Abstract:The Moderate Resolution Imaging Spectroradiometer (MODIS), flown on board the Terra Earth Observing System (EOS) platform launched in December 1999, produces a snow-covered area (SCA) product. This product is expected to be of better quality than SCA products based on operational satellites (notably GOES and AVHRR), due both to improved spectral resolution and higher spatial resolution of the MODIS instrument. The gridded MODIS SCA product was compared with the SCA product produced and distributed by the National Weather Service National Operational Hydrologic Remote Sensing Center (NOHRSC) for 46 selected days over the Columbia River basin and 32 days over the Missouri River basin during winter and spring of 2000-01. Snow presence or absence was inferred from ground observations of snow depth at 1330 stations in the Missouri River basin and 762 stations in the Columbia River basin, and was compared with the presence/absence classification for the corresponding pixels in the MODIS and NOHRSC SCA products. On average, the MODIS SCA images classified fewer pixels as cloud than NOHRSC, the effect of which was that 15% more of the Columbia basin area could be classified as to presence-absence of snow, while overall there was a statistically insignificant difference over the Missouri basin. Of the pixels classified as cloud free, MODIS misclassified 4% and 5% fewer overall (for the Columbia and Missouri basins respectively) than did the NOHRSC product. When segregated by vegetation cover, forested areas had the greatest differences in fraction of cloud cover reported by the two SCA products, with MODIS classifying 13% and 17% less of the images as cloud for the Missouri and Columbia basins respectively. These differences are particularly important in the Columbia River basin, 39% of which is forested. The ability of MODIS to classify significantly greater amounts of snow in the presence of cloud in more topographically complex, forested, and snow-dominated areas of these two basins provides valuable information for hydrologic prediction.

Abstract. This research examines the feasibility of using remotely sensed surface temperature for validation and updating of land surface hydrologic models. Surface temperature simulated by the Variable Infiltration Capacity (VIC) hydrologic model is compared over the Arkansas-Red River basin with surface temperature retrievals from TOVS and GOES. The results show that modeled and satellite-derived surface temperatures agree well when aggregated in space or time. In particular, monthly mean temperatures agree on the pixel scale, and basin mean temperatures agree instantaneously. At the pixel scale, however, surface temperatures from both satellites were found to have higher spatial and temporal variabilities than the modeled temperatures, although the model and satellites display similar patterns of variability through space and time. The largest differences between modeled and remotely sensed surface temperature variability occur at times of maximum net radiation both diurnally and seasonally, i.e., afternoon and summer. Comparison of temporal and spatial patterns of VIC-predicted surface temperature variability with similar predictions by nine other models involved in the PILPS-2c experiment show that the VIC patterns are similar to those of the other models. Observed surface temperature and air temperature from FII•'E are used to identify possible errors in satellite-retrieved surface temperatures. The lql•E comparisons show that satellite retrieved surface temperatures likely contain errors that increase variability.

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Remote sensing observations increasingly are used to obtain the detailed information needed about land surface state required for hydrological analyses. However, many of the surface state parameters required for such analyses are related to the vertical structures of surface vegetation and topography, that are often difficult to measure using passive optical and radar remote sensing technologies. A new technology, lidar (light detection and ranging) remote sensing, has proven highly effective for characterizing land surface structure in great detail, including subcanopy topography, canopy height, foliar profile and biomass, among others. In this article, we review the theory and application of lidar remote sensing for characterizing land surface states for hydrological analysis. First we present a brief overview of lidar, and discuss similarities and distinctions between the small‐footprint systems commonly used in commercial applications and large‐footprint approaches used in research and space‐based systems. We next describe common land surface variables that are often used in hydrological analysis and how these are related to vertical structures observable from lidar. Lastly, we present how lidar may be used to recover or parameterize these surface variables; in particular we focus on the observation of forest and topographic structures.