Recent advances in radar remote sensing techniques illustrate the potential for monitoring soil moisture conditions at spatial and temporal scales required for regional and local modeling efforts. This research examined the feasibility of producing accurate and spatially distributed estimates of soil moisture using a time series of ERS-2 radar images for a tallgrass prairie ecosystem in northeast Kansas. Methods used included field data collection of soil moisture, digital image interpretation of optical (NOAA AVHRR and LANDSAT TM) and radar (ERS-2) imagery, and environmental modeling in a raster geographic information system (GIS) and image processing environment. Critical to this study was determining the scattering behavior of overlying vegetation, or the contribution of vegetation backscatter ( sigmaoveg) to the total backscatter coefficient (sigmao total), which was simulated using a modified water cloud model. By removing sigmaoveg from sigmaototal, the amount of backscatter contributed by the soil surface (sigmaosoil) was isolated and the linear relationship between sigmaosoil and volumetric soil moisture determined. Single-date correlations averaged r = 0.62 and r = 0.67 for a burned and unburned watershed, respectively, within the study area. While previous studies have questioned the sensitivity of C-band radars to near-surface soil moisture conditions, these results show that ERS-2 data may be capable of monitoring soil moisture conditions over even extremely dense natural grassland vegetation.
Estimating near-surface soil moisture using active microwave satellite imagery and optical sensor inputs
Transactions of the ASAE ; 46 , 2 ; 225-236
2003
12 Seiten, 41 Quellen
Article (Journal)
English
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