HR: 1330h
AN: ED22C-1246    [PDF]
TI: Validating the MODIS snow product with GLOBE student observations
AU: * Czajkowski, K P
EM: kczajko@utnet.utoledo.edu
AF: University of Toledo, 2801 W. Bancroft St., Toledo, OH 43606 United States
AU: Spongberg, A
EM: aspongb@utnet.utoledo.edu
AF: University of Toledo, 2801 W. Bancroft St., Toledo, OH 43606 United States
AU: Struble, J
EM: jstrubl2@utnet.utoledo.edu
AF: University of Toledo, 2801 W. Bancroft St., Toledo, OH 43606 United States
AU: Benko, T
EM: tbenko@utnet.utoledo.edu
AF: University of Toledo, 2801 W. Bancroft St., Toledo, OH 43606 United States
AU: Templin, M
EM: mtempli@utnet.utoledo.edu
AF: University of Toledo, 2801 W. Bancroft St., Toledo, OH 43606 United States
AU: Ault, T
EM: tault@utnet.utoledo.edu
AF: University of Toledo, 2801 W. Bancroft St., Toledo, OH 43606 United States
AU: Witter, J
EM: jwitter@utnet.utoledo.edu
AF: University of Toledo, 2801 W. Bancroft St., Toledo, OH 43606 United States
AB: For this project, we validated the Moderate Resolution Imaging Spectroradiometer (MODIS) snow product and cloud masking algorithms using GLOBE student, SATELLITES (a K-12 program developed at the University of Toledo) and National Weather Service (NWS) Cooperative Extension observations. The study area is the lower Great Lakes region that includes the lake effect snowbelt areas to the east of Lakes Michigan and Erie. Student observations were taken during intense field campaigns with the winter of 2001-2002 having very little snow and 2000-2001 and 2002-2003 having significant snow cover. The student observers are able to gather data over a large spatial area that would be difficult to obtain through other means. In addition, the students collected snow as well as cloud data near the satellite overpass time as well as snow water equivalent that is an improvement over the NWS cooperative station data that is just snow depth. Quantitative analysis of the Version 4 MODIS snow algorithm produced an accuracy of 94 percent when compared to student observations. The largest errors were associated with partly cloudy conditions. A qualitative study was performed by a tenth grade student and her teacher at St. Ursula's Academy in Toledo found that the snow product produces errors when there are different levels of clouds in the images.
UR: http://remotesensing.utoledo.edu/edu/SATEL.html
DE: 1640 Remote sensing
DE: 1800 HYDROLOGY
DE: 1863 Snow and ice (1827)
DE: 3360 Remote sensing
DE: 6605 Education
SC: Education and Human Resources [ED]
MN: 2003 Fall Meeting