Passive microwave remote sensing of snow constrained by hydrological simulations

This paper describes a snow parameter retrieval algorithm from passive microwave remote sensing measurements. The three components of the retrieval algorithm include a dense media radiative transfer (DMRT) model, which is based on the quasicrystalline approximation (QCA) with the sticky particle ass...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2001-08, Vol.39 (8), p.1744-1756
Main Authors: Chi-Te Chen, Nijssen, B., Jianjun Guo, Leung Tsang, Wood, A.W., Jenq-Neng Hwang, Lettenmaier, D.P.
Format: Article
Language:English
Subjects:
Algorithms
Applied geophysics
Approximation algorithms
Brightness temperature
Computer simulation
Earth sciences
Earth, ocean, space
Exact sciences and technology
Grain size
Hydrologic measurements
Hydrology
Hydrology. Hydrogeology
Information retrieval
Internal geophysics
Inversions
Mathematical models
Meteorology
Microwave measurements
Neural networks
Passive microwave remote sensing
Quantum cellular automata
Remote sensing
Retrieval
Snow
Weather forecasting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper describes a snow parameter retrieval algorithm from passive microwave remote sensing measurements. The three components of the retrieval algorithm include a dense media radiative transfer (DMRT) model, which is based on the quasicrystalline approximation (QCA) with the sticky particle assumption, a physically-based snow hydrology model (SHM) that incorporates meteorological and topographical data, and a neural network (NN) for computational efficient inversions. The DMRT model relates physical snow parameters to brightness temperatures. The SHM simulates the mass and heat balance and provides initial guesses for the neural network. The NN is used to speed up the inversion of parameters. The retrieval algorithm can provide speedy parameter retrievals for desired temporal and spatial resolutions, Four channels of brightness temperature measurements: 19V, 19H, 37V, and 37H are used. The algorithm was applied to stations in the northern hemisphere. Two sets of results are shown. For these cases, the authors use ground-truth precipitation data, and estimates of snow water equivalent (SWE) from SHM give good results. For the second set, a weather forecast model is used to provide precipitation inputs for SHM. Additional constraints in grain size and density are used. They show that inversion results compare favorably with ground truth observations.
ISSN:0196-2892
1558-0644
DOI:10.1109/36.942553