129 citations as recorded by crossref.

1. Study on Spatial Changes in PM2.5 before and after the COVID-19 Pandemic in Southwest China X. Li et al. 10.3390/atmos14040671
2. National Civil Space Infrastructure Satellite Aerosol Product Validation Network (SIAVNET) measurements: Supporting satellite aerosol product validation for different surface types over China T. Cheng et al. 10.1016/j.atmosres.2022.106508
3. Spatio-temporal changes of AOD in Xinjiang of China from 2000 to 2019: Which factor is more influential, natural factor or human factor? J. Li et al. 10.1371/journal.pone.0253942
4. Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe Y. Xue et al. 10.1016/j.rse.2017.06.036
5. Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign M. Choi et al. 10.5194/amt-12-4619-2019
6. Investigating the Spatial and Temporal Limitations for Remote Sensing of Wildfire Smoke Using Satellite and Airborne Imagers During FIREX‐AQ Y. Shi et al. 10.1029/2023JD039085
7. Retrieval of 500 m Aerosol Optical Depths from MODIS Measurements over Urban Surfaces under Heavy Aerosol Loading Conditions in Winter S. Jin et al. 10.3390/rs11192218
8. Empirical Correlation Weighting (ECW) Spatial Interpolation Method for Satellite Aerosol Optical Depth Products by MODIS AOD over Northern China in 2016 Y. Wang et al. 10.3390/rs15184462
9. Fusing Retrievals of High Resolution Aerosol Optical Depth from Landsat-8 and Sentinel-2 Observations over Urban Areas H. Lin et al. 10.3390/rs13204140
10. Spatial characteristics and temporal evolution of the relationship between PM2.5 and aerosol optical depth over the eastern USA during 2003–2017 Q. Jin et al. 10.1016/j.atmosenv.2020.117718
11. MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison J. Wei et al. 10.1016/j.atmosenv.2018.12.004
12. Retrieval uncertainty and consistency of Suomi-NPP VIIRS Deep Blue and Dark Target aerosol products under diverse aerosol loading scenarios over South Asia K. Aditi et al. 10.1016/j.envpol.2023.121913
13. Evaluation and comparison of MODIS and VIIRS aerosol optical depth (AOD) products over regions in the Eastern Mediterranean and the Black Sea P. Ettehadi Osgouei et al. 10.1016/j.atmosenv.2021.118784
14. Factor analysis for aerosol optical depth and its prediction from the perspective of land-use change W. Zhang et al. 10.1016/j.ecolind.2018.05.026
15. Characteristics of Remotely Sensed Urban Pollution Island (UPI) & its Linkage with Surface Urban Heat Island (SUHI) over Eastern India A. Barat & P. Parth Sarthi 10.1007/s41810-023-00176-7
16. The health impacts of Indonesian peatland fires L. Hein et al. 10.1186/s12940-022-00872-w
17. A Simple Band Ratio Library (BRL) Algorithm for Retrieval of Hourly Aerosol Optical Depth Using FY-4A AGRI Geostationary Satellite Data X. Jiang et al. 10.3390/rs14194861
18. Performance of MODIS Collection 6.1 Level 3 aerosol products in spatial-temporal variations over land J. Wei et al. 10.1016/j.atmosenv.2019.03.001
19. Evaluation of the MODIS Collection 6.1 3 km aerosol optical depth product over China M. Zhang et al. 10.1016/j.atmosenv.2022.118970
20. Review of retrieval of aerosol optical depth to estimate particle concentration and its challenges based on spatiotemporal relationships by various spectroradiometer models C. Samuel et al. 10.1002/gj.4780
21. A comprehensive reappraisal of long-term aerosol characteristics, trends, and variability in Asia S. Jin et al. 10.5194/acp-23-8187-2023
22. Improving the Estimation of PM2.5 Concentration in the North China Area by Introducing an Attention Mechanism into Random Forest L. Zhang et al. 10.3390/atmos15030384
23. A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing A. Sayer et al. 10.5194/amt-13-373-2020
24. Assessment of the impact of discontinuity in satellite instruments and retrievals on global PM2.5 estimates M. Hammer et al. 10.1016/j.rse.2023.113624
25. Assessment of the Representativeness of MODIS Aerosol Optical Depth Products at Different Temporal Scales Using Global AERONET Measurements Y. Tong et al. 10.3390/rs12142330
26. Assesment of Coronavirus Pandemic Effect on Air Quality in Cyprus S. ÇAKIR & M. PRİNCEWİLL 10.31590/ejosat.1038697
27. Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns H. Lim et al. 10.5194/amt-14-4575-2021
28. Continuing the MODIS Dark Target Aerosol Time Series with VIIRS V. Sawyer et al. 10.3390/rs12020308
29. Retrieving aerosols single scattering albedo from MODIS reflectances Q. Wang et al. 10.1016/j.atmosres.2022.106381
30. An Improved Aerosol Retrieval Algorithm Based on Nonlinear Surface Model From FY-3D/MERSI-II Remote Sensing Data Y. Si et al. 10.1109/TGRS.2024.3367883
31. The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future L. Remer et al. 10.3390/rs12182900
32. Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas M. Bilal et al. 10.3390/rs14020373
33. Instant and delayed effects of March biomass burning aerosols over the Indochina Peninsula A. Zhu et al. 10.5194/acp-22-15425-2022
34. Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East Asia E. Kang et al. 10.1016/j.atmosenv.2023.119951
35. Himawari-8/AHI and MODIS Aerosol Optical Depths in China: Evaluation and Comparison T. Jiang et al. 10.3390/rs11091011
36. Validation and inter-comparison of MODIS and VIIRS aerosol optical depth products against data from multiple observation networks over East China Y. Su et al. 10.1016/j.atmosenv.2021.118205
37. Comparison and evaluation of the simulated annual aerosol characteristics over China with two global aerosol models Y. Cheng et al. 10.1016/j.scitotenv.2020.143003
38. Surface and satellite observations of air pollution in India during COVID-19 lockdown: Implication to air quality Y. Sathe et al. 10.1016/j.scs.2020.102688
39. Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments Z. Chen et al. 10.1016/j.scitotenv.2024.170593
40. Investigating multiple aerosol optical depth products from MODIS and VIIRS over Asia: Evaluation, comparison, and merging Y. Wang et al. 10.1016/j.atmosenv.2020.117548
41. Retrieval of hourly PM2.5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II H. Choi et al. 10.1016/j.envpol.2023.121169
42. A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data Z. Ma et al. 10.1016/j.rse.2021.112827
43. Preliminary Investigation of a New AHI Aerosol Optical Depth (AOD) Retrieval Algorithm and Evaluation with Multiple Source AOD Measurements in China F. Yang et al. 10.3390/rs10050748
44. Evaluation of Terra-MODIS C6 and C6.1 Aerosol Products against Beijing, XiangHe, and Xinglong AERONET Sites in China during 2004-2014 M. Bilal et al. 10.3390/rs11050486
45. AERONET Remotely Sensed Measurements and Retrievals of Biomass Burning Aerosol Optical Properties During the 2015 Indonesian Burning Season T. Eck et al. 10.1029/2018JD030182
46. Performance of MODIS C6.1 Dark Target and Deep Blue aerosol products in Delhi National Capital Region, India: Application for aerosol studies V. Sharma et al. 10.1016/j.apr.2021.01.023
47. A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges A. Ranjan et al. 10.1007/s13143-020-00215-0
48. Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm X. Su et al. 10.1016/j.envint.2022.107343
49. Aerosol Retrieval Algorithm for Sentinel-2 Images Over Complex Urban Areas Y. Yang et al. 10.1109/TGRS.2022.3158061
50. Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm T. Zhang et al. 10.1080/15481603.2022.2060596
51. Evaluating VIIRS EPS Aerosol Optical Depth in China: An intercomparison against ground-based measurements and MODIS C. Li et al. 10.1016/j.jqsrt.2018.12.002
52. Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8 T. Su et al. 10.1016/j.rse.2020.112093
53. Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager M. Kim et al. 10.5194/amt-17-1913-2024
54. Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation Y. Wang et al. 10.1016/j.isprsjprs.2019.08.017
55. AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, Validation and Merged Products H. Lim et al. 10.3390/rs10050699
56. Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China N. Liu et al. 10.5194/acp-19-8243-2019
57. Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales X. Su et al. 10.1016/j.scitotenv.2023.163794
58. Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over mainland China Y. Che et al. 10.1016/j.isprsjprs.2018.09.004
59. Mitigating MODIS AOD non-random sampling error on surface PM2.5 estimates by a combined use of Bayesian Maximum Entropy method and linear mixed-effects model D. Fu et al. 10.1016/j.apr.2019.11.020
60. Exploring Aerosols Near Clouds With High‐Spatial‐Resolution Aircraft Remote Sensing During SEAC4RS R. Spencer et al. 10.1029/2018JD028989
61. 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. 10.5194/amt-11-385-2018
62. 20 years of columnar aerosol properties at Valencia area (Eastern Spain) by ground-based sun-photometry M. Garcia-Suñer et al. 10.1016/j.atmosres.2023.107198
63. Synergistic Use of Hyperspectral UV-Visible OMI and Broadband Meteorological Imager MODIS Data for a Merged Aerosol Product S. Go et al. 10.3390/rs12233987
64. Validation of MODIS Aerosol Products with AERONET Measurements of Different Land Cover Types in Areas over Eastern Europe and China M. Filonchyk & V. Hurynovich 10.1007/s41651-020-00052-9
65. Validation and Accuracy Analysis of the Collection 6.1 MODIS Aerosol Optical Depth Over the Westernmost City in China Based on the Sun‐Sky Radiometer Observations From SONET G. Huang et al. 10.1029/2019EA001041
66. Comparison and evaluation of multiple satellite aerosol products over China in different scenarios under a unified criterion: Preparation for consistent and high-quality dataset construction H. Zhu et al. 10.1016/j.atmosres.2022.106374
67. Exploring systematic offsets between aerosol products from the two MODIS sensors R. Levy et al. 10.5194/amt-11-4073-2018
68. Characterizing the 2015 Indonesia fire event using modified MODIS aerosol retrievals Y. Shi et al. 10.5194/acp-19-259-2019
69. High-resolution aerosol retrieval over urban areas using sentinel-2 data Y. Yang et al. 10.1016/j.atmosres.2021.105829
70. Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data N. Bhatia et al. 10.3390/rs10060947
71. Difference between global and regional aerosol model classifications and associated implications for spaceborne aerosol optical depth retrieval P. Zhou et al. 10.1016/j.atmosenv.2023.119674
72. A Spatio-Temporal Weighted Filling Method for Missing AOD Values R. Gao et al. 10.3390/atmos13071080
73. How Long Is Too Long? Variogram Analysis of AERONET Data to Aid Aerosol Validation and Intercomparison Studies A. Sayer 10.1029/2020EA001290
74. Validation and Accuracy Assessment of MODIS C6.1 Aerosol Products over the Heavy Aerosol Loading Area X. Tian & Z. Gao 10.3390/atmos10090548
75. Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia Y. Kang et al. 10.1016/j.isprsjprs.2021.11.016
76. How Does COVID-19 Lockdown Impact Air Quality in India? Z. Hu et al. 10.3390/rs14081869
77. Unveiling the Transparency of Prediction Models for Spatial PM2.5 over Singapore: Comparison of Different Machine Learning Approaches with eXplainable Artificial Intelligence M. Sunder et al. 10.3390/ai4040040
78. Columnar optical characteristics and radiative properties of aerosols of the AERONET site in Minsk, Belarus M. Filonchyk et al. 10.1016/j.atmosenv.2021.118237
79. Quantifying PM2.5 mass concentration and particle radius using satellite data and an optical-mass conversion algorithm M. Liu et al. 10.1016/j.isprsjprs.2019.10.010
80. Evaluation of MODIS DT, DB, and MAIAC Aerosol Products over Different Land Cover Types in the Yangtze River Delta of China J. Jiang et al. 10.3390/rs15010275
81. Global Validation of MODIS C6 and C6.1 Merged Aerosol Products over Diverse Vegetated Surfaces M. Bilal et al. 10.3390/rs10030475
82. Evaluating MODIS and MISR aerosol optical depth retrievals over environmentally distinct sites in Pakistan G. Ali et al. 10.1016/j.jastp.2018.12.008
83. An Investigation on Shenzhen Urban Green Space Changes and Their Effect on Local Eco-Environment in Recent Decades Y. Liu et al. 10.3390/su132212549
84. Aerosol optical depth climatology from the high-resolution MAIAC product over Europe: differences between major European cities and their surrounding environments L. Di Antonio et al. 10.5194/acp-23-12455-2023
85. Geographical coverage analysis and usage suggestions of temporal averaged aerosol optical depth product from GOES-R satellite data X. Jiang et al. 10.1080/01431161.2024.2331978
86. Comparison of MODIS- and CALIPSO-Derived Temporal Aerosol Optical Depth over Yellow River Basin (China) from 2007 to 2015 Z. Zhang et al. 10.1007/s41748-020-00181-7
87. Satellite-based PM2.5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm J. Liu et al. 10.1016/j.atmosenv.2019.04.002
88. Influence of industrial sustainability transition on air quality in a typical resource-exhausted city J. Wang & X. Li 10.1016/j.heliyon.2024.e25138
89. Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land A. Lipponen et al. 10.5194/amt-11-1529-2018
90. Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation M. Tao et al. 10.1016/j.atmosenv.2019.06.004
91. Adapting the Dark Target Algorithm to Advanced MERSI Sensor on the FengYun-3-D Satellite: Retrieval and Validation of Aerosol Optical Depth Over Land S. Jin et al. 10.1109/TGRS.2020.3021021
92. Aerosol optical depth regime over megacities of the world K. Papachristopoulou et al. 10.5194/acp-22-15703-2022
93. Estimation of ground-level particulate matter concentrations based on synergistic use of MODIS, MERRA-2 and AERONET AODs over a coastal site in the Eastern Mediterranean G. Tuna Tuygun et al. 10.1016/j.atmosenv.2021.118562
94. Spatiotemporal Variations of Aerosols in China during the COVID-19 Pandemic Lockdown J. Yao et al. 10.3390/rs14030696
95. Validation and Comparison of MODIS C6.1 and C6 Aerosol Products over Beijing, China X. Tian et al. 10.3390/rs10122021
96. Google Earth Engine based spatio-temporal analysis of air pollutants before and during the first wave COVID-19 outbreak over Turkey via remote sensing F. Ghasempour et al. 10.1016/j.jclepro.2021.128599
97. The Influence of Underlying Land Cover on the Accuracy of MODIS C6.1 Aerosol Products—A Case Study over the Yangtze River Delta Region of China K. Sun et al. 10.3390/rs14040938
98. Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface Y. Huang et al. 10.1029/2019EA000809
99. Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018) M. Hammer et al. 10.1021/acs.est.0c01764
100. Temporal evolution of aerosols and their extreme events in polluted Asian regions during Terra's 20-year observations Z. Hu et al. 10.1016/j.rse.2021.112541
101. A High-Precision Aerosol Retrieval Algorithm (HiPARA) for Advanced Himawari Imager (AHI) data: Development and verification X. Su et al. 10.1016/j.rse.2020.112221
102. Improvement of snow/haze confusion data gaps in MODIS Dark Target aerosol retrievals in East China X. Zhang et al. 10.1016/j.atmosres.2020.105063
103. The polarization crossfire (PCF) sensor suite focusing on satellite remote sensing of fine particulate matter PM2.5 from space Z. Li et al. 10.1016/j.jqsrt.2022.108217
104. Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder L. Liang et al. 10.1038/s41597-023-02696-w
105. Understanding MODIS dark-target collection 5 and 6 aerosol data over China: Effect of surface type, aerosol loading and aerosol absorption L. Mei et al. 10.1016/j.atmosres.2019.05.023
106. Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring C. Chen et al. 10.5194/essd-12-3573-2020
107. Applying the Dark Target aerosol algorithm with Advanced Himawari Imager observations during the KORUS-AQ field campaign P. Gupta et al. 10.5194/amt-12-6557-2019
108. Evaluation of MODIS C6 and C6.1 Dark Target AOD products over Turkey based on NDVI and aerosol type G. Tuna Tuygun et al. 10.1016/j.apr.2020.07.003
109. Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products J. Wei et al. 10.5194/acp-19-7183-2019
110. SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe N. Ajtai et al. 10.3390/rs13050844
111. Validation of MODIS 3 km land aerosol optical depth from NASA's EOS Terra and Aqua missions P. Gupta et al. 10.5194/amt-11-3145-2018
112. MODIS AOD sampling rate and its effect on PM2.5 estimation in North China Z. Song et al. 10.1016/j.atmosenv.2019.04.020
113. MODIS Collection 6.1 3 km resolution aerosol optical depth product: global evaluation and uncertainty analysis J. Wei et al. 10.1016/j.atmosenv.2020.117768
114. Evaluation of MODIS, MISR, and VIIRS daily level-3 aerosol optical depth products over land Q. Chen et al. 10.1016/j.atmosres.2021.105810
115. Improved merge schemes for MODIS Collection 6.1 Dark Target and Deep Blue combined aerosol products J. Wei et al. 10.1016/j.atmosenv.2019.01.016
116. High-Resolution Gridded Level 3 Aerosol Optical Depth Data from MODIS P. Gupta et al. 10.3390/rs12172847
117. Aerosol optical depth (AOD): spatial and temporal variations and association with meteorological covariates in Taklimakan desert, China J. Li et al. 10.7717/peerj.10542
118. Investigating the spatio-temporal correlation between urban heat island and atmospheric pollution island interaction over Delhi, India using geospatial techniques M. Mishra & A. Mathew 10.1007/s12517-022-10854-4
119. A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land G. Zhong et al. 10.3390/rs9060524
120. Merging regional and global aerosol optical depth records from major available satellite products L. Sogacheva et al. 10.5194/acp-20-2031-2020
121. An improved deep learning network for AOD retrieving from remote sensing imagery focusing on sub-pixel cloud H. Cai et al. 10.1080/15481603.2023.2262836
122. How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth A. Sayer & K. Knobelspiesse 10.5194/acp-19-15023-2019
123. VIIRS Environmental Data Record and Deep Blue aerosol products: validation, comparison, and spatiotemporal variations from 2013 to 2018 in China L. He et al. 10.1016/j.atmosenv.2021.118265
124. Local region partition for person re-identification H. Chu et al. 10.1007/s11042-017-4817-4
125. Long-term validation of MODIS C6 and C6.1 Dark Target aerosol products over China using CARSNET and AERONET H. Che et al. 10.1016/j.chemosphere.2019.06.238
126. Aerosol data assimilation in the MOCAGE chemical transport model during the TRAQA/ChArMEx campaign: lidar observations L. El Amraoui et al. 10.5194/amt-13-4645-2020
127. Fusion of Multisource Satellite AOD Products via Bayesian Maximum Entropy With Explicit A Priori Knowledge H. Zhu et al. 10.1109/TGRS.2023.3285943
128. A Dark Target Method for Himawari-8/AHI Aerosol Retrieval: Application and Validation B. Ge et al. 10.1109/TGRS.2018.2854743
129. Assessment of aerosol burden over Ghana K. Fosu-Amankwah et al. 10.1016/j.sciaf.2021.e00971