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REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (1) : 122-128     DOI: 10.6046/gtzyyg.2017.01.19
Technology Application |
Verification of the retrieval algorithm and analysis of influencing factors of fog physical parameters based on MODIS data
MA Huiyun, ZHAO Guoqing, ZOU Zhengrong, ZHANG Weikang
Department of Surveying and Geo-informatics, Central South University, Changsha 410083, China
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Abstract  

Retrieving fog physical parameters becomes one of the major hot spots of study in recent years based on remote sensing data. The visibility, top height of fog, effective particle radius, and liquid water path (LWP) of fog are the fundamental physical parameters for fog monitoring. In this study, the authors retrieved fog physical parameters from southwest Jiangsu Province according to the path model of fog radioactive phenomena and SBDART based on the MODIS images. The authors verified the visibility and top height of fog according to the data from the Nanjing Information Engineering University and analyzed the influencing factors for the changes of physical parameters. The results showed that the correlation coefficient of visibility and top height of fog was 0.908 3 and 0.980 7, indicating that the retrieval of remote sensing data was feasible. The study also found positive correlations between the fog physical parameters,the surface elevation and vegetation index. The vegetation index was negatively correlated with the radius and optical depth and positively correlated with the liquid water. There was a positively correlation between the visibility and the surface elevation.

Keywords GF-2      image fusion      quality assessment     
:  TP79  
Issue Date: 23 January 2017
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SUN Pan
DONG Yusen
CHEN Weitao
MA Jiao
ZOU Yi
WANG Jinpeng
CHEN Hua
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SUN Pan,DONG Yusen,CHEN Weitao, et al. Verification of the retrieval algorithm and analysis of influencing factors of fog physical parameters based on MODIS data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 122-128.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.01.19     OR     https://www.gtzyyg.com/EN/Y2017/V29/I1/122

[1] Stephens G L,Ackerman S,Smith E A.A shortwave parameterization revised to improve cloud absorption[J].Journal of Atmospheric Sciences,1984,41(4):687-690.
[2] Bendix J.A case study on the determination of fog optical depth and liquid water path using AVHRR data and relations to fog liquid water content and horizontal visibility[J].International Journal of Remote Sensing,1995,16(3):515-530.
[3] Bendix J.A satellite-based climatology of fog and low-level stratus in Germany and adjacent areas[J].Atmospheric Research,2002,64(1/4):3-18.
[4] 吴晓京,陈云浩,李三妹.应用MODIS数据对新疆北部大雾地面能见度和微物理参数的反演[J].遥感学报,2005,9(6):688-696. Wu X J,Chen Y H,Li S M.Utilizing MODIS data to retrieve the visibility and microphysical properties of fog happens in the northwest China[J].Journal of Remote Sensing,2005,9(6):688-696.
[5] 邓军,白洁,刘健文.基于EOS/MODIS的云雾光学厚度和有效粒子半径反演研究[J].遥感技术与应用,2006,21(3):220-226. Deng J,Bai J,Liu J W.Remoting sensing cloud's optical thickness and effective radius using MODIS MULTSPECTRAL data[J].Remote Sensing Technology and Application,2006,21(3):220-226.
[6] 张纪伟,张苏平,吴晓京,等.基于MODIS的黄海海雾研究——海雾特征量反演[J].中国海洋大学学报:自然科学版,2009,39(S1):311-318. Zhang J W,Zhang S P,Wang X J,et al.The research on yellow sea fog based on MODIS date:Sea fog properties retrieval and spatial-temporal distribution[J].Periodical of Ocean University of China,2009,39(S1):311-318.
[7] 蒋璐璐,魏鸣.FY-3A卫星资料在雾监测中的应用研究[J].遥感技术与应用,2011,26(4):489-495. Jiang L L,Wei M.Application of fog monitoring with FY-3A data[J].Remote Sensing Technology and Application,2011,26(4):489-495.
[8] 杨岚,魏鸣,徐永明.长江三角洲雾的MODIS遥感检测[J].科技创新导报,2008(13):1. Yang L,Wei M,Xu Y M.Detection the Yangzi river delta fog by MODIS remote sensing[J].Science and Technology Innovation Herald,2008(13):1.
[9] 严文莲.南京冬季典型雾生消物理过程及爆发性增强特征[D].南京:南京信息工程大学,2008. Yan W L.The Physical Process of Genesis and Dissipation and the Characteristics of Burst Reinforcement on Typical Winter Fog in Nanjing[D].Nanjing:Nanjing University of Information Science & Technology,2008.
[10] 张伟康,马慧云,邹峥嵘,等.基于SBDART辐射传输模型的夜间辐射雾自动检测及时间序列分析[J].国土资源遥感,2014,26(2):80-86.doi:10.6046/gtzyyg.2014.02.14. Zhang W K,Ma H Y,Zou Z R,et al.Automatic detection of night time radiation fog based on SBDART radiative transfer model and the analysis of time series[J].Remote Sensing for Land and Resources,2014,26(2):80-86.doi:10.6046/gtzyyg.2014.02.14.
[11] 何卓臣,马慧云,邹峥嵘,等.基于MODIS影像的陆地辐射雾微物理参数反演及动态变化分析[J].测绘与空间地理信息,2013,36(10):35-39. He Z C,Ma H Y,Zou Z R,et al.Retrieval of fog microphysical parameters and analysis of change condition based on MODIS sequence images about land radiation fog in the daytime[J].Geomatics & Spatial Information Technology,2013,36(10):35-39.
[12] Stephens G L.Radiation profiles in extended water clouds.II:Parameterization schemes[J].Journal of Atmospheric Sciences,1978,35(11):2123-2132.
[13] Bendix J,Thies B,Cermak J,et al.Ground fog detection from space based on MODIS daytime data-a feasibility study[J].Weather and Forecasting,2005,20(6):989-1005.
[14] 李亚春,孙涵,徐萌.卫星遥感在大雾生消动态监测中的应用[J].灾害学,2001,16(1):45-49. Li Y C,Sun H,Xu M.A study on the application of remote sensing technique to monitoring of the tendency of fog dissipation[J].Journal of Catastrophology,2001,16(1):45-49.

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