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一种用于风糙化海面的蒙特卡洛发射率模型。

A Monte Carlo Emissivity Model for Wind-Roughened Sea Surface.

作者信息

Cheng Jie, Cheng Xiaolong, Meng Xiangchen, Zhou Guanhua

机构信息

State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China.

Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.

出版信息

Sensors (Basel). 2019 May 10;19(9):2166. doi: 10.3390/s19092166.

DOI:10.3390/s19092166
PMID:31083293
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6539143/
Abstract

Sea surface emissivity (SSE) is a key variable in the estimation of sea surface temperature and the sea surface radiation budget. A physical base SSE model with adequate accuracy and acceptable computational efficiency is highly desired. This paper develops a Monte Carlo ray-tracing model to compute the SSE of a wind-roughened sea surface. The adoption of a two-dimensional continuous surface model and averaging the two polarization components in advance before ray-tracing gives the model acceptable computational efficiency. The developed model can output the contributions of direct emission and the reflected component to the effective emissivity. The contribution of the reflected component to the effective emissivity reaches 0.035 at an 80° emission angle for a wind speed larger than 10 m/s. The emissivity spectra and channel emissivities collected from two field campaigns and one set of outdoor measurements are used to validate the developed model. Statistical results indicate that the absolute value of bias or difference is less than 0.5% when the view angle is less than 65°, which means the retrieval accuracy of sea surface temperature (SST) is guaranteed from the view of SSE. When the view angle increases, the accuracy of the developed model degraded, especially at the view angle of 85°. Without considering this view angle, the absolute value of bias or difference is less than 0.016, and the root mean square difference (RMSD) is less than 0.018.

摘要

海面发射率(SSE)是估算海面温度和海面辐射收支的关键变量。人们迫切需要一个具有足够精度和可接受计算效率的基于物理原理的SSE模型。本文开发了一种蒙特卡罗射线追踪模型来计算风致粗糙海面的SSE。采用二维连续表面模型并在射线追踪之前预先对两个偏振分量进行平均,使该模型具有可接受的计算效率。所开发的模型可以输出直接发射和反射分量对有效发射率的贡献。对于风速大于10 m/s的情况,在80°发射角下,反射分量对有效发射率的贡献达到0.035。从两次实地测量活动和一组户外测量中收集的发射率光谱和通道发射率用于验证所开发的模型。统计结果表明,当视角小于65°时,偏差或差异的绝对值小于0.5%,这意味着从SSE的角度保证了海面温度(SST)的反演精度。当视角增大时,所开发模型的精度下降,特别是在85°视角时。不考虑这个视角时,偏差或差异的绝对值小于0.016,均方根差(RMSD)小于0.018。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/99f2738d2388/sensors-19-02166-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/47e892c88112/sensors-19-02166-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/1d7a5650d571/sensors-19-02166-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/81f4b0e8a6af/sensors-19-02166-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/f0b31b0be176/sensors-19-02166-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/f6657f771a36/sensors-19-02166-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/818874cd6e9e/sensors-19-02166-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/75bf397a4aa1/sensors-19-02166-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/99f2738d2388/sensors-19-02166-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/47e892c88112/sensors-19-02166-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/1d7a5650d571/sensors-19-02166-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/81f4b0e8a6af/sensors-19-02166-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/f0b31b0be176/sensors-19-02166-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/f6657f771a36/sensors-19-02166-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/818874cd6e9e/sensors-19-02166-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/75bf397a4aa1/sensors-19-02166-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba67/6539143/99f2738d2388/sensors-19-02166-g008.jpg

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