Towards an integrated geographic analysis system with remote sensing, GIS and consecutive modelling for snow cover monitoring

Distributed hydrological model for mapping evapotranspiration using remote sensing inputs

“…The application of remote sensing techniques in hydrological studies and water resources management has progressed in the past decades (see review by Kite and Pietroniro, 1996). In general, remote sensing data are used in the following ways (Ritchie and Rango, 1996;Kite and Pietroniro, 1996;Schultz, 1996): (i) original remote sensing imagery is directly used to identify hydrologically significant areal phenomena, such as flooded areas (Barber et al, 1997;Brivio et al, 2002;Islam and Sado, 2002), snow cover (Baumgartner and Apfl, 1994;Tait et al, 2000) or plumes (Ouillon et al, 1997); (ii) processed remote sensing data are used to provide fields of hydrological parameters, such as precipitation (Kite and Pietroniro, 1996;Wang et al, 2001), and soil moisture (Jackson, 1993;Hollenbeck et al, 1996;Kim and Barros, 2002). This requires the understanding and development of relationships between electromagnetic signals and hydrological parameters of interest; and (iii) multispectral remote sensing data are used to quantify surface parameters, such as vegetation (land cover) types and density.…”

On the usefulness of remote sensing input data for spatially distributed hydrological modelling: case of the Tarim River basin in China

“…Many recent studies have proven the advantage of supplying distributed hydrological models with RS data. For instance, flood mapping (Brakenridge et al , 1994; Barber et al , 1996) and snow cover estimation (Baumgartner and Apfl, 1994; Tait et al , 2000) supported by RS data sets can be considered as direct applications. Indirect applications include hydrological analysis and modelling (Engman, 1996; Kite and Pietroniro, 1996): Caparrini et al (1998) derived soil hydrological parameters from Landsat TM data; Biftu and Gan (2001) assimilated RS data in a semi‐distributed model in support of model parameterization; Chen et al (2005) implemented various spatial meteorological data sets and leaf area index (LAI) derived from Landsat in a small Canadian watershed to track the impact of potential ET; Stisen et al (2008) used RS‐based precipitation and ET to simulate surface runoff.…”

Improving Distributed Runoff Prediction in Urbanized Catchments with Remote Sensing based Estimates of Impervious Surface Cover

“…In general, remote sensing data are used for hydrological modelling in the following ways: (1) to quantify surface parameters, such as land-cover type and density [4, 7] or surface roughness [8, 9]; (2) to identify hydrologically significant areal phenomena for spatial model output verification, such as flooded areas [10-12] and snow cover [13, 14]; (3) to produce field representations of hydrologically important parameters, such as soil moisture and leaf area index (LAI), used for calculation of interception and evapotranspiration, and thus the water balance of a watershed [15-18]. …”