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Wei Gong
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

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Journal article
Published: 16 April 2021 in Atmospheric Research
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Fine particle matter (PM2.5) significantly affects the atmospheric environment and human health. The satellite-derived aerosol optical depth (AOD), which could represent the concentration of atmospheric particles to a certain extent, is widely used for estimating ambient PM2.5 concentration, in combination with diverse auxiliary information. However, the general satellite-derived PM2.5 products exhibit limitation in the application and aggregate analysis of PM2.5 in urban areas, because of the moderate spatial resolution to match the urban landscape and low spatial coverage making it hard to describe airmass trajectory. In order to explore the potential application value of PM2.5 concentration products with relatively high spatial coverage and resolution, a two-stage machine learning and geo-statistics coupled model incorporating with a feedback mechanism was proposed in this study. To be specific, we firstly develop a hybrid back-propagation neural network coupled kriging with external drifting approach (BPNN-KED) for estimating 1-km daily PM2.5 concentration maps at high coverage over four urban agglomerations in China. The model performs well, with R2 up to 0.83 and root mean square error of 14.7 μg/m3 from cross-validation. The daily PM2.5 maps display an average spatial coverage exceeding 95%, and on an average, each grid produces 350 days of valid estimations annually. In addition, the extra value of the high-coverage PM2.5 estimates were explored through the more accurate aggregate analysis of urban PM2.5 pollution level. The advantage of the high-coverage PM2.5 estimation is demonstrated through daily PM2.5 hotspot identification over urban areas, providing substantially fine spatially resolved PM2.5 trends, which offers the potential for daily pollutant emission sources location through satellite remote sensing technology. Moreover, the spatiotemporally continuous PM2.5 concentrations possess the ability to capture polluted air mass trajectories, thereby offering observational support not only for evaluating the contribution from exogenous pollutants to local PM2.5 concentrations and but also for providing empirical references for haze warning.

ACS Style

Yusi Huang; Tianhao Zhang; Zhongmin Zhu; Wei Gong; Xinghui Xia. PM2.5 concentration estimation with 1-km resolution at high coverage over urban agglomerations in China using the BPNN-KED approach and potential application. Atmospheric Research 2021, 258, 105628 .

AMA Style

Yusi Huang, Tianhao Zhang, Zhongmin Zhu, Wei Gong, Xinghui Xia. PM2.5 concentration estimation with 1-km resolution at high coverage over urban agglomerations in China using the BPNN-KED approach and potential application. Atmospheric Research. 2021; 258 ():105628.

Chicago/Turabian Style

Yusi Huang; Tianhao Zhang; Zhongmin Zhu; Wei Gong; Xinghui Xia. 2021. "PM2.5 concentration estimation with 1-km resolution at high coverage over urban agglomerations in China using the BPNN-KED approach and potential application." Atmospheric Research 258, no. : 105628.

Journal article
Published: 17 October 2020 in Sensors
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For high-precision measurements of the CO2 column concentration in the atmosphere with airborne integrated path differential absorption (IPDA) Lidar, the exact distance of the Lidar beam to the scattering surface, that is, the length of the column, must be measured accurately. For the high-precision inversion of the column length, we propose a set of methods on the basis of the actual conditions, including autocorrelation detection, adaptive filtering, Gaussian decomposition, and optimized Levenberg–Marquardt fitting based on the generalized Gaussian distribution. Then, based on the information of a pair of laser pulses, we use the direct adjustment method of unequal precision to eliminate the error in the distance measurement. Further, the effect of atmospheric delay on distance measurements is considered, leading to further correction of the inversion results. At last, an airborne experiment was carried out in a sea area near Qinhuangdao, China on March 14, 2019. The results showed that the ranging accuracy can reach 0.9066 m, which achieved an excellent ranging accuracy on 1.57-μm IPDA Lidar and met the requirement for high-precision CO2 column length inversion.

ACS Style

Xin Ma; Haowei Zhang; Ge Han; Hao Xu; Tianqi Shi; Wei Gong; Yue Ma; Song Li. High-Precision CO2 Column Length Analysis on the Basis of a 1.57-μm Dual-Wavelength IPDA Lidar. Sensors 2020, 20, 5887 .

AMA Style

Xin Ma, Haowei Zhang, Ge Han, Hao Xu, Tianqi Shi, Wei Gong, Yue Ma, Song Li. High-Precision CO2 Column Length Analysis on the Basis of a 1.57-μm Dual-Wavelength IPDA Lidar. Sensors. 2020; 20 (20):5887.

Chicago/Turabian Style

Xin Ma; Haowei Zhang; Ge Han; Hao Xu; Tianqi Shi; Wei Gong; Yue Ma; Song Li. 2020. "High-Precision CO2 Column Length Analysis on the Basis of a 1.57-μm Dual-Wavelength IPDA Lidar." Sensors 20, no. 20: 5887.

Journal article
Published: 25 July 2020 in Remote Sensing
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Integrated-path differential absorption (IPDA) LiDAR is a promising means of measuring the global distributions of the column weighted xCO2 (dry-air mixing ratio of CO2) with adequate accuracy and precision. Most IPDA LiDARs are incapable of discerning the vertical information of CO2 diffusion, which is of great significance for studies on the carbon cycle and climate change. Hence, we developed an inversion method using the constrained linear least-squares technique for a pulsed direct-detection multi-wavelength IPDA LiDAR to obtain sliced xCO2. In the proposed inversion method, the atmosphere is sliced into three different layers, and the xCO2 of those layers is then retrieved using the constrained linear least-squares technique. Assuming complete knowledge of the water vapor content, the accuracy of the retrieved sliced xCO2 could be as high as 99.85% when the signal-to-noise ratio of central wavelength retrievals is higher than 25 (with a log scale). Further experiments demonstrated that different carbon characteristics can be identified by the sign of the carbon gradient of the retrieved xCO2 between the ABL (atmospheric boundary layer) and FT (free troposphere). These results highlight the potential applications of multiple wavelength IPDA LiDAR.

ACS Style

Ge Han; Tianqi Shi; Xin Ma; Hao Xu; Miao Zhang; Qi Liu; Wei Gong. Obtaining Gradients of XCO2 in Atmosphere Using the Constrained Linear Least-Squares Technique and Multi-Wavelength IPDA LiDAR. Remote Sensing 2020, 12, 2395 .

AMA Style

Ge Han, Tianqi Shi, Xin Ma, Hao Xu, Miao Zhang, Qi Liu, Wei Gong. Obtaining Gradients of XCO2 in Atmosphere Using the Constrained Linear Least-Squares Technique and Multi-Wavelength IPDA LiDAR. Remote Sensing. 2020; 12 (15):2395.

Chicago/Turabian Style

Ge Han; Tianqi Shi; Xin Ma; Hao Xu; Miao Zhang; Qi Liu; Wei Gong. 2020. "Obtaining Gradients of XCO2 in Atmosphere Using the Constrained Linear Least-Squares Technique and Multi-Wavelength IPDA LiDAR." Remote Sensing 12, no. 15: 2395.

Journal article
Published: 14 July 2020 in Remote Sensing
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Previous studies have shown that it is feasible to retrieve multiple cloud properties simultaneously based on the space-borne hyperspectral observation in the oxygen A-band, such as cloud optical depth, cloud-top height, and cloud geometrical thickness. However, hyperspectral remote sensing is time-consuming if based on the precise radiative transfer solution that counts multiple scatterings of light. To speed up the radiation transfer solution in cloud scenarios for nadir space-borne observations, we developed a physical parameterization of hyperspectral reflectance in the oxygen A-band for single-layer water clouds. The parameterization takes into account the influences of cloud droplet forward-scattering and nonlinear oxygen absorption on hyperspectral reflectance, which are improvements over the previous studies. The performance of the parameterization is estimated through comparison with DISORT (Discrete Ordinates Radiative Transfer Program Multi-Layered Plane-Parallel Medium) on the cases with solar zenith angle θ, the cloud optical depth τc, and the single-scattering albedo ω in the range of 0 ≤ θ ≤ 75, 5 ≤ τc ≤ 50, 0.5 ≤ ω ≤ 1. The relative error of the cloud reflectance is within 5% for most cases, even for clouds with optical depths around five or at strong absorption wavelengths. We integrate the parameterization with a slit function and a simplified atmosphere to evaluate its performance in simulating the observed cloud reflection at the top of the atmosphere by OCO-2 (Orbiting Carbon Observatory-2). To better visualize the possible errors from the new parameterization, gas molecular scattering, aerosol scattering, and reflection from the underlying surface are ignored. The relative error of the out-of-band radiance is less than 4% and the relative error of the intra-band radiance ratio is less than 4%. The radiance ratio is the ratio of simulated observations with and without in-cloud absorption and is used to assess the accuracy of the parameterization in quantifying the in-cloud absorption. The parameterization is a preparation for rapid hyperspectral remote sensing in the oxygen A-band. It would help to improve retrieval efficiency and provide cloud geometric thickness products.

ACS Style

Jie Yang; Siwei Li; Feiyue Mao; Qilong Min; Wei Gong; Lei Zhang; Sheng Liu. Physical Parameterization of Hyperspectral Reflectance in the Oxygen A-Band for Single-Layer Water Clouds. Remote Sensing 2020, 12, 2252 .

AMA Style

Jie Yang, Siwei Li, Feiyue Mao, Qilong Min, Wei Gong, Lei Zhang, Sheng Liu. Physical Parameterization of Hyperspectral Reflectance in the Oxygen A-Band for Single-Layer Water Clouds. Remote Sensing. 2020; 12 (14):2252.

Chicago/Turabian Style

Jie Yang; Siwei Li; Feiyue Mao; Qilong Min; Wei Gong; Lei Zhang; Sheng Liu. 2020. "Physical Parameterization of Hyperspectral Reflectance in the Oxygen A-Band for Single-Layer Water Clouds." Remote Sensing 12, no. 14: 2252.

Journal article
Published: 21 May 2020 in Remote Sensing
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The turbulent mixing and dispersion of air pollutants is strongly dependent on the vertical structure of the wind, which constitutes one of the major challenges affecting the determination of boundary layer height (BLH). Here, an adaptive method is proposed to estimate BLH from measurements of radar wind profilers (RWPs) in Beijing (BJ), Nanjing (NJ), Chongqing (CQ), and Wulumuqi (WQ), China, during the summer of 2019. Validation against simultaneous BLH estimates from radiosondes (RSs) yielded a correlation coefficient of 0.66, indicating that the method can be used to derive BLH from RWPs. Diurnal variations of BLH and the ventilation coefficient (VC) at four sites were then examined. A distinct diurnal cycle of BLH was observed over all four cities; BLH gradually increased from sunset, reached a maximum in the afternoon, and then dropped sharply after sunset. The maximum hourly average BLH (1.426 ± 0.46 km) occurred in WQ, consistent with the maximum hourly mean VC larger than 5000 m2/s observed there. By comparison, the diurnal variation of VC was not strong, with values ranging between 2000 and 3000 m2/s, likely owing to the high-humidity environment. Furthermore, surface sensible heat flux, latent heat flux, and dry mass of particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) concentrations were found to somehow affect the vertical structure of wind and thermodynamic features, leading to a difference between RS and RWP BLH estimates. This indicates that the atmospheric environment can affect BLH estimates using RWP data. The BLH results from RWPs were better in some specific cases. These findings show great potential of RWP measurements in air quality research, and will provide key data references for policy-making toward emission reductions.

ACS Style

Boming Liu; Jianping Guo; Wei Gong; Yifan Shi; Shikuan Jin. Boundary Layer Height as Estimated from Radar Wind Profilers in Four Cities in China: Relative Contributions from Aerosols and Surface Features. Remote Sensing 2020, 12, 1657 .

AMA Style

Boming Liu, Jianping Guo, Wei Gong, Yifan Shi, Shikuan Jin. Boundary Layer Height as Estimated from Radar Wind Profilers in Four Cities in China: Relative Contributions from Aerosols and Surface Features. Remote Sensing. 2020; 12 (10):1657.

Chicago/Turabian Style

Boming Liu; Jianping Guo; Wei Gong; Yifan Shi; Shikuan Jin. 2020. "Boundary Layer Height as Estimated from Radar Wind Profilers in Four Cities in China: Relative Contributions from Aerosols and Surface Features." Remote Sensing 12, no. 10: 1657.

Journal article
Published: 27 March 2020 in Atmosphere
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Haze is an atmospheric phenomenon in which different types of particulates obscure the sky, and hence affect almost all human activities. Over a couple of recent decades, China has witnessed increasingly worse air quality as well as atmospheric haziness in its cities. There are various haze contributing factors including the rapid industrialization, excessive biomass burning, and an increase in the number of vehicles. This study proposes a methodology based on the aerosols scattering and absorption properties, to predict the likelihood of an episode of hazy days. This case study employs the aerosol optical properties data from integrated nephelometer and aethalometer sensors from December 2009 to September 2014 over Wuhan. The role and contribution of each aerosol optical parameter (e.g., aerosol scattering and absorption coefficients, single scattering albedo, scattering, and absorption Ångström exponents, backscatter ratio, and asymmetry factor) in distinguishing haze and haze-free conditions has been quantitatively determined based on a machine learning approach. Each aerosol optical parameter was classified independently by the support vector machine (SVM) algorithm, and the aerosol scattering (85.37%) and absorption (74.53%) coefficients were found to be primary potential indicators. Through the Kolmogorov-Smirnov test and traditional statistical analysis, the aerosol scattering and absorption coefficients were then verified as important indicators in distinguishing haze and haze-free days. Finally, through a probability density diagram and frequency histogram, we propose a simple quantitative standard to distinguish between haze and haze-free conditions based on the aerosol scattering coefficient and absorption coefficient in Wuhan City. The accuracy of the standard was determined to be 81.49% after testing, which indicates an accurate result. An error in aerosol optical properties may lead to an error in the calculation of aerosol radiative forcing, the earth’s energy budget, and climate prediction. Therefore, understanding of the aerosol properties during haze-free and haze-days will help policymakers to make new policies to control urban pollution and their effects on human health.

ACS Style

Miao Zhang; Jing Liu; Muhammad Bilal; Chun Zhang; Majid Nazeer; Luqman Atique; Ge Han; Wei Gong. Aerosol Optical Properties and Contribution to Differentiate Haze and Haze-Free Weather in Wuhan City. Atmosphere 2020, 11, 322 .

AMA Style

Miao Zhang, Jing Liu, Muhammad Bilal, Chun Zhang, Majid Nazeer, Luqman Atique, Ge Han, Wei Gong. Aerosol Optical Properties and Contribution to Differentiate Haze and Haze-Free Weather in Wuhan City. Atmosphere. 2020; 11 (4):322.

Chicago/Turabian Style

Miao Zhang; Jing Liu; Muhammad Bilal; Chun Zhang; Majid Nazeer; Luqman Atique; Ge Han; Wei Gong. 2020. "Aerosol Optical Properties and Contribution to Differentiate Haze and Haze-Free Weather in Wuhan City." Atmosphere 11, no. 4: 322.

Journal article
Published: 11 February 2020 in Remote Sensing
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The current full-waveform data at a single wavelength can mainly retrieve the geometric attributes of targets along the light path by detecting waveform components, resulting in the lack of spectral or color attribute information. This kind of device relies on a digital camera for acquiring the color information, however, which is inevitably limited by the lighting conditions and geometric registration errors. With the development of multispectral light detection and ranging (LiDAR) or even hyperspectral LiDAR that often utilize a supercontinuum laser source covering the whole visible light band, including red, green and blue bands, the simultaneous acquisition of color and spatial information becomes possible and makes passive imaging data no longer necessary. In this study, we propose a color restoration method for a full-waveform multispectral LiDAR (FWMSL) system. Additionally, we develop a multispectral lognormal function to fit the tailing echoes measured by FWMSL further accurately. Experimental data from our FWMSL system are used to evaluate the performance of the proposed method. The relative standard deviation, correlation coefficient (R2) and color difference ( Δ E ) metrics suggest that the color restoration for the full-waveform multispectral data is feasible.

ACS Style

Binhui Wang; Shalei Song; Wei Gong; Xiong Cao; Dong He; Zhenwei Chen; Xin Lin; Faquan Li; Jia Sun. Color Restoration for Full-Waveform Multispectral LiDAR Data. Remote Sensing 2020, 12, 593 .

AMA Style

Binhui Wang, Shalei Song, Wei Gong, Xiong Cao, Dong He, Zhenwei Chen, Xin Lin, Faquan Li, Jia Sun. Color Restoration for Full-Waveform Multispectral LiDAR Data. Remote Sensing. 2020; 12 (4):593.

Chicago/Turabian Style

Binhui Wang; Shalei Song; Wei Gong; Xiong Cao; Dong He; Zhenwei Chen; Xin Lin; Faquan Li; Jia Sun. 2020. "Color Restoration for Full-Waveform Multispectral LiDAR Data." Remote Sensing 12, no. 4: 593.

Journal article
Published: 10 February 2020 in Sensors
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Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Active laser-induced fluorescence (LIF) only measures the photosynthesis and biochemical status of vegetation and lacks information about spatial structures. In this work, we present a new Multi-Wavelength Fluorescence LiDAR (MWFL) system. The system extended the multi-channel fluorescence detection of LIF on the basis of the LiDAR scanning and ranging mechanism. Based on the principle prototype of the MWFL system, we carried out vegetation-monitoring experiments in the laboratory. The results showed that MWFL simultaneously acquires the 3D spatial structure and physiological states for precision vegetation monitoring. Laboratory experiments on interior scenes verified the system’s performance. Fluorescence point cloud classification results were evaluated at four wavelengths and by comparing them with normal vectors, to assess the MWFL system capabilities. The overall classification accuracy and Kappa coefficient increased from 70.7% and 0.17 at the single wavelength to 88.9% and 0.75 at four wavelengths. The overall classification accuracy and Kappa coefficient improved from 76.2% and 0.29 at the normal vectors to 92.5% and 0.84 at the normal vectors with four wavelengths. The study demonstrated that active 3D fluorescence imaging of vegetation based on the MWFL system has a great application potential in the field of remote sensing detection and vegetation monitoring.

ACS Style

Xingmin Zhao; Shuo Shi; Jian Yang; Wei Gong; Jia Sun; Biwu Chen; Kuanghui Guo; Bowen Chen. Active 3D Imaging of Vegetation Based on Multi-Wavelength Fluorescence LiDAR. Sensors 2020, 20, 935 .

AMA Style

Xingmin Zhao, Shuo Shi, Jian Yang, Wei Gong, Jia Sun, Biwu Chen, Kuanghui Guo, Bowen Chen. Active 3D Imaging of Vegetation Based on Multi-Wavelength Fluorescence LiDAR. Sensors. 2020; 20 (3):935.

Chicago/Turabian Style

Xingmin Zhao; Shuo Shi; Jian Yang; Wei Gong; Jia Sun; Biwu Chen; Kuanghui Guo; Bowen Chen. 2020. "Active 3D Imaging of Vegetation Based on Multi-Wavelength Fluorescence LiDAR." Sensors 20, no. 3: 935.

Journal article
Published: 04 January 2020 in Remote Sensing
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Spectra of reflectance (Sr) and fluorescence (Sf) are significant for crop monitoring and ecological environment research, and can be used to indicate the leaf nitrogen content (LNC) of crops indirectly. The aim of this work is to use the Sr-Sf features obtained with hyperspectral and laser-induced fluorescence LiDAR (HSL, LIFL) systems to construct novel combined spectral indices (NCIH-F) for multi-year rice LNC estimation. The NCIH-F is in a form of FWs* Φ + GSIs* Φ , where Φ is the Sr-Sf features, and FWs and GSIs are the feature weights and global sensitive indices for each characteristic band. In this study, the characteristic bands were chosen in different ways. Firstly, the Sr-Sf characteristics which can be the intensity or derivative variables of spectra in 685 and 740 nm, have been assigned as the Φ value in NCIH-F formula. Simultaneously, the photochemical reflectance index (PRI) formed with 531 and 570 nm was modified based on a variant spectral index, called PRIfraction, with the Sf intensity in 740 nm, and then compared its potential with NCIH-F on LNC estimation. During the above analysis, both NCIH-F and PRIfraction values were utilized to model rice LNC based on the artificial neural networks (ANNs) method. Subsequently, four prior bands were selected, respectively, with high FW and GSI values as the ANNs inputs for rice LNC estimation. Results show that FW- and GSI-based NCIH-F are closely related to rice LNC, and the performance of previous spectral indices used for LNC estimation can be greatly improved by multiplying their FWs and GSIs. Thus, it can be included that the FW- and GSI-based NCIH-F constitutes an efficient and reliable constructed form combining HSL (Sr) and LIFL (Sf) data together for rice LNC estimation.

ACS Style

Lin Du; Jian Yang; Bowen Chen; Jia Sun; Biwu Chen; Shuo Shi; Shalei Song; Wei Gong. Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice. Remote Sensing 2020, 12, 185 .

AMA Style

Lin Du, Jian Yang, Bowen Chen, Jia Sun, Biwu Chen, Shuo Shi, Shalei Song, Wei Gong. Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice. Remote Sensing. 2020; 12 (1):185.

Chicago/Turabian Style

Lin Du; Jian Yang; Bowen Chen; Jia Sun; Biwu Chen; Shuo Shi; Shalei Song; Wei Gong. 2020. "Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice." Remote Sensing 12, no. 1: 185.

Letter
Published: 23 September 2019 in Remote Sensing
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Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products are used worldwide for their reliable accuracy. However, the aerosol optical depth (AOD) usually retrieved by the operational dark target (DT) algorithm of MODIS has been missing for most of the urban regions in Central China. This was due to a high surface reflectance and heavy aerosol loading, especially in winter, when a high cloud cover fraction and the frequent occurrence of haze events reduce the number of effective satellite observations. The retrieval of the AOD from limited satellite data is much needed and important for further aerosol investigations. In this paper, we propose an improved AOD retrieval method for 500 m MODIS data, which is based on an extended surface reflectance estimation scheme and dynamic aerosol models derived from ground-based sun-photometric observations. This improved method was applied to retrieve AOD during heavy aerosol loading and effectively complements the scarcity of AOD in correspondence with urban surface of a higher spatial resolution. The validation results showed that the retrieved AOD was consistent with MODIS DT AOD (R = ~0.87; RMSE = ~0.11) and ground measurements (R = ~0.89; RMSE = ~0.15) from both the Terra and the Aqua satellite. The method can be easily applied to different urban environments affected by air pollution and contributes to the research on aerosol.

ACS Style

Shikuan Jin; Yingying Ma; Ming Zhang; Wei Gong; Oleg Dubovik; Boming Liu; Yifan Shi; Changlan Yang. Retrieval of 500 m Aerosol Optical Depths from MODIS Measurements over Urban Surfaces under Heavy Aerosol Loading Conditions in Winter. Remote Sensing 2019, 11, 2218 .

AMA Style

Shikuan Jin, Yingying Ma, Ming Zhang, Wei Gong, Oleg Dubovik, Boming Liu, Yifan Shi, Changlan Yang. Retrieval of 500 m Aerosol Optical Depths from MODIS Measurements over Urban Surfaces under Heavy Aerosol Loading Conditions in Winter. Remote Sensing. 2019; 11 (19):2218.

Chicago/Turabian Style

Shikuan Jin; Yingying Ma; Ming Zhang; Wei Gong; Oleg Dubovik; Boming Liu; Yifan Shi; Changlan Yang. 2019. "Retrieval of 500 m Aerosol Optical Depths from MODIS Measurements over Urban Surfaces under Heavy Aerosol Loading Conditions in Winter." Remote Sensing 11, no. 19: 2218.

Journal article
Published: 28 June 2019 in Remote Sensing
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True-color three-dimensional (3D) imaging exploits spatial and spectral information and can enable accurate feature extraction and object classification. The existing methods, however, are limited by data collection mechanisms when realizing true-color 3D imaging. We overcome this problem and present a novel true-color 3D imaging method based on a 32-channel hyperspectral LiDAR (HSL) covering a 431–751 nm spectral range. We conducted two experiments, one with nine-color card papers and the other with seven different colored objects. We used the former to investigate the effect of true-color 3D imaging and determine the optimal spectral bands for compositing true-color, and the latter to explore the classification potential based on the true-color feature using polynomial support vector machine (SVM) and Gaussian naive Bayes (NB) classifiers. Since using all bands of HSL will cause color distortions, the optimal spectral band combination for better compositing the true-color were selected by principal component analysis (PCA) and spectral correlation measure (SCM); PCA emphasizes the amount of information in band combinations, while SCM focuses on correlation between bands. The results show that the true-color 3D imaging can be realized based on HSL measurements, and three spectral bands of 466, 546, and 626 nm were determined. Comparing reflectance of the three selected bands, the overall classification accuracy of seven different colored objects was improved by 14.6% and 8.25% based on SVM and NB, respectively, classifiers after converting spectral intensities into true-color information. Overall, this study demonstrated the potential of HSL system in retrieving true-color and facilitating target recognition, and can serve as a guide in developing future three-channel or multi-channel true-color LiDAR.

ACS Style

Bowen Chen; Shuo Shi; Wei Gong; Jia Sun; Lin Du; Jian Yang; Kuanghui Guo; Xingmin Zhao. True-Color Three-Dimensional Imaging and Target Classification Based on Hyperspectral LiDAR. Remote Sensing 2019, 11, 1541 .

AMA Style

Bowen Chen, Shuo Shi, Wei Gong, Jia Sun, Lin Du, Jian Yang, Kuanghui Guo, Xingmin Zhao. True-Color Three-Dimensional Imaging and Target Classification Based on Hyperspectral LiDAR. Remote Sensing. 2019; 11 (13):1541.

Chicago/Turabian Style

Bowen Chen; Shuo Shi; Wei Gong; Jia Sun; Lin Du; Jian Yang; Kuanghui Guo; Xingmin Zhao. 2019. "True-Color Three-Dimensional Imaging and Target Classification Based on Hyperspectral LiDAR." Remote Sensing 11, no. 13: 1541.

Journal article
Published: 01 October 2018 in Remote Sensing of Environment
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Satellite-derived aerosol optical depth (AOD) has been widely used to estimate ground-level PM2.5 concentrations due to its spatially continuous observation. However, the coarse spatial resolutions (e.g., 3 km, 6 km, or 10 km) of the primary satellite AOD products have weakness to capture the characteristics of urban-scale PM2.5 patterns. Moreover, high-resolution (e.g., 1 km) PM2.5 estimations are still unable to be related to the urban landscape or to small geographical units, which is crucial for analyzing the urban pollution structure. In this study, the daily PM2.5 concentrations were estimated using the new AOD data with a 160 m spatial resolution retrieved by the Gaofen-1 (GF) wide field of view (WFV) along with the nested linear mixed effects model and ancillary variables from the Weather Research & Forecasting (WRF) meteorological simulation data. The experiment was conducted in Wuhan, Beijing, and Shanghai, which suffers from severe atmospheric fine particle pollution in recent years. The proposed model performed well for both GF and Moderate Resolution Imaging Spectroradiometer (MODIS), with slight over-fitting and little spatial autocorrelation. Regarding to the GF PM2.5 estimation, model fitting yielded R2 values of 0.96, 0.91 and 0.95 and mean prediction error (MPE) of 10.13, 11.89 and 7.34 μg/m3 for Wuhan, Beijing and Shanghai, respectively. The site-based cross validation achieved R2 values of 0.92, 0.88 and 0.89, and MPE of 13.69, 16.76 and 12.59 μg/m3 for Wuhan, Beijing and Shanghai, respectively. The day-of-years based cross validation resulted in R2 of 0.54, 0.58 and 0.50, and MPE of 30.46, 27.12 and 31.58 μg/m3 for Wuhan, Beijing and Shanghai, respectively, indicating that it was practicable to estimate the GF PM2.5 in the days without enough AOD-PM2.5 matchups. The ultrahigh resolution PM2.5 estimations offer substantial advantages for providing finer spatially resolved PM2.5 trends. Additionally, it offers new approaches to locate main PM2.5 emission sources, evaluate the local PM2.5 contribution proportion, and quantify the daily PM2.5 emission level via remote sensing techniques. Along with the joint observations via other high-resolution satellites, the temporal resolution of GF PM2.5 will be further improved. In all, this study not only provides possibilities for further applications in the precise analysis of urban inner PM2.5 pollution patterns but also establishes a foundation for constructing a high-resolution satellite air monitoring network in China.

ACS Style

Tianhao Zhang; Zhongmin Zhu; Wei Gong; Zerun Zhu; Kun Sun; Lunche Wang; Yusi Huang; Feiyue Mao; Huanfeng Shen; Zhiwei Li; Kai Xu. Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals. Remote Sensing of Environment 2018, 216, 91 -104.

AMA Style

Tianhao Zhang, Zhongmin Zhu, Wei Gong, Zerun Zhu, Kun Sun, Lunche Wang, Yusi Huang, Feiyue Mao, Huanfeng Shen, Zhiwei Li, Kai Xu. Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals. Remote Sensing of Environment. 2018; 216 ():91-104.

Chicago/Turabian Style

Tianhao Zhang; Zhongmin Zhu; Wei Gong; Zerun Zhu; Kun Sun; Lunche Wang; Yusi Huang; Feiyue Mao; Huanfeng Shen; Zhiwei Li; Kai Xu. 2018. "Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals." Remote Sensing of Environment 216, no. : 91-104.

Journal article
Published: 20 July 2018 in Sensors
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Atmospheric CO2 plays an important role in controlling climate change and its effect on the carbon cycle. However, detailed information on the dynamics of CO2 vertical mixing remains lacking, which hinders the accurate understanding of certain key features of the carbon cycle. Differential absorption lidar (DIAL) is a promising technology for CO2 detection due to its characteristics of high precision, high time resolution, and high spatial resolution. Ground-based CO2-DIAL can provide the continuous observations of the vertical profile of CO2 concentration, which can be highly significant to gaining deeper insights into the rectification effect of CO2, the ratio of respiration photosynthesis, and the CO2 dome in urban areas. A set of ground-based CO2-DIAL systems were developed by our team and highly accurate long-term laboratory experiments were conducted. Nonetheless, the performance suffered from low signal-to-noise ratio (SNR) in field explorations because of decreasing aerosol concentrations with increasing altitude and surrounding interference according to the results of our experiments in Wuhan and Huainan. The concentration of atmospheric CO2 is derived from the difference of signals between on-line and off-line wavelengths; thus, low SNR will cause the superimposition of the final inversion error. In such a situation, an efficient and accurate denoising algorithm is critical for a ground-based CO2-DIAL system, particularly in field experiments. In this study, a method based on lifting wavelet transform (LWT) for CO2-DIAL signal denoising was proposed. This method, which is an improvement of the traditional wavelet transform, can select different predictive and update functions according to the characteristics of lidar signals, thereby making it suitable for the signal denoising of CO2-DIAL. Experiment analyses were conducted to evaluate the denoising effect of LWT. For comparison, ensemble empirical mode decomposition denoising was also performed on the same lidar signal. In addition, this study calculated the coefficient of variation (CV) at the same altitude among multiple original signals within 10 min and then performed the same calculation on the denoised signal. Finally, high-quality signal of ground-based CO2-DIAL was obtained using the LWT denoising method. The differential absorption optical depths of the denoised signals obtained via LWT were calculated, and the profile distribution information of CO2 concentration was acquired during field detection by using our developed CO2-DIAL systems.

ACS Style

Chengzhi Xiang; Ge Han; Yuxin Zheng; Xin Ma; Wei Gong. Improvement of CO2-DIAL Signal-to-Noise Ratio Using Lifting Wavelet Transform. Sensors 2018, 18, 2362 .

AMA Style

Chengzhi Xiang, Ge Han, Yuxin Zheng, Xin Ma, Wei Gong. Improvement of CO2-DIAL Signal-to-Noise Ratio Using Lifting Wavelet Transform. Sensors. 2018; 18 (7):2362.

Chicago/Turabian Style

Chengzhi Xiang; Ge Han; Yuxin Zheng; Xin Ma; Wei Gong. 2018. "Improvement of CO2-DIAL Signal-to-Noise Ratio Using Lifting Wavelet Transform." Sensors 18, no. 7: 2362.

Journal article
Published: 02 May 2018 in Remote Sensing
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The characteristics of aerosol optical depth (AOD) over the Tibetan Plateau (TP) were analyzed using 8-year (from January 2007 to December 2014) Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) level 2 aerosol layer products. Firstly, the overall feature of AOD over the Tibetan Plateau was investigated, including the seasonal diversities of AODS (the sum of AODs from all aerosol layers), and A (the amounts of aerosol layers). Then we deeply studied the characteristics of AOD within the lowest aerosol layer over TP, including the seasonal variations of AOD1 (The AOD of the first aerosol layer), HB1 (the height of the first aerosol layer base), TL1 (the thickness of the first aerosol layer) and PAOD1 (The AOD proportion of the first aerosol layer). The AODS was generally low (0.9) indicated that the aerosols were mainly concentrated in the lowest layer in summer, fall, and winter in the main body of TP. In spring, the PAOD1 value was relatively low (~0.7–0.85) and the distribution exhibited obvious differences between the southern (~0.85) and the northern (~0.75) TP, which appeared to be consistent with A. Most of the aerosol loads in summer were concentrated in the lowest aerosol layer with high aerosol loads. Most of the aerosol loads in fall and winter were also concentrated in the lowest aerosol layer, but with low aerosol loads.

ACS Style

Miao Zhang; Lunche Wang; Muhammad Bilal; Wei Gong; Ziyue Zhang; Guangmeng Guo. The Characteristics of the Aerosol Optical Depth within the Lowest Aerosol Layer over the Tibetan Plateau from 2007 to 2014. Remote Sensing 2018, 10, 696 .

AMA Style

Miao Zhang, Lunche Wang, Muhammad Bilal, Wei Gong, Ziyue Zhang, Guangmeng Guo. The Characteristics of the Aerosol Optical Depth within the Lowest Aerosol Layer over the Tibetan Plateau from 2007 to 2014. Remote Sensing. 2018; 10 (5):696.

Chicago/Turabian Style

Miao Zhang; Lunche Wang; Muhammad Bilal; Wei Gong; Ziyue Zhang; Guangmeng Guo. 2018. "The Characteristics of the Aerosol Optical Depth within the Lowest Aerosol Layer over the Tibetan Plateau from 2007 to 2014." Remote Sensing 10, no. 5: 696.

Journal article
Published: 23 April 2018 in Remote Sensing
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South Asia is experiencing a levelling-off trend in solar radiation and even a transition from dimming to brightening. Any change in incident solar radiation, which is the only significant energy source of the global ecosystem, profoundly affects our habitats. Here, we use multiple observations of the A-Train constellation to evaluate the impacts of three-dimensional (3D) aerosol, cloud, and water vapor variations on the changes in surface solar radiation during the monsoon season (June–September) in South Asia from 2006 to 2015. Results show that surface shortwave radiation (SSR) has possibly increased by 16.2 W m−2 during this period. However, an increase in aerosol loading is inconsistent with the SSR variations. Instead, clouds are generally reduced and thinned by approximately 8.8% and 280 m, respectively, with a decrease in both cloud water path (by 34.7 g m−2) and particle number concentration under cloudy conditions. Consequently, the shortwave cloud radiative effect decreases by approximately 45.5 W m−2 at the surface. Moreover, precipitable water in clear-sky conditions decreases by 2.8 mm (mainly below 2 km), and related solar brightening increases by 2.5 W m−2. Overall, the decreases in 3D water vapor and clouds distinctly result in increased absorption of SSR and subsequent surface brightening.

ACS Style

Zengxin Pan; Feiyue Mao; Wei Wang; Bo Zhu; Xin Lu; Wei Gong. Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season. Remote Sensing 2018, 10, 651 .

AMA Style

Zengxin Pan, Feiyue Mao, Wei Wang, Bo Zhu, Xin Lu, Wei Gong. Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season. Remote Sensing. 2018; 10 (4):651.

Chicago/Turabian Style

Zengxin Pan; Feiyue Mao; Wei Wang; Bo Zhu; Xin Lu; Wei Gong. 2018. "Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season." Remote Sensing 10, no. 4: 651.

Journal article
Published: 18 February 2018 in Remote Sensing
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Aerosols greatly influence global and regional atmospheric systems, and human life. However, a comprehensive understanding of the source regions and three-dimensional (3D) characteristics of aerosol transport over central China is yet to be achieved. Thus, we investigate the 3D macroscopic, optical, physical, and transport properties of the aerosols over central China based on the March 2007 to February 2016 data obtained from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission and the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model. Our results showed that approximately 60% of the aerosols distributed over central China originated from local areas, whereas non-locally produced aerosols constituted approximately 40%. Anthropogenic aerosols constituted the majority of the aerosol pollutants (69%) that mainly distributed less than 2.0 km above mean sea level. Natural aerosols, which are mainly composed of dust, accounted for 31% of the total aerosols, and usually existed at an altitude higher than that of anthropogenic aerosols. Aerosol particles distributed in the near surface were smaller and more spherical than those distributed above 2.0 km. Aerosol optical depth (AOD) and the particulate depolarization ratio displayed decreasing trends, with a total decrease of 0.11 and 0.016 from March 2007 to February 2016, respectively. These phenomena indicate that during the study period, the extinction properties of aerosols decreased, and the degree of sphericity in aerosol particles increased. Moreover, the annual anthropogenic and natural AOD demonstrated decreasing trends, with a total decrease of 0.07 and 0.04, respectively. This study may benefit the evaluation of the effects of the 3D properties of aerosols on regional climates.

ACS Style

Xin Lu; Feiyue Mao; Zengxin Pan; Wei Gong; Wei Wang; Liqiao Tian; Shenghui Fang. Three-Dimensional Physical and Optical Characteristics of Aerosols over Central China from Long-Term CALIPSO and HYSPLIT Data. Remote Sensing 2018, 10, 314 .

AMA Style

Xin Lu, Feiyue Mao, Zengxin Pan, Wei Gong, Wei Wang, Liqiao Tian, Shenghui Fang. Three-Dimensional Physical and Optical Characteristics of Aerosols over Central China from Long-Term CALIPSO and HYSPLIT Data. Remote Sensing. 2018; 10 (2):314.

Chicago/Turabian Style

Xin Lu; Feiyue Mao; Zengxin Pan; Wei Gong; Wei Wang; Liqiao Tian; Shenghui Fang. 2018. "Three-Dimensional Physical and Optical Characteristics of Aerosols over Central China from Long-Term CALIPSO and HYSPLIT Data." Remote Sensing 10, no. 2: 314.

Journal article
Published: 11 October 2017 in Remote Sensing
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As China is suffering from severe fine particle pollution from dense industrialization and urbanization, satellite-derived aerosol optical depth (AOD) has been widely used for estimating particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5). However, the correlation between satellite AOD and ground-level PM2.5 could be influenced by aerosol vertical distribution, as satellite AOD represents the entire column, rather than just ground-level concentration. Here, a new column-to-surface vertical correction scheme is proposed to improve separation of the near-surface and elevated aerosol layers, based on the ratio of the integrated extinction coefficient within 200–500 m above ground level (AGL), using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)) aerosol profile products. There are distinct differences in climate, meteorology, terrain, and aerosol transmission throughout China, so comparisons between vertical correction via CALIOP ratio and planetary boundary layer height (PBLH) were conducted in different regions from 2014 to 2015, combined with the original Pearson coefficient between satellite AOD and ground-level PM2.5 for reference. Furthermore, the best vertical correction scheme was suggested for different regions to achieve optimal correlation with PM2.5, based on the analysis and discussion of regional and seasonal characteristics of aerosol vertical distribution. According to our results and discussions, vertical correction via PBLH is recommended in northwestern China, where the PBLH varies dramatically, stretching or compressing the surface aerosol layer; vertical correction via the CALIOP ratio is recommended in northeastern China, southwestern China, Central China (excluding summer), North China Plain (excluding Beijing), and the spring in the southeast coast, areas that are susceptible to exogenous aerosols and exhibit the elevated aerosol layer; and original AOD without vertical correction is recommended in Beijing and the southeast coast (excluding spring), where the elevated aerosol layer rarely occurs and a large proportion of aerosol is aggregated in near-surface. Moreover, validation experiments in 2016 agreed well with our discussions and conclusions drawn from the experiments of the first two years. Furthermore, suggested vertical correction scheme was applied into linear mixed effect (LME) model, and high cross validation (CV) R2 (~85%) and relatively low root mean square errors (RMSE, ~20 μg/m3) were achieved, which demonstrated that the PM2.5 estimation agreed well with the measurements. When compared to the original situation, CV R2 values and RMSE after vertical correction both presented improvement to a certain extent, proving that the suggested vertical correction schemes could further improve the estimation accuracy of PM2.5 based on sophisticated model in China. Estimating PM2.5 with better accuracy could contribute to a more precise research of ecology and epidemiology, and provide a reliable reference for environmental policy making by governments.

ACS Style

Wei Gong; Yusi Huang; Tianhao Zhang; Zhongmin Zhu; Yuxi Ji; Hao Xiang. Impact and Suggestion of Column-to-Surface Vertical Correction Scheme on the Relationship between Satellite AOD and Ground-Level PM2.5 in China. Remote Sensing 2017, 9, 1038 .

AMA Style

Wei Gong, Yusi Huang, Tianhao Zhang, Zhongmin Zhu, Yuxi Ji, Hao Xiang. Impact and Suggestion of Column-to-Surface Vertical Correction Scheme on the Relationship between Satellite AOD and Ground-Level PM2.5 in China. Remote Sensing. 2017; 9 (10):1038.

Chicago/Turabian Style

Wei Gong; Yusi Huang; Tianhao Zhang; Zhongmin Zhu; Yuxi Ji; Hao Xiang. 2017. "Impact and Suggestion of Column-to-Surface Vertical Correction Scheme on the Relationship between Satellite AOD and Ground-Level PM2.5 in China." Remote Sensing 9, no. 10: 1038.

Journal article
Published: 10 October 2017 in Remote Sensing
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CO2 is one of the most important greenhouse gases. Its concentration and distribution in the atmosphere have always been important in studying the carbon cycle and the greenhouse effect. This study is the first to validate the XCO2 of satellite observations with total carbon column observing network (TCCON) data and to compare the global XCO2 distribution for the passive satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT), which are on-orbit greenhouse gas satellites. Results show that since GOSAT was launched in 2009, its mean measurement accuracy was −0.4107 ppm with an error standard deviation of 2.216 ppm since 2009, and has since decreased to −0.62 ppm with an error standard deviation of 2.3 ppm during the past two more years (2014–2016), while the mean measurement accuracy of the OCO-2 was 0.2671 ppm with an error standard deviation of 1.56 ppm from September 2014 to December 2016. GOSAT observations have recently decreased and lagged behind OCO-2 on the ability to monitor the global distribution and monthly detection of XCO2. Furthermore, the XCO2 values gathered by OCO-2 are higher by an average of 1.765 ppm than those by GOSAT. Comparison of the latitude gradient characteristics, seasonal fluctuation amplitude, and annual growth trend of the monthly mean XCO2 distribution also showed differences in values but similar line shapes between OCO-2 and GOSAT. When compared with the NOAA statistics, both satellites’ measurements reflect the growth trend of the global XCO2 at a low and smooth level, and reflect the seasonal fluctuation with an absolutely different line shape.

ACS Style

Ailin Liang; Wei Gong; Ge Han; Chengzhi Xiang. Comparison of Satellite-Observed XCO2 from GOSAT, OCO-2, and Ground-Based TCCON. Remote Sensing 2017, 9, 1033 .

AMA Style

Ailin Liang, Wei Gong, Ge Han, Chengzhi Xiang. Comparison of Satellite-Observed XCO2 from GOSAT, OCO-2, and Ground-Based TCCON. Remote Sensing. 2017; 9 (10):1033.

Chicago/Turabian Style

Ailin Liang; Wei Gong; Ge Han; Chengzhi Xiang. 2017. "Comparison of Satellite-Observed XCO2 from GOSAT, OCO-2, and Ground-Based TCCON." Remote Sensing 9, no. 10: 1033.

Articles
Published: 21 September 2017 in International Journal of Geographical Information Science
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Maps are often animated to help users make comparisons and comprehend trends. However, large and complex differences between sequential maps can inhibit users from doing so. This paper proposes a morphing technique to highlight trends without manual intervention. Changes between sequential maps are considered as the diffusion processes of expanding classes, with these processes simulated by cellular automata. A skeleton extraction technique is introduced to handle special cases. Experimental results demonstrate that the proposed morphing technique can reveal obvious trends between dramatically changed maps. The potential application of the proposed morphing technique in sequential spatial data (e.g. remote-sensing images) is discussed.

ACS Style

Heng Lin; Wei Gong. Gradually morphing a thematic map series based on cellular automata. International Journal of Geographical Information Science 2017, 32, 102 -119.

AMA Style

Heng Lin, Wei Gong. Gradually morphing a thematic map series based on cellular automata. International Journal of Geographical Information Science. 2017; 32 (1):102-119.

Chicago/Turabian Style

Heng Lin; Wei Gong. 2017. "Gradually morphing a thematic map series based on cellular automata." International Journal of Geographical Information Science 32, no. 1: 102-119.

Research article
Published: 07 September 2017 in Journal of Optics
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Overlap factor is an instrumental phenomenon caused by the incomplete overlay of the transmitting and receiving systems of a light detection and ranging (lidar) system. Conventional methods of overlap calculation for Raman lidar by combining Mie and N2-Raman signals is based on a user-assumed lidar ratio, assumption of which may introduce larger uncertainties when the characters of an aerosol loading is unknown. In this study, a physical constraint method is proposed to obtain an appropriate lidar ratio for overlap profile calculation of Raman lidar. The experiment of six representative cases verified that the correction of the overlap profile obtained by our method is practical and feasible. The signal of the experiment was derived from the Raman lidar at the Southern Great Plains site (SGPRL) of Atmospheric Radiation Measurement Program. The particle extinction coefficient of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation was used as a reference value. The mean absolute errors of the particle extinction coefficient derived based on the proposed method is small (7.0–22.9 Mm−1) for 0–2 km by comparing the reference value. Additionally, the large bias below 0.8 km between the particle extinction coefficient corrected by the SGPRL-released overlap profile and the reference value suggest that the overlap profile applied in SGPRL still has larger room to be improved.

ACS Style

Wei Wang; Wei Gong; Feiyue Mao; Zengxin Pan. Physical constraint method to determine optimal overlap factor of Raman lidar. Journal of Optics 2017, 47, 83 -90.

AMA Style

Wei Wang, Wei Gong, Feiyue Mao, Zengxin Pan. Physical constraint method to determine optimal overlap factor of Raman lidar. Journal of Optics. 2017; 47 (1):83-90.

Chicago/Turabian Style

Wei Wang; Wei Gong; Feiyue Mao; Zengxin Pan. 2017. "Physical constraint method to determine optimal overlap factor of Raman lidar." Journal of Optics 47, no. 1: 83-90.