Cloud Detection Algorithm for Greenhouse Gas Retrieval

Hao Wu; Xianhua Wang; Hanhan Ye; Yun Jiang; Song Lü; Qinqin Li; Shichao Wu; Jun Wu

doi:10.3788/AOS201939.0501001

Acta Optica Sinica, Volume. 39, Issue 5, 0501001(2019)

Cloud Detection Algorithm for Greenhouse Gas Retrieval

Hao Wu^1,2, Xianhua Wang^1、*, Hanhan Ye¹, Yun Jiang^1,2, Song Lü^1,2, Qinqin Li^1,2, Shichao Wu^1,2, and Jun Wu¹

¹ Key Laboratory of Optical Calibration and Characterization of Chinese Academy of Sciences, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, Anhui 230031, China

² University of Science and Technology of China, Hefei, Anhui 230026, China

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Figures & Tables(8)

Fig. 1. Space matching diagram of GMI, DPC and MODIS

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Fig. 2. Flow chart of GMI and DPC combined cloud-screening algorithm

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Fig. 3. Cloud screening results of 16 d GMI observation points. (a) Land; (b) ocean

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Fig. 4. Detection results of CGDC algorithm. (a) Land Nadir observation mode; (b) ocean glint observation mode

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Table 1. Parameters of DPC on board GF-5 satellite

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Table 1. Parameters of DPC on board GF-5 satellite

Parameter	Swath width /km	Field of view /(°)	Polarization angle /(°)	Spatial resolution /km	Number of angles	Spectral band /nm	Bandwidth /nm
Value	1850	±50	0,60,120	3.3	≥9	443,490(P),565,670(P),763,765,865(P),910	20,20,20,20,10,40,40,20
P:polarization.

Table 2. Parameters of GMI on board GF-5 satellite

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Table 2. Parameters of GMI on board GF-5 satellite

Band	Band 1	Band 2	Band 3	Band 4
Spectral coverage of wavelength /nmSpectral coverage of wavenumber /cm^-1	759-769	1568-1583	1642-1658	2043-2058
	13004-13175	6317.1-6377.6	6031.4-6090.1	4859.1-4894.8
Target gase	O₂	CO₂	CH₄	CO₂
Signal to noise ratio	>300	>300	>250	>250
Spatial resolution	Nadir 10 km diameter (projected on Earth’s surface)

Table 3. Statistics of detection results by CGDC algorithm for land and ocean scenes

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Table 3. Statistics of detection results by CGDC algorithm for land and ocean scenes

Type		Total detection points	Pixel No.
Type		Total detection points	Clear	Cloudy	undefined
Land surface	0°-30°	8726	2518(28.86%)	5276(60.46%)	932(10.68%)
	30°-45°	23456	2054(37.60%)	2431(44.50%)	978(17.90%)
	45°-70°	5463	2026(16.92%)	8220(68.65%)	1728(14.43%)
Ocean surface	0°-30°	14526	1803(7.69%)	18283(77.95%)	3370(14.37%)
	30°-45°	11974	827(5.69%)	12414(85.46%)	1285(8.85%)
	45°-70°	13436	280(2.08%)	12732(94.76%)	424(3.16%)
Globe		77581	9508(12.26%)	59356(76.51%)	8717(11.23%)

Table 4. Comparison of detection results of CGDC algorithm and fused MODIS

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Table 4. Comparison of detection results of CGDC algorithm and fused MODIS

Condition	MODIS (clear)					MODIS (cloudy)					${R_{ACC}}_{}$ /%	${R_{PPV}}_{}$ /%
	CGDC (clear)			CGDC (cloudy)		CGDC (clear)			CGDC (cloudy)
	N_TP	${R_{TP}}_{}$ /%		N_FN	${R_{FN}}_{}$ /%	N_FP	${R_{FP}}_{}$ /%		N_TN	${R_{FP}}_{}$ /%
Land	1201		90.11	117	9.89	61		5.12	1139	94.88	92.93	95.17
Ocean	531		78.33	147	21.67	289		16.70	1443	83.30	81.91	64.76

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Hao Wu, Xianhua Wang, Hanhan Ye, Yun Jiang, Song Lü, Qinqin Li, Shichao Wu, Jun Wu. Cloud Detection Algorithm for Greenhouse Gas Retrieval[J]. Acta Optica Sinica, 2019, 39(5): 0501001

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Paper Information

Category: Atmospheric Optics and Oceanic Optics

Received: Nov. 11, 2018

Accepted: Dec. 29, 2018

Published Online: May. 10, 2019

The Author Email:

DOI:10.3788/AOS201939.0501001

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