Pergamon www.elsevier.nl/locatelas zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Adv. Space Res. Vol. 24, No. 7, pp. 935-940, 1999 0 1999 CCISPAR. Published by Elsevier Science Ltd. All rights reserved Printed in Great Britain 0273- 1177199 $20.00 + 0.00 273-I 177(99)00367-l zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONM SPATIAL ESTIMATION OF SOIL MOISTURE USING SYNTHETIC APERTURE RADAR IN ALASKA N. G. Meade, L. D. Hinzman, and D. L. Kane ABSTRACT A spatially distributed Model of Arctic Thermal and Hydrologic processes (MATH) has been developed. One of the attributes of this model is the spatial and temporal prediction of soil moisture in the active layer. The spatially distributed output from this model required verification data obtained through remote sensing to assess performance at the watershed scale indep~dently. Therefore, a neural network was trained to predict soil moisture contents near the ground surface. The input to train the neural network is synthetic aperture radar (SAR) pixel value, and field measurements of soil moisture, and vegetation, which were used as a surrogate for surface roughness. Once the network was trained, soil moisture predictions were made based on SAR pixel value and vegetation. These results were then used for comparison with results from the hydrologic model. The quality of neural network input was less than anticipated. Our digital elevation model (DEM) was not of high enough resolution to allow exact co- registration with soil moisture measurements; therefore, the statistical correlations were not as good as hoped. However, the spatial pattern of the SAR derived soil moisture contents compares favorably with the hydrologic MATH model results. Primary surface parameters that effect SAR include topography, surface roughness, vegetation cover and soil texture. Single parameters that are considered to influence SAR include incident angle of the radar, pol~i~tion of the radiation, signal strength and returning signal integration, to name a few. These factors influence the reflectance, but if one adequately quantities the influences of terrain and roughness, it is considered possible to extract information on soil moisture from SAR imagery analysis and in turn use SAR imagery to validate hydrologic models. 0 1999 COSPAR.Published by Elsevier Science Ltd. Soil moisture is highly variable both spatially and temporally, primarily as a result of the heterogeneous nature of soil properties, topography, land cover, and the nonuniformity of rainfall and evapotranspiration. A soil moisture measurement at a scale larger than a point is difficult to obtain (Engmanand Gurney, 1991). It is envisaged that new remote monito~ng techniques and the development of suppo~ng models will make the operational estimation of soil moisture through remote sensing a viable option. A unique characteristic of the Arctic is the existence of continuous permafrost; this permafrost produces a very shallow subsurface system called the active layer that is important both biologically and hydrologically. This shallow (25 cm to 100 cm) soil layer is generally made up of a layer of organic soil with underlying mineral soil that completely freezes during winter and reaches a maximum depth of thaw by summer’s end. Carbon-rich arctic soils have the potential to produce large amounts of greenhouse gases when decomposition exceeds photosynthesis. In the past, these soils have accumulated large amounts of carbon. However, such soils could reverse from a sink to a source of carbon under a warming climate scenario. Soil moisture is one of the critical variables that influences soil decomposition and dete~ines the ma~i~de of the emitting methane and carbon dioxide fluxes (~eeb~g and Whalen 1992, Oechel et al., 1993). Spatial and temporal variation of soil moisture is useful as input variables into models for the purpose of prediction of these gas fluxes (Vourlitis et al., 1993). A Model of Arctic Thermal and 935