التفاصيل البيبلوغرافية
| العنوان: |
Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements |
| المؤلفون: |
Cheng Fan, Guangliang Fu, Antonio Di Noia, Martijn Smit, Jeroen H.H. Rietjens, Richard A. Ferrare, Sharon Burton, Zhengqiang Li, Otto P. Hasekamp |
| المصدر: |
Remote Sensing; Volume 11; Issue 23; Pages: 2877 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2019 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
neural network, aerosols, multi-angle, polarimetry |
| جغرافية الموضوع: |
agris |
| الوصف: |
For aerosol retrieval from multi-angle polarimetric (MAP) measurements over the ocean it is important to accurately account for the contribution of the ocean-body to the top-of-atmosphere signal, especially for wavelengths <500 nm. Performing online radiative transfer calculations in the coupled atmosphere ocean system is too time consuming for operational retrieval algorithms. Therefore, mostly lookup-tables of the ocean body reflection matrix are used to represent the lower boundary in an atmospheric radiative transfer model. For hyperspectral measurements such as those from Spectro-Polarimeter for Planetary Exploration (SPEXone) on the NASA Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission, also the use of look-up tables is unfeasible because they will become too big. In this paper, we propose a new method for aerosol retrieval over ocean from MAP measurements using a neural network (NN) to model the ocean body reflection matrix. We apply the NN approach to synthetic SPEXone measurements and also to real data collected by SPEX airborne during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. We conclude that the NN approach is well capable for aerosol retrievals over ocean, introducing no significant error on the retrieved aerosol properties |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
Atmospheric Remote Sensing; https://dx.doi.org/10.3390/rs11232877 |
| DOI: |
10.3390/rs11232877 |
| الاتاحة: |
https://doi.org/10.3390/rs11232877 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.DD850A6F |
| قاعدة البيانات: |
BASE |