43. Perspectives of Using MODIS and VIIRS Observations on Snow
Fraction for Modeling Surface Properties
Author：
Igor Appel
Affiliation： I.M. Systems Group
Snow cover dramatically influences the processes of land-atmosphere interaction as
well as retrieval of various atmosphere and land products, which explains an
increasing interest to implementation of remote sensing information on snow cover
in a wide range of applications including data assimilation and radiative transfer
models.
Recent changes in data assimilation introduced the Northern Hemisphere snow
analysis created on the basis of remote sensing into the global European Center for
Medium-Range Weather Forecasts (ECMWF) model.
The progress in the
development of Community Radiative Transfer Model (CRTM) included modeling
of multi-layer snow emissivity depending on snow properties
More realistic description of surface properties includes allowing for fractional snow
cover in cells (grid boxes) especially significant for the period of melting. The
National Polar-Orbiting Environmental Satellite System (NPOESS) Preparatory
Project (NPP) and the Joint Polar Satellite System (JPSS) will generate snow cover
Environmental Data Records (EDR), including fractional snow cover, from the
Visible Infrared Radiometer Suite (VIIRS) instrument. It has been decided to
develop improved VIIRS fractional snow cover algorithm for NPP on the basis of the
heritage Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm
currently routinely employed to generate fractional snow cover. A central feature of
the MODIS snow cover algorithms is the Normalized Difference Snow Index
(NDSI). Salomonson and Appel demonstrated that the NDSI approach could be
extended to calculate the fraction of snow cover in a pixel and showed that a NDSI
based regression technique provided fairly robust determination of the fraction of
snow cover within 500 m cells. The task of snow remote sensing is significantly
complicated by variability of natural conditions. Improved snow cover retrieval is
achievable by an algorithm that takes into account the variability in space and time
of snow and non-snow spectral signatures. The enhancement could be made by
employing scene-specific parameters characterizing local properties of snow and
non-snow spectral endmembers.Proper validation is a critically important means to
develop snow cover retrieval. Daily snow depth data acquired from more than 1000
World Meteorological Organization (WMO) stations and approximately 1500 US
Cooperative stations are currently used to estimate the accuracy of snow derivation
from simulated VIIRS observations. Another method implemented to analyze the
performance of snow algorithms utilizes high-resolution observations as an effective
source of ground truth information.
The results of validation valuable to tune algorithms for better performance confirm
that the development of scene-based algorithms suppressing sensitivity to viewing
geometry, illumination, snow, and non-snow cover conditions is a very powerful way
to significantly improve the accuracy of fractional snow cover derivation.