HR: 0800h
AN: B51B-0204    [Abstracts]
TI: Simultaneous Retrieval of Aerosol and Snow/ice Properties Using Multi- and Hyperspectral Data
AU: Li, W
EM: wli1@odin.mat.stevens-tech.edu
AF: Stevens Institute of Technology, Department of Physics and Engineering Physics, Light and Life Laboratory, Castle Point on Hudson, Hoboken, NJ 07030 United States
AU: * Eide, H
EM: heide@stevens-tech.edu
AF: Stevens Institute of Technology, Department of Physics and Engineering Physics, Light and Life Laboratory, Castle Point on Hudson, Hoboken, NJ 07030 United States
AU: Stamnes, K
EM: kstamnes@stevens.edu
AF: Stevens Institute of Technology, Department of Physics and Engineering Physics, Light and Life Laboratory, Castle Point on Hudson, Hoboken, NJ 07030 United States
AU: Spurr, R
EM: rtsolutions@verizon.net
AF: RT Solutions Inc., 9 Channing Street, Cambridge, MA 02138 United States
AU: Aoki, T
EM: teaoki@mri-jma.go.jp
AF: Meterological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki, 305-0052 Japan
AU: Hori, M
EM: hori@eorc.jaxa.jp
AF: Japan Aerospace Exploration Agency, Harumi 1-8-10,Chuo-Ku, Tokyo, 104-6023 Japan
AB: Retrieval of surface properties of highly reflecting targets such as snow and ice is a challenging problem due to the influence of aerosols, which vary considerably in space and time. Also, accounting for the bidirectional properties of a bright surface such as snow is very important for reliable retrievals. Here we explore the opportunities and possibilities offered by multi- and hyperspectral data, such as those provided by the MODIS, GLI, VIIRS, the Advanced Land Imager (ALI), and Hyperion sensors, to retrieve reliable aerosol and surface properties. Over snow and ice surfaces these properties include aerosol optical depth, single scattering albedo, the mean size of snow grains and ice 'particles' (inclusions), and the spectral and broadband snow/ice albedo. We emphasize the use of linearized forward radiative transfer models for the coupled snow/ice system, because it allows us to compute weighting functions (Jacobians) required in state-of-the-art nonlinear, iterative inversion schemes. By using several wavelengths with different penetration depths we may retrieve depth information about snow properties, and thereby accurate spectral albedo of snow. In particular the following question will be addressed: To what extent can multi- and hyperspectral data help improve our knowledge of snow and ice parameters that are important for understanding global climate change?
DE: 0480 Remote sensing
DE: 0758 Remote sensing
DE: 1855 Remote sensing (1640)
DE: 1863 Snow and ice (0736, 0738, 0776, 1827)
DE: 4275 Remote sensing and electromagnetic processes (0689, 2487, 3285, 4455, 6934)
SC: Biogeosciences [B]
MN: Fall Meeting 2005