|
|
|
|
|
|
Study on Global Spatial Distribution of Surface-Atmosphere Clutter in Mid-Infrared Atmospheric Strong Absorption Band |
YAO Qian1, 2, LI Zheng-qiang1, 2, FAN Cheng1, XU Hua1, WANG Si-heng3*, CHEN Zhen-ting4 |
1. State Environment Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Remote Sensing Satellite General Department, China Academy of Space Technology, Beijing 100094, China
4. School of Information Engineering, Kunming University, Kunming 650214, China
|
|
|
Abstract The mid-infrared(MIR) atmospheric strong absorption wavelength bands(near 2.7 and 4.3 μm) have the characteristics of suppressing background clutter signals, which are often used on the payload of infrared early warning satellites to achieve stable detection and tracking of targets. The background clutter fluctuation level has an important impact on MIR target detection, and it is of great significance to study the spatial domain fluctuation of surface-atmosphere clutter near the 2.7 and 4.3 μm bands. First, the MODTRAN radiative transfer model is used to iteratively simulate the optical depth (OD) of water vapor and CO2 under six standard atmospheric models, screen the strong absorption bands according to the criterion of OD>1, and intersect the band calculation results under various atmospheric models. The final strong absorption bands were 2.52~2.83 and 4.18~4.47 μm. Then, global monthly averaged surface and atmosphere background products are produced based on remote sensing satellite data products and data assimilation information. Among them, combined with the monthly average surface reflectance/emissivity products (MYD09A1/ MOD11C3) of MODIS shortwave infrared and mid-infrared bands (2.13, 3.75, 3.96 and 4.05 μm), the non-negative matrix factorization (NMF) method was used to reconstruct global surface reflectance/emissivity products at strongly absorbing bands; Produce monthly-averaged global surface temperature products based on ERA5 reanalysis data; The global monthly average products of water vapor and cloud optical thickness (COT) are obtained by splicing and fusing MODIS atmospheric products (MOD05_L2 and MOD06_L2) respectively; The CO2 global monthly average product comes from the data assimilation product of the OCO-2 satellite; In order to spatially match various data products and save computing resources, global products are limited to the 60° north and south latitude range and resampled to a spatial resolution of 1°×1°; Next, under cloudy and cloud-free conditions, the background clutter intensity of two strong absorption bands was simulated and calculated pixel by pixel, and the spatial distribution pattern of clutter was analyzed. Finally, the 11×11 square window neighborhood statistics method was used to calculate the clutter spatial domain fluctuation in the simulation results, convert the clutter fluctuation under 95% probability, and conduct histogram statistics. From the perspective of clutter suppression, a comparison of infrared target detection performance in two absorption bands is given. Study results show that under cloud conditions, the fluctuation level of background clutter in the 2.52~2.83 μm band has the characteristics of “a point-like peak, regional enhancement, and overall low values”. In contrast, the clutter fluctuation level in the 4.18~4.47 μm band shows the characteristics of “regional high values, patchy enhancement, and low values at high latitudes”. Under cloud-free conditions, the background clutter fluctuation level in the 2.52~2.83 μm band increases significantly in the low water vapor content area. In contrast, the clutter fluctuation level in the 4.18~4.47 μm band is generally low, and most of them are controlled within 2×10-4 W/m2/sr/μm. On the global scale, the infrared target detection performance is better in the 2.52~2.83 μm band when there are clouds and in the 4.18~4.47 μm band when there are no clouds. The results of this study can provide a reference basis for detecting infrared targets in terms of spatial domain law and spectral band optimization, which is of great value for enhancing the detectability of targets.
|
Received: 2023-10-24
Accepted: 2024-03-15
|
|
Corresponding Authors:
WANG Si-heng
E-mail: rswangsiheng@163.com
|
|
[1] Liu Y, Zhang W J, Zhang B. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 452.
[2] LIU Yao, ZHANG Wen-juan, ZHANG Bing, et al(刘 瑶, 张文娟, 张 兵, 等). Remote Sensing for Land & Resources(国土资源遥感), 2017, 29(3): 98.
[3] Deng H, Sun X P, Zhou X. IEEE Transactions on Cybernetics, 2019, 49(5): 1694.
[4] HU Rui-jie, CHE Dou(胡睿杰, 车 逗). Computer and Modernization(计算机与现代化), 2023, (8): 79.
[5] Bai X Z, Bi Y G. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 2452.
[6] Qin Y, Li B. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1890.
[7] Han J H, Liang K, Zhou B, et al. IEEE Geoscience and Remote Sensing Letters, 2018, 15(4): 612.
[8] Wang Y H, Xu X P, Yue N N, et al. International Journal of Advanced Robotic Systems, 2017, 14(6): 1729881417744822.
[9] Bae T W. Infrared Physics & Technology, 2014, 63: 42.
[10] Yu J G, Xia G S, Deng J J, et al. Infrared Physics & Technology, 2015, 73: 175.
[11] Hou W Z, Li Z Q, Dong X G, et al. Hyperspectral Surface Reflectance Reconstruction Based on Non-Negative Matrix Factorization and Multispectral Results. Proceedings of the AOPC 2021: Optical Spectroscopy and Imaging, 2021.
[12] LI Yin-na, LI Zheng-qiang, ZHENG Yang, et al(李殷娜, 李正强, 郑 杨, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2024, 44(2): 563.
[13] Hou W Z, Wang J, Xu X G, et al. Journal of Quantitative Spectroscopy and Radiative Transfer, 2016, 178: 400.
[14] Hou W Z, Wang J, Xu X G, et al. Journal of Quantitative Spectroscopy and Radiative Transfer, 2017, 192: 14.
[15] Hou W Z, Wang J, Xu X G, et al. Journal of Quantitative Spectroscopy and Radiative Transfer, 2020, 253: 107161.
[16] Zeng H, Ren H Z, Nie J, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 7724.
[17] Zhao E Y, Qian Y G, Gao C X, et al. Remote Sensing, 2014, 6(12): 12667.
[18] Li Z L, Tang B H, Wu H, et al. Remote Sensing of Environment, 2013, 131: 14.
|
[1] |
HAO Yi-shuo1, 2, NIU Yi-fang1*, WANG Li1, BI Kai-yi1. Exploring the Capability of Airborne Hyperspectral LiDAR Based on
Radiative Transfer Models for Detecting the Vertical Distribution of
Vegetation Biochemical Components[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 2083-2092. |
[2] |
WANG Dao-qi1,WANG Hou-mao2,HE Wei-wei1,HU Xiang-rui1,LI Juan3,LI Fa-quan4,WU Kui-jun1*. Radiative Transfer Characteristics of the 1.27 μm O2(a1Δg) Airglow in Limb-Viewing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1088-1097. |
[3] |
LIANG Shou-zhen1, SUI Xue-yan1, WANG Meng1, WANG Fei1, HAN Dong-rui1, WANG Guo-liang1, LI Hong-zhong2, MA Wan-dong3. The Influence of Anthocyanin on Plant Optical Properties and Remote Sensing Estimation at the Scale of Leaf[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 275-282. |
[4] |
ZHANG Xuan-yi1, 2, 3, WEI Fei1, 2, 3*, PENG Song-wu1, 3, FENG Peng-yuan1, 3, LENG Shuang1, 3. Study on Solar FUV Radiation Characteristics in Near Space[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 374-382. |
[5] |
ZHANG Yu-xiao1, WANG Xi3, CHEN Shu-guo1, 2, 3*, LIU Zhao-wei3, HU Lian-bo1, 2. Variation of Water Leaving Radiance Originated From Bioluminescence in the Yellow Sea and Its Relationship With Inherent Optical Properties and Depth[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1899-1906. |
[6] |
YANG Jun-jie1, HUANG Miao-fen2*, LUO Wei-jian3, WANG Zhong-lin2, XING Xu-feng2. The Effect of Oil-in-Water on the Upward Radiance Spectrum in Seawater[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1648-1653. |
[7] |
REN Shen-he1, 2, GAO Ming1*, WANG Ming-jun3, LI Yan1, GUO Lei-li3. Attenuation and Transmission Characteristics of Laser Propagation in Cirrus Clouds With a Spherical Boundary[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 316-321. |
[8] |
CHEN Hao1,2, WANG Hao3*, HAN Wei3, GU Song-yan4, ZHANG Peng4, KANG Zhi-ming1. Impacts Analysis of Typical Spectral Absorption Models on Geostationary Millimeter Wave Atmospheric Radiation Simulation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1858-1862. |
[9] |
SU Wei1,2, WU Jia-yu1,2, WANG Xin-sheng1,2, XIE Zi-xuan1,2, ZHANG Ying1,2, TAO Wan-cheng1,2, JIN Tian1,2. Retrieving Corn Canopy Leaf Area Index Based on Sentinel-2 Image and PROSAIL Model Parameter Calibration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1891-1897. |
[10] |
WANG Da-xin1,2, FU Li-ping1,3,4*, JIANG Fang1,3,4, JIA Nan1,2,3,4, DOU Shuang-tuan1,2. Investigation of Transmission Characteristic of O+ 83.4 nm Dayglow in the Ionosphere[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(05): 1334-1339. |
[11] |
WANG Nian1,2, SHEN Hua1,2*, ZHU Ri-hong1,2. Spectral Radiation Transmission Model of Plasma in Laser Welding[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(05): 1362-1366. |
[12] |
WANG Qi, LIU Lei*, GAO Tai-chang, HU Shuai, ZENG Qing-wei. A Study on the Computational Model for High Spectral Infrared Sounder by Fourier Transform Technique and its Influence Factors[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1711-1716. |
[13] |
ZHANG Hong-hai1, 2, 3, GAO Yi-bo1, 2, 3, LI Chao1, 2, 3, MA Jin-ji1, 2, 3*, FANG Xue-jing4, XIONG Wei4. Simulation of Limb Measurements for Mesospheric Hydroxyl Radical Based on SHS Detector[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(09): 2685-2691. |
[14] |
WANG Jie1, 2, 3, HUANG Chun-lin1*, WANG Jian1, CAO Yong-pan1,2, HAO Xiao-hua1. A Kind of Snow Grains and Shape Parameters Retrieval New Algorithm Based on Spectral Library[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(05): 1502-1506. |
[15] |
YANG Bin1, 2, YAN Chang-xiang1* . Multi-Spectral Polarized Properties of Ocean Aerosol [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(09): 2736-2741. |
|
|
|
|