Multi-layer Cloud Detection Based on the Polarization Crossfire

Shu Li; Shimiao Zhang; Xiaoxue Chu; Song Ye; Xinqiang Wang; Fangyuan Wang; Ziyang Zhang; Yongying Gan; Jiawen Guo

doi:10.3788/AOS241814

Acta Optica Sinica, Volume. 45, Issue 7, 0728001(2025)

Multi-layer Cloud Detection Based on the Polarization Crossfire

Shu Li^1,3, Shimiao Zhang^1,3, Xiaoxue Chu^2,3、*, Song Ye^1,3, Xinqiang Wang^1,3, Fangyuan Wang^1,3, Ziyang Zhang^1,3, Yongying Gan^1,3, and Jiawen Guo⁴

Author Affiliations

¹School of Optoelectronic Engineering, Guilin University of Electronic Technology, Guilin 541004, Guangxi , China

²School of Life & Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, Guangxi , China

³Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin 541004, Guangxi , China

⁴Institute of New Functional Materials of Guangxi Institute of Industrial Technology, Nanning 530022, Guangxi , China

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Figures & Tables(10)

Fig. 1. Variation of 865 nm polarized reflectance with scattering angle under different optical thicknesses. (a) Single-layer ice cloud; (b) single-layer water cloud; (c) multi-layer cloud

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Fig. 2. Variation of reflectance ratio ΔR between single-layer cloud and multi-layer cloud at 763 nm and 765 nm channels with cloud optical thickness. (a)(d) Different cloud top altitudes; (b)(e) different ice cloud models; (c)(f) different effective particle radii

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Fig. 3. Reflectance of single-layer cloud and multi-layer cloud at 1380 nm channel under different cloud top altitudes. (a) Ice cloud altitude is 9 km and water cloud altitude is 2 km; (b) ice cloud altitude is 8 km and water cloud altitude is 3 km

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Fig. 4. Variation of reflectance difference $Δ R$ between single-layer cloud and multi-layer cloud at 2250 nm and 1610 nm channels with cloud optical thickness. (a)(d) Different cloud top heights; (b)(e) different ice cloud models; (c)(f) different effective particle radii

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Fig. 5. Flowchart of the multi-layer cloud detection algorithm

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Fig. 6. Distribution of reflectance ratio in the oxygen-A absorption channel and reflectance difference in short-wave infrared channel for single-layer cloud and multi-layer cloud. (a) Reflectance ratio; (b) reflectance difference

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Fig. 7. MODIS cloud products and the multi-layer cloud identification results obtained by the proposed algorithm. (a) MODIS multi-layer cloud products; (b) the proposed algorithm

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Fig. 8. Cloud pixels misclassified by the multi-layer cloud threshold algorithm. (a) Multi-layer cloud misclassified as single-layer cloud; (b) single-layer cloud misclassified as multi-layer cloud

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Table 1. Satellite instruments and corresponding datasets

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Table 1. Satellite instruments and corresponding datasets

Instrument

MODIS/Aqua

POLDER

Dataset

MYD021 (L1), MYD06 (L2), MYD03 (L3)

POLDER3_L1B, PARASOL_PM02-L2

Quantities

M6/M7/M26 reflectance, cloud optical thickness,

multi-layer cloud flag

765NP reflectance, 865P polarized

reflectance, cloud phase

Table 2. Detection results of multi-layer cloud algorithm
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Table 2. Detection results of multi-layer cloud algorithm
Parameter Multi-layer cloud Single-layer cloud
Cloud flag accuracy /% 88.3
Number of pixel 6785 5367
Accuracy /% 92.3 83.0

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Shu Li, Shimiao Zhang, Xiaoxue Chu, Song Ye, Xinqiang Wang, Fangyuan Wang, Ziyang Zhang, Yongying Gan, Jiawen Guo. Multi-layer Cloud Detection Based on the Polarization Crossfire[J]. Acta Optica Sinica, 2025, 45(7): 0728001

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