22.2.22 Atmospheric, Aerosols, Aerosol Optical Depth, Measurements, Air Quality, Remote Sensing

You, Y., Lu, C., Wang, W., Tang, C.,
Relative CNN-RNN: Learning Relative Atmospheric Visibility From Images,
IP(28), No. 1, January 2019, pp. 45-55.
IEEE DOI 1810
Atmospheric modeling, Atmospheric measurements, Estimation, Meteorology, Observatories, Support vector machines, large-scale image collection BibRef

Rushton, C.[Christopher], Galatioto, F.[Fabio], Wright, J.[James], Nielsen, E.[Erik], Tsotskas, C.[Christos],
City-wide emissions modelling using fleet probe vehicles,
IET-ITS(12), No. 9, November 2018, pp. 1181-1188.
DOI Link 1810
BibRef

Liu, L.[Li], Mishchenko, M.I.[Michael I.],
Scattering and Radiative Properties of Morphologically Complex Carbonaceous Aerosols: A Systematic Modeling Study,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Behera, S.K., Dash, A.K., Dogra, D.P., Roy, P.P.,
Air Signature Recognition Using Deep Convolutional Neural Network-Based Sequential Model,
ICPR18(3525-3530)
IEEE DOI 1812
Feature extraction, Convolutional neural networks, Convolution, Atmospheric modeling BibRef

Xu, H., Chen, Y., Wang, L.,
Cross-Track Infrared Sounder Spectral Gap Filling Toward Improving Intercalibration Uncertainties,
GeoRS(57), No. 1, January 2019, pp. 509-519.
IEEE DOI 1901
Calibration, Atmospheric measurements, Instruments, Atmospheric modeling, Extraterrestrial measurements, spectrum gap filling BibRef

Bitta, J.[Jan], Pavlíková, I.[Irena], Svozilík, V.[Vladislav], Jancík, P.[Petr],
Air Pollution Dispersion Modelling Using Spatial Analyses,
IJGI(7), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Zhao, K.[Kunrong], He, T.T.[Ting-Ting], Wu, S.[Shuang], Wang, S.L.[Song-Ling], Dai, B.L.[Bi-Lan], Yang, Q.F.[Qi-Fan], Lei, Y.[Yutao],
Research on video classification method of key pollution sources based on deep learning,
JVCIR(59), 2019, pp. 283-291.
Elsevier DOI 1903
Pollution sources, Deep learning, Surveillance video classification, Convolution neural network BibRef

She, L., Xue, Y., Yang, X., Leys, J., Guang, J., Che, Y., Fan, C., Xie, Y., Li, Y.,
Joint Retrieval of Aerosol Optical Depth and Surface Reflectance Over Land Using Geostationary Satellite Data,
GeoRS(57), No. 3, March 2019, pp. 1489-1501.
IEEE DOI 1903
aerosols, atmospheric optics, geophysical signal processing, radiative transfer, remote sensing, aerosol optical depth, optimal estimation BibRef

Xie, Y., Xue, Y., Guang, J., Mei, L., She, L., Li, Y., Che, Y., Fan, C.,
Deriving a Global and Hourly Data Set of Aerosol Optical Depth Over Land Using Data From Four Geostationary Satellites: GOES-16, MSG-1, MSG-4, and Himawari-8,
GeoRS(58), No. 3, March 2020, pp. 1538-1549.
IEEE DOI 2003
Aerosols, Monitoring, Geostationary satellites, Remote sensing, Earth, Optical sensors, Aerosol optical depth (AOD), MSG-4 BibRef

Schuster, G.L.[Gregory L.], Espinosa, W.R.[W. Reed], Ziemba, L.D.[Luke D.], Beyersdorf, A.J.[Andreas J.], Rocha-Lima, A.[Adriana], Anderson, B.E.[Bruce E.], Martins, J.V.[Jose V.], Dubovik, O.[Oleg], Ducos, F.[Fabrice], Fuertes, D.[David], Lapyonok, T.[Tatyana], Shook, M.[Michael], Derimian, Y.[Yevgeny], Moore, R.H.[Richard H.],
A Laboratory Experiment for the Statistical Evaluation of Aerosol Retrieval (STEAR) Algorithms,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Ravindrababu, S., Ratnam, M.V.[M. Venkat], Basha, G.[Ghouse], Liou, Y.A.[Yuei-An], Reddy, N.N.[N. Narendra],
Large Anomalies in the Tropical Upper Troposphere Lower Stratosphere (UTLS) Trace Gases Observed during the Extreme 2015-16 El Niño Event by Using Satellite Measurements,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Fan, X.L.[Xian-Lei], Qu, Y.[Ying],
Retrieval of High Spatial Resolution Aerosol Optical Depth from HJ-1 A/B CCD Data,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Xu, F.[Feng], Diner, D.J.[David J.], Dubovik, O.[Oleg], Schechner, Y.Y.[Yoav Y.],
A Correlated Multi-Pixel Inversion Approach for Aerosol Remote Sensing,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Shi, Z.[Zheng], Xing, T.Y.[Ting-Yan], Guang, J.[Jie], Xue, Y.[Yong], Che, Y.H.[Ya-Hui],
Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Zhang, H.P.[Hao-Peng], Deng, Q.[Qin],
Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
Monitoring pollution. BibRef

Dongre, P.K.[Prateek Kumar], Havemann, S.[Stephan], Hargrave, P.[Peter], Orlando, A.[Angiola], Sudiwala, R.[Rashmikant], Thomas, C.[Christopher], Goldie, D.[David], Withington, S.[Stafford],
End-to-End Instrument Performance Simulation System (EIPS) Framework: Application to Satellite Microwave Atmospheric Sounding Systems,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Kurmi, I.[Indrajit], Schedl, D.C.[David C.], Bimber, O.[Oliver],
Thermal Airborne Optical Sectioning,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Deng, G.R.[Guo-Rong], Zhang, H.Y.[Hong-Yan], Guo, X.Y.[Xiao-Yi], Shan, Y.[Yu], Ying, H.[Hong], Rihan, W.[Wu], Li, H.[Hui], Han, Y.L.[Yang-Li],
Asymmetric Effects of Daytime and Nighttime Warming on Boreal Forest Spring Phenology,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Li, W.Z.[Wen-Zhao], Ali, E.[Elham], El-Magd, I.A.[Islam Abou], Mourad, M.M.[Moustafa Mohamed], El-Askary, H.[Hesham],
Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Zhang, W.Z.[Wen-Zhong], Deng, S.M.[Shu-Mei], Luo, T.[Tao], Wu, Y.[Yang], Liu, N.[Nana], Li, X.B.[Xue-Bin], Huang, Y.B.[Yin-Bo], Zhu, W.[Wenyue],
New Global View of Above-Cloud Absorbing Aerosol Distribution Based on CALIPSO Measurements,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Mei, L., Rozanov, V., Jethva, H., Meyer, K.G., Lelli, L., Vountas, M., Burrows, J.P.,
Extending XBAER Algorithm to Aerosol and Cloud Condition,
GeoRS(57), No. 10, October 2019, pp. 8262-8275.
IEEE DOI 1910
aerosols, atmospheric optics, atmospheric techniques, clouds, dust, radiative transfer, radiometers, remote sensing, cloud condition, satellite BibRef

Klimov, P.[Pavel], Khrenov, B.[Boris], Kaznacheeva, M.[Margarita], Garipov, G.[Gali], Panasyuk, M.[Mikhail], Petrov, V.[Vasily], Sharakin, S.[Sergei], Shirokov, A.[Andrei], Yashin, I.[Ivan], Zotov, M.[Mikhail], Grebenyuk, V.[Viktor], Grinyuk, A.[Andrei], Lavrova, M.[Maria], Tkachenko, A.[Artur], Tkachev, L.[Leonid], Botvinko, A.[Alla], Saprykin, O.[Oleg], Puchkov, A.[Andrei], Senkovsky, A.[Alexander],
Remote Sensing of the Atmosphere by the Ultraviolet Detector TUS Onboard the Lomonosov Satellite,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Fan, C.[Cheng], Fu, G.L.[Guang-Liang], di Noia, A.[Antonio], Smit, M.[Martijn], Rietjens, J.H.H.[Jeroen H.H.], Ferrare, R.A.[Richard A.], Burton, S.[Sharon], Li, Z.Q.[Zheng-Qiang], Hasekamp, O.P.[Otto P.],
Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Malmgren-Hansen, D.[David], Laparra, V.[Valero], Nielsen, A.A.[Allan Aasbjerg], Camps-Valls, G.[Gustau],
Statistical retrieval of atmospheric profiles with deep convolutional neural networks,
PandRS(158), 2019, pp. 231-240.
Elsevier DOI 1912
Atmospheric measurements, Neural networks, Infrared measurements, Information retrieval BibRef

Rosenkranz, P.W., Cimini, D.,
Speed Dependence of 22- and 118-GHz Line Shapes for Tropospheric Remote Sensing,
GeoRS(57), No. 12, December 2019, pp. 9702-9708.
IEEE DOI 1912
Shape, Absorption, Doppler effect, Atmospheric measurements, Atmosphere, Resonant frequency, Remote sensing, spectral shape BibRef

She, L.[Lu], Zhang, H.[Hankui], Wang, W.[Weile], Wang, Y.J.[Yu-Jie], Shi, Y.[Yun],
Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm for Himawari-8 Data,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Ceamanos, X.[Xavier], Moparthy, S.[Suman], Carrer, D.[Dominique], Seidel, F.C.[Felix C.],
Assessing the Potential of Geostationary Satellites for Aerosol Remote Sensing Based on Critical Surface Albedo,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Wang, Y.W.[Ya-Wen], Trentmann, J.[Jörg], Pfeifroth, U.[Uwe], Yuan, W.P.[Wen-Ping], Wild, M.[Martin],
Improvement of Air Pollution in China Inferred from Changes between Satellite-Based and Measured Surface Solar Radiation,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Li, L.[Lianfa],
Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Zhao, L.[Lewen], Václavovic, P.[Pavel], Douša, J.[Jan],
Performance Evaluation of Troposphere Estimated from Galileo-Only Multi-Frequency Observations,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Wang, W.Y.[Wen-Yu], Wang, Z.Z.[Zhen-Zhan], Duan, Y.Q.[Yong-Qiang],
Preliminary Evaluation of the Error Budgets in the TALIS Measurements and Their Impact on the Retrievals,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
THz Atmospheric Limb Sounder. Measure atmosphere. BibRef

Augustin, P.[Patrick], Billet, S.[Sylvain], Crumeyrolle, S.[Suzanne], Deboudt, K.[Karine], Dieudonné, E.[Elsa], Flament, P.[Pascal], Fourmentin, M.[Marc], Guilbaud, S.[Sarah], Hanoune, B.[Benjamin], Landkocz, Y.[Yann], Méausoone, C.[Clémence], Roy, S.[Sayahnya], Schmitt, F.G.[François G.], Sentchev, A.[Alexei], Sokolov, A.[Anton],
Impact of Sea Breeze Dynamics on Atmospheric Pollutants and Their Toxicity in Industrial and Urban Coastal Environments,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Banach, M.[Marzena], Dlugosz, R.[Rafal], Pauk, J.[Jolanta], Talaska, T.[Tomasz],
Hardware Efficient Solutions for Wireless Air Pollution Sensors Dedicated to Dense Urban Areas,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Siomos, N.[Nikolaos], Fountoulakis, I.[Ilias], Natsis, A.[Athanasios], Drosoglou, T.[Theano], Bais, A.[Alkiviadis],
Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Zou, B.[Bin], Liu, N.[Ning], Wang, W.[Wei], Feng, H.H.[Hui-Hui], Liu, X.P.[Xiang-Ping], Lin, Y.[Yan],
An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Chen, B.[Bin],
Air Quality Index Forecasting via Deep Dictionary Learning,
IEICE(E103-D), No. 5, May 2020, pp. 1118-1125.
WWW Link. 2005
BibRef

Zawadzka-Manko, O.[Olga], Stachlewska, I.S.[Iwona S.], Markowicz, K.M.[Krzysztof M.],
Near-Real-Time Application of SEVIRI Aerosol Optical Depth Algorithm,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Li, C.[Chong], Li, J.[Jing], Dubovik, O.[Oleg], Zeng, Z.C.[Zhao-Cheng], Yung, Y.L.[Yuk L.],
Impact of Aerosol Vertical Distribution on Aerosol Optical Depth Retrieval from Passive Satellite Sensors,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Zhang, T.[Taixin], Zang, L.[Lin], Mao, F.Y.[Fei-Yue], Wan, Y.C.[You-Chuan], Zhu, Y.N.[Yan-Nian],
Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Liang, Z.[Ze], Wei, F.L.[Fei-Li], Wang, Y.Y.[Yue-Yao], Huang, J.[Jiao], Jiang, H.[Hong], Sun, F.Y.[Fu-Yue], Li, S.C.[Shuang-Cheng],
The Context-Dependent Effect of Urban Form on Air Pollution: A Panel Data Analysis,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Mei, L., Rozanov, V., Ritter, C., Heinold, B., Jiao, Z., Vountas, M., Burrows, J.P.,
Retrieval of Aerosol Optical Thickness in the Arctic Snow-Covered Regions Using Passive Remote Sensing: Impact of Aerosol Typing and Surface Reflection Model,
GeoRS(58), No. 7, July 2020, pp. 5117-5131.
IEEE DOI 2006
Aerosols, Arctic, Clouds, Optical surface waves, Satellites, Sea surface, Remote sensing, Aerosol, arctic, retrieval, satellite, snow-covered BibRef

Zhang, M.[Miao], Su, B.[Bo], Bilal, M.[Muhammad], Atique, L.[Luqman], Usman, M.[Muhammad], Qiu, Z.F.[Zhong-Feng], Ali, M.A.[Md. Arfan], Han, G.[Ge],
An Investigation of Vertically Distributed Aerosol Optical Properties over Pakistan Using CALIPSO Satellite Data,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Camps, A.[Adriano], Alonso-Arroyo, A.[Alberto], Park, H.[Hyuk], Onrubia, R.[Raul], Pascual, D.[Daniel], Querol, J.[Jorge],
L-Band Vegetation Optical Depth Estimation Using Transmitted GNSS Signals: Application to GNSS-Reflectometry and Positioning,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

de Vito, S.[Saverio], di Francia, G.[Girolamo], Esposito, E.[Elena], Ferlito, S.[Sergio], Formisano, F.[Fabrizio], Massera, E.[Ettore],
Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices,
PRL(136), 2020, pp. 264-271.
Elsevier DOI 2008
BibRef

Hoque, H.M.S.[Hossain Mohammed Syedul], Irie, H.[Hitoshi], Damiani, A.[Alessandro], Momoi, M.[Masahiro],
Primary Evaluation of the GCOM-C Aerosol Products at 380 nm Using Ground-Based Sky Radiometer Observations,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

González, J.A.[Josep-Abel], Calbó, J.[Josep],
Assessing Rapid Variability in Atmospheric Apparent Optical Depth with an Array Spectrometer System,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Song, Z.G.[Zi-Geng], He, X.Q.[Xian-Qiang], Bai, Y.[Yan], Wang, D.F.[Di-Feng], Hao, Z.Z.[Zeng-Zhou], Gong, F.[Fang], Zhu, Q.K.[Qian-Kun],
Changes and Predictions of Vertical Distributions of Global Light-Absorbing Aerosols Based on CALIPSO Observation,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Remer, L.A.[Lorraine A.], Levy, R.C.[Robert C.], Mattoo, S.[Shana], Tanré, D.[Didier], Gupta, P.[Pawan], Shi, Y.X.[Ying-Xi], Sawyer, V.[Virginia], Munchak, L.A.[Leigh A.], Zhou, Y.P.[Ya-Ping], Kim, M.[Mijin], Ichoku, C.[Charles], Patadia, F.[Falguni], Li, R.R.[Rong-Rong], Gassó, S.[Santiago], Kleidman, R.G.[Richard G.], Holben, B.N.[Brent N.],
The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Wu, D.[Dong], Gong, J.H.[Jian-Hua], Liang, J.M.[Jian-Ming], Sun, J.[Jin], Zhang, G.Y.[Guo-Yong],
Analyzing the Influence of Urban Street Greening and Street Buildings on Summertime Air Pollution Based on Street View Image Data,
IJGI(9), No. 9, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Barreto, Á.[África], García, O.E.[Omaira Elena], Schneider, M.[Matthias], García, R.D.[Rosa Delia], Hase, F.[Frank], Sepúlveda, E.[Eliezer], Almansa, A.F.[Antonio Fernando], Cuevas, E.[Emilio], Blumenstock, T.[Thomas],
Spectral Aerosol Optical Depth Retrievals by Ground-Based Fourier Transform Infrared Spectrometry,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Brisco, B.[Brian], Mahdianpari, M.[Masoud], Mohammadimanesh, F.[Fariba],
Hybrid Compact Polarimetric SAR for Environmental Monitoring with the RADARSAT Constellation Mission,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Galtier, S.[Sandrine], Pivard, C.[Clément], Rairoux, P.[Patrick],
Towards DCS in the UV Spectral Range for Remote Sensing of Atmospheric Trace Gases,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Sasi, S.[Sruthy], Natraj, V.[Vijay], García, V.M.[Víctor Molina], Efremenko, D.S.[Dmitry S.], Loyola, D.[Diego], Doicu, A.[Adrian],
Model Selection in Atmospheric Remote Sensing with an Application to Aerosol Retrieval from DSCOVR/EPIC, Part 1: Theory,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Sasi, S.[Sruthy], Natraj, V.[Vijay], García, V.M.[Víctor Molina], Efremenko, D.S.[Dmitry S.], Loyola, D.[Diego], Doicu, A.[Adrian],
Model Selection in Atmospheric Remote Sensing with Application to Aerosol Retrieval from DSCOVR/EPIC. Part 2: Numerical Analysis,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Zhao, M., Si, F., Wang, Y., Zhou, H., Wang, S., Jiang, Y., Liu, W.,
First Year On-Orbit Calibration of the Chinese Environmental Trace Gas Monitoring Instrument Onboard GaoFen-5,
GeoRS(58), No. 12, December 2020, pp. 8531-8540.
IEEE DOI 2012
Electromagnetic interference, Calibration, Monitoring, Instruments, Radiometry, Optical sensors, Earth, Calibration, solar diffusers (SDs) BibRef

Go, S.J.[Su-Jung], Kim, J.[Jhoon], Park, S.S.[Sang Seo], Kim, M.[Mijin], Lim, H.K.[Hyun-Kwang], Kim, J.Y.[Ji-Young], Lee, D.W.[Dong-Won], Im, J.H.[Jung-Ho],
Synergistic Use of Hyperspectral UV-Visible OMI and Broadband Meteorological Imager MODIS Data for a Merged Aerosol Product,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Kalajdjieski, J.[Jovan], Zdravevski, E.[Eftim], Corizzo, R.[Roberto], Lameski, P.[Petre], Kalajdziski, S.[Slobodan], Pires, I.M.[Ivan Miguel], Garcia, N.M.[Nuno M.], Trajkovik, V.[Vladimir],
Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

She, L.[Lu], Zhang, H.K.[Hankui K.], Li, Z.Q.[Zheng-Qiang], de Leeuw, G.[Gerrit], Huang, B.[Bo],
Himawari-8 Aerosol Optical Depth (AOD) Retrieval Using a Deep Neural Network Trained Using AERONET Observations,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Yang, F.[Fukun], Fan, M.[Meng], Tao, J.H.[Jin-Hua],
An Improved Method for Retrieving Aerosol Optical Depth Using Gaofen-1 WFV Camera Data,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Doicu, A.[Adrian], Efremenko, D.S.[Dmitry S.], Trautmann, T.[Thomas],
A Proof-of-Concept Algorithm for the Retrieval of Total Column Amount of Trace Gases in a Multi-Dimensional Atmosphere,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Huang, J.T.[Jing-Ting], Arnott, W.P.[William Patrick], Barnard, J.C.[James C.], Holmes, H.A.[Heather A.],
Theoretical Uncertainty Analysis of Satellite Retrieved Aerosol Optical Depth Associated with Surface Albedo and Aerosol Optical Properties,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Karpachev, A.[Alexander],
Sub-Auroral, Mid-Latitude, and Low-Latitude Troughs during Severe Geomagnetic Storms,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Choi, W.[Wonei], Lee, H.[Hanlim], Park, J.[Jeonghyeon],
A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Zhao, H.M.[Hong-Mei], Yang, G.Y.[Guang-Yi], Tong, D.Q.[Daniel Q.], Zhang, X.L.[Xue-Lei], Xiu, A.[Aijun], Zhang, S.C.[Shi-Chun],
Interannual and Seasonal Variability of Greenhouse Gases and Aerosol Emissions from Biomass Burning in Northeastern China Constrained by Satellite Observations,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Nicolae, A.[Ajtai], Alexandru, M.[Mereuta], Horatiu, S.[Stefanie], Andrei, R.[Radovici], Camelia, B.[Botezan], Olga, Z.M.[Zawadzka-Manko], Stachlewska, I.S.[Iwona S.], Kerstin, S.[Stebel], Claus, Z.[Zehner],
SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Várnai, T.[Tamás], Marshak, A.[Alexander],
Analysis of Near-Cloud Changes in Atmospheric Aerosols Using Satellite Observations and Global Model Simulations,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Lin, J.Y.[Jian-Yu], Zheng, Y.[Yu], Shen, X.Y.[Xin-Yong], Xing, L.[Lizhu], Che, H.Z.[Hui-Zheng],
Global Aerosol Classification Based on Aerosol Robotic Network (AERONET) and Satellite Observation,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Ma, H., Liang, S., Shi, H., Zhang, Y.,
An Optimization Approach for Estimating Multiple Land Surface and Atmospheric Variables From the Geostationary Advanced Himawari Imager Top-of-Atmosphere Observations,
GeoRS(59), No. 4, April 2021, pp. 2888-2908.
IEEE DOI 2104
Land surface, Atmospheric modeling, Clouds, Soil, MODIS, Advanced Himawari imager (AHI), remote sensing BibRef

Choi, W.[Wonei], Lee, H.[Hanlim], Kim, D.W.[Dae-Won], Kim, S.[Serin],
Improving Spatial Coverage of Satellite Aerosol Classification Using a Random Forest Model,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Wang, W.L.[Wei-Lin], Mao, W.J.[Wen-Jing], Tong, X.L.[Xue-Li], Xu, G.[Gang],
A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Lin, T.H.[Tang-Huang], Tsay, S.C.[Si-Chee], Lien, W.H.[Wei-Hung], Lin, N.H.[Neng-Huei], Hsiao, T.C.[Ta-Chih],
Spectral Derivatives of Optical Depth for Partitioning Aerosol Type and Loading,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Robles, J.[José], Zamorano, J.[Jaime], Pascual, S.[Sergio], de Miguel, A.S.[Alejandro Sánchez], Gallego, J.[Jesús], Gaston, K.J.[Kevin J.],
Evolution of Brightness and Color of the Night Sky in Madrid,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Burgués, J.[Javier], Esclapez, M.D.[María Deseada], Doñate, S.[Silvia], Pastor, L.[Laura], Marco, S.[Santiago],
Aerial Mapping of Odorous Gases in a Wastewater Treatment Plant Using a Small Drone,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Calassou, G.[Gabriel], Foucher, P.Y.[Pierre-Yves], Léon, J.F.[Jean-François],
Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Boselli, A.[Antonella], Sannino, A.[Alessia], d'Emilio, M.[Mariagrazia], Wang, X.[Xuan], Amoruso, S.[Salvatore],
Aerosol Characterization during the Summer 2017 Huge Fire Event on Mount Vesuvius (Italy) by Remote Sensing and In Situ Observations,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Yu, Z.Q.[Zhong-Qi], Qu, Y.H.[Yuan-Hao], Wang, Y.[Yunxin], Ma, J.H.[Jing-Hui], Cao, Y.[Yu],
Application of Machine-Learning-Based Fusion Model in Visibility Forecast: A Case Study of Shanghai, China,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Choi, W.[Wonei], Kang, H.W.[Hyeong-Woo], Shin, D.H.[Dong-Ho], Lee, H.[Hanlim],
Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Rao, L.[Lanlan], Xu, J.[Jian], Efremenko, D.S.[Dmitry S.], Loyola, D.G.[Diego G.], Doicu, A.[Adrian],
Optimization of Aerosol Model Selection for TROPOMI/S5P,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Ou, Y.[Yang], Li, L.[Lei], Li, Z.Q.[Zheng-Qiang], Zhang, Y.[Ying], Dubovik, O.[Oleg], Derimian, Y.[Yevgeny], Chen, C.[Cheng], Fuertes, D.[David], Xie, Y.S.[Yi-Song], Lopatin, A.[Anton], Ducos, F.[Fabrice], Peng, Z.[Zongren],
Spatio-Temporal Variability of Aerosol Components, Their Optical and Microphysical Properties over North China during Winter Haze in 2012, as Derived from POLDER/PARASOL Satellite Observations,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef
And: Correction: RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Zeng, Z.L.[Zhao-Liang], Wang, Z.[Zemin], Zhang, B.[Baojun],
An Adjustment Approach for Aerosol Optical Depth Inferred from CALIPSO,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Tong, C.[Chao], Zhang, C.X.[Cheng-Xin], Liu, C.[Cheng],
Investigation on the Relationship between Satellite Air Quality Measurements and Industrial Production by Generalized Additive Modeling,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Xu, S.Q.[Shi-Qi], Wang, S.S.[Shan-Shan], Xia, M.[Men], Lin, H.[Hua], Xing, C.Z.[Cheng-Zhi], Ji, X.G.[Xiang-Guang], Su, W.J.[Wen-Jing], Tan, W.[Wei], Liu, C.[Cheng], Hu, Q.H.[Qi-Hou],
Observations by Ground-Based MAX-DOAS of the Vertical Characters of Winter Pollution and the Influencing Factors of HONO Generation in Shanghai, China,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Ji, X.G.[Xiang-Guang], Hu, Q.H.[Qi-Hou], Hu, B.[Bo], Wang, S.T.[Shun-Tian], Liu, H.Y.[Han-Yang], Xing, C.Z.[Cheng-Zhi], Lin, H.[Hua], Lin, J.[Jinan],
Vertical Structure of Air Pollutant Transport Flux as Determined by Ground-Based Remote Sensing Observations in Fen-Wei Plain, China,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhang, L.[Luo], Liu, P.[Peng], Wang, L.[Lizhe], Liu, J.B.[Jian-Bo], Song, B.Z.[Bing-Ze], Zhang, Y.W.[Yu-Wei], He, G.J.[Guo-Jin], Zhang, H.[Hui],
Improved 1-km-Resolution Hourly Estimates of Aerosol Optical Depth Using Conditional Generative Adversarial Networks,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
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Lin, H.[Hao], Li, S.W.[Si-Wei], Xing, J.[Jia], Yang, J.[Jie], Wang, Q.X.[Qing-Xin], Dong, L.[Lechao], Zeng, X.Y.[Xiao-Yue],
Fusing Retrievals of High Resolution Aerosol Optical Depth from Landsat-8 and Sentinel-2 Observations over Urban Areas,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
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Guan, J.[Jian], Jin, B.[Bohan], Ding, Y.Z.[Yi-Zhe], Wang, W.[Wen], Li, G.X.[Guo-Xiang], Ciren, P.[Pubu],
Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks Technique,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
Formaldehyde. HCHO. BibRef

Jin, C.L.[Chun-Lin], Xue, Y.[Yong], Jiang, X.X.[Xing-Xing], Sun, Y.X.[Yu-Xin], Wu, S.H.[Shu-Hui],
Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Leckey, J.P.[John P.], Damadeo, R.[Robert], Hill, C.A.[Charles A.],
Stratospheric Aerosol and Gas Experiment (SAGE) from SAGE III on the ISS to a Free Flying SAGE IV Cubesat,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Marseille, C.[Charles], Aubé, M.[Martin], Barreto, A.[Africa], Simoneau, A.[Alexandre],
Remote Sensing of Aerosols at Night with the CoSQM Sky Brightness Data,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Choi, W.[Wonei], Lee, H.[Hanlim], Kim, J.[Jhoon], Park, J.[Junsung],
First TROPOMI Retrieval of Aerosol Effective Height Using O4 Absorption Band at 477 nm and Aerosol Classification,
GeoRS(59), No. 12, December 2021, pp. 9873-9886.
IEEE DOI 2112
Aerosols, Absorption, Artificial intelligence, Sensors, Monitoring, Atmospheric measurements, Pollution measurement, Aerosol height, TROPOspheric Monitoring Instrument (TROPOMI) BibRef

Shi, C.[Chong], Hashimoto, M.[Makiko], Shiomi, K.[Kei], Nakajima, T.[Teruyuki],
Development of an Algorithm to Retrieve Aerosol Optical Properties Over Water Using an Artificial Neural Network Radiative Transfer Scheme: First Result From GOSAT-2/CAI-2,
GeoRS(59), No. 12, December 2021, pp. 9861-9872.
IEEE DOI 2112
Aerosols, Atmospheric modeling, Sea measurements, Water, Satellites, Estimation, Atmospheric measurements, Aerosols, neural network, remote sensing BibRef

Li, H.[Hui], Shi, R.[Rui], Jin, S.K.[Shi-Kuan], Wang, W.[Weiyan], Fan, R.N.[Ruo-Nan], Zhang, Y.Q.[Yi-Qun], Liu, B.M.[Bo-Ming], Zhao, P.[Peitao], Gong, W.[Wei], Zhao, Y.F.[Yue-Feng],
Study of Persistent Haze Pollution in Winter over Jinan (China) Based on Ground-Based and Satellite Observations,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Shelestov, A.[Andrii], Yailymova, H.[Hanna], Yailymov, B.[Bohdan], Kussul, N.[Nataliia],
Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Zheng, L.M.[Lian-Ming], Lin, R.[Rui], Wang, X.M.[Xue-Mei], Chen, W.H.[Wei-Hua],
The Development and Application of Machine Learning in Atmospheric Environment Studies,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Xia, X.H.[Xing-Hui], Zhao, B.[Bin], Zhang, T.H.[Tian-Hao], Wang, L.[Luyao], Gu, Y.[Yu], Liou, K.N.[Kuo-Nan], Mao, F.Y.[Fei-Yue], Liu, B.M.[Bo-Ming], Bo, Y.C.[Yan-Chen], Huang, Y.[Yusi], Dong, J.[Jiadan], Gong, W.[Wei], Zhu, Z.M.[Zhong-Min],
Satellite-Derived Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing Based on Bayesian Maximum Entropy,
GeoRS(60), 2022, pp. 1-13.
IEEE DOI 2112
Aerosols, Cloud computing, Sensors, Satellites, MODIS, Spatiotemporal phenomena, Remote sensing, Active-passive fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) BibRef

Chang, C.W.[Chiao-Wei], Chen, W.T.[Wei-Ting], Chen, Y.C.[Yi-Chun],
Susceptibility of East Asian Marine Warm Clouds to Aerosols in Winter and Spring from Co-Located A-Train Satellite Observations,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Jha, S.S.[Sudhanshu Shekhar], Nidamanuri, R.R.[Rama Rao], Ientilucci, E.J.[Emmett J.],
Influence of atmospheric modeling on spectral target detection through forward modeling approach in multi-platform remote sensing data,
PandRS(183), 2022, pp. 286-306.
Elsevier DOI 2201
Target detection, Radiative transfer, Aerosol optical thickness, Atmospheric profiles, Multi-platform dataset BibRef

Kang, Y.[Yoojin], Kim, M.[Miae], Kang, E.[Eunjin], Cho, D.J.[Dong-Jin], Im, J.[Jungho],
Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia,
PandRS(183), 2022, pp. 253-268.
Elsevier DOI 2201
Aerosol optical depth, Fine mode fraction, Geostationary Ocean Color Imager, Machine learning, Shapley Additive exPlanations values BibRef

Grzegorski, M.[Michael], Poli, G.[Gabriele], Cacciari, A.[Alessandra], Jafariserajehlou, S.[Soheila], Holdak, A.[Andriy], Lang, R.[Ruediger], Vazquez-Navarro, M.[Margarita], Munro, R.[Rosemary], Fougnie, B.[Bertrand],
Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Banach, M.[Marzena], Dlugosz, R.[Rafal], Talaska, T.[Tomasz], Pedrycz, W.[Witold],
Air Pollution Monitoring System with Prediction Abilities Based on Smart Autonomous Sensors Equipped with ANNs with Novel Training Scheme,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Wei, S.W.[Shih-Wei], Lu, C.H.(.[Cheng-Hsuan (Sarah)], Johnson, B.T.[Benjamin T.], Dang, C.[Cheng], Stegmann, P.[Patrick], Grogan, D.[Dustin], Ge, G.Q.[Guo-Qing], Hu, M.[Ming],
The Influence of Aerosols on Satellite Infrared Radiance Simulations and Jacobians: Numerical Experiments of CRTM and GSI,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Tong, Y.C.[Yi-Cheng], Chen, S.[Sijie], Xiao, D.[Da], Zhang, K.[Kai], Fang, J.[Jing], Liu, C.[Chong], Shen, Y.B.[Yi-Bing], Liu, D.[Dong],
Lidar Ratio Regional Transfer Method for Extinction Coefficient Accuracy Improvement in Lidar Networks,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
Lidar for aerosol analysis. BibRef

Ou, Y.[Yang], Li, Z.Q.[Zheng-Qiang], Chen, C.[Cheng], Zhang, Y.[Ying], Li, K.[Kaitao], Shi, Z.[Zheng], Dong, J.T.[Jian-Tao], Xu, H.[Hua], Peng, Z.[Zongren], Xie, Y.S.[Yi-Song], Luo, J.[Jie],
Evaluation of MERRA-2 Aerosol Optical and Component Properties over China Using SONET and PARASOL/GRASP Data,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Sun, J.[Jie], Ding, K.[Kaihua], Lai, Z.L.[Zu-Long], Huang, H.J.[Hai-Jun],
Global and Regional Variations and Main Drivers of Aerosol Loadings over Land during 1980-2018,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Chen, X.F.[Xing-Feng], Zhao, L.M.[Li-Min], Zheng, F.J.[Feng-Jie], Li, J.G.[Jia-Guo], Li, L.[Lei], Ding, H.[Haonan], Zhang, K.N.[Kai-Nan], Liu, S.[Shumin], Li, D.H.[Dong-Hui], de Leeuw, G.[Gerrit],
Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Chiesa, S.[Stefano], di Pietro, A.[Antonio], Pollino, M.[Maurizio], Taraglio, S.[Sergio],
Urban Air Pollutant Monitoring through a Low-Cost Mobile Device Connected to a Smart Road,
IJGI(11), No. 2, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Popp, T.[Thomas], Mittaz, J.P.D.[Jonathan P.D.],
Systematic Propagation of AVHRR AOD Uncertainties: A Case Study to Demonstrate the FIDUCEO Approach,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Fajardo-Zambrano, C.M.[Carlos Mario], Bravo-Aranda, J.A.[Juan Antonio], Granados-Muñoz, M.J.[María José], Montilla-Rosero, E.[Elena], Casquero-Vera, J.A.[Juan Andrés], Rejano, F.[Fernando], Castillo, S.[Sonia], Alados-Arboledas, L.[Lucas],
Lidar and Radar Signal Simulation: Stability Assessment of the Aerosol-Cloud Interaction Index,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

She, L.[Lu], Zhang, H.K.[Hankui K.], Bu, Z.Q.[Zi-Qiang], Shi, Y.[Yun], Yang, L.[Lu], Zhao, J.T.[Jin-Tao],
A Deep-Neural-Network-Based Aerosol Optical Depth (AOD) Retrieval from Landsat-8 Top of Atmosphere Data,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
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Lee, S.[Seunghee], Kim, G.[Ganghan], Lee, M.I.[Myong-In], Choi, Y.H.[Yong-Han], Song, C.K.[Chang-Keun], Kim, H.K.[Hyeon-Kook],
Seasonal Dependence of Aerosol Data Assimilation and Forecasting Using Satellite and Ground-Based Observations,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Wang, P.D.[Pei-Dong], Holloway, T.[Tracey], Bindl, M.[Matilyn], Harkey, M.[Monica], de Smedt, I.[Isabelle],
Ambient Formaldehyde over the United States from Ground-Based (AQS) and Satellite (OMI) Observations,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Keppens, A.[Arno], Compernolle, S.[Steven], Hubert, D.[Daan], Verhoelst, T.[Tijl], Granville, J.[José], Lambert, J.C.[Jean-Christopher],
Removing Prior Information from Remotely Sensed Atmospheric Profiles by Wiener Deconvolution Based on the Complete Data Fusion Framework,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Su, Y.Y.[Yue-Yuan], Han, Y.[Yong], Luo, H.[Hao], Zhang, Y.[Yuan], Shao, S.Y.[Shi-Yong], Xie, X.X.[Xin-Xin],
Physical-Optical Properties of Marine Aerosols over the South China Sea: Shipboard Measurements and MERRA-2 Reanalysis,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Qin, X.N.[Xue-Ning], Do, T.H.[Tien Huu], Hofman, J.[Jelle], Bonet, E.R.[Esther Rodrigo], Manna, V.P.L.[Valerio Panzica La], Deligiannis, N.[Nikos], Philips, W.[Wilfried],
Fine-Grained Urban Air Quality Mapping from Sparse Mobile Air Pollution Measurements and Dense Traffic Density,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Li, W.T.[Wei-Tao], Su, X.[Xin], Feng, L.[Lan], Wu, J.Y.[Jin-Yang], Zhang, Y.J.[Yu-Jie], Cao, M.D.[Meng-Dan],
Comprehensive Validation and Comparison of Three VIIRS Aerosol Products over the Ocean on a Global Scale,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
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Chen, L.R.[Li-Rong], Wang, J.[Junyi], Wang, H.[Hui], Jin, T.C.[Tian-Cheng],
Urban Air Quality Assessment by Fusing Spatial and Temporal Data from Multiple Study Sources Using Refined Estimation Methods,
IJGI(11), No. 6, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Rahman, M.M.[Md Masudur], Shuo, W.[Wang], Zhao, W.X.[Wei-Xiong], Xu, X.Z.[Xue-Zhe], Zhang, W.J.[Wei-Jun], Arshad, A.[Arfan],
Investigating the Relationship between Air Pollutants and Meteorological Parameters Using Satellite Data over Bangladesh,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Chen, Y.L.[Yuan-Lin], Fan, M.[Meng], Li, M.Y.[Ming-Yang], Li, Z.B.[Zhong-Bin], Tao, J.H.[Jin-Hua], Wang, Z.B.[Zhi-Bao], Chen, L.[Liangfu],
Himawari-8/AHI Aerosol Optical Depth Detection Based on Machine Learning Algorithm,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
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Suel, E.[Esra], Sorek-Hamer, M.[Meytar], Moise, I.[Izabela], von Pohle, M.[Michael], Sahasrabhojanee, A.[Adwait], Asanjan, A.A.[Ata Akbari], Arku, R.E.[Raphael E.], Alli, A.S.[Abosede S.], Barratt, B.[Benjamin], Clark, S.N.[Sierra N.], Middel, A.[Ariane], Deardorff, E.[Emily], Lingenfelter, V.[Violet], Oza, N.C.[Nikunj C.], Yadav, N.[Nishant], Ezzati, M.[Majid], Brauer, M.[Michael],
What You See Is What You Breathe? Estimating Air Pollution Spatial Variation Using Street-Level Imagery,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
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Han, J.[Jie], Wu, J.J.[Jia-Ji], Zhang, L.J.[Li-Jun], Wang, H.G.[Hong-Guang], Zhu, Q.L.[Qing-Lin], Zhang, C.[Chao], Zhao, H.[Hui], Zhang, S.B.[Shou-Bao],
A Classifying-Inversion Method of Offshore Atmospheric Duct Parameters Using AIS Data Based on Artificial Intelligence,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Freitas, A.D.[Arthur Dias], Fornaro, A.[Adalgiza],
Atmospheric Formaldehyde Monitored by TROPOMI Satellite Instrument throughout 2020 over Sao Paulo State, Brazil,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Falah, S.[Somaya], Mhawish, A.[Alaa], Omar, A.H.[Ali H.], Sorek-Hamer, M.[Meytar], Lyapustin, A.I.[Alexei I.], Banerjee, T.[Tirthankar], Kizel, F.[Fadi], Broday, D.M.[David M.],
Intercomparison of Aerosol Types Reported as Part of Aerosol Product Retrieval over Diverse Geographic Regions,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Phillips, M.C.[Mark C.], Bernacki, B.E.[Bruce E.], Conry, P.T.[Patrick T.], Brown, M.J.[Michael J.],
Standoff Infrared Measurements of Chemical Plume Dynamics in Complex Terrain Using a Combination of Active Swept-ECQCL Laser Spectroscopy with Passive Hyperspectral Imaging,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Ge, B.Y.[Bang-Yu], Li, Z.Q.[Zheng-Qiang], Chen, C.[Cheng], Hou, W.Z.[Wei-Zhen], Xie, Y.S.[Yi-Song], Zhu, S.[Sifeng], Qie, L.[Lili], Zhang, Y.[Ying], Li, K.[Kaitao], Xu, H.[Hua], Ma, Y.[Yan], Yan, L.[Lei], Mei, X.D.[Xiao-Dong],
An Improved Aerosol Optical Depth Retrieval Algorithm for Multiangle Directional Polarimetric Camera (DPC),
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Tang, F.Y.[Fu-Ying], Wang, W.H.[Wei-He], Si, F.Q.[Fu-Qi], Zhou, H.J.[Hai-Jin], Luo, Y.H.[Yu-Han], Qian, Y.Y.[Yuan-Yuan],
Successful Derivation of Absorbing Aerosol Index from the Environmental Trace Gases Monitoring Instrument (EMI),
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Li, S.Q.[Shu-Qun], Hu, H.[Hao], Fang, C.G.[Cheng-Gege], Wang, S.C.[Si-Chen], Xun, S.P.[Shang-Pei], He, B.F.[Bin-Fang], Wu, W.Y.[Wen-Yu], Huo, Y.F.[Yan-Feng],
Hyperspectral Infrared Atmospheric Sounder (HIRAS) Atmospheric Sounding System,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Sun, Y.Q.[Yue-Qiang], Wang, B.[Bowen], Meng, X.G.[Xiang-Guang], Tang, X.C.[Xin-Chun], Yan, F.[Feng], Zhang, X.G.[Xian-Guo], Bai, W.H.[Wei-Hua], Du, Q.F.[Qi-Fei], Wang, X.[Xianyi], Cai, Y.R.[Yue-Rong], Guo, B.[Bibo], Wei, S.[Shilong], Qiao, H.[Hao], Hu, P.[Peng], Li, Y.P.[Yong-Ping], Wang, X.Y.[Xin-Yue],
Analysis of Orbital Atmospheric Density from QQ-Satellite Precision Orbits Based on GNSS Observations,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Fang, L.[Li], Hasekamp, O.[Otto], Fu, G.L.[Guang-Liang], Gong, W.S.[Wei-Shu], Wang, S.P.[Shu-Peng], Wang, W.H.[Wei-He], Han, Q.J.[Qi-Jin], Tang, S.H.[Shi-Hao],
Retrieval of Aerosol Optical Properties over Land Using an Optimized Retrieval Algorithm Based on the Directional Polarimetric Camera,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Li, H.[Hui], Liu, B.M.[Bo-Ming], Ma, X.[Xin], Ma, Y.Y.[Ying-Ying], Jin, S.K.[Shi-Kuan], Fan, R.N.[Ruo-Nan], Wang, W.[Weiyan], Fang, J.[Jing], Zhao, Y.F.[Yue-Feng], Gong, W.[Wei],
The Influence of Temperature Inversion on the Vertical Distribution of Aerosols,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Jiang, X.X.[Xing-Xing], Xue, Y.[Yong], Jin, C.L.[Chun-Lin], Bai, R.[Rui], Sun, Y.X.[Yu-Xin], Wu, S.H.[Shu-Hui],
A Simple Band Ratio Library (BRL) Algorithm for Retrieval of Hourly Aerosol Optical Depth Using FY-4A AGRI Geostationary Satellite Data,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Bai, J.Y.[Jing-Yu], Bai, L.[Lu], Li, J.[Jinlu], Wang, Y.K.[Yan-Kun], Xie, J.[Jinyu], Zhang, D.[Danmeng], Guo, L.X.[Li-Xin],
Sensitivity Analysis of 1,3-Butadiene Monitoring Based on Space-Based Detection in the Infrared Band,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Yu, X.Y.[Xin-Yu], Nichol, J.[Janet], Lee, K.H.[Kwon Ho], Li, J.[Jing], Wong, M.S.[Man Sing],
Analysis of Long-Term Aerosol Optical Properties Combining AERONET Sunphotometer and Satellite-Based Observations in Hong Kong,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Ma, Q.H.[Qi-Han], Liu, Y.Y.[Ying-Ying], Qiu, T.[Ting], Huang, T.X.[Ting-Xuan], Deng, T.[Tao], Hu, Z.Y.[Zhi-Yuan], Cui, T.W.[Ting-Wei],
Satellite-Observed Four-Dimensional Spatiotemporal Characteristics of Maritime Aerosol Types over the Coastal Waters of the Guangdong-Hong Kong-Macao Greater Bay Area and the Northern South China Sea,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Ding, H.N.[Hao-Nan], Zhao, L.M.[Li-Min], Liu, S.W.[Shan-Wei], Chen, X.F.[Xing-Feng], de Leeuw, G.[Gerrit], Wang, F.[Fu], Zheng, F.J.[Feng-Jie], Zhang, Y.[Yuhuan], Liu, J.[Jun], Li, J.G.[Jia-Guo], She, L.[Lu], Si, Y.[Yidan], Gu, X.F.[Xing-Fa],
FY-4A/AGRI Aerosol Optical Depth Retrieval Capability Test and Validation Based on NNAeroG,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Han, Y.Y.[Yuan-Yuan], Xie, F.[Fei], Cui, F.[Fei], Wang, F.[Feiyang], Li, X.[Xin], Feng, W.[Wuhu],
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Hashim, M.[Mazlan], Ng, H.L.[Hui Lin], Zakari, D.M.[Dahiru Mohammed], Sani, D.A.[Dalhatu Aliyu], Chindo, M.M.[Musa Muhammad], Hassan, N.[Noordyana], Azmy, M.M.[Muna Maryam], Pour, A.B.[Amin Beiranvand],
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Aerosol Retrieval Study from a Particulate Observing Scanning Polarimeter Onboard Gao-Fen 5B without Prior Surface Knowledge, Based on the Optimal Estimation Method,
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Evaluating the Value of CrIS Shortwave-Infrared Channels in Atmospheric-Sounding Retrievals,
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Zang, Z.L.[Zeng-Liang], Bao, X.[Xulun], Li, Y.[Yi], Qu, Y.M.[You-Ming], Niu, D.[Dan], Liu, N.[Ning], Chen, X.[Xisong],
A Modified RNN-Based Deep Learning Method for Prediction of Atmospheric Visibility,
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Tao, S.C.[Si-Chen], Sun, Z.C.[Zong-Chen], Lin, X.[Xingwen], Zhang, Z.Z.[Zhen-Zhen], Wu, C.F.[Chao-Fan], Zhang, Z.Y.[Zhao-Yang], Zhou, B.Z.[Ben-Zhi], Zhao, Z.[Zhen], Cao, C.C.[Chen-Chen], Guan, X.Y.[Xin-Yu], Zhuang, Q.J.[Qian-Jin], Wen, Q.Q.[Qing-Qing], Xu, Y.L.[Yu-Ling],
Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Wang, Y.X.[Yu-Xuan], Sun, X.B.[Xia-Bing], Huang, H.L.[Hong-Lian], Ti, R.[Rufang], Liu, X.[Xiao], Fan, Y.Z.[Yi-Zhe],
Study on Influencing Factors of the Information Content of Satellite Remote-Sensing Aerosol Vertical Profiles Using Oxygen A-Band,
RS(15), No. 4, 2023, pp. xx-yy.
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Li, S.[Sheng], Liu, Y.[Yanna], Du, K.[Ke],
Quantifying Emissions from Fugitive Area Sources Using a Hybrid Method of Multi-Path Optical Remote Sensing and Tomographic Inverse-Dispersion Techniques,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Huang, C.[Congwu], Niu, T.[Tao], Wu, H.[Hao], Qu, Y.[Yawei], Wang, T.[Tijian], Li, M.M.[Meng-Meng], Li, R.[Rong], Liu, H.L.[Hong-Li],
A Data Assimilation Method Combined with Machine Learning and Its Application to Anthropogenic Emission Adjustment in CMAQ,
RS(15), No. 6, 2023, pp. 1711.
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Lemmouchi, F.[Farouk], Cuesta, J.[Juan], Lachatre, M.[Mathieu], Brajard, J.[Julien], Coman, A.[Adriana], Beekmann, M.[Matthias], Derognat, C.[Claude],
Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations Using MODIS Satellite Observations,
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Scarlatti, F.[Francesco], Gómez-Amo, J.L.[José L.], Valdelomar, P.C.[Pedro C.], Estellés, V.[Víctor], Utrillas, M.P.[María Pilar],
A Machine Learning Approach to Derive Aerosol Properties from All-Sky Camera Imagery,
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Demeyer, S.[Séverine], Kristoffersen, S.K.[Samuel K.], Le Pichon, A.[Alexis], Larsonnier, F.[Franck], Fischer, N.[Nicolas],
Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems,
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Xia, X.H.[Xing-Hui], Zhang, T.H.[Tian-Hao], Wang, L.[Lunche], Gong, W.[Wei], Zhu, Z.M.[Zhong-Min], Wang, W.[Wei], Gu, Y.[Yu], Lin, Y.[Yun], Zhou, X.Y.[Xiang-Yang], Dong, J.[Jiadan], Fan, S.[Shumin], Xu, W.F.[Wen-Fa],
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Maulik, U.[Ujjwal], Kundu, S.[Srimanta],
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IEEE DOI 2305
Surveillance, Engines, Deep learning, Air pollution, Roads, Iterative methods, Image color analysis, Vehicle pollution, majority voting BibRef

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Shah, S.S.A.[Syed Shakeel Ahmad], Huang, Z.W.[Zhong-Wei], ul Haq, E.[Ehtiram], Alam, K.[Khan], and Its Relationship with Meteorology over the
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Long, Z.Y.[Zhi-Yong], Jin, Z.[Zichun], Meng, Y.Z.[Yi-Zhen], Ma, J.[Jin],
Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data,
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Li, H.[Han], Gu, M.J.[Ming-Jian], Zhang, C.M.[Chun-Ming], Xie, M.Z.[Meng-Zhen], Yang, T.[Tianhang], Hu, Y.[Yong],
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Koushafar, M.[Mohammad], Sohn, G.[Gunho], Gordon, M.[Mark],
Deep Convolutional Neural Network for Plume Rise Measurements in Industrial Environments,
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Kokhanovsky, A.A.[Alexander A.], Brell, M.[Maximillian], Segl, K.[Karl], Bianchini, G.[Giovanni], Lanconelli, C.[Christian], Lupi, A.[Angelo], Petkov, B.[Boyan], Picard, G.[Ghislain], Arnaud, L.[Laurent], Stone, R.S.[Robert S.], Chabrillat, S.[Sabine],
First Retrievals of Surface and Atmospheric Properties Using EnMAP Measurements over Antarctica,
RS(15), No. 12, 2023, pp. xx-yy.
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Guo, Y.H.[Yu-Hang], Li, X.Y.[Xiao-Ying], Cheng, T.H.[Tian-Hai], Li, S.S.[Shen-Shen], Zhang, X.Y.[Xin-Yuan], Lu, W.J.[Wen-Jing], Fang, W.F.[Wei-Fang],
Construction of the Global Reference Atmospheric Profile Database,
RS(15), No. 12, 2023, pp. xx-yy.
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Kim, J.[Juhuhn], Emmerich, M.T.M.[Michael T. M.], Voors, R.[Robert], Ording, B.[Barend], Lee, J.S.[Jong-Seok],
A Systematic Approach to Identify Shipping Emissions Using Spatio-Temporally Resolved TROPOMI Data,
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Kim, M.[Mijeong], Lee, K.[Kyunghwa], Choi, M.[Myungje],
Spectral and Spatial Dependencies in the Validation of Satellite-Based Aerosol Optical Depth from the Geostationary Ocean Color Imager Using the Aerosol Robotic Network,
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Du, J.[Jia], Li, D.[Dianjia], Song, K.[Kaishan], Zheng, Z.[Zhi], Wang, Y.[Yan],
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Li, S.J.[Shi-Jie], Wu, T.[Tong], Zhong, K.[Kai], Zhang, X.Z.[Xian-Zhong], Sun, Y.[Yue], Zhang, Y.J.[Yi-Jian], Wang, Y.[Yu], Li, X.[Xinqi], Xu, D.[Degang], Yao, J.Q.[Jian-Quan],
Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer,
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laser in AoD BibRef

Xie, M.Z.[Meng-Zhen], Gu, M.J.[Ming-Jian], Zhang, C.M.[Chun-Ming], Hu, Y.[Yong], Yang, T.H.[Tian-Hang], Huang, P.Y.[Peng-Yu], Li, H.[Han],
Comparative Study of the Atmospheric Gas Composition Detection Capabilities of FY-3D/HIRAS-I and FY-3E/HIRAS-II Based on Information Capacity,
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Liu, J.J.[Jing-Jing], Li, M.[Mengping], Zhou, L.[Luyao], Ge, J.M.[Jin-Ming], Liu, J.T.[Jing-Tao], Guo, Z.[Zhuqi], Liu, Y.Y.[Yang-Yang], Wang, J.[Jun], Yan, Q.[Qing], Hua, D.X.[Deng-Xin],
Analysis of Aerosol Optical Depth and Forward Scattering in an Ultraviolet Band Based on Sky Radiometer Measurements,
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Wang, Y.M.[Yong-Mei], Zhang, Z.[Zhuo], Mao, J.H.[Jing-Hua], Wang, H.[Houmao], Shi, E.[Entao], Liu, X.H.[Xiao-Hong], Li, P.D.[Peng-Da], Liu, J.[Jiu],
In-Flight Preliminary Performance of GF-5B/Absorbing Aerosol Sensor,
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Chen, H.L.[Hui-Lin], Yang, W.M.[Wen-Ming], Liao, Q.M.[Qing-Min],
Two-Stream Non-Uniform Concentration Reasoning Network for Single Image Air Pollution Estimation,
ICIP22(501-505)
IEEE DOI 2211
Costs, Aggregates, Estimation, Streaming media, Feature extraction, Air pollution, Cognition, Air pollution estimation, attention mechanism BibRef

Zhang, T.[Tony], Dick, R.P.[Robert P.],
Image-Based Air Quality Forecasting Through Multi-Level Attention,
ICIP22(686-690)
IEEE DOI 2211
Visualization, Atmospheric measurements, Fuses, Atmospheric modeling, Predictive models, Time measurement, attention BibRef

Dubey, R., Bharadwaj, S., Zafar, M.I., Biswas, S.,
Collaborative Air Quality Mapping of Different Metropolitan Cities Of India,
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Ridzuan, N., Ujang, U., Azri, S., Choon, T.L.,
3d Air Pollution Computational Fluid Modelling Data Analysis Using Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) Approach,
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Garcia, D., Vázquez-Gallego, F., Parés, M.E.,
On the Organization and Validation of a Pilot Test of a Mobile Crowdsourced Air Quality Monitoring System,
ISPRS21(B4-2021: 361-366).
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Chen, Z.[Zikun],
The Application of Artificial Intelligence on the Traceability and Dispersion of Air Pollution,
ICIVC21(404-407)
IEEE DOI 2112
Image resolution, Atmospheric modeling, Computational modeling, Stochastic processes, Interference, Position measurement, fuzzy data processing BibRef

Mukherjee, R.[Ryan], Rollend, D.[Derek], Christie, G.[Gordon], Hadzic, A.[Armin], Matson, S.[Sally], Saksena, A.[Anshu], Hughes, M.[Marisa],
Towards Indirect Top-Down Road Transport Emissions Estimation,
EarthVision21(1092-1101)
IEEE DOI 2109
Training, Climate change, Visualization, Satellites, Uncertainty, Roads, Estimation BibRef

Hofman, J.[Jelle], Do, T.H.[Tien Huu], Qin, X.[Xuening], Rodrigo, E.[Esther], Nikolaou, M.E.[Martha E.], Philips, W.[Wilfried], Deligiannis, N.[Nikos], La Manna, V.P.[Valerio Panzica],
Spatiotemporal Air Quality Inference of Low-cost Sensor Data; Application on a Cycling Monitoring Network,
MAES20(139-147).
Springer DOI 2103
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Alpan, K., Sekeroglu, B.,
Prediction of Pollutant Concentrations By Meteorological Data Using Machine Learning Algorithms,
SmartCityApp20(21-27).
DOI Link 2012
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Casella, V., Franzini, M., Bellazzi, R., Larizza, C., Pala, D.,
Dynamic Assessment of Personal Exposure to Air Pollution for Everyone: A Smartphone-based Approach,
ISPRS20(B4:655-663).
DOI Link 2012
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Parés, M.E., Garcia, D., Vázquez-Gallego, F.,
Mapping Air Quality with A Mobile Crowdsourced Air Quality Monitoring System (C-AQM),
ISPRS20(B4:685-690).
DOI Link 2012
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Ridzuan, N., Ujang, U., Azri, S., Choon, T.L.,
Visualising Urban Air Quality Using Aermod, Calpuff and Cfd Models: A Critical Review,
SmartCityApp20(355-363).
DOI Link 2012
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Calassou, G., Foucher, P.Y., Leon, J.F.,
Aerosol Plumes Characterization By Hyperspectral Images Coupled With Sentinel-2 Products,
ISPRS20(B3:791-797).
DOI Link 2012
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Luo, Z., Yu, Y., Zhang, D., Feng, S., Yu, H., Chang, Y., Shen, W.,
Air Quality Inference with Deep Convolutional Conditional Random Field,
ICIVC20(296-302)
IEEE DOI 2009
Air quality, Convolution, Neural networks, Indexes, Data models, Biological system modeling, air quality inference BibRef

Zhang, T., Dick, R.P.,
Estimation of Multiple Atmospheric Pollutants Through Image Analysis,
ICIP19(2060-2064)
IEEE DOI 1910
Air Quality, Light Attenuation, Support Vector Regression, Atmospheric Modeling BibRef

Ma, J.[Jian], Li, K.[Kun], Han, Y.H.[Ya-Hong], Yang, J.Y.[Jing-Yu],
Image-based Air Pollution Estimation Using Hybrid Convolutional Neural Network,
ICPR18(471-476)
IEEE DOI 1812
Air pollution, Feature extraction, Atmospheric measurements, Pollution measurement, Convolutional neural networks, Scattering, Training BibRef

Mechtley, B.[Brandon], Roberts, C.[Christopher], Stein, J.[Julian], Nandin, B.[Benjamin], Sha, X.W.[Xin Wei],
Enactive Steering of an Experiential Model of the Atmosphere,
VAMR18(I: 126-144).
Springer DOI 1807
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Yoshida, T., Wasenmüller, O., Stricker, D.,
Time-of-flight sensor depth enhancement for automotive exhaust gas,
ICIP17(1955-1959)
IEEE DOI 1803
Automotive engineering, Gaussian distribution, Gaussian mixture model, Mathematical model, Solids, Standards, image processing BibRef

Luffarelli, M., Govaerts, Y., Goossens, C., Wolters, E.L.A., Swinnen, E.,
Joint retrieval of surface reflectance and aerosol properties from PROBA-V observations, part I: Algorithm performance evaluation,
MultiTemp17(1-6)
IEEE DOI 1712
BibRef
And: A4, A5, A1, A3, A2:
Joint Surface Reflectance and AeRosol properties retrieval in the PV-LAC framework, part II: Validation,
MultiTemp17(1-4)
IEEE DOI 1712
aerosols, PROBA-V observations, aerosol properties, algorithm performance evaluation, atmospheric state variables, joint retrieval. aerosols, atmospheric optics, AOT observations, Aerosol Optical Thickness, validation BibRef

Eu, K.S.[Kok Seng], Yap, K.M.[Kian Meng], Tan, W.C.[Wan Chew],
A Simulation Study of Micro-Drone Chemical Plume Tracking Performance in Tree Farm Environments,
IVIC17(260-269).
Springer DOI 1711
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Presles, B., Debayle, J.,
A distance-based shape descriptor invariant to similitude and its application to shape classification,
ICPR16(2598-2603)
IEEE DOI 1705
Atmospheric measurements, Noise measurement, Particle measurements, Robustness, Sensitivity, Shape, Shape, measurement BibRef

Ghosh, R., Ghosh, D., Roy, S., Mukherjee, A.,
Exploring the self similar properties for monitoring of air quality information,
ICAPR15(1-6)
IEEE DOI 1511
air pollution control BibRef

Lin, T.H.[Tang-Huang], Liu, G.R.[Gin-Rong], Liu, C.Y.[Chian-Yi],
A Novel Index For Atmospheric Aerosol Types Categorization With Spectral Optical Depths From Satellite Retrieval,
ISPRS16(B8: 277-279).
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Marek, L., Campbell, M., Epton, M., Storer, M., Kingham, S.,
Real-time Environmental Sensors To Improve Health In The Sensing City,
ISPRS16(B2: 729-733).
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Levis, A.[Aviad], Schechner, Y.Y.[Yoav Y.], Davis, A.B.,
Multiple-Scattering Microphysics Tomography,
CVPR17(5797-5806)
IEEE DOI 1711
Atmospheric modeling, Clouds, Computational modeling, Mathematical model, Scattering, Tomography BibRef

Levis, A.[Aviad], Schechner, Y.Y.[Yoav Y.], Aides, A.[Amit], Davis, A.B.,
Airborne Three-Dimensional Cloud Tomography,
ICCV15(3379-3387)
IEEE DOI 1602
Atmospheric modeling BibRef

Veikherman, D.[Dmitry], Aides, A.[Amit], Schechner, Y.Y.[Yoav Y.], Levis, A.[Aviad],
Clouds in the Cloud,
ACCV14(IV: 659-674).
Springer DOI 1504
Light-field imaging for spaceborne instruments. Monitor the atmosphere. BibRef

Rajitha, K., Mohan, M.M.P.[M.M. Prakash], Varma, M.R.R.,
Effect of cirrus cloud on normalized difference Vegetation Index (NDVI) and Aerosol Free Vegetation Index (AFRI): A study based on LANDSAT 8 images,
ICAPR15(1-5)
IEEE DOI 1511
aerosols BibRef

Julien, Y., Sobrino, J.A.,
CloudSim: A fair benchmark for comparison of methods for times series reconstruction from cloud and atmospheric contamination,
MultiTemp15(1-4)
IEEE DOI 1511
clouds BibRef

Liu, G.L.[Gui-Liang],
Seemingly unrelated regression modeling of urban air quality by direct Monte Carlo algorithm,
ICWAPR15(171-174)
IEEE DOI 1511
Bayes methods BibRef

Bhattacharjee, S., Ghosh, S.K.,
Exploring spatial dependency of meteorological attributes for multivariate analysis: A granger causality test approach,
ICAPR15(1-6)
IEEE DOI 1511
atmospheric humidity BibRef

Wijeratne, I.K., Bijker, W.,
Mapping Dispersion of Urban Air Pollution with Remote Sensing,
IfromI06(xx-yy).
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Aerosols, Aerosol Optical Depth, Air Quality, Specific Sites .

Last update:Oct 29, 2023 at 22:16:34