HR: 0800h
AN: H11F-0354    [Abstracts]
TI: Comparison Between SSM/I Filtering Algorithm and Neural Networks for Snow Cover Identification in the Northern Midwest States
AU: * AREVALO, J C
EM: jc@ce.ccny.cuny.edu
AF: The City College of the City University of New York / NOAA-CREST, The City College of the City University of New York. Convent Avenue at 140th St, Steinman Hall., New York, NY 10031 United States
AU: HAMILTON, N
EM: melodies2000@yahoo.com
AF: The City College of the City University of New York / NOAA-CREST, The City College of the City University of New York. Convent Avenue at 140th St, Steinman Hall., New York, NY 10031 United States
AU: GHEDIRA, H
EM: ghedira@ce.ccny.cuny.edu
AF: The City College of the City University of New York / NOAA-CREST, The City College of the City University of New York. Convent Avenue at 140th St, Steinman Hall., New York, NY 10031 United States
AB: Snow coverage and depth are two key parameters that are essential to be estimated and applied in a wide range of hydrological applications. However, the traditional field sampling methods and the ground-based data collection are often very sparse, time consuming, and expensive compared to the coverage provided by remote sensing techniques. Passive microwave remote sensing data have been investigated by numerous researchers and have been demonstrated to be effective for monitoring snow pack parameters. Those researches have resulted that the microwave brightness temperature are related to the snow cover structure with different correlation degrees. The primary objective of this research is to produce a spatial estimation of snow water equivalent with sufficient spatial and temporal resolution using passive microwave data. The final product of this project will be an additional tool for flood warning and water resource forecasts, which can be an additional input to the actual hydrological models. The focus of this paper is to investigate the performance of filtering algorithm (developed by NESDIS NOAA) and Neural Network algorithm for snow cover identification in the Northern Midwest States. Artificial neural networks have been successfully applied to image processing, and have shown a great potential in the classification of a wide range of remote sensing data. The study area is located in the Northern Midwest of the United States within $109\deg$$30\prime$W - $100\deg$$50\prime$W and $48\deg$$40\prime$N - $41\deg$$00\prime$N. A total of 180 ground stations covering the study area have been identified for this experiment. The passive microwave data from the current SSM/I (Special Sensor Microwave Imager) sensors on board the DMSP F13 and F14 satellites are used in both ascending and descending orbits. These images provide (twice-a-day) measurements of the brightness temperature in seven channels with different frequencies and polarizations (19 V, 19 H, 22 V, 37V, 37 H, 85 V, and 85 H). All the seven channels were tested in this project. The preliminary results showed that the filtering algorithm performance in identifying the non-snow pixels was acceptable with accuracy varying between 79% and 99% for both ascending and descending modes. However, the performance in identifying snow pixels was very poor.
DE: 3360 Remote sensing
DE: 1800 HYDROLOGY
DE: 1863 Snow and ice (1827)
DE: 1894 Instruments and techniques
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting