Pachniak, E.; Li, W.; Tanikawa, T.; Gatebe, C.; Stamnes, K. Remote Sensing of Snow Parameters: A Sensitivity Study of Retrieval Performance Based on Hyperspectral Versus Multispectral Data. Algorithms2023, 16, 493.
Pachniak, E.; Li, W.; Tanikawa, T.; Gatebe, C.; Stamnes, K. Remote Sensing of Snow Parameters: A Sensitivity Study of Retrieval Performance Based on Hyperspectral Versus Multispectral Data. Algorithms 2023, 16, 493.
Pachniak, E.; Li, W.; Tanikawa, T.; Gatebe, C.; Stamnes, K. Remote Sensing of Snow Parameters: A Sensitivity Study of Retrieval Performance Based on Hyperspectral Versus Multispectral Data. Algorithms2023, 16, 493.
Pachniak, E.; Li, W.; Tanikawa, T.; Gatebe, C.; Stamnes, K. Remote Sensing of Snow Parameters: A Sensitivity Study of Retrieval Performance Based on Hyperspectral Versus Multispectral Data. Algorithms 2023, 16, 493.
Abstract
Snow parameters have traditionally been retrieved using discontinuous, multi-band sensors; however, continuous hyperspectral sensor 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 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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
