Open AccessRetrieval of snow physical parameters by neural networks and optimal estimation: case study for ground-based spectral radiometer system
Author(s) -
Tomonori Tanikawa,
Wei Li,
Katsuyuki Kuchiki,
Teruo Aoki,
Masahiro Hori,
Knut Stamnes
Publication year - 2015
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.23.0a1442
Subject(s) - remote sensing , snow , radiometer , optimal estimation , radiative transfer , radiance , computer science , a priori and a posteriori , artificial neural network , radiometry , atmospheric radiative transfer codes , computation , environmental science , algorithm , meteorology , optics , artificial intelligence , physics , geology , philosophy , epistemology , machine learning
A new retrieval algorithm for estimation of snow grain size and impurity concentration from spectral radiation data is developed for remote sensing applications. A radiative transfer (RT) model for the coupled atmosphere-snow system is used as a forward model. This model simulates spectral radiant quantities for visible and near-infrared channels. The forward RT calculation is, however, the most time-consuming part of the forward-inverse modeling. Therefore, we replaced it with a neural network (NN) function for fast computation of radiances and Jacobians. The retrieval scheme is based on an optimal estimation method with a priori constraints. The NN function was also employed to obtain an accurate first guess in the retrieval scheme. Validation with simulation data shows that a combination of NN techniques and optimal estimation method can provide more accurate retrievals than by using only NN techniques. In addition, validation with in-situ measurements conducted by using ground-based spectral radiometer system shows that comparison between retrieved snow parameters with in-situ measurements is acceptable with satisfactory accuracy. The algorithm provides simultaneous, accurate and fast retrieval of the snow properties. The algorithm presented here is useful for airborne/satellite remote sensing.