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

Journals >Acta Optica Sinica >Volume 39 >Issue 5 >Page 0501001 > Article

Acta Optica Sinica
Vol. 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

Author Affiliations

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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DOI: 10.3788/AOS201939.0501001 Cite this Article Set citation alerts

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 Copy Citation Text

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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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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 1. Parameters of DPC 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 2. Parameters of GMI on board GF-5 satellite

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

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

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

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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