1692 citations as recorded by crossref.

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56. Variation of Aerosol Optical Properties over Cluj-Napoca, Romania, Based on 10 Years of AERONET Data and MODIS MAIAC AOD Product H. Ștefănie et al. https://doi.org/10.3390/rs15123072
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896. A robust aerosol impact on clouds along the subtropical to tropical transition N. Yeheskel et al. https://doi.org/10.5194/acp-26-8765-2026
897. Spatial-Temporal Variation of AOD Based on MAIAC AOD in East Asia from 2011 to 2020 P. Wang et al. https://doi.org/10.3390/atmos13121983
898. Evaluation of the NDVI-Based Pixel Selection Criteria of the MODIS C6 Dark Target and Deep Blue Combined Aerosol Product M. Bilal & J. Nichol https://doi.org/10.1109/JSTARS.2017.2693289
899. Variability in the correlation between satellite-derived liquid cloud droplet effective radius and aerosol index over the northern Pacific Ocean S. Tan et al. https://doi.org/10.1080/16000889.2017.1391656
900. The impact of resolution on meteorological, chemical and aerosol properties in regional simulations with WRF-Chem P. Crippa et al. https://doi.org/10.5194/acp-17-1511-2017
901. Precipitable water vapor over oceans from the Maritime Aerosol Network: Evaluation of global models and satellite products under clear sky conditions D. Pérez-Ramírez et al. https://doi.org/10.1016/j.atmosres.2018.09.007
902. Radiative Impacts of Aerosols During COVID-19 Lockdown Period Over the Indian Region R. Bhawar et al. https://doi.org/10.3389/fenvs.2021.746090
903. Exploring the trend, prediction and driving forces of aerosols using satellite and ground data, and implications for climate change mitigation X. Li et al. https://doi.org/10.1016/j.jclepro.2019.03.121
904. Trends in the types and absorption characteristics of ambient aerosols over the Indo-Gangetic Plain and North China Plain in last two decades S. Ramachandran & M. Rupakheti https://doi.org/10.1016/j.scitotenv.2022.154867
905. Improving XCO2 retrieval under high aerosol loads with fused satellite aerosol Data: Advancing understanding of anthropogenic emissions H. Zhu et al. https://doi.org/10.1016/j.isprsjprs.2025.03.009
906. Investigating the Spatial and Temporal Limitations for Remote Sensing of Wildfire Smoke Using Satellite and Airborne Imagers During FIREX‐AQ Y. Shi et al. https://doi.org/10.1029/2023JD039085
907. A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula A. Alban et al. https://doi.org/10.1007/s41748-024-00398-w
908. A satellite-measured view of aerosol component content and optical property in a haze-polluted case over North China Plain L. Li et al. https://doi.org/10.1016/j.atmosres.2021.105958
909. Applying the Dark Target aerosol algorithm with Advanced Himawari Imager observations during the KORUS-AQ field campaign P. Gupta et al. https://doi.org/10.5194/amt-12-6557-2019
910. A hybrid method for reconstructing the historical evolution of aerosol optical depth from sunshine duration measurements W. Wandji Nyamsi et al. https://doi.org/10.5194/amt-13-3061-2020
911. Spatiotemporal analysis of hydrological and environmental changes in lake Urmia (2000–2024) using google earth engine H. Li et al. https://doi.org/10.1007/s12145-026-02089-8
912. Variability of mineral dust deposition in the western Mediterranean basin and south-east of France J. Vincent et al. https://doi.org/10.5194/acp-16-8749-2016
913. Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach T. Li et al. https://doi.org/10.1002/2017GL075710
914. Evaluation of WRF-Chem-RTFDDA dust forecasts over the MENA region using in-situ and remote-sensing observations D. Rostkier-Edelstein et al. https://doi.org/10.3389/fenvs.2022.981852
915. Aerosols under heatwaves: Interactions between pollution and heatwave extremes M. Caushaj et al. https://doi.org/10.1051/epjconf/202636504004
916. Study of optical scattering properties and direct radiative effects of high-altitude cirrus clouds in Barcelona, Spain, with 4 years of lidar measurements C. Gil-Díaz et al. https://doi.org/10.5194/acp-25-3445-2025
917. Assessing the performance of the Copernicus Atmosphere Monitoring Service reanalysis: a comprehensive analysis of aerosol optical depth over India K. Shukla et al. https://doi.org/10.1007/s11356-025-37286-3
918. Statistics and Meteorology of Air Pollution Episodes over the South African Highveld Based on Satellite–Model Datasets M. Jury https://doi.org/10.1175/JAMC-D-16-0354.1
919. Long-term Changes in Solar Radiation in Northern Eurasia during the Warm Season According to Measurements and Reconstruction Model E. Volpert & N. Chubarova https://doi.org/10.3103/S1068373921080021
920. ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data set A. Gkikas et al. https://doi.org/10.5194/amt-14-309-2021
921. Spaceborne observations of aerosol vertical distribution over Indian mainland (2009–2018) M. Mehta et al. https://doi.org/10.1016/j.atmosenv.2020.117902
922. Effects of COVID-19 pandemic lockdown: A satellite data-based appraisal of air quality in Guwahati, Assam J. Biswas & B. Pathak https://doi.org/10.1016/j.matpr.2022.06.218
923. Landscape determinants of spatio-temporal patterns of aerosol optical depth in the two most polluted metropolitans in the United States C. Wang et al. https://doi.org/10.1016/j.scitotenv.2017.07.273
924. Technical note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers – first steps toward assessing the feasibility of a global observation strategy D. Anderson et al. https://doi.org/10.5194/acp-23-6319-2023
925. Spatiotemporal variability of aerosol-cloud interactions in Northwestern Pacific and impact on NPP X. Qiu et al. https://doi.org/10.1016/j.atmosenv.2025.121151
926. Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models J. Yeom et al. https://doi.org/10.1109/TGRS.2021.3107542
927. An exploratory study on the aerosol height retrieval from OMI measurements of the 477  nm O2 − O2 spectral band using a neural network approach J. Chimot et al. https://doi.org/10.5194/amt-10-783-2017
928. CALIOP-Based Quantification of Central Asian Dust Transport Y. Han et al. https://doi.org/10.3390/rs14061416
929. Assessments of Satellite-Based Aerosol Optical Depth for Monitoring Air Quality of the Large Port of Busan, Korea U. Jeong et al. https://doi.org/10.3390/atmos16101123
930. Spatial and seasonal variations in atmospheric aerosols over Nigeria: Assessment of influence of intertropical discontinuity movement A. Ayanlade et al. https://doi.org/10.1177/1759313118820306
931. The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations Q. Yang et al. https://doi.org/10.1016/j.envpol.2019.02.071
932. Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing F. Bao et al. https://doi.org/10.1016/j.rse.2024.114275
933. Spatio-temporal variations in fine particulate matter and evaluation of associated health risk over Pakistan A. Mariam et al. https://doi.org/10.1002/ieam.4446
934. Analysis of the MODIS above-cloud aerosol retrieval algorithm using MCARS G. Wind et al. https://doi.org/10.5194/gmd-15-1-2022
935. Retrieval of global aerosol and surface properties from the Gaofen-5 Directional Polarimetric Camera measurements Z. Zhang et al. https://doi.org/10.5194/amt-19-2555-2026
936. Evaluation and utilization of MODIS and CALIPSO aerosol retrievals over a complex terrain in Himalaya A. Kumar et al. https://doi.org/10.1016/j.rse.2017.12.019
937. Impact of atmospheric circulation types on southwest Asian dust and Indian summer monsoon rainfall D. Kaskaoutis et al. https://doi.org/10.1016/j.atmosres.2017.11.002
938. Impacts of Sun Glint Off Ice Clouds on DSCOVR EPIC Cloud Products T. Várnai et al. https://doi.org/10.1109/TGRS.2024.3400253
939. Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation A. Sayer et al. https://doi.org/10.5194/amt-8-5277-2015
940. The Sensitivity of AOD Retrieval to Aerosol Type and Vertical Distribution over Land with MODIS Data Y. Wu et al. https://doi.org/10.3390/rs8090765
941. SI-traceable solar irradiance measurements for aerosol optical depth retrieval N. Kouremeti et al. https://doi.org/10.1088/1681-7575/ac6cbb
942. The regime of aerosol asymmetry parameter over Europe, the Mediterranean and the Middle East based on MODIS satellite data: evaluation against surface AERONET measurements M. Korras-Carraca et al. https://doi.org/10.5194/acp-15-13113-2015
943. Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh A. Gupta et al. https://doi.org/10.1007/s42452-020-03829-1
944. Long-term aerosol optical depth trend over Iran and identification of dominant aerosol types R. Yousefi et al. https://doi.org/10.1016/j.scitotenv.2020.137906
945. High-Resolution Satellite-Based PM2.5 Concentration Data Acquired During the COVID-19 Outbreak Throughout China: Model, Variations, and Reasons H. Guo et al. https://doi.org/10.1109/JSTARS.2021.3119383
946. SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm L. She et al. https://doi.org/10.3390/rs9030253
947. Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China Y. Che et al. https://doi.org/10.5194/acp-16-9655-2016
948. Global validation and comparison of MODIS DT and POLDER-3 GRASP aerosol products over ocean X. Pei et al. https://doi.org/10.1016/j.atmosres.2022.106480
949. Global and regional evaluation of a global model simulated AODs with AERONET and MODIS observations V. Sheel et al. https://doi.org/10.1002/joc.5367
950. Description and performance of a sectional aerosol microphysical model in the Community Earth System Model (CESM2) S. Tilmes et al. https://doi.org/10.5194/gmd-16-6087-2023
951. A critical view of long-term AVHRR aerosol data record in China: Retrieval frequency and heavy pollution M. Tao et al. https://doi.org/10.1016/j.atmosenv.2019.117246
952. Evaluation of MODIS columnar aerosol retrievals using AERONET in semi-arid Nevada and California, U.S.A., during the summer of 2012 S. Loría-Salazar et al. https://doi.org/10.1016/j.atmosenv.2016.08.070
953. Evaluation of the Aqua-MODIS C6 and C6.1 Aerosol Optical Depth Products in the Yellow River Basin, China M. Zhang et al. https://doi.org/10.3390/atmos10080426
954. Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm T. Zhang et al. https://doi.org/10.1080/15481603.2022.2060596
955. Increase of surface solar irradiance across East China related to changes in aerosol properties during the past decade J. Li et al. https://doi.org/10.1088/1748-9326/aaa35a
956. Models transport Saharan dust too low in the atmosphere: a comparison of the MetUM and CAMS forecasts with observations D. O'Sullivan et al. https://doi.org/10.5194/acp-20-12955-2020
957. Satellite-based PM2.5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm J. Liu et al. https://doi.org/10.1016/j.atmosenv.2019.04.002
958. GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia M. Choi et al. https://doi.org/10.5194/amt-11-385-2018
959. Dual‐Isotope Constraints on Seasonally Resolved Source Fingerprinting of Black Carbon Aerosols in Sites of the Four Emission Hot Spot Regions of China W. Fang et al. https://doi.org/10.1029/2018JD028607
960. Mitigating Satellite‐Based Fire Sampling Limitations in Deriving Biomass Burning Emission Rates: Application to WRF‐Chem Model Over the Northern sub‐Saharan African Region J. Wang et al. https://doi.org/10.1002/2017JD026840
961. Coupling meteorological variables with Moderate Resolution Imaging Spectroradiometer atmospheric products for estimating global solar radiation J. Chen et al. https://doi.org/10.1016/j.enconman.2019.112383
962. Accuracy assessment and climatology of MODIS aerosol optical properties over North Africa A. Merdji et al. https://doi.org/10.1007/s11356-022-22997-8
963. 准东煤田吸光气溶胶特性及辐射强迫模拟 张. Zhang Zikang et al. https://doi.org/10.3788/AOS241868
964. Changing air pollution scenario during COVID-19: Redefining the hotspot regions over India B. Tyagi et al. https://doi.org/10.1016/j.envpol.2020.116354
965. High‐Resolution Post‐Process Corrected Satellite AOD H. Taskinen et al. https://doi.org/10.1029/2022GL099733
966. Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak A. Rudke et al. https://doi.org/10.1016/j.rse.2023.113514
967. Optimal estimation of spectral surface reflectance in challenging atmospheres D. Thompson et al. https://doi.org/10.1016/j.rse.2019.111258
968. The relationship between cloud condensation nuclei (CCN) concentration and light extinction of dried particles: indications of underlying aerosol processes and implications for satellite-based CCN estimates Y. Shinozuka et al. https://doi.org/10.5194/acp-15-7585-2015
969. Study of Chemical and Optical Properties of Biomass Burning Aerosols during Long-Range Transport Events toward the Arctic in Summer 2017 T. Zielinski et al. https://doi.org/10.3390/atmos11010084
970. Aerosols impact on the convective and non-convective rain distribution over the Indian region: Results from WRF-Chem simulation S. Kedia et al. https://doi.org/10.1016/j.atmosenv.2019.01.020
971. Long term trend in aerosol direct radiative effects over Indian Ocean region from multi- satellite observations S. Jose et al. https://doi.org/10.1080/2150704X.2021.1957509
972. An improved meteorological variables-based aerosol optical depth estimation method by combining a physical mechanism model with a two-stage model F. Li et al. https://doi.org/10.1016/j.chemosphere.2024.142820
973. Role of aerosol–cloud–radiation interactions in modulating summertime quasi-biweekly rainfall intensity over South China H. Chen et al. https://doi.org/10.5194/acp-25-15765-2025
974. Spatiotemporal Trends of Aerosols over Urban Regions in Pakistan and Their Possible Links to Meteorological Parameters G. Ali et al. https://doi.org/10.3390/atmos11030306
975. Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy J. Park et al. https://doi.org/10.5194/amt-16-3039-2023
976. Verification of dust forecast over the Indian region with satellite and ground based observations P. Das et al. https://doi.org/10.1080/01431161.2016.1239285
977. Observations of the Interaction and Transport of Fine Mode Aerosols With Cloud and/or Fog in Northeast Asia From Aerosol Robotic Network and Satellite Remote Sensing T. Eck et al. https://doi.org/10.1029/2018JD028313
978. Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements A. Sayer et al. https://doi.org/10.1002/2017JD026934
979. Seasonality of the mass concentration and chemical composition of aerosols around an urbanized basin in East Asia C. Chou et al. https://doi.org/10.1002/2016JD025728
980. Large air quality and human health impacts due to Amazon forest and vegetation fires E. Butt et al. https://doi.org/10.1088/2515-7620/abb0db
981. Error characterization and analysis of aerosol products from the geostationary environment monitoring spectrometer (GEMS) G. Du et al. https://doi.org/10.1016/j.atmosres.2025.108682
982. Synchronous Atmospheric Correction of High Spatial Resolution Images from Gao Fen Duo Mo Satellite L. Xu et al. https://doi.org/10.3390/rs14174427
983. Evaluation of the MISR fine resolution aerosol product using MODIS, MISR, and ground observations over China Y. Si et al. https://doi.org/10.1016/j.atmosenv.2019.117229
984. Impact of boundary layer schemes in RegCM-Chem on East Asian climate and its response to the aerosol-radiation interaction H. Cao et al. https://doi.org/10.1016/j.atmosres.2026.108951
985. Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1) L. Zhang et al. https://doi.org/10.5194/gmd-15-5337-2022
986. Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation M. Tao et al. https://doi.org/10.1016/j.atmosenv.2019.06.004
987. Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter T. Dai et al. https://doi.org/10.1029/2018MS001475
988. Seasonality biases arise from the interplay of retrieval quality and solar zenith angle effects in passive sensor AOD products S. Smith et al. https://doi.org/10.5194/acp-25-14333-2025
989. The influence of simulated surface dust lofting and atmospheric loading on radiative forcing S. Saleeby et al. https://doi.org/10.5194/acp-19-10279-2019
990. A strengthened and southward-shifted westerly jet mitigates warming-induced drying across Asian drylands J. Jiang & T. Zhou https://doi.org/10.1126/sciadv.aed7890
991. Climatology of the aerosol optical depth by components from the Multi-angle Imaging SpectroRadiometer (MISR) and chemistry transport models H. Lee et al. https://doi.org/10.5194/acp-16-6627-2016
992. Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD S. Kim et al. https://doi.org/10.3390/app112210755
993. Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions G. Saponaro et al. https://doi.org/10.5194/acp-20-1607-2020
994. Investigating the relationship of aerosols with enhanced vegetation index and meteorological parameters over Pakistan S. Tariq et al. https://doi.org/10.1016/j.apr.2021.101080
995. Evaluation of the multi‐angle implementation of atmospheric correction (MAIAC) aerosol algorithm through intercomparison with VIIRS aerosol products and AERONET S. Superczynski et al. https://doi.org/10.1002/2016JD025720
996. A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI: Development and Verification over Asia D. Fu et al. https://doi.org/10.1016/j.eng.2023.09.023
997. Long-Term Trends in Aerosol Single Scattering Albedo Cause Bias in MODIS Aerosol Optical Depth Trends Z. Zhang et al. https://doi.org/10.1109/TGRS.2024.3424981
998. Impact of Holuhraun volcano aerosols on clouds in cloud-system-resolving simulations M. Haghighatnasab et al. https://doi.org/10.5194/acp-22-8457-2022
999. Bias in modeled solar radiation by non-resolved intra-daily AOD variability J. Ruiz-Arias https://doi.org/10.1016/j.solener.2020.04.082
1000. Long-term MAX-DOAS measurements of NO2, HCHO, and aerosols and evaluation of corresponding satellite data products over Mohali in the Indo-Gangetic Plain V. Kumar et al. https://doi.org/10.5194/acp-20-14183-2020
1001. Fusion of the Arctic Aerosol Optical Depth Based on Improved Bayesian Maximum Entropy Y. Han et al. https://doi.org/10.1109/TGRS.2026.3654145
1002. Assessment of Aerosol Radiative Forcing with 1-D Radiative Transfer Modeling in the U. S. South-East E. Alston & I. Sokolik https://doi.org/10.3390/atmos9070271
1003. Effect of Reduced Emissions from Thermal Power Plants in China on Local Air Quality Improvement K. Lee & S. Shin https://doi.org/10.5572/KOSAE.2022.38.2.304
1004. Assessing the relationship between atmospheric aerosols and maximum surface air temperature over the Indian region T. Sarin & V. Vinoj https://doi.org/10.1038/s41598-026-40641-0
1005. Extended aerosol and surface characterization from S5P/TROPOMI with GRASP algorithm. Part I: Conditions, approaches, performance and new possibilities P. Litvinov et al. https://doi.org/10.1016/j.rse.2024.114355
1006. Global changes in aerosol single scattering albedo during COVID-19 K. Ansari & S. Ramachandran https://doi.org/10.1016/j.atmosenv.2024.120649
1007. Long Temporal Analysis of 3-km MODIS Aerosol Product Over East China Q. Ma et al. https://doi.org/10.1109/JSTARS.2017.2650144
1008. Large Contribution of Coarse Mode to Aerosol Microphysical and Optical Properties: Evidence from Ground-Based Observations of a Transpacific Dust Outbreak at a High-Elevation North American Site E. Kassianov et al. https://doi.org/10.1175/JAS-D-16-0256.1
1009. A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances M. Penning de Vries et al. https://doi.org/10.5194/acp-15-10597-2015
1010. Impact of Chinese air pollutants on a record-breaking PMs episode in the Republic of Korea for 11–15 January 2019 H. Oh et al. https://doi.org/10.1016/j.atmosenv.2020.117262
1011. Aerosol optical depth trend over the Middle East K. Klingmüller et al. https://doi.org/10.5194/acp-16-5063-2016
1012. The Uncharacteristic Occurrence of the June 2013 Biomass-Burning Haze Event in Southeast Asia: Effects of the Madden-Julian Oscillation and Tropical Cyclone Activity Y. Oozeer et al. https://doi.org/10.3390/atmos11010055
1013. Assessing the radiative impacts of an extreme desert dust outbreak and the potential improvements on short-term weather forecasts: The case of February 2015 A. Gkikas et al. https://doi.org/10.1016/j.atmosres.2019.04.020
1014. Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols G. Wind et al. https://doi.org/10.5194/gmd-9-2377-2016
1015. Impact of environmental pollution on the retrieval of hourly aerosol products from Advanced Himawari Imager (AHI) over Beijing Q. Xu et al. https://doi.org/10.1016/j.apr.2020.03.007
1016. Improving aerosol optical depth retrievals from Himawari-8 with ensemble learning enhancement: Validation over Asia D. Fu et al. https://doi.org/10.1016/j.atmosres.2023.106624
1017. Monitoring cyanobacterial harmful algal blooms at high spatiotemporal resolution by fusing Landsat and MODIS imagery Y. Zhao et al. https://doi.org/10.1016/j.envadv.2020.100008
1018. Aerosol chemistry, transport, and climatic implications during extreme biomass burning emissions over the Indo-Gangetic Plain N. Singh et al. https://doi.org/10.5194/acp-18-14197-2018
1019. An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness, part 1: Algorithm development X. Yan et al. https://doi.org/10.1016/j.rse.2017.02.005
1020. Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning J. Fuchs et al. https://doi.org/10.5194/acp-18-16537-2018
1021. Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea S. Park et al. https://doi.org/10.5194/acp-19-1097-2019
1022. Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China W. Wang et al. https://doi.org/10.3390/rs9080858
1023. An Enhanced Aerosol Optical Depth Retrieval Algorithm for Particulate Observing Scanning Polarimeter (POSP) Data Over Land Z. Ji et al. https://doi.org/10.1109/TGRS.2024.3514170
1024. A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches X. Yan et al. https://doi.org/10.5194/essd-14-1193-2022
1025. Aerosol Optical Depth Over the Nepalese Cryosphere Derived From an Empirical Model B. Bhattarai et al. https://doi.org/10.3389/feart.2019.00178
1026. The global spatial-temporal distribution and EOF analysis of AOD based on MODIS data during 2003–2021 X. Tian et al. https://doi.org/10.1016/j.atmosenv.2023.119722
1027. Integration of GEMS and MODIS AOD for enhanced long-term aerosol monitoring over East Asia H. Jeon et al. https://doi.org/10.1080/01431161.2026.2632162
1028. Extreme levels of Canadian wildfire smoke in the stratosphere over central Europe on 21–22 August 2017 A. Ansmann et al. https://doi.org/10.5194/acp-18-11831-2018
1029. Nocturnal, seasonal and intra-annual variability of tropospheric aerosols observed using ground-based and space-borne lidars over a tropical location of India P. Prasad et al. https://doi.org/10.1016/j.atmosenv.2019.06.008
1030. Coupling Hourly Land Surface Models With Spatiotemporal Variability Characteristics: A Study on FY-4B/AGRI Land Aerosol Retrieval Algorithm Y. Si et al. https://doi.org/10.1109/TGRS.2025.3591646
1031. Annual and Seasonal Variations in Aerosol Optical Characteristics in the Huai River Basin, China from 2007 to 2021 X. Deng et al. https://doi.org/10.3390/rs16091571
1032. Climatological variations in aerosol optical depth and aerosol type identification in Liaoning of Northeast China based on MODIS data from 2002 to 2019 H. Zhao et al. https://doi.org/10.1016/j.scitotenv.2021.146810
1033. A synergic study on estimating surface downward shortwave radiation from satellite data D. Wang et al. https://doi.org/10.1016/j.rse.2021.112639
1034. A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images O. Hagolle et al. https://doi.org/10.3390/rs70302668
1035. A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing A. Sayer et al. https://doi.org/10.5194/amt-13-373-2020
1036. The impact of moderately absorbing aerosol on surface sensible, latent, and net radiative fluxes during the summer of 2015 in Central Europe K. Markowicz et al. https://doi.org/10.1016/j.jaerosci.2020.105627
1037. Scattering and absorbing aerosols in the climate system J. Li et al. https://doi.org/10.1038/s43017-022-00296-7
1038. Advanced Time series Multi-wavelength AOD Transformer (TMAT) model: joint retrieval of aerosol optical and microphysical properties using Himawari-8 AHI data L. She et al. https://doi.org/10.1016/j.jag.2026.105252
1039. A deep hybrid CNN–BiLSTM–attention framework with metaheuristic optimization and uncertainty quantification for solar power forecasting in dust-affected arid climates M. Bou-Rabee et al. https://doi.org/10.3389/fenrg.2026.1750871
1040. Towards gridded nighttime aerosol optical thickness retrievals using VIIRS day–night band observations over regions with artificial light sources J. Zhang et al. https://doi.org/10.5194/amt-18-1787-2025
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1499. A theoretical study of the effect of subsurface oceanic bubbles on the enhanced aerosol optical depth band over the southern oceans as detected from MODIS and MISR M. Christensen et al. https://doi.org/10.5194/amt-8-2149-2015
1500. Assessment of dominating aerosol properties and their long-term trend in the Pan-Third Pole region: A study with 10-year multi-sensor measurements T. Wang et al. https://doi.org/10.1016/j.atmosenv.2020.117738
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1502. Retrievals of aerosol optical depth over the western North Atlantic Ocean during ACTIVATE L. Siu et al. https://doi.org/10.5194/amt-17-2739-2024
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1505. Multi-Satellite Assessment of Factors Controlling Biomass Burning Aerosol Formation over the South China Sea L. Liang et al. https://doi.org/10.3390/rs18101462
1506. Extending XBAER Algorithm to Aerosol and Cloud Condition L. Mei et al. https://doi.org/10.1109/TGRS.2019.2919910
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1515. High-Resolution Gridded Level 3 Aerosol Optical Depth Data from MODIS P. Gupta et al. https://doi.org/10.3390/rs12172847
1516. Validation of MODIS 3 km land aerosol optical depth from NASA's EOS Terra and Aqua missions P. Gupta et al. https://doi.org/10.5194/amt-11-3145-2018
1517. Evaluating the potential for application of MODIS satellite data for urban air quality monitoring in Vietnam B. Hieu et al. https://doi.org/10.1088/1757-899X/869/2/022035
1518. Spatial and temporal gradients in the rate of dust deposition and aerosol optical thickness in southwestern Iran M. Foroushani et al. https://doi.org/10.1007/s40333-020-0079-5
1519. China Surface Ozone Remote Sensing Dataset in 2021–2022 at 1 km Spatial Resolution S. Zhang et al. https://doi.org/10.1007/s44408-026-00119-0
1520. Smoke emissions from agricultural fires in Mexico and Central America B. Rios & G. Raga https://doi.org/10.1117/1.JRS.13.036509
1521. Assessment of urban aerosol pollution over the Moscow megacity by the MAIAC aerosol product E. Zhdanova et al. https://doi.org/10.5194/amt-13-877-2020
1522. The role of aerosol layer height in quantifying aerosol absorption from ultraviolet satellite observations J. Sun et al. https://doi.org/10.5194/amt-12-6319-2019
1523. The observational evidence of association between types of aerosol mode-cloud-precipitation interaction over Iran M. Rezaei et al. https://doi.org/10.1016/j.apr.2023.101760
1524. Evaluation and Comparison of Long-Term MODIS C5.1 and C6 Products against AERONET Observations over China A. Fan et al. https://doi.org/10.3390/rs9121269
1525. EARLINET evaluation of the CATS Level 2 aerosol backscatter coefficient product E. Proestakis et al. https://doi.org/10.5194/acp-19-11743-2019
1526. Comprehensive Evaluation of Spatial Distribution and Temporal Trend of NO2, SO2 and AOD Using Satellite Observations over South and East Asia from 2011 to 2021 M. Rahman et al. https://doi.org/10.3390/rs15205069
1527. Combining low-cost, surface-based aerosol monitors with size-resolved satellite data for air quality applications P. deSouza et al. https://doi.org/10.5194/amt-13-5319-2020
1528. Satellite Remote Sensing for Estimating PM2.5 and Its Components Y. Li et al. https://doi.org/10.1007/s40726-020-00170-4
1529. Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder L. Liang et al. https://doi.org/10.1038/s41597-023-02696-w
1530. Merging regional and global aerosol optical depth records from major available satellite products L. Sogacheva et al. https://doi.org/10.5194/acp-20-2031-2020
1531. Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis S. Falah et al. https://doi.org/10.1016/j.atmosenv.2021.118659
1532. Estimation of daily photosynthetically active radiation (PAR) in presence of low to high aerosol loads: application to OLCI-like satellite data T. Harmel & M. Chami https://doi.org/10.1364/OE.24.0A1390
1533. Change in precipitation pattern over South Asia in response to the trends in regional warming and free-tropospheric aerosol loading S. Jana et al. https://doi.org/10.1038/s41598-024-64842-7
1534. How Do Aerosol Properties Affect the Temporal Variation of MODIS AOD Bias in Eastern China? M. Tao et al. https://doi.org/10.3390/rs9080800
1535. Investigating the relationship between aerosol and cloud optical properties inferred from the MODIS sensor in recent decades over East China J. Huang et al. https://doi.org/10.1016/j.atmosenv.2020.117812
1536. Aerosol Optical Depth Retrieval Over Land from Particulate Observing Scanning Polarimeter (POSP) Using a New Look-Up Table (LUT) Method Z. Ji et al. https://doi.org/10.1007/s41810-024-00236-6
1537. Microphysics and Radiation Effect of Dust on Saharan Air Layer: An HS3 Case Study Z. Tao et al. https://doi.org/10.1175/MWR-D-17-0279.1
1538. Aerosol Optical Depth from MODIS satellite data above the Pierre Auger Observatory N. Borodai et al. https://doi.org/10.1051/epjconf/201919702011
1539. Retrieving hourly aerosol optical depth for geostationary satellite FY-4B/AGRI by surface-related dynamic spectral reflectance ratio method W. Wang et al. https://doi.org/10.1016/j.asr.2024.10.057
1540. Exploring the effect of waterbodies coupled with other environmental parameters to model PM2.5 over Delhi-NCT in northwest India B. Gayen et al. https://doi.org/10.1016/j.apr.2022.101614
1541. Global aerosol retrieval over land from Landsat imagery integrating Transformer and Google Earth Engine J. Wei et al. https://doi.org/10.1016/j.rse.2024.114404
1542. Particulate matter variability in Kathmandu based on in-situ measurements, remote sensing, and reanalysis data S. Becker et al. https://doi.org/10.1016/j.atmosres.2021.105623
1543. Multi-dimensional satellite observations of aerosol properties and aerosol types over three major urban clusters in eastern China Y. Liu et al. https://doi.org/10.5194/acp-21-12331-2021
1544. Characterisation of dust aerosols and their absorbing efficiency over South Asia using infrared observations from INSAT-3D Imager C. Lima et al. https://doi.org/10.1080/01431161.2024.2431176
1545. Spatiotemporal changes in aerosols over Bangladesh using 18 years of MODIS and reanalysis data M. Ali et al. https://doi.org/10.1016/j.jenvman.2022.115097
1546. Trends in AOD, Clouds, and Cloud Radiative Effects in Satellite Data and CMIP5 and CMIP6 Model Simulations Over Aerosol Source Regions R. Cherian & J. Quaas https://doi.org/10.1029/2020GL087132
1547. Evaluation of Aerosol Optical Depth and Aerosol Models from VIIRS Retrieval Algorithms over North China Plain J. Zhu et al. https://doi.org/10.3390/rs9050432
1548. Spatial and temporal variability in dust storms in the Middle East, 2002–2018: three case studies in July 2009 N. Hamzeh et al. https://doi.org/10.1007/s12517-021-06859-0
1549. AeroTrans: Hourly AOD retrieval over land from MSG-1/SEVIRI imagery integrating Transformer and transfer learning Y. Fan et al. https://doi.org/10.1016/j.rse.2026.115268
1550. Assimilation of INSAT-3D imager retrieved aerosol optical depth for enhanced dust storm prediction using WRF-Chem model A. Khan et al. https://doi.org/10.1088/2515-7620/ad9712
1551. Characterization of Historical Aerosol Optical Depth Dynamics Using LSTM and Peak Enhancement Techniques H. Cămărășan et al. https://doi.org/10.3390/atmos16060743
1552. Description and evaluation of the tropospheric aerosol scheme in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS-AER, cycle 45R1) S. Rémy et al. https://doi.org/10.5194/gmd-12-4627-2019
1553. Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation M. Grzegorski et al. https://doi.org/10.3390/rs14010085
1554. Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004–2013 Z. Ma et al. https://doi.org/10.1289/ehp.1409481
1555. Improved simulations of biomass burning aerosol optical properties and lifetimes in the NASA GEOS Model during the ORACLES-I campaign S. Das et al. https://doi.org/10.5194/acp-24-4421-2024
1556. Investigating the frequency and interannual variability in global above-cloud aerosol characteristics with CALIOP and OMI R. Alfaro-Contreras et al. https://doi.org/10.5194/acp-16-47-2016
1557. Satellite-Derived Correlation of SO2, NO2, and Aerosol Optical Depth with Meteorological Conditions over East Asia from 2005 to 2015 C. Lin et al. https://doi.org/10.3390/rs11151738
1558. Simultaneous Monitoring of Wildfire and Smoke Using Geostationary Satellite Infrared Channel Observations K. Lee et al. https://doi.org/10.5572/KOSAE.2024.40.3.337
1559. Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation Y. Wang et al. https://doi.org/10.1016/j.isprsjprs.2019.08.017
1560. On the build-up of dust aerosols and possible indirect effect during Indian summer monsoon break spells using recent satellite observations of aerosols and cloud properties V. Arya et al. https://doi.org/10.1007/s12040-020-01526-6
1561. Performance of the Semi-Empirical Precipitable Water Vapor Retrieval Algorithm Developed for Polarized Scanning Atmospheric Corrector (PSAC) in the Presence of Sensor Decay Y. Xie et al. https://doi.org/10.1109/TGRS.2023.3304129
1562. Mapping PM2.5 concentration from the top-of-atmosphere reflectance of Himawari-8 via an ensemble stacking model X. Chen et al. https://doi.org/10.1016/j.atmosenv.2024.120560
1563. The Global Atmosphere‐aerosol Model ICON‐A‐HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness M. Salzmann et al. https://doi.org/10.1029/2021MS002699
1564. Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS) M. Kim et al. https://doi.org/10.5194/acp-16-1789-2016
1565. Dust emission and transport in Northwest China: WRF-Chem simulation and comparisons with multi-sensor observations J. Zhao et al. https://doi.org/10.1016/j.atmosres.2020.104978
1566. Long range transport of Canadian wildfire smoke to Europe in 2023: aerosol properties and spectral features of smoke particles A. Masoom et al. https://doi.org/10.5194/acp-26-10801-2026
1567. Surface Reflectance and Aerosol Retrieval from SPOT-VGT and PROBA-V in the Mission Exploitation Platform Environment M. Luffarelli et al. https://doi.org/10.3390/rs15215109
1568. Circular polarization in atmospheric aerosols S. Gassó & K. Knobelspiesse https://doi.org/10.5194/acp-22-13581-2022
1569. Why rainfall alone cannot mitigate urban pollution: Two decades (2003–2024) of satellite observation over Delhi G. Balachandran & S. S https://doi.org/10.1016/j.jastp.2026.106936
1570. Continuing the MODIS Dark Target Aerosol Time Series with VIIRS V. Sawyer et al. https://doi.org/10.3390/rs12020308
1571. Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa W. Tang et al. https://doi.org/10.5194/gmd-16-6001-2023
1572. Retrieval of Aerosol Optical Depth in the Arid or Semiarid Region of Northern Xinjiang, China X. Tian et al. https://doi.org/10.3390/rs10020197
1573. Effect of Aerosols, Tropospheric NO2 and Clouds on Surface Solar Radiation over the Eastern Mediterranean (Greece) G. Alexandri et al. https://doi.org/10.3390/rs13132587
1574. Emission Reductions Significantly Reduce the Hemispheric Contrast in Cloud Droplet Number Concentration in Recent Two Decades Y. Cao et al. https://doi.org/10.1029/2022JD037417
1575. Results From the Deep Convective Clouds-Based Response Versus Scan-Angle Characterization for the MODIS Reflective Solar Bands A. Angal et al. https://doi.org/10.1109/TGRS.2017.2759660
1576. Estimation of Net Surface Shortwave Radiation From Remotely Sensed Data Under Dust Aerosol Conditions Y. Sun & B. Tang https://doi.org/10.1109/ACCESS.2021.3069791
1577. Sources, seasonality, and trends of southeast US aerosol: an integrated analysis of surface, aircraft, and satellite observations with the GEOS-Chem chemical transport model P. Kim et al. https://doi.org/10.5194/acp-15-10411-2015
1578. Clouds and the Earth’s Radiant Energy System (CERES) Cloud Radiative Swath (CRS) Edition 4 Data Product R. Scott et al. https://doi.org/10.1175/JTECH-D-22-0021.1
1579. Contrasting changes in snow cover and its sensitivity to aerosol optical properties in Hindukush-Karakoram-Himalaya region M. Ahmad et al. https://doi.org/10.1016/j.scitotenv.2019.134356
1580. Satellite-derived spatiotemporal PM2.5 concentrations and variations from 2006 to 2017 in China W. Xue et al. https://doi.org/10.1016/j.scitotenv.2019.134577
1581. A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data Z. Ma et al. https://doi.org/10.1016/j.rse.2021.112827
1582. Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum J. Zhang et al. https://doi.org/10.5194/gmd-14-27-2021
1583. Impacts of dust aerosol and adjacency effects on the accuracy of Landsat 8 and RapidEye surface reflectances R. Houborg & M. McCabe https://doi.org/10.1016/j.rse.2017.03.013
1584. Accuracy assessment of MODIS land aerosol optical thickness algorithms using AERONET measurements over North America H. Jethva et al. https://doi.org/10.5194/amt-12-4291-2019
1585. Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS L. Wang et al. https://doi.org/10.1016/j.isprsjprs.2024.06.022
1586. Two decades of high-resolution aerosol product over the Sierra Nevada Mountain region (SE Spain): Spatio-temporal distribution and impact on ecosystems A. del Águila et al. https://doi.org/10.1016/j.atmosres.2024.107515
1587. Investigation on spatiotemporal distribution of aerosol optical properties over two oceanic regions surrounding Indian subcontinent during summer monsoon season C. Vachaspati et al. https://doi.org/10.1007/s11356-018-2682-y
1588. Lower tropospheric ozone over the North China Plain: variability and trends revealed by IASI satellite observations for 2008–2016 G. Dufour et al. https://doi.org/10.5194/acp-18-16439-2018
1589. Advancing the quantification of aerosol-cloud interactions with the CALIPSO-CloudSat-Aqua/MODIS record Z. Li et al. https://doi.org/10.5194/acp-26-7705-2026
1590. Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion G. Geng et al. https://doi.org/10.1021/acs.est.1c01863
1591. Physical-Guided Transfer Deep Neural Network for High-Resolution AOD Retrieval D. Chen et al. https://doi.org/10.3390/rs17213606
1592. Analysis of Atmospheric Aerosol Optical Properties in the Northeast Brazilian Atmosphere with Remote Sensing Data from MODIS and CALIOP/CALIPSO Satellites, AERONET Photometers and a Ground-Based Lidar A. de Oliveira et al. https://doi.org/10.3390/atmos10100594
1593. MAIA Level 2 Aerosol Retrieval: Prototype Algorithm Test and Improvement Using MISR and MODIS Measurements W. Zhang et al. https://doi.org/10.1109/TGRS.2026.3720586
1594. Decreased dust particles amplify the cloud cooling effect by regulating cloud ice formation over the Tibetan Plateau J. Chen et al. https://doi.org/10.1126/sciadv.ado0885
1595. Seasonal evolution of aerosol loading and its vertical distribution in northeastern China from long-term satellite observations and model reanalysis H. Zhao et al. https://doi.org/10.1016/j.atmosenv.2023.119720
1596. Cloud condensation nuclei concentrations derived from the CAMS reanalysis K. Block et al. https://doi.org/10.5194/essd-16-443-2024
1597. Analysis and Validation of the Aerosol Optical Depth of MODIS Products in Gansu Province, Northwest China F. Huang et al. https://doi.org/10.3390/rs15122972
1598. Dust suppresses aerosol first indirect effects in marine warm clouds over the North Atlantic Ocean S. Pandey et al. https://doi.org/10.1038/s43247-026-03693-8
1599. Impact of California Fires on Local and Regional Air Quality: The Role of a Low‐Cost Sensor Network and Satellite Observations P. Gupta et al. https://doi.org/10.1029/2018GH000136
1600. Evaluating the consistency between Sentinel-2 and Planet constellations at field scale: illustration over winter wheat Y. Ma et al. https://doi.org/10.1007/s11119-025-10226-4
1601. 融合MODIS和OMI卫星数据的准东煤田吸光气溶胶辐射强迫估算研究 王. Wang Shuo et al. https://doi.org/10.3788/AOS250830
1602. Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. X. Ye et al. https://doi.org/10.3390/rs14236113
1603. 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 Y. Xie et al. https://doi.org/10.1109/TGRS.2019.2944949
1604. Temporal variation of surface reflectance and cloud fraction used to identify background aerosol retrieval information over East Asia S. Park et al. https://doi.org/10.1016/j.atmosenv.2023.119916
1605. Spatial downscaling of surface ozone concentration calculation from remotely sensed data based on mutual information X. Wang et al. https://doi.org/10.3389/fenvs.2022.925979
1606. A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements L. Clarisse et al. https://doi.org/10.1029/2018JD029701
1607. The Sub-Daily Variability of Aerosol Loading and Associated Radiative Forcing Over the Indian Region T. Mukherjee & V. Vinoj https://doi.org/10.3389/feart.2021.727169
1608. PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models H. Chu & M. Bilal https://doi.org/10.1007/s11356-018-3763-7
1609. Long-term study of desert dust deposition effects on phytoplankton biomass in the Persian Gulf using Google Earth Engine H. Asgari & A. Soleimany https://doi.org/10.1016/j.marpolbul.2023.115564
1610. Global distribution of maritime low clouds with an emphasis on different aerosol types and meteorological parameters inferred from multi-satellite and reanalysis data during 2007–2016 B. Pan et al. https://doi.org/10.1016/j.atmosenv.2020.118082
1611. An Ensemble Machine-Learning Model To Predict Historical PM2.5Concentrations in China from Satellite Data Q. Xiao et al. https://doi.org/10.1021/acs.est.8b02917
1612. The role of cloud contamination, aerosol layer height and aerosol model in the assessment of the OMI near-UV retrievals over the ocean S. Gassó & O. Torres https://doi.org/10.5194/amt-9-3031-2016
1613. High resolution aerosol optical depth retrieval over urban areas from Landsat-8 OLI images H. Lin et al. https://doi.org/10.1016/j.atmosenv.2021.118591
1614. Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM2.5 concentrations in Taiwan from 2005 to 2015 C. Jung et al. https://doi.org/10.1016/j.envpol.2017.11.016
1615. A study of 15-year aerosol optical thickness and direct shortwave aerosol radiative effect trends using MODIS, MISR, CALIOP and CERES R. Alfaro-Contreras et al. https://doi.org/10.5194/acp-17-13849-2017
1616. Improving aerosol retrieval from FY-3D/MERSI by parameterizing a binary quadratic spectral surface reflectance model with urban percentage and vegetation index W. Ji et al. https://doi.org/10.1016/j.atmosres.2025.108291
1617. Source-Specific PM2.5 Exposure and Associated Health Risks During Beijing Winter X. Liu et al. https://doi.org/10.3390/toxics13121081
1618. Large contrast in the vertical distribution of aerosol optical properties and radiative effects across the Indo-Gangetic Plain during the SWAAMI–RAWEX campaign A. Vaishya et al. https://doi.org/10.5194/acp-18-17669-2018
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