Remote Sensing of Snow Parameters: A Sensitivity Study of Retrieval Performance Based on Hyperspectral versus Multispectral Data

Snow parameters have traditionally been retrieved using discontinuous, multi-band sensors; however, continuous hyperspectral sensors are now being developed as an alternative. In this paper, we investigate the performance of various sensor configurations using machine learning neural networks traine...

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Bibliographic Details
Published in:Algorithms 2023-10, Vol.16 (10), p.493
Main Authors: Pachniak, Elliot, Li, Wei, Tanikawa, Tomonori, Gatebe, Charles, Stamnes, Knut
Format: Article
Language:English
Subjects:
Algorithms
Artificial satellites in remote sensing
Atmosphere
Atmospheric aerosols
Configurations
Datasets
Earth
Grain size
Hydrology
hyperspectral
Ice
Machine learning
MODIS
Neural networks
Optical properties
Parameter sensitivity
Physical properties
Radiation
Remote sensing
Retrieval
Satellites
Sensors
Snow
Spectrum analysis
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Summary:Snow parameters have traditionally been retrieved using discontinuous, multi-band sensors; however, continuous hyperspectral sensors are now being developed as an alternative. In this paper, we investigate the performance of various sensor configurations using machine learning neural networks trained on a simulated dataset. Our results show improvements in the accuracy of retrievals of snow grain size and impurity concentration for continuous hyperspectral channel configurations. Retrieval accuracy of snow albedo was found to be similar for all channel configurations.
ISSN:1999-4893
1999-4893
DOI:10.3390/a16100493