia, where the climate is strongly influenced by El Niño-Southern Oscillation (ENSO). By contrast, western Australia, a region predominantly controlled by the Indian Ocean Dipole (IOD), exhibited a continuous decline in TWS during the ‘big dry’ and only a subtle increase during the ‘big wet’, indicating a weak recovery of water storage. Southeastern Australia, influenced by combined ENSO, IOD and SAM interactions, exhibited a pronounced TWS drying trend during the ‘big dry’ followed by rapid TWS increases during the ‘big wet’, with complete water storage recoveries. A spatial intensification of the water cycle was further identified, with a wetting trend over wetter regions (northern and northeastern Australia) and a drying trend over drier regions (western Australia). Our results highlight the value of GRACE derived TWSA as an important indicator of hydrological system performance for improved water impact assessments and management of water resources across space and time.

Nitrogen (N) fertilization is crucial for the growth and development of wheat crops, and yet increased use of N can also result in increased stripe rust severity. Stripe rust infection and N deficiency both cause changes in foliar physiological activity and reduction in plant pigments that result in chlorosis. Furthermore, stripe rust produce pustules on the leaf surface which similar to chlorotic regions have a yellow color. Quantifying the severity of each factor is critical for adopting appropriate management practices. Eleven widely-used vegetation indices, based on mathematic combinations of narrow-band optical reflectance measurements in the visible/near infrared wavelength range were evaluated for their ability to discriminate and quantify stripe rust severity and N deficiency in a rust-susceptible wheat variety (H45) under varying conditions of nitrogen status. The physiological reflectance index (PhRI) and leaf and canopy chlorophyll index (LCCI) provided the strongest correlation with levels of rust infection and N-deficiency, respectively. When PhRI and LCCI were used in a sequence, both N deficiency and rust infection levels were correctly classified in 82.5 and 55 % of the plots at Zadoks growth stage 47 and 75, respectively. In misclassified plots, an overestimation of N deficiency was accompanied by an underestimation of the rust infection level or vice versa. In 18 % of the plots, there was a tendency to underestimate the severity of stripe rust infection even though the N-deficiency level was correctly predicted. The contrasting responses of the PhRI and LCCI to stripe rust infection and N deficiency, respectively, and the relative insensitivity of these indices to the other parameter makes their use in combination suitable for quantifying levels of stripe rust infection and N deficiency in wheat crops under field conditions.

Land surface phenology (LSP) characterizes episodes of greening and browning of the vegetated land surface from remote sensing imagery. LSP is of interest for quantification and monitoring of crop yield, wildfire fuel accumulation, vegetation condition, ecosystem response and resilience to climate variability and change. Deriving LSP represents an effort for end users and existing global products may not accommodate conditions in Australia, a country with a dry climate and high rainfall variability. To fill this information gap we developed the Australian LSP Product in contribution to AusCover/Terrestrial Ecosystem Research Network (TERN).We describe the product's algorithm and information content consisting of metrics that characterize LSP greening and browning episodes of the vegetated land surface. Our product allows tracking LSP metrics over time and thereby quantifying inter- and intraannual variability across Australia. We demonstrate the metrics' response to ENSO-driven climate variability. Lastly, we discuss known limitations of the current product and future development plans. (C) 2014 Elsevier Ltd. All rights reserved.

Australia recently experienced a long-term continental drought (“big dry”, 2001-2009) followed by an anomalous wet two-year period (“big wet”, 2010-2011). Despite the significance of the two extreme events, continental-wide information regarding the effects of the high and low precipitation conditions on the hydrological components, stress and recovery is not available. In this paper, we use terrestrial total water storage changes (ATWS) from the Gravity Recovery and Climate Experiment (GRACE) and precipitation data from the Tropical Rainfall Measuring Mission (TRMM) spanning from 2002 to 2013, where ATWS represents the main source of water available for human consumption, agriculture and natural ecosystems. We rely on a combination of temporal trend analysis and spatial statistics methods in order to evaluate the terrestrial total water storage (TWS) dynamics and the relationship between TWS and rainfall during the “big dry” and “big wet” events. Here we report the occurrence of hydrological cycle intensification during the study period in Australia which exhibited strong spatial variations: the wet areas (the northern and northeast regions) got wetter while the dry areas (the west and interior of the continent) became drier. By contrast, in southeastern Australia TWS changes over time showed sudden extreme responses to both events. Our results constitute a step beyond quantifying droughts/anomalous wet years that rely solely on precipitation data. This work demonstrates the ability of TWS observations as a significant indicator of hydrological system performance during hydroclimatic events and also an important tool for understanding continental-wide and regional spatial and temporal patterns of water availability.