基于云特性的多源卫星日间海雾探测技术研究*

doi:10.11978/2023015

热带海洋学报 ›› 2023, Vol. 42 ›› Issue (6): 15-28.doi: 10.11978/2023015CSTR: 32234.14.2023015

基于云特性的多源卫星日间海雾探测技术研究^*

王钰¹(), 胡晨悦¹, 丘仲锋¹(), 赵冬至¹, 吴到懋², 廖廓³

1.南京信息工程大学, 江苏南京 210044
2.江苏省宿迁环境监测中心, 江苏宿迁 223800
3.福建省气象科学研究所, 福建福州 350008

收稿日期:2023-02-08 修回日期:2023-03-11 出版日期:2023-11-10 发布日期:2023-03-23
通讯作者: 王钰,丘仲锋
作者简介:王钰(1997—), 女, 山东省诸城市人, 硕士研究生, 从事气象遥感应用研究。email: yuwang@nuist.edul.cn
第一联系人：
*感谢美国国家航空航天局(https://ladsweb.modaps.eosdis.nasa.gov/)及美国国家海洋和大气管理局(https://www.avl.class.noaa.gov/saa/)提供的数据支撑。
基金资助:
国家自然科学基金(41976165);风云卫星应用先行计划(2022)(FY-APP-2022.0610)

Research on the multi-source satellite daytime sea fog detection technology based on cloud characteristics*

WANG Yu¹(), HU Chenyue¹, QIU Zhongfeng¹(), ZHAO Dongzhi¹, WU Daomao², LIAO Kuo³

1. Nanjing University of Information Science and Technology, Nanjing 210044, China
2. Suqian Environmental Monitoring Center, Suqian 223800, China
3. Fujian Institute of Meteorological Sciences, Fuzhou 350008, China

Received:2023-02-08 Revised:2023-03-11 Online:2023-11-10 Published:2023-03-23
Contact: WANG Yu, QIU Zhongfeng
Supported by:
National Natural Science Foundation of China(41976165);Advanced Program for FY Satellite Applications (2022)(FY-APP-2022.0610)

摘要/Abstract

摘要：

海雾与低云的分离是当前海雾遥感监测的难点, 为提高日间海雾监测的准确度和实时性, 本文针对2015—2020年在渤海、黄海和东海的11次海雾过程, 在分析海雾与低云在云特性、可见光反射率、亮温和亮温差及纹理特征差异的基础上, 使用Terra/Aqua卫星MODIS(moderate resolution imaging spectroradiometer)传感器及S-NPP/NOAA-20卫星VIIRS(visible infrared imaging radiometer suite)传感器的云和反射率产品, 建立基于云特性的多源卫星日间海雾探测模型, 有效分离了海雾与低云。利用CALIOP(cloud aerosol lidar with orthogonal polarization)后向散射和垂直特征对模型进行精度验证, MODIS(Terra)、MODIS(Aqua)、VIIRS(S-NPP)海雾识别的召回率最高为0.97、0.96、0.89, VIIRS(NOAA-20)与VIIRS(S-NPP)精度相当, 与VIIRS(S-NPP)一致性超过0.8的VIIRS(NOAA-20)海雾监测结果达到93.15%, 表明本文模型能够有效监测日间海雾; 同时, 基于本文模型, 开展了MODIS与VIIRS数据的一致性研究, 结果表明该模型对不同传感器有较强的适用性和稳定性, 能够实现多源卫星对同一次海雾过程的协同观测。

关键词: 海雾, 云特性, 云底高度, MODIS, VIIRS

Abstract:

The separation of sea fog and low clouds is the current difficulty of sea fog monitoring, in order to improve the accuracy and real-time of daytime sea fog monitoring, a model of multi-satellite daytime sea fog detection based on cloud properties is established by analyzing the difference in the features of cloud properties, visible reflectance, brightness temperature, brightness temperature difference and texture features in the infrared bands between sea fog and cloud using the cloud and reflectivity products of MODIS (moderate resolution imaging spectroradiometer) on Terra/Aqua and VIIRS (visible infrared imaging radiometer suite) on S-NPP/NOAA-20 during eleven sea fog events from 2015 to 2020, which effectively separate low clouds from sea fog. Model precision was validated based on the true value of sea fog identified by CALIOP (cloud aerosol lidar with orthogonal polarization) backscattering and vertical feature mask products. The results showed that the highest probability of detection for MODIS(Terra), MODIS(Aqua), and VIIRS(S-NPP) sea fog identification were 0.97, 0.96, 0.89, respectively. There are more than 93.15% of the VIIRS(NOAA-20) sea fog detection images that show 80% consistency with VIIRS(S-NPP), indicating that the model can effectively monitor daytime sea fog. Meanwhile, based on the model presented in this paper, the consistency study of MODIS and VIIRS data is carried out, and the results show that the model has strong applicability and stability for different sensors and can realize the synergistic observation of the same sea fog process by multiple source satellites.

Key words: sea fog, cloud properties, cloud base height, MODSI, VIIRS

中图分类号:

P714.2

王钰, 胡晨悦, 丘仲锋, 赵冬至, 吴到懋, 廖廓. 基于云特性的多源卫星日间海雾探测技术研究^*[J]. 热带海洋学报, 2023, 42(6): 15-28.

WANG Yu, HU Chenyue, QIU Zhongfeng, ZHAO Dongzhi, WU Daomao, LIAO Kuo. Research on the multi-source satellite daytime sea fog detection technology based on cloud characteristics*[J]. Journal of Tropical Oceanography, 2023, 42(6): 15-28.

图/表 17

表1

表2

图1

图2

图3

图4

表3

表4

图5

图6

图7

图8

图9

表5

图10

图11

图12

参考文献 23

[1]	傅刚, 王菁茜, 张美根, 等, 2004. 一次黄海海雾事件的观测与数值模拟研究——以2004年4月11日为例[J]. 中国海洋大学学报(自然科学版), 34(5): 720-726, 926.
	FU GANG, WANG JINGQIAN, ZHANG MEIGEN, et al, 2004. An observational and numerical study of a sea fog event over the Yellow Sea on 11 April, 2004[J]. Periodical of Ocean University of China, 34(5): 720-726, 926 (in Chinese with English abstract).
[2]	耿丹, 刘婷婷, 李超, 2022. 结合FY-4A卫星及随机森林的日间沿海海雾识别模型的研究[J]. 海洋预报, 39(3): 83-93.
	GENG DAN, LIU TINGTING, LI CHAO, 2022. Research on a daytime sea fog identification model based on FY-4A satellite data and random forest algorithm[J]. Marine Forecasts, 39(3): 83-93 (in Chinese with English abstract).
[3]	郝姝馨, 郝增周, 黄海清, 等, 2021. 基于Himawari-8数据的夜间海雾识别[J]. 海洋学报, 43(11): 166-180.
	HAO SHUXIN, HAO ZENGZHOU, HUANG HAIQING, et al, 2021. Nighttime sea fog recognition based on Himawari-8 data[J]. Haiyang Xuebao, 43(11): 166-180 (in Chinese with English abstract).
[4]	胡晨悦, 丘仲锋, 廖廓, 等, 2022. 福建海雾的CALIOP遥感监测及基于Himawari-8的云下雾光谱特征分析[J]. 热带海洋学报: 1-10.
	HU CHENYUE, QIU ZHONGFENG, LIAOKUO, et al, 2022. CALIOP remote sensing monitoring of Fujian sea fog and spectral characteristics analysis of cloud fog based on Himawari-8[J]. Journal of Tropical Oceanography: 1-10 (in Chinese with English abstract).
[5]	刘照民, 康琮, 2011. 琼州海峡船舶雾航安全管理[J]. 中国海事, (2): 28-30.
	LIU ZHAOMIN, KANG ZONG, 2011. Safety control of ships navigating in the Qiongzhou Strait in foggy weather[J]. China Maritime Safety, (2): 28-30 (in Chinese with English abstract).
[6]	史得道, 黄彬, 吴振玲, 2018. 2016年春季一次黄渤海明显海雾过程的大气海洋特征分析[J]. 海洋预报, 35(5): 85-92.
	SHI DEDAO, HUANG BIN, WU ZHENLING, 2018. Analysis of atmosphere and sea characteristics under an obvious sea fog process over the Bohai and Yellow Sea in spring 2016[J]. Marine Forecasts, 35(5): 85-92 (in Chinese with English abstract).
[7]	苏婧, 2019. 基于主被动遥感的海雾探测方法研究[D]. 青岛: 中国石油大学(华东):19-21.
	SU JING, 2019. Research on sea fog detection method based on active and passive remote sensing[D]. Qingdao: China University of Petroleum (East China): 19-21 (in Chinese with English abstract).
[8]	张苏平, 鲍献文, 2008. 近十年中国海雾研究进展[J]. 中国海洋大学学报(自然科学版), 38(3): 359-366.
	ZHANG SUPING, BAO XIANWEN, 2008. The main advances in sea fog research in China[J]. Periodical of Ocean University of China, 38(3): 359-366 (in Chinese with English abstract).
[9]	AMEUR Z, AMEUR S, ADANE A, et al, 2004. Cloud classification using the textural features of Meteosat images[J]. International Journal of Remote Sensing, 25(21): 4491-4503. DOI: 10.1080/01431160410001735120.
[10]	BADARINATH K V S, KHAROL S K, SHARMA A R, et al, 2009. Fog over Indo-Gangetic Plains—A study using multisatellite data and ground observations[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(3): 185-195. DOI: 10.1109/JSTARS.2009.2019830.
[11]	BENDIX J, CERMAK J, THIES B, 2004. New perspectives in remote sensing of fog and low stratus-TERRA/AQUA-MODIS and MSG[C/OL]. Cape Town, South Africa:3^rd Int. Conf. on Fog, Fog Collection and Dew, 11-15 Oct. 2004 [2023-02-08]. https://www.researchgate.net/deref/https%3A%2F%2Fwww.researchgate.net%2Fpublication%2F228860444_New_perspectives_in_remote_sensing_of_fog_and_low_stratus-TERRAAQUA-MODIS_and_MSG?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIiwicHJldmlvdXNQYWdlIjoicHVibGljYXRpb24ifX0
[12]	CERMAK J, BENDIX J, 2011. Detecting ground fog from space - a microphysics-based approach[J]. International Journal of Remote Sensing, 32(12): 3345-3371. DOI: 10.1080/01431161003747505.
[13]	DOZIER J, PAINTER T H, 2004. Multispectral and hyperspectral remote sensing of alpine snow properties[J]. Annual Review of Earth and Planetary Sciences, 32(1): 465-494. DOI: 10.1146/annurev.earth.32.101802.120404.
[14]	HAN J H, SUH M S, YU H Y, et al, 2020. Development of fog detection algorithm using GK2A/AMI and ground data[J]. Remote Sensing, 12(19): 3181. DOI: 10.3390/rs12193181.
[15]	JEON J, KIM S, YANG CHANSU, 2016. Fundamental research on spring season daytime sea fog detection using MODIS in the Yellow Sea[J]. Korean Journal of Remote Sensing, 32(4): 339-351. doi: 10.7780/kjrs.2016.32.4.1
[16]	LIU LIANGMING, WEN XIONGFEI, GONZALEZ A, et al, 2011. An object-oriented daytime land-fog-detection approach based on the mean-shift and full lambda-schedule algorithms using EOS/MODIS data[J]. International Journal of Remote Sensing, 32(17): 4769-4785. DOI: 10.1080/01431161.2010.489067.
[17]	QU J J, GAO WEI, KAFATOS M, et al, 2006. Earth science satellite remote sensing[M]. Beijing, China: Tsinghua University Press.
[18]	THIES B, NAUSS T, BENDIX J, 2008. Precipitation process and rainfall intensity differentiation using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager data[J]. Journal of Geophysical Research: Atmospheres, 113(D23). DOI: 10.1029/2008JD010464.
[19]	WEN XIONGFEI, HU DUNMEI, DONG XINYI, et al, 2014. An object-oriented daytime land fog detection approach based on NDFI and fractal dimension using EOS/MODIS data[J]. International Journal of Remote Sensing, 35(13): 4865-4880. DOI: 10.1080/01431161.2014.930564.
[20]	WU DONG, LU BO, ZHANG TIANCHE, et al, 2015. A method of detecting sea fogs using CALIOP data and its application to improve MODIS-based sea fog detection[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 153: 88-94. DOI: 10.1016/j.jqsrt.2014.09.021.
[21]	WU XIAOJING, LI SANMEI, 2014. Automatic sea fog detection over Chinese adjacent oceans using Terra/MODIS data[J]. International Journal of Remote Sensing, 35(21): 7430-7457. DOI: 10.1080/01431161.2014.968685.
[22]	YUAN YIBO, QIU ZHONGFENG, SUN DEDONG, et al, 2016. Daytime sea fog retrieval based on GOCI data: a case study over the Yellow Sea[J]. Optics Express, 24(2): 787-801. DOI: 10.1364/OE.24.000787. pmid: 26832463
[23]	ZHANG SUPING, YI LI, 2013. A comprehensive dynamic threshold algorithm for daytime sea fog retrieval over the Chinese adjacent seas[J]. Pure and Applied Geophysics, 170(11): 1931-1944. DOI: 10.1007/s00024-013-0641-6.

传感器	波段数目	空间分辨率/m	时间分辨率	每日过境时当地时间	可获取时间段
MODIS(Terra)	36	1000	2次·d^-1	10:30	2000-02-24至今
MODIS(Aqua)	36	1000	2次·d^-1	13:30	2002-07-04至今
VIIRS(NOAA-20)	22	750	4h	12:40	2018-01-05至今
VIIRS(S-NPP)	22	750	4h	13:30	2012-01-19至今

序号	日期(年-月-日)	区域	观测时间(UTC)
序号	日期(年-月-日)	区域	MODIS(Terra)	MODIS(Aqua)	VIIRS(S-NPP)	VIIRS(NOAA-20)
1	2015-01-10	黄海	02:20	05:35	04:48, 04:54	—
2	2015-03-30	黄海	03:15	—	05:06, 03:30	—
3	2015-04-29	黄海、渤海	—	05:10	04:06, 04:29	—
4	2015-04-30	黄海	02:35	—	05:24	—
4	2015-05-01	黄海、东海	01:40, 03:15	04:55	05:06	—
5	2015-06-09	黄海	03:20	05:00	04:36	—
6	2016-03-03	黄海	02:10	05:25	04:06, 05:48	—
6	2016-03-04	黄海、渤海	02:50	04:30	03:48, 05:30	—
7	2018-03-24	黄海	03:05	04:40, 04:45	04:24	03:36, 05:12, 05:18
	2018-03-25	黄海	02:10	05:25	04:06, 05:48	04:54, 05:00
	2018-03-26	黄海	02:50	04:30	03:48, 05:30	—
8	2018-05-10	黄海	02:20	04:00, 05:35	04:42	03:54, 05:36
9	2020-02-12	渤海	02:50	04:30	03:48, 05:30	—
10	2020-05-16	黄海	—	04:40	04:24	05:18
11	2020-05-23	黄海	03:10	04:45	03:54	04:42
总计影像数			14景	15景	23景	9景

	相关系数	显著性
云掩膜	0.95	1
云相态	0.92	1
云顶高度	0.84	1
云有效半径	0.99	1

校正数据	MODIS波段	MODIS中心波长/μm	VIIRS中心波长/μm	拟合斜率	拟合截距	决定系数
反射率	1	0.645	0.672	1.03	0.01	0.87
	3	0.469	0.488	1.01	0.01	0.87
	7	2.13	2.25	1.12	0.00	0.84
辐亮度	20	3.75	3.70	0.99	-0.03	0.72
亮温	29	8.55	8.55	0.85	39.90	0.72
	31	11.03	10.763	0.86	39.70	0.72
	32	12.02	12.013	0.85	43.17	0.70

卫星	评价指标	时间窗口/h
卫星	评价指标	1.0	1.5	2.0	2.5	3.0	3.5
MODIS(Terra)	雾点数目	0	0	0	17	1855	1977
	召回率	0	0	0	0	0.97	0.94
	准确度	0	0	1.00	0.94	0.93	0.90
MODIS(Aqua)	雾点数目	98	1958	1994	1994	1994	1994
	召回率	0.66	0.96	0.94	0.94	0.94	0.94
	准确度	0.86	0.89	0.89	0.89	0.89	0.89
VIIRS(S-NPP)	雾点数目	2056	2081	2185	2185	2185	2185
	召回率	0.89	0.88	0.84	0.84	0.84	0.84
	准确度	0.96	0.97	0.96	0.96	0.96	0.96
VIIRS(NOAA-20)	雾点数目	210	211	327	327	327	327
	召回率	0.49	0.48	0.57	0.57	0.57	0.57
	准确度	0.89	0.91	0.91	0.91	0.91	0.91

基于云特性的多源卫星日间海雾探测技术研究^*

Research on the multi-source satellite daytime sea fog detection technology based on cloud characteristics*

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 23

相关文章 9

Metrics

本文评价

推荐阅读 0

[1]	廖廓, 李恺霖, 党皓飞, 林彬, 赵冬至, 李慧. 基于沿岸自动气象站的台湾海峡西岸海雾生消过程及特征^*[J]. 热带海洋学报, 2024, 43(1): 79-93.
[2]	胡晨悦, 丘仲锋, 廖廓, 赵冬至, 吴到懋. 福建海雾的CALIOP遥感监测及基于Himawari-8的云下雾光谱特征分析^*[J]. 热带海洋学报, 2023, 42(4): 104-112.
[3]	杨威, 董园, 俎婷婷, 刘长建, 修鹏. 南海北部夏季叶绿素a分布规律及影响因素[J]. 热带海洋学报, 2019, 38(6): 9-20.
[4]	詹伟康,吴颉,韦惺,唐世林,詹海刚. 基于遥感反演的珠江河口表层悬沙浓度分位数趋势分析 ^*[J]. 热带海洋学报, 2019, 38(3): 32-42.
[5]	欧素英. 华南不同类型热带风暴驱动下珠江口表层悬沙分布趋势[J]. 热带海洋学报, 2019, 38(3): 22-31.
[6]	尹小青, 杨顶田, 周立柱. 黄、东海边界悬浮物输运量的卫星遥感估算[J]. 热带海洋学报, 2018, 37(2): 10-16.
[7]	郭甜甜, 陈圣波, 陆天启. 基于MODIS数据的多时相海表温度反演——以海南岛西南部近海海域为例^*[J]. 热带海洋学报, 2017, 36(1): 9-14.
[8]	杨棋, 欧建军, 李永平. 洋山海域海雾客观预报方法研究[J]. 热带海洋学报, 2013, 32(5): 59-64.
[9]	姚月,许惠平. 福建围填海及其对海洋环境影响的遥感初探 [J]. 热带海洋学报, 2012, 31(1): 72-78.