Snow Cover and SWE


 

MODIS and AMSR-E Snow Products Validation and Applications

Snow cover pattern has a significant impact on climate processes, surface hydrological cycles and ecological processes (Simpson et al., 1998; Simic et al., 2004). Frequent and long term snow observation and accurate snow cover (SC) mappings and snow water equivalent (SWE) estimates are critical for snowmelt-runoff prediction, operational flood control, water supply planning, and water resource management in snow cover-dominated basins (Gutzler & Rosen, 1992; Brown, 2000; Dressler et al., 2006; Pulliainen´╝î2006). Conventional ground snow monitoring is normally based on point measurements. However, sparse point observation networks can not provide accurate data on regional and global scales, due to their low density even no existence especially at inaccessible mountainous or high latitude regions (Derksen et al., 2005). Satellite images are readily available and provide spatio-temporal characteristics needed for snow monitoring and snow cover mapping. Satellite data from visible and infrared spectrum as well as from passive microwaves provide important sources of information on snow cover. Since the middle of the 1960s, a number of satellite-derived snow products have been available, with a few available in near-real time through Internet (Bitner et al, 2002).

Space-board passive microwave radiometer, such as SMMR (Scanning Multichannel Microwave Radiometer), SSM/I (Special Sensor Microwave/Imager), and AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System), can penetrate clouds to detect microwave energy emitted by snow and ice and provide information on SWE or snow depth and thus estimating runoff (Pulliainen & Hallikainen´╝î2001; Wulder et al., 2000). Since the 1970s, SWE retrieval from space-borne passive microwave has been investigated (Pulliainen, 2006; Derksen et al., 2005). Space-borne passive microwave data are well suited to snow cover monitoring because of characteristics such as all weather imaging, a wide swath width with frequent overpass times, and a long available time series (Derksen et al., 2004). But the coarse spatial resolution (25 km of AMSR-E is the best available now) hinders their application in operational hydrological modeling and snow-caused disasters monitoring (kelly et al., 2003; Dressler, et al. 2006; Pulliainen, 2006). Optical sensors such as AVHRR (Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectraradiometer), SPOT and Landsat have been well developed to produce snow cover maps with high spatial resolution (Salonmonson & Appel, 2004; Brown et al., 2007; Dozier & Painter, 2004). But due to the inherent limitation, optical sensors cannot see the earth surface when cloud is present. High cloud blockage becomes the biggest problem in applying snow products from optical sensor (Klein & Barnett, 2003; Zhou et al., 2005; Tekeli et al., 2005; Ault et al., 2006; Liang et al. 2008 a, b; Wang et al., 2008a, b; Wang and Xie, 2008).

Objectives

The goals of the LRSG group for the MODIS and AMSR-E snow products study, supported by the DoEd, USGS, and Chinese NSF, are to:

  1. Validate the MODIS and AMSR-E snow products
  2. Develop algorithms to general advanced daily and/or multi-day MODIS/Terra and Aqua and AMSR-E/Aqua snow cover and SWE products, with a purpose of reducing cloud contamination and increase spatial resolution of SWE product
  3. Use the new products to generate image-based snow cover onset date, snow melt onset date, snow cover duration date, and snow cover index to better study the snow cover spatial and temporal variations, and their climate connection
  4. Perform snow-runoff modeling and predict the water availability for regions depending on snow pack