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In this study we tested the feasibility of the thermal infrared (TIR) wavelength region (within the atmospheric window between 8 and 11.5 µm) together with the traditional solar reflective wavelengths for quantifying soil properties for coarse-textured soils from the Australian wheat belt region. These soils have very narrow ranges of texture and organic carbon contents. Soil surface spectral signatures were acquired in the laboratory, using a directional emissivity spectrometer (µFTIR) in the TIR, as well as a bidirectional reflectance spectrometer (ASD FieldSpec) for the solar reflective wavelengths (0.4-2.5 µm). Soil properties were predicted using multivariate analysis techniques (partial least square regression). The spectra were resampled to operational imaging spectroscopy sensor characteristics (HyMAP and TASI-600). To assess the relevance of specific wavelength regions in the prediction, the drivers of the PLS models were interpreted with respect to the spectral characteristics of the soils' chemical and physical composition. The study

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The Earth’s surface comprises minerals diagnostic of weathering, deposition and erosion. The first continental-scale mineral maps generated from an imaging satellite with spectral bands designed to measure clays, quartz and other minerals were released in 2012 for Australia. Here we show how these satellite mineral maps improve our understanding of weathering, erosional and depositional processes in the context of changing weather, climate and tectonics. The clay composition map shows how kaolinite has developed over tectonically stable continental crust in response to deep weathering during northwardly migrating tropical conditions from 45 to 10 Ma. The same clay composition map, in combination with one sensitive to water content, enables the discrimination of illite from montmorillonite clays that typically develop in large depositional environments over thin (sinking) continental crust such as the Lake Eyre Basin. Cutting across these clay patterns are sandy deserts that developed <10 Ma and are well mapped using another satellite product sensitive to the particle size of silicate minerals. This product can also be used to measure temporal gains/losses of surface clay caused by periodic wind erosion (dust) and rainfall inundation (flood) events. The accuracy and information content of these satellite mineral maps are validated using published data.

Abstract. Methane emissions across Queensland’s Surat Basin, Australia, result from a mix of activities, including the production and processing of coal seam gas (CSG). We measured methane concentrations over 1.5 years from two monitoring stations established 80 km apart on either side of the main CSG belt located within a study area of 350 × 350 km2. Coupling bottom-up inventory and inverse modelling approaches, we quantify methane emissions from this area. The inventory suggests that the total emission is 173 × 106 kg CH4/yr, with grazing cattle contributing about half of that, cattle feedlots 25 %, and CSG Processing 8 %. Using the inventory emissions in a forward regional transport model indicates that the above sources are significant contributors to methane at both monitors. However, the model underestimates approximately the highest 15 % of the observed methane concentrations, suggesting underestimated or missing emissions. An efficient regional Bayesian inverse model is developed, incorporating an hourly source-receptor relationship based on a backward-in-time configuration of the forward regional transport model, a posterior sampling scheme, and the hourly methane observations. The inferred emissions obtained from one of the inverse model setups that uses a Gaussian prior whose averages are identical the gridded bottom-up inventory emissions across the domain with an uncertainty of 3 % of the averages best describes the observed methane. Having only two stations is not adequate at sampling distant source areas of the study domain, and this necessitates a small prior uncertainty. This inverse setup yields a total emission that is very similar to the total inventory emission. However, in a subdomain covering the CSG development areas, the inferred emissions are 33 % larger than those from the inventory.

Summary This study assessed the feasibility of remote mapping and, thus, monitoring of soils contaminated by acid mine drainage. We report on the use of laboratory and airborne spectroscopy to determine pH. Reflectance spectra were obtained for rock and soil samples collected at our test site, the abandoned Brukunga Pyrite Mine in South Australia, using a laboratory‐based Analytical Spectral Devices Inc. (ASD) spectroradiometer. A partial least squares (PLS) regression was used to develop a predictive equation for pH based on the reflectance spectra. The validation results indicated that it is possible to generate satisfactory predictions of pH from spectral data, as demonstrated by the ratio of performance to deviation (RPD) of 1.53, a relatively small root mean square error of prediction (RMSE) of 0.91 and an R2 value of 0.58. Evaluation of the predictive equation indicated that it depends on diagnostic spectral features related to secondary iron minerals resulting from acid mine drainage (AMD). The presence of these minerals was validated independently using X‐ray diffraction (XRD). The predictive equation was applied to airborne hyperspectral data, using a form of remote sensing that simultaneously acquires spatially co‐registered images in many spectrally contiguous bands (> 50). Hyperspectral data acquired by the HyMap sensor between 1998 and 2001 were used to produce multi‐temporal pH maps. Despite the inaccuracy of the global positioning system (GPS), locations of the validation samples and geographical inaccuracy of the airborne imagery, validation of the maps indicated that pH can be generated reliably from airborne hyperspectral data, as indicated by the relatively small RMSE of 0.57 and the R2 value of 0.72. These maps demonstrate the potential to provide environmental practitioners, including soil scientists, with a spatially comprehensive view of pH related to AMD conditions. This has implications for the application of remotely sensed hyperspectral data for monitoring soil pH related to AMD conditions, especially in the near future when such data will be available from satellite sensors such EnMap (Stuffler et al., 2009). Some examples of their use include a better understanding of the progress of restoration efforts and or to pinpoint areas where future efforts should be concentrated and to evaluate the extent of downstream impacts, including contamination of soil. More generically, soil pH data are routinely required by soil scientists as part of the suite of data for understanding soil characteristics and the ability to provide spatially comprehensive soil pH data, as demonstrated by this study, would be valuable.

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