The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) “Index-then-Blend” (IB); and (ii) “Blend-then-Index” (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R²; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm.

Study region: Australian continent. Study focus: With increasing groundwater development around the world, a method is required to identify and map groundwater dependent ecosystems (GDEs) across broad landscape scales. Identifying the location of GDEs, will ensure that the environmental impacts of increasing water development are understood and will lead to better management of water resources to protect GDEs. In this study, a method is demonstrated that underpinned the development of an online national GDE mapping tool in Australia (GDE Atlas; http://www.bom.gov.au/water/groundwater/gde/map.shtml). Known GDEs and their locations were extrapolated to regional scales using a process that relied on the integration of expert opinion, remote sensing data obtained between 2000 and 2010 and GIS analysis. New hydrological insights: It was identified that 34% of Australia’s landscape potentially contains GDEs of which 5% are classified with a high GDE potential. In addition, new continental scale insights into landscape processes where provided by the derivation and integration of remote sensing products using MODIS and Landsat. These products identify landscapes which are ‘wetter’ or ‘greener’ than surrounding areas, indicating these landscapes are accessing additional water, such as groundwater, supplementary to rainfall. The method reported also demonstrates the importance of expert knowledge, obtained through literature and expert elicitation, in order to provide a conceptual understanding of regional ecohydrological processes to develop rules of GDE dependency that would guide the extrapolation of known GDEs