Abstract
Synthetic aperture radar (SAR) data are often used to determine the physical properties of the soil surface, such as soil moisture and surface roughness. Although these analyses are commonly applied in agricultural environments, there has been limited application in more natural environments, particularly at high latitudes. For the research reported here, an artificial neural network (ANN) is developed to model surface roughness in the Canadian High Arctic. This research represents the first phase of the overall goal of developing an operational methodology for estimating surface roughness, vegetation cover and soil moisture using SAR and limited field measurements. Multiple incidence angle data and fully polarimetric data from RADARSAT-2 are combined with long and short profile in situ surface roughness measurements from 134 sample locations located across two distinct High Arctic study sites. Multiple ANN models were developed using various backscatter, textural, and polarimetric variables. The ANN models exhibited a moderate to strong agreement to field-measured surface roughness. This study demonstrates that operational surface roughness modeling in the Canadian High Arctic is feasible with RADARSAT-2 polarimetric data.
Published Version
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