@article{oai:jaxa.repo.nii.ac.jp:00022632,
 author = {谷川, 朋範 and Li, Wei and 朽木, 勝幸 and 青木, 輝夫 and 堀, 雅裕 and Stamnes, Knut and Tanikawa, Tomonori and Wei, Li and Kuchiki, Katsuyuki and Aoki, Teruo and Hori, Masahiro and Stamnes, Knut},
 issue = {24},
 journal = {Optics Express},
 month = {Nov},
 note = {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., 形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: PA1610001000},
 pages = {1442--1462},
 title = {Retrieval of snow physical parameters by neural networks and optimal estimation: case study for ground based spectral radiometer system},
 volume = {23},
 year = {2015}
}