S6–P1

㻕㻖㻕

S6–P2㻕㻖㻖S6–P3

Visible Remote Sensing Information for Snow Monitoring
Igor Appe,IMSG/NOAA

iappel@earthlink.net
The launch of advanced instruments onboard of new operational and research satellites
greatly improves the potential of mapping and monitoring snow cover. Increasing spectral
coverage of the data and frequency of observations allows for more accurate snow coverdetection and timely identification of changes in the snow pack properties. A higher spatial
resolution of satellite observations provides better opportunity for a much more detailedcharacterization of the snow cover distribution needed for various applications. As a resultof the advancement in remote sensing, the information on snow cover from satellites isbeing increasingly used not only in global and continental-scale studies but also in regional
and local scale investigations. Snow monitoring to a great degree depends on using visible
channels even though such data of satellite observations could have significant gaps.
The advance in snow remote sensing is accompanied by increasing requirements tosnow products – resolution finer than 1 km, snow fraction in addition to snow extent, awider range of snow properties. To meet those growing requirements, improved retrieval
algorithms and snow models should be developed for visible spectral bands.
A promising direction to create automatic snow retrievals of improved quality isdeveloped by the author of the presentation and implemented in several algorithms. The
algorithms were accepted to retrieve all the Visible Infrared Radiometer Suite (VIIRS)
snow products in NOAA and have been successfully used to routinely generate daily global
maps of snow cover fraction from the Moderate Resolution Imaging Spectroradiometer(MODIS) at GSFC/NASA.
Proper validation is a critically important means to develop snow cover retrieval. Dailysnow depth data acquired from more than 1000 World Meteorological Organization(WMO) stations and approximately 1500 US Cooperative stations are currently used toestimate the accuracy of snow derivation from daily VIIRS observations. Another method
implemented to analyze the performance of snow algorithms utilizes high-resolution
observations as an effective source of ground truth information for snow fraction.
To further improve processing of satellite observations on snow, the strategy
characterized by the following distinguishing features is currently under development.
Creating snow algorithms, first of all, it is proposed to derive scene-specific (or evenpixel-specific) parameters characterizing local properties of snow and non-snowendmembers. The application of such approach requires adjustment of algorithms tovarying regional conditions.
Another opportunity to improve snow retrieval is related to the use of snow BRDF
models. It has been demonstrated that the means of geometric optics could reliably describe
angular dependencies related to bidirectional snow reflectance, and a simple asymptotic
analytical model could be used to calculate bidirectional reflectance. Possible applicationsof the asymptotic analytical BRDF model for remote sensing include sensor calibration,
calculating snow albedo, normalizing reflectances, and retrieval of snow grain size needed,in particular, for microwave observations on snow.
The combination of scene-specific algorithms with analytical BRDF model and synergyof remote sensing retrieval algorithms with conventional observations will help create
unbiased and consistent information on snow cover.

㻕㻖㻗