DocumentCode :
339495
Title :
Passive remote sensing of snow with hydrological model and constraints on grain size and snow density
Author :
Chi-Te Chen ; Nijssen, B.
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA
Volume :
3
fYear :
1999
fDate :
1999
Firstpage :
1801
Abstract :
Predicting the snow parameters, such as snow depth or snow water equivalent is an important task in the geoscience study. In the past, parameter retrieval algorithms mainly use the relationship between a subset of the snow parameters and passive remote sensing measurements. However, the brightness temperatures depend not only on snow depth but also on other snow parameters. Thus, it is desirable to develop a multi-parametric inversion algorithm using multi-frequency and dual polarization measurements. The authors´ current approach is using a constrained neural network iterative inversion algorithm incorporating a priori estimates provided by the snow hydrology model to retrieve the snow parameters. For the real-time application, the weather forecast model precipitation input instead of station precipitation data is used to provide the precipitation input for the snow hydrology model. The results of the constrained inversion will be illustrated for the stations in the Northern Hemisphere
Keywords :
geophysical signal processing; geophysics computing; hydrological techniques; iterative methods; neural nets; polarimetry; radiometry; remote sensing; snow; 19 GHz; 37 GHz; EHF; SHF; a priori estimate; brightness temperature; constrained neural network; dual polarization; grain size; hydrological model; hydrology; inversion algorithm; iterative method; measurement technique; microwave radiometry; multi-frequency method; multi-parametric inversion; multifrequency method; neural net; parameter retrieval algorithm; remote sensing; snow cover; snow density; snow depth; snow water equivalent; snowcover; snowpack; Brightness temperature; Geoscience; Hydrologic measurements; Hydrology; Iterative algorithms; Polarization; Predictive models; Remote sensing; Snow; Weather forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International
Conference_Location :
Hamburg
Print_ISBN :
0-7803-5207-6
Type :
conf
DOI :
10.1109/IGARSS.1999.772100
Filename :
772100
Link To Document :
بازگشت