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The geostationary Himawari-8 satellite offers a unique opportunity to monitor sub-daily thermal dynamics over Asia and Oceania, and several operational land surface temperature (LST) retrieval algorithms have been developed for this purpose. However, studies have reported inconsistency between LST data obtained from geostationary and polar-orbiting platforms, particularly for daytime LST, which can arise from variations in viewing geometries and inherent differences in sensor types and LST algorithms. Despite this, previous research has primarily focused on analysing the directionality of LST without thoroughly exploring systematic differences between platforms. Hence, we presented a Solar Zenith Angle-based Calibration (SZAC) method to harmonise the daytime component of a split-window retrieved Himawari-8 LST (referred to here as the baseline) with the MODerate-resolution Imaging Spectroradiometer (MODIS) LST. SZAC describes the spatial heterogeneity and magnitude of diurnal LST discrepancies from different platforms, which is anticipated to complement typical directionality analyses. We evaluated the harmonised LST data, referred to as the Australian National University LST with SZAC (ANU SZAC), against MODIS LST and the Visible Infrared Imaging Radiometer Suite (VIIRS) LST, as well as in-situ LST from the OzFlux network. Two peer Himawari-8 LST products from Chiba University and the Copernicus Global Land Service were also collected for comparison. The median daytime bias of ANU SZAC LST against Terra-MODIS LST, Aqua-MODIS LST and VIIRS LST was 1.52 K, 0.98 K and −0.63 K, respectively, which demonstrated improved performance compared to baseline (5.37 K, 4.85 K and 3.02 K, respectively) and Chiba LST (3.71 K, 2.90 K and 1.07 K, respectively). All four Himawari-8 LST products showed comparable performance of unbiased root mean squared error (ubRMSE), ranging from 2.47 to 3.07 K, compared to LST from polar-orbiting platforms. In the evaluation against in-situ LST, the mean values of bias (ubRMSE) of baseline, Chiba, Copernicus and ANU SZAC LST during daytime were 4.23 K (3.74 K), 2.16 K (3.62 K), 1.73 K (3.31 K) and 1.41 K (3.24 K), respectively, based on 171,289 hourly samples from 20 OzFlux sites across Australia between 01/Jan/2016 and 31/Dec/2020. In summary, the SZAC method offers a promising approach to enhance the reliability of geostationary LST retrievals by incorporating the spatiotemporal characteristics observed by accurate polar-orbiting LST data. SZAC can also reduce LST angular effects due to its capability of quantifying spatial inhomogeneity of surface heat dynamics. Furthermore, it is possible to implement SZAC using LST data acquired by geostationary satellites in other regions, e.g., Europe, Africa and Americas. This could improve our understanding of the error characteristics of coincident imageries from geostationary and polar-orbiting platforms, allowing for targeted refinements and global harmonisations to further enhance applicability. [Display omitted] • We introduce the SZAC method to harmonise daytime Himawari-8 LST with MODIS LST. • The SZAC coefficient was optimised against MODIS best-quality LST over Australia. • Four Himawari-8 LST products using different algorithms were compared. • Harmonised LST showed improved diurnal agreement with both in-situ and polar-orbiting LST. • SZAC can quantify inhomogeneity of surface heat dynamics and reduce LST angular effects. [ABSTRACT FROM AUTHOR]

The land surface emissivity (LSE) in the MYD21 product contains the effects of viewing zenith angle. The influence of the angular variation of LSE on the surface upwelling longwave radiation (SULR) estimation is still unexplored at the satellite scale. We performed statistical analyses of MYD21 emissivity retrievals over different land surface types for three longwave bands centred around 8.55 μm (Band 29), 11 μm (Band 31) and 12 μm (Band 32), respectively. A look-up table was generated to describe the angular variations for both single-band and broadband emissivities. The results showed that the angular variation of directional emissivity in Band 29 could reach up to 0.03, but was <0.01 for Bands 31 and 32. The angular variation in broadband emissivity was intermediate to that for individual bands. In all cases, the directional emissivity was greatest and symmetric around nadir. By integrating the directional broadband emissivity, the influence of angular variation of the LSE on estimated SULR was quantified using simulation and measurements at seven stations from the US surface radiation budget network (SURFRAD). The difference between the directional and integrated hemispheric broadband emissivity was within 0.01. As a result, the influence of angular variation of LSE on the SULR estimation was modest. For the SURFRAD stations, the differences of root-mean-square error (RMSE) before and after considering the angular variation of LSE were generally <1 W m−2. We conclude that the angular variation of broadband emissivity is not pronounced because of the small linear weight for Band 29 in the calculation of broadband emissivity. Ignoring the anisotropy of emissivity does not introduce large errors in SULR estimation generally. • MODIS 8.5–12 μm emissivities retrieved using a physical algorithm were analysed. • Anisotropy in emissivity was demonstrated and quantified. • Emissivity differences were found between surface types, day/night, and seasons. • Ignoring emissivity anisotropy does not affect longwave radiation estimation greatly. [ABSTRACT FROM AUTHOR]

Fine spatial resolution (i.e., ≤ 100 m) land surface temperature (LST) data are crucial to study heterogeneous landscapes (e.g., agricultural and urban). Some well-known spatiotemporal fusion methods like the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), which were originally developed to fuse surface reflectance data, may not be suitable for direct application in LST studies due to the high sub-diurnal dynamics of LST. Furthermore, the effectiveness of spatiotemporal fusion methods for LST data has not been thoroughly evaluated in previous studies that only focused on relatively small spatiotemporal extents. To address these limitations, we proposed a variant of ESTARFM, referred to as the unbiased ESTARFM (ubESTARFM), specifically designed to accommodate the high temporal dynamics of LST to generate fine-resolution LST estimates. We evaluated ubESTARFM and ESTARFM against in-situ LST and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST across 12 regions throughout Australia, encompassing various land covers and environments. Independent validation showed that ubESTARFM had a bias of 2.55 K, unbiased root mean squared error (ubRMSE) of 2.57 K, and Pearson correlation coefficient (R) of 0.95 against the in-situ LST over 11,290 observations at the 12 sites, all of which were considerably better than those calculated for ESTARFM, being a bias of 4.73 K, ubRMSE of 3.80 K and R of 0.92. When compared to ECOSTRESS data, ubESTARFM LST had a bias of −1.69 K, ubRMSE of 2.00 K, and R of 0.70 over 43 near clear-sky scenes, while ESTARFM LST had a bias of 1.79 K, ubRMSE of 2.68 K, and R of 0.59. Overall, our results demonstrated that ubESTARFM can avoid systematic bias accumulation, substantially reduce uncertainty deviation, and maintain a good level of correlation with validation datasets when compared to ESTARFM. A further assessment underscored the potential of ubESTARFM for application using LST data acquired from geostationary platforms (e.g., Himawari-8), with a mean ubRMSE (R) of 2.22 K (0.97) against in-situ LST over 1327 observations at 3 sites from southeast Australia at the overpass time of MODIS/Terra. This promising method leverages reliable numeric values from coarse-resolution LST while borrowing spatial heterogeneity from fine-resolution LST and has the potential to be coupled with energy balance and/or radiative transfer models thus enabling better farm and/or regional-scale water management strategies to be implemented. Furthermore, both the input and generated LST data, encompassing a comprehensive spatial extent over diverse land covers and climatic conditions, are publicly available for benchmarking future algorithmic refinements. • We developed an unbiased variant of ESTARFM (ubESTARFM) to fuse MODIS and Landsat LST. • ubESTARFM uses coarse LST as reference to locally correct systematic bias of fine LST. • We evaluated ubESTARFM against in-situ LST and ECOSTRESS LST across Australia. • ubESTARFM reduces deviation of uncertainty while it maintains spatial details. • ubESTARFM shows potential for application on geostationary LST (e.g., Himawari-8). [ABSTRACT FROM AUTHOR]

Model-data fusion offers considerable promise in remote sensing for improved state and parameter estimation particularly when applied to multi-sensor image products. This paper demonstrates the application of a ‘multiple constraints’ model-data fusion (MCMDF) scheme to integrating AMSR-E soil moisture content (SMC) and MODIS land surface temperature (LST) data products with a coupled biophysical model of surface moisture and energy budgets for savannas of northern Australia. The focus in this paper is on the methods, difficulties and error sources encountered in developing an MCMDF scheme and enhancements for future schemes. An important aspect of the MCMDF approach emphasized here is the identification of inconsistencies between model and data, and among data sets. The MCMDF scheme was able to identify that an inconsistency existed between AMSR-E SMC and LST data when combined with the coupled SEB-MRT model. For the example presented, an optimal fit to both remote sensing data sets together resulted in an 84% increase in predicted SMC and 0.06% increase for LST relative to the fit to each data set separately. That is the model predicted on average cooler LST''s (∼1.7 K) and wetter SMC values (∼0.04 g cm−3) than the satellite image products. In this instance we found that the AMSR-E SMC data on their own were poor constraints on the model. Incorporating LST data via the MCMDF scheme ameliorated deficiencies in the SMC data and resulted in enhanced characterization of the land surface soil moisture and energy balance based on comparison with the MODIS evapotranspiration (ET) product of Mu et al. [Mu, Q., Heinsch, F.A, Zhao, M. and Running, S.W. (in press), Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sensing of Environment.]. Canopy conductance, g C, and latent heat flux, λE, from the MODIS ET product were in good agreement with RMSEs for g C =0.5 mm s−1 and for λE =18 W m−2, respectively. Differences were attributable to a greater canopy-to-air vapor pressure gradient in the MCMDF approach obtained from a more realistic partitioning of soil surface and canopy temperatures. [Copyright &y& Elsevier]

Drought indices based on thermal remote sensing have been developed and have merit for effective early warning of agricultural droughts, but approaches so far are relatively complex or sensitive to land surface temperature (LST) estimation uncertainties. Here, we propose the temperature rise index (TRI), a drought index that is comparatively robust and easy to calculate, as the anomaly of the intrinsic morning rise of LST. The underlying principle is that the rate of LST rise between 1.5 and 3.5 h after the sunrise is approximately linear and occurs more rapidly under dry conditions than under wet conditions over vegetated surfaces as a consequence of stomatal control. TRI during the growing seasons of 2010–2014 was calculated over the Australian wheatbelt from LST retrievals from the geostationary Multifunction Transport Satellite-2 (MTSAT-2) instrument. The calculated TRI was compared with indices based on precipitation integrated over 1-, 3- and 6-month time scales, on Soil Moisture and Ocean Salinity (SMOS) soil moisture derived from passive microwave remote sensing, and on vegetation condition (normalized difference vegetation index, NDVI) derived from optical remote sensing. The various indices were also compared to annual wheat yield over large areas. The correlation coefficient between TRI and precipitation anomaly that serves as an operational drought index in Australia was above 0.6 in general with 3-month integrative time scale for precipitation. TRI produced spatiotemporal dryness patterns that were very similar to those in soil moisture, but with more detail due to its finer resolution. A time lag of >1 month was found between TRI and observed vegetation condition, supporting the use of TRI in early warning. Among the compared drought indices, TRI explained the largest fraction (35%) of wheat yield variations. TRI correlations with wheat yields peaked higher and earlier by almost one month in comparison to other indices. We conclude that the thermal drought index proposed here shows considerable potential for use in drought early warning as an effective complement. • Land surface temperature morning rise at 1.5–3.5 h after sunrise is quasi-linear. • Vegetation canopy under water stress has a faster temperature morning rise. • Temperature rise index provides earlier drought monitoring than greenness indices. • Temperature rise index peaked higher and earlier by 1 month in correlation to yields. [ABSTRACT FROM AUTHOR]

Low frequency passive microwave remote sensing is a proven technology for providing soil moisture estimates, but the coarse resolution of its data restricts the range of applications. Downscaling, otherwise known as disaggregation, has been proposed as the solution to spatially enhance these coarse resolution soil moisture observations, through association with complementary observations, or ancillary information about land surface features at higher spatial resolution. Such information includes solar reflectance, thermal emission, passive microwave emissions at a higher frequency, radar backscatter, soil or surface attributes such as topography and soil properties, and land surface modelling. Each of these ancillary data sources has its own strengths and limitations in terms of, for example, sensitivity to surface soil moisture dynamics and availability. This paper provides an extensive review of the capabilities and opportunities of current soil moisture downscaling approaches which provide a deterministic pattern of soil moisture, together with their strengths and limitations. [ABSTRACT FROM AUTHOR]

Purpose: Our goal was to determine whether use of the VasoNova™ VPS™ tip positioning system would improve accuracy of PICC placement versus "blind" bedside insertion in the adult intensive care unit (ICU) patient population. Materials and Methods: This study was performed prospectively and with institutional review board approval. Between August 27, 2012 and February 25, 2013, 68 PICCs were inserted, 34 with VPS™ technology and 34 using standard “blind” technique. Chest x-ray (CXR) was performed after all insertions and used as the reference standard to determine tip location; interpreting radiologists were blinded to the technique of PICC placement. Using Doppler ultrasound and ECG data, the VPS™ system displays a blue bull's eye when the PICC is thought to be in the caudal superior vena cava (SVC) or at the SVC-right atrium junction. Results: Tip position in the distal superior vena cava or right atrium (defined as ideal tip position) was obtained in 30/34 (88%) for the VPS™ group and 22/34 (65%) for the "blind" insertion group (p=0.04). No jugular venous placements occurred in the VPS™ group versus 3/34 (9%) in the CXR group. The table details the distal tip position in both groups. Conclusion: In the adult ICU population, use of the VasoNova™ VPS™ system improves final tip position. [Copyright &y& Elsevier]

Sustainable management of groundwater-dependent vegetation (GDV) requires the accurate identification of GDVs, characterisation of their water use dynamics and an understanding of associated errors. This paper presents sensitivity and uncertainty analyses of one GDV mapping method which uses temperature differences between time-series of modelled and observed land surface temperature (LST) to detect groundwater use by vegetation in a subtropical woodland. Uncertainty in modelled LST was quantified using the Jacobian method with error variances obtained from literature. Groundwater use was inferred where modelled and observed LST were significantly different using a Student's t -test. Modelled LST was most sensitive to low-range wind speeds (<1.5 m s −1 ), low-range vegetation height (<=0.5 m), and low-range leaf area index (<=0.5 m 2 m −2 ), limiting the detectability of groundwater use by vegetation under such conditions. The model-data approach was well-suited to detection of GDV because model-data errors were lowest for climatic conditions conducive to groundwater use. [ABSTRACT FROM AUTHOR]

This study presents a novel ‘model-data’ approach to detect groundwater-dependent vegetation (GDV), through differences in modelled and observed land surface temperatures (LST) in space and time. Vegetation groundwater use is inferred where modelled LST exceeds observed LST by more than a threshold determined from consideration of systematic and random errors in model and observations. Modelled LST was derived from a surface energy balance model and LST observations were obtained from Terra-MODIS thermal imagery. The model-data approach, applied in the Condamine River Catchment, Queensland, Australia, identified GDV coincident to existing mapping. GDV were found to use groundwater up to 48% of the time and for as many as 56 consecutive days. Under driest of conditions, groundwater was estimated to contribute up to 0.2 mm h −1 to total ET for GDV. The ability to both detect the location and water-use dynamics of GDV is a significant advancement on previous remote-sensing GDV methods. [ABSTRACT FROM AUTHOR]

The land parameter retrieval model (LPRM) is a methodology that retrieves soil moisture from low frequency dual polarized microwave measurements and has been extensively tested on C-, X- and Ku-band frequencies. Its performance on L-band is tested here by using observations from the Soil Moisture and Ocean Salinity (SMOS) satellite. These observations have potential advantages compared to higher frequencies: a low sensitivity to cloud and vegetation contamination, an increased thermal sampling depth and a greater sensitivity to soil moisture fluctuations. These features make it desirable to add SMOS-derived soil moisture retrievals to the existing European Space Agency (ESA) long-term climatological soil moisture data record, to be harmonized with other passive microwave soil moisture estimates from the LPRM. For multi-channel observations, LPRM infers the effective soil temperature ( T eff ) from higher frequency channels. This is not possible for a single channel mission like SMOS and therefore two alternative sources for T eff were tested: (1) MERRA-Land and (2) ECMWF numerical weather prediction systems, respectively. SMOS measures brightness temperature at a range of incidence angles, different incidence angle bins (45°, 52.5° and 60°) were tested for both ascending and descending swaths. Three LPRM algorithm parameters were optimized to match remotely sensed soil moisture with ground based observations: the single scattering albedo, roughness and polarization mixing factor. The soil moisture retrievals were optimized and evaluated against ground-based data from the Murrumbidgee Soil Moisture Monitoring Network (OzNet) in southeast Australia. The agreement with single-angle SMOS LPRM retrievals was close to the official SMOS L3 product, provided the three parameters were optimized for the OzNet dataset, with linear correlation of 0.70–0.75 (0.75–0.77 for SMOS L3), root-mean-square error of 0.069–0.085 m 3 m − 3 (0.084–0.106 m 3 m − 3 for SMOS L3) and small bias of − 0.02–0.01 m 3 m − 3 (0.03–0.06 m 3 m − 3 for SMOS L3). These results suggest that the LPRM can be applied successfully to single-angle SMOS L-band observations, but further testing is required to determine if the same set of parameters can be used in other geographic areas. [ABSTRACT FROM AUTHOR]

Abstract: Background: Several epidemiological studies have demonstrated an increased mortality from cardiovascular causes in patients with Klinefelter Syndrome (KS). Little information is available about the nature of the underlying cardiovascular abnormalities. Aim of the study was to investigate exercise performance, left ventricular architecture and function, vascular reactivity, and carotid intima-media thickness in a group of patients with KS. Materials and methods: Sixty-nine patients with KS and 48 age-matched controls participated in our population-controlled study. Forty-eight Klinefelter subjects were on testosterone treatment at the time of the investigation while 21 were naive and underwent a complete Doppler echocardiographic examination, a cardiopulmonary exercise test as well as a vascular study including measures of carotid intima-media thickness and endothelial function with flow-mediated dilation of the brachial artery. Patients with KS on testosterone therapy (n=48) were also matched against a population of men with treated secondary hypogonadism (n=21). Results: Patients with KS exhibited a wide array of cardiovascular abnormalities including left ventricular diastolic dysfunction, reduced maximal oxygen consumption (p<0.01), increased intima-media thickness (p<0.05) (−34% and +42% vs. controls, respectively) and a high prevalence of chronotropic incompetence (55% of patients, p<0.01). No significant difference was found between treated and untreated KS in variance with men treated for secondary hypogonadism. Conclusion: Left ventricular diastolic dysfunction, impaired cardiopulmonary performance, chronotropic incompetence, and increased intima-media thickness suggest that cardiovascular abnormalities are a common finding in KS that is not reversed by testosterone replacement therapy and may represent the pathophysiological underpinnings of the increased risk of dying from heart disease. [Copyright &y& Elsevier]

Quantitative estimation of fractional cover of photosynthetic vegetation (f PV), non-photosynthetic vegetation (f NPV) and bare soil (f BS) is critical for natural resource management and for modeling carbon dynamics. Accurate estimation of fractional cover is especially important for monitoring and modeling savanna systems, subject to highly seasonal rainfall and drought, grazing by domestic and native animals, and frequent burning. This paper describes a method for resolving f PV, f NPV and f BS across the ~2 million km2 Australian tropical savanna zone with hyperspectral and multispectral imagery. A spectral library compiled from field campaigns in 2005 and 2006, together with three EO-1 Hyperion scenes acquired during the 2005 growing season were used to explore the spectral response space for f PV, f NPV and f BS. A linear unmixing approach was developed using the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI). Translation of this approach to MODerate resolution Imaging Spectroradiometer (MODIS) scale was assessed by comparing multiple linear regression models of NDVI and CAI with a range of indices based on the seven MODIS bands in the visible and shortwave infrared region (SWIR) using synthesized MODIS surface reflectance data on the same dates as the Hyperion acquisitions. The best resulting model, which used NDVI and the simple ratio of MODIS bands 7 and 6 (SWIR3/SWIR2), was used to generate a time series of fractional cover from 16 day MODIS nadir bidirectional reflectance distribution function-adjusted reflectance (NBAR) data from 2000–2006. The results obtained with MODIS NBAR were validated against grass curing measurement at ten sites with good agreement at six sites, but some underestimation of f NPV proportions at four other sites due to substantial sub-pixel heterogeneity. The model was also compared with remote sensing measurements of fire scars and showed a good matching in the spatio-temporal patterns of grass senescence and posterior burning. The fractional cover profiles for major grassland cover types showed significant differences in relative proportions of f PV, f NPV and f BS, as well as large intra-annual seasonal variation in response to monsoonal rainfall gradients and soil type. The methodology proposed here can be applied to other mixed tree-grass ecosystems across the world. [Copyright &y& Elsevier]

Recent advances in L-band passive microwave remote sensing provide an unprecedented opportunity to monitor soil moisture at ~40 km spatial resolution around the globe. Nevertheless, retrieval of the accurate high spatial resolution soil moisture maps that are required to satisfy hydro-meteorological and agricultural applications remains a challenge. Currently, a variety of downscaling, otherwise known as disaggregation techniques have been proposed as the solution to disaggregate the coarse passive microwave soil moisture into high-to-medium resolutions. These techniques take advantage of the strengths of both the passive microwave observations of soil moisture having low spatial resolution and the spatially detailed information on land surface features that either influence or represent soil moisture variability. However, such techniques have typically been developed and tested individually under differing weather and climate conditions, meaning that there is no clear guidance on which technique performs the best. Consequently, this paper presents a quantitative assessment of the existing radar-, optical-, radiometer-, and oversampling-based downscaling techniques using a singular extensive data set collected specifically for that purpose, being the Soil Moisture Active Passive Experiment (SMAPEx)-4 and -5 airborne field campaigns, and the OzNet in situ stations, to determine the relative strengths and weaknesses of their performances. The oversampling-based soil moisture product best captured the temporal and spatial variability of the reference soil moisture overall, though the radar-based products had a better temporal agreement with airborne soil moisture during the short SMAPEx-4 period. Moreover, the difference between temporal analysis of products against in situ and airborne soil moisture reference data sets pointed to the fact that relying on in situ measurements alone is not appropriate for validation of spatially enhanced soil moisture maps. • Benchmarking downscaled product performance against in-situ and airborne data. • A comprehensive inter-comparison of a variety of downscaled soil moisture products. • Determine the downscaling methodology yielding the best soil moisture estimation. [ABSTRACT FROM AUTHOR]

Improving crop productivity is crucial to food security and rural livelihoods in southern Africa. However, agricultural intensification at irrigation schemes may place greater demands on water resources, even where water use efficiency (WUE) is improved, due to the paradox of irrigation efficiency. We analysed trends in water use efficiency (WUE), Gross Primary Productivity (GPP), evapotranspiration (ET), evaporation (E), and transpiration (T) at six irrigation schemes which received technological and social interventions. GPP was decoupled from ET at some schemes. For example, at the Landela sub-block of the Silalatshani irrigation scheme in Zimbabwe, GPP and T increased at 4.8% and 4.6% per year, respectively. Conversely, E decreased at 13.5% per year, giving a 3.6% decrease in ET per year from 2013 to 2021. This suggests that crop production can be decoupled from water consumption at southern African irrigation schemes, meaning it is possible to produce more food with less water. [Display omitted] • Water use efficiency was estimated with remote sensing for smallholder irrigation schemes in southern Africa. • Gross primary productivity (GPP) was decoupled from evapotranspiration (ET) at some schemes. This means more crops have been grown with less water. • Decoupling was not observed at all irrigation schemes and is likely to be context dependent. [ABSTRACT FROM AUTHOR]

This study compared surface soil moisture from 11 separate remote sensing and modelled products across Australia in a common framework. The comparison was based on a correlation analysis between soil moisture products and in situ data collated from three separate ground-based networks: OzFlux, OzNet and CosmOz. The correlation analysis was performed using both original data sets and temporal anomalies, and was supported by examination of the time series plots. The interrelationships between the products were also explored using cluster analyses. The products considered in this study include: Soil Moisture Ocean Salinity (SMOS; both Land Parameter Retrieval Model (LPRM) and L-band Microwave Emission of the Biosphere (LMEB) algorithms), Advanced Microwave Scanning Radiometer 2 (AMSR2; both LPRM and Japan Aerospace Exploration Agency (JAXA) algorithms) and Advanced Scatterometer (ASCAT) satellite-based products, and WaterDyn, Australian Water Resource Assessment Landscape (AWRA-L), Antecedent Precipitation Index (API), Keetch-Byram Drought Index (KBDI), Mount's Soil Dryness Index (MSDI) and CABLE/BIOS2 model-based products. The comparison of the satellite and model data sets showed variation in their ability to reflect in situ soil moisture conditions across Australia owing to individual product characteristics. The comparison showed the satellite products yielded similar ranges of correlation coefficients, with the possible exception of AMSR2_JAXA. SMOS (both algorithms) achieved slightly better agreement with in situ measurements than the alternative satellite products overall. Among the models, WaterDyn yielded the highest correlation most consistently across the different locations and climate zones considered. All products displayed a weaker performance in estimating soil moisture anomalies than the original data sets ( i.e. the absolute values), showing all products to be more effective in detecting interannual and seasonal soil moisture dynamics rather than individual events. Using cluster analysis we found satellite products generally grouped together, whereas models were more similar to other models. SMOS (based on LMEB algorithm and ascending overpass) and ASCAT (descending overpass) were found to be very similar to each other in terms of their temporal soil moisture dynamics, whereas AMSR2 (based on LPRM algorithm and descending overpass) and AMSR2 (based on JAXA algorithm and ascending overpass) were dissimilar. Of the model products, WaterDyn and CABLE were similar to each other, as were the API/AWRA-L and KBDI/MSDI pairs. The clustering suggests systematic commonalities in error structure and duplication of information may exist between products. This evaluation has highlighted relative strengths, weaknesses, and complementarities between products, so the drawbacks of each may be minimised through a more informed assessment of fitness for purpose by end users. [ABSTRACT FROM AUTHOR]

Vegetation fractional cover is a key metric for monitoring land management, both in pastoral and agricultural settings. Monitoring vegetation fractional cover continuously across large areas needs good remote sensing techniques underpinned by high quality field data to calibrate and validate algorithms. Here Landsat and MODIS surface reflectance data together with 1171 field observations across Australia were used to estimate vegetation fractional cover using a linear unmixing technique. The aim was to estimate the fractions of photosynthetic and non-photosynthetic vegetation (PV and NPV, respectively) and the remaining fraction of bare soil (BS). Landsat surface reflectance was averaged over a 3 × 3 pixel window representing the field area measured and also “degraded” using a 17 × 17 pixel window (~ 0.26 km 2 ) to approximate the coarser MODIS sensor's response. These two Landsat surface reflectances were used to calculate a site heterogeneity metric. Data from two MODIS-derived surface reflectance products with a pixel size of ~ 0.25 km 2 were used: (i) the 16-day nadir BRDF-Adjusted Reflectance product (MCD43A4); and (ii) the MODIS 8-day surface reflectance (MOD09A1). Log transforms and band interaction terms were added to account for non-linearities in the spectral mixing. A cross-validation step was also included to select the optimal number of singular values to avoid over-fitting. For each surface reflectance source we investigated the residuals' correlation with site heterogeneity, soil colour and soil moisture. The best model was obtained when Landsat data for a small region around each observation were used. Root mean square error (RMSE) values of 0.112, 0.162 and 0.130 for PV, NPV and BS, respectively, were obtained. As expected, degrading the Landsat data to ~ 0.26 km 2 around each site decreased model goodness of fit to RMSE of 0.119, 0.174 and 0.150, respectively, for the three fractions. Using MODIS surface reflectance data gave worse results than the “degraded” Landsat surface reflectance, with MOD09A1 performing slightly better than MCD43A4. No strong evidence of soil colour or soil moisture influence on model performance was found, suggesting that the unmixing models are insensitive to soil colour and/or that the soil moisture in the top few millimetres of soil, which influence surface reflectance in optical sensors, is decoupled from the soil moisture in the top layer (i.e., a few centimetres) as measured by passive microwave sensors or estimated by models. This study outlines an operational combined Landsat/MODIS product to benefit users with varying requirements of spatial resolution and temporal frequency and latency that could be applied to other regions in the world. [ABSTRACT FROM AUTHOR]

Schmallenberg virus (SBV), an arthropod-borne orthobunyavirus was first detected in 2011 in cattle suffering from diarrhea and fever. The most severe impact of an SBV infection is the induction of malformations in newborns and abortions. Between 2011 and 2013 SBV spread throughout Europe in an unprecedented epidemic wave. SBV contains a tripartite genome consisting of the three negative-sense RNA segments L, M, and S. The virus is usually isolated from clinical samples by inoculation of KC (insect) or BHK-21 (mammalian) cells. Several virus passages are required to allow adaptation of SBV to cells in vitro . In the present study, the porcine SK-6 cell line was used for isolation and passaging of SBV. SK-6 cells proved to be more sensitive to SBV infection and allowed to produce higher titers more rapidly as in BHK-21 cells after just one passage. No adaptation was required. In order to determine the in vivo genetic stability of SBV during an epidemic spread of the virus the nucleotide sequence of the genome from seven SBV field isolates collected in summer 2012 in Switzerland was determined and compared to other SBV sequences available in GenBank. A total of 101 mutations, mostly transitions randomly dispersed along the L and M segment were found when the Swiss isolates were compared to the first SBV isolated late 2011 in Germany. However, when these mutations were studied in detail, a previously described hypervariable region in the M segment was identified. The S segment was completely conserved among all sequenced SBV isolates. To assess the in vitro genetic stability of SBV, three isolates were passage 10 times in SK-6 cells and sequenced before and after passaging. Between two and five nt exchanges per genome were found. This low in vitro mutation rate further demonstrates the suitability of SK-6 cells for SBV propagation. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical systems model, which integrates a simulator of the mine water use process with a multi-objective optimiser for assessing mine water management strategies. The simulator advances prior tools in simulating complicated strategies by placing operation rules on model objects and process states. An optimisation framework that interacts with the simulator is used to identify optimal strategies for water storage, use and reuse that are 'fit-for-purpose', easy to handle, and economic. The model has been used to assess mine water use strategies in a coal mine in Queensland, Australia. The simulation results indicate that some tested strategies could reduce water use costs by more than 40%, and raw water needed by more than 50%. Further, the optimisation revealed more easy-to-handle strategies that could save more than 40% of pipeline water and reduce the risks of penalties associated with discharge and of losing production due to insufficient water. [ABSTRACT FROM AUTHOR]

Abstract: Toggenburg Orbivirus (TOV) is the prototype of bluetongue virus serotype 25 (BTV-25). It was first detected in goats in Switzerland in 2008. The virus does not induce clinical signs in infected goats. In field samples viral RNA could be detected only in goats and never in other ruminants. BTV-25 RNA was repeatedly detected for more than one year in the blood of goats from a single flock in Principality of Liechtenstein. Since viral persistence over such a long period has never been reported for bluetongue, blood samples from 110 goats and 2 sheep of that flock were collected during a period of up to two years and analyzed for the presence of BTV-25 RNA and antibodies. Most of the animals which tested positive for BTV-25 RNA, remained positive during the whole investigation period. Moreover, five of these goats were BTV-25 RNA positive over a period of 19–25 months. A weak antibody response against BTV VP7 was commonly observed. As BTV-25 cannot be propagated in any culture system, the presence of virus could only be demonstrated in samples by viral RNA detection using RT-qPCR. To address the question of infectivity of the virus in blood from long-term positive animals, goats were experimentally infected with this blood. Viral replication was demonstrated by increasing RNA amounts. Thus, our findings provide evidence that BTV-25 can persist much longer in an infected host than known so far for other BTV serotypes. Hence, persistence of infectious BTV represents an additional important factor in BTV epidemiology. [Copyright &y& Elsevier]

ABSTRACT: Importance: NEC is a common and severe complication in premature neonates, particularly those with very-low-birth-weight (VLBW, <1500 g at birth). Probiotics including lactobacillus rhamnosus GG (LGG) proved effective in preventing NEC in preterm infants in several RCTs. Objective: Lactoferrin, a mammalian milk glycoprotein involved in innate immune host defences, can reduce the incidence of NEC in animal models, and its action is enhanced by LGG. We tried to assess whether bovine lactoferrin (BLF), alone or with the probiotic LGG, has a similar effect in human infants, something that has not yet been studied. Design: An international, multicenter, randomized, double-blind, placebo-controlled trial conducted from October 1st, 2007 through July 31st, 2010. Setting: Thirteen Italian and New Zealand tertiary neonatal intensive care units. Participants: 743 VLBW neonates were assessed until discharge for development of NEC. Intervention: Infants were randomly assigned to receive orally either BLF (100 mg/day) alone (group LF; n = 247) or with LGG (at 6×109 CFU/day; group BLF + LGG; n = 238), or placebo (Control group; n = 258) from birth until day 30 of life (45 for neonates <1000 g at birth). Main outcome measures: ≥ stage 2 NEC; death-and/or-≥ stage 2 NEC prior to discharge. Results: Demographics, clinical and management characteristics of the 3 groups were similar, including type of feeding and maternal milk intakes. NEC incidence was significantly lower in groups BLF and BLF + LGG [5/247 (2.0%)] and 0/238 (0%), respectively] than in controls [14/258 (5.4%)] (RR = 0.37; 95% CI: 0.136–1.005; p = 0.055 for BLF vs. control; RR = 0.00; p < 0.001 for BLF + LGG vs. control). The incidence of death-and/or-NEC was significantly lower in both treatment groups (4.0% and 3.8% in BLF and BLF + LGG vs. 10.1% in control; RR = 0.39; 95% CI: 0.19–0.80; p = 0.008. RR = 0.37; 95% CI: 0.18–0.77; p = 0.006, respectively). No adverse effects or intolerances to treatment occurred. Conclusions and relevance: Compared with placebo, BLF supplementation alone or in combination with LGG reduced the incidence of ≥ stage 2 NEC and of death-and/or ≥ stage 2 NEC in VLBW neonates. BLF might be a promising strategy to prevent NEC in NICU settings. Further data on larger sample sizes are warranted before BLF can be widespreadly used in clinical settings. Trial registration: ISRCTN53107700–http://www.controlled-_trials.com/ISRCTN53107700. [Copyright &y& Elsevier]