Abstract
Various methods are used to determine soil moisture information from synthetic aperture radar (SAR) data, but none specific to High Arctic regions and their unique physical characteristics. This research presents a method for determining, at high spatial and temporal resolutions, surface soil moisture and its changes through time in the Canadian High Arctic. An artificial neural network (ANN) is implemented using input variables derived from RADARSAT-2 SAR data and previously modelled surface roughness information. The model is applied to SAR data collected at various incidence angles and acquisition dates across two study sites on Melville Island, Nunavut. The model results in absolute soil moisture errors of approximately 15% (r2 = 0.46) for the primary study sites and 12% (r2 = 0.26) for the verification study area. The ANN model is accurate for modelling (i) the spatial distribution of soil moisture and (ii) the changes in moisture through time across the study areas, two characteristics that are very important for inputs to hydrologic or climate models. In addition, the models appear to be scalable when applied at coarser spatial resolutions, showing potential for large-area mapping or modelling.
Highlights
Site Description. e majority of the field work for this study was undertaken at the Cape Bounty Arctic Watershed Observatory (CBAWO), located on the southern coast of Melville Island, Nunavut, Canada (74.91°N, 109.44°W) (Figure 1) in 2009 and 2010. e CBAWO was used to develop, calibrate, and validate the Artificial neural networks (ANNs) models. is High Arctic site is composed of two parallel watersheds, each covering approximately 15 km2. e area is characterized by rolling topography of low to medium relief, with elevation varying between 5 m and 125 m above sea level. e site has been impacted by periods of glaciation, during which various tills have been deposited in the study region, including Bolduc, Dundas, and Winter Harbour tills [47]
An unsupervised classi cation was conducted on the GeoEye imagery, to distinguish between the three main vegetation communities in the region. e digital elevation model (DEM) and unsupervised classi cation results were used to set up a strati ed random sampling scheme across three elevations (90 m) and three vegetation classes (Figure 2). e number of samples across each vegetation class was determined on a relative basis by the spatial coverage of each class in the unsupervised classi cation. e elevation groupings were chosen to exploit the current knowledge of di erent till layers, with marine sediments thought to be present between approximately 35 m and 90 m above sea level, as explained previously
E model was created from a single date and beam mode (July 11, 2010, U75) and applied to multiple dates and beam modes, a methodology necessary to model soil moisture through time. e model was applied to the CBAWO, where it was calibrated, and the Cape Collingwood study area, where it was validated under different biophysical and geological conditions
Summary
Information gathered from synthetic aperture radar (SAR) is ideal for the spatial estimation or modelling of soil moisture, at a range of different spatial scales [20,21,22]. In many cases, these data can be gathered instantaneously over large areas, day or night, in any weather, which is a tremendous advantage over traditional techniques. Highresolution multi-incidence angle SAR data from sensors such as RADARSAT-2 have not been examined to any significant degree for their utility in modelling surface moisture conditions in the High Arctic at fine spatial scales. Given the spatial detail captured by RADARSAT-2 SAR data and the utility of ANNs for modelling environmental
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