518 citations as recorded by crossref.

1. Evaluation of MAIAC aerosol retrievals over China Z. Zhang et al. 10.1016/j.atmosenv.2019.01.013
2. 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
3. Climatological Characteristics and Aerosol Loading Trends from 2001 to 2020 Based on MODIS MAIAC Data for Tianjin, North China Plain Z. Wu et al. 10.3390/su14031072
4. High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China L. Li 10.3390/rs13122324
5. Effects of COVID-19 lockdowns on fine particulate matter concentrations M. Hammer et al. 10.1126/sciadv.abg7670
6. Long-term validation and error analysis of DB and MAIAC aerosol products over bright surface of China W. Ji et al. 10.1016/j.atmosres.2023.107106
7. Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover J. Daniels et al. 10.1016/j.scitotenv.2024.171122
8. Impact of Model Spatial Resolution on Global Geophysical Satellite-Derived Fine Particulate Matter D. Zhang et al. 10.1021/acsestair.4c00084
9. Remote sensing estimation of surface PM2.5 concentrations using a deep learning model improved by data augmentation and a particle size constraint S. Yin et al. 10.1016/j.atmosenv.2022.119282
10. 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. 10.1016/j.atmosres.2024.107515
11. From lowland plains to the Altiplano: The impacts of regional transport of wildfire smoke on the air quality of Bolivian cities E. Mollinedo et al. 10.1016/j.atmosenv.2023.120137
12. Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020 Q. He et al. 10.1016/j.atmosres.2022.106481
13. 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. 10.1016/j.chemosphere.2024.142820
14. A Generalized Aerosol Algorithm for Multi‐Spectral Satellite Measurement With Physics‐Informed Deep Learning Method J. Jiang et al. 10.1029/2023GL106806
15. Assessment of the vertical distribution of speciated aerosol absorption over South Asia using spaceborne LIDAR and ground-based observations N. Lakshmi et al. 10.1016/j.rse.2020.112164
16. 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
17. Comparison of different methods of determining land surface reflectance for AOD retrieval Q. Wang et al. 10.1016/j.apr.2021.101143
18. Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment H. Bai et al. 10.3390/rs14122933
19. Continuous mapping of fine particulate matter (PM2.5) air quality in East Asia at daily 6  ×  6 km2 resolution by application of a random forest algorithm to 2011–2019 GOCI geostationary satellite data D. Pendergrass et al. 10.5194/amt-15-1075-2022
20. Short-term effects of exposure to ambient PM1, PM2.5, and PM10 on ischemic and hemorrhagic stroke incidence in Shandong Province, China H. Wu et al. 10.1016/j.envres.2022.113350
21. Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees J. Wei et al. 10.5194/acp-20-3273-2020
22. On the retrieval of aerosol optical depth over cryosphere using passive remote sensing L. Mei et al. 10.1016/j.rse.2020.111731
23. Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling L. Li et al. 10.1016/j.rse.2019.111584
24. Validation and Comparison of Long-Term Accuracy and Stability of Global Reanalysis and Satellite Retrieval AOD X. Su et al. 10.3390/rs16173304
25. Improving MODIS Aerosol Estimates Over Land With the Surface BRDF Reflectances Using the 3-D Discrete Cosine Transform and RossThick-LiSparse Models X. Tian et al. 10.1109/TGRS.2020.3048109
26. Shapley values reveal the drivers of soil organic carbon stock prediction A. Wadoux et al. 10.5194/soil-9-21-2023
27. Quantifying uncertainty for remote spectroscopy of surface composition D. Thompson et al. 10.1016/j.rse.2020.111898
28. Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model T. Jiang et al. 10.1016/j.atmosres.2020.105146
29. Satellite-Derived 1-km-Resolution PM1 Concentrations from 2014 to 2018 across China J. Wei et al. 10.1021/acs.est.9b03258
30. Deterioration of air quality associated with the 2020 US wildfires M. Filonchyk et al. 10.1016/j.scitotenv.2022.154103
31. Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm T. Zhang et al. 10.1080/15481603.2022.2060596
32. Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States D. Goldberg et al. 10.1016/j.atmosenv.2018.11.049
33. Harmonized Landsat and Sentinel-2 Data with Google Earth Engine E. Berra et al. 10.3390/rs16152695
34. Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions L. Liang et al. 10.1016/j.envpol.2023.121832
35. Ensemble averaging based assessment of spatiotemporal variations in ambient PM2.5 concentrations over Delhi, India, during 2010–2016 S. Mandal et al. 10.1016/j.atmosenv.2020.117309
36. The Relationship Between MAIAC Smoke Plume Heights and Surface PM M. Cheeseman et al. 10.1029/2020GL088949
37. Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach J. Wei et al. 10.1016/j.rse.2019.111221
38. Inferring iron-oxide species content in atmospheric mineral dust from DSCOVR EPIC observations S. Go et al. 10.5194/acp-22-1395-2022
39. Employing relaxed smoothness constraints on imaginary part of refractive index in AERONET aerosol retrieval algorithm A. Sinyuk et al. 10.5194/amt-15-4135-2022
40. Identification of Aerosol Pollution Hotspots in Jiangsu Province of China Y. Wang et al. 10.3390/rs13142842
41. Accuracy assessment and climatology of MODIS aerosol optical properties over North Africa A. Merdji et al. 10.1007/s11356-022-22997-8
42. Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire X. Ye et al. 10.5194/acp-21-14427-2021
43. A Deep-Neural-Network-Based Aerosol Optical Depth (AOD) Retrieval from Landsat-8 Top of Atmosphere Data L. She et al. 10.3390/rs14061411
44. Robust Analysis of PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina W. Hernandez et al. 10.3390/s19214648
45. Impact of COVID-19 induced lockdown on land surface temperature, aerosol, and urban heat in Europe and North America B. Parida et al. 10.1016/j.scs.2021.103336
46. An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation N. Schutgens et al. 10.5194/acp-20-12431-2020
47. A deep learning-based imputation method for missing gaps in satellite aerosol products by fusing numerical model data N. Liu et al. 10.1016/j.atmosenv.2024.120440
48. First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI S. Li et al. 10.3390/rs11242990
49. Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire” P. Makkaroon et al. 10.1029/2022JD037298
50. Accuracy assessment of MODIS land aerosol optical thickness algorithms using AERONET measurements over North America H. Jethva et al. 10.5194/amt-12-4291-2019
51. 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
52. Review: Strategies for using satellite-based products in modeling PM2.5 and short-term pollution episodes M. Sorek-Hamer et al. 10.1016/j.envint.2020.106057
53. 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
54. Effects of Air Pollutants on Summer Precipitation in Different Regions of Beijing Y. Yang et al. 10.3390/atmos13010141
55. Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI F. Yin et al. 10.5194/gmd-15-7933-2022
56. Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. X. Ye et al. 10.3390/rs14236113
57. Comparative evaluation of backpropagation neural network and genetic algorithm-backpropagation neural network models for PM2.5 concentration prediction based on aerosol optical depth, meteorological factors, and air pollutants J. Gu et al. 10.1117/1.JRS.18.012006
58. Evaluation and comparison of VIIRS dark target and deep blue aerosol products over land Q. Wang et al. 10.1016/j.scitotenv.2023.161667
59. Using deep ensemble forest for high-resolution mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran H. Bagheri 10.1007/s10661-023-10951-1
60. Spatial downscaling of surface ozone concentration calculation from remotely sensed data based on mutual information X. Wang et al. 10.3389/fenvs.2022.925979
61. A Hybrid Approach to Estimate Spatially and Temporally Resolved Pm2.5 Distributions from Multi-Sourced Aod Data Q. Pu & Y. Eun-Hye 10.2139/ssrn.4094009
62. Benefits of High Resolution PM2.5 Prediction using Satellite MAIAC AOD and Land Use Regression for Exposure Assessment: California Examples H. Lee 10.1021/acs.est.9b03799
63. Accuracy assessment on eight public PM2.5 concentration datasets across China Y. Di et al. 10.1016/j.atmosenv.2024.120799
64. Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018) M. Hammer et al. 10.1021/acs.est.0c01764
65. A physical knowledge-based machine learning method for near-real-time dust aerosol properties retrieval from the Himawari-8 satellite data J. Li et al. 10.1016/j.atmosenv.2022.119098
66. 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
67. Big data analyses for determining the spatio-temporal trends of air pollution due to wildfires in California using Google Earth Engine A. Saim & M. Aly 10.1016/j.apr.2024.102226
68. Hourly Mapping of the Layer Height of Thick Smoke Plumes Over the Western U.S. in 2020 Severe Fire Season Z. Lu et al. 10.3389/frsen.2021.766628
69. Radiative interaction of atmosphere and surface: Write-up with elements of code S. Korkin & A. Lyapustin 10.1016/j.jqsrt.2023.108663
70. Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK K. Dimakopoulou et al. 10.3390/ijerph19095401
71. Mineral dust optical properties for remote sensing and global modeling: A review P. Castellanos et al. 10.1016/j.rse.2023.113982
72. 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
73. A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5 L. Li 10.3390/rs12020264
74. Long short-term memory network model to estimate PM2.5 concentrations with missing-filled satellite data in Beijing S. Jia et al. 10.1007/s00477-022-02253-8
75. Global aerosol retrieval over land from Landsat imagery integrating Transformer and Google Earth Engine J. Wei et al. 10.1016/j.rse.2024.114404
76. Instantaneous aerosol and surface retrieval using satellites in geostationary orbit (iAERUS-GEO) – estimation of 15 min aerosol optical depth from MSG/SEVIRI and evaluation with reference data X. Ceamanos et al. 10.5194/amt-16-2575-2023
77. Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products M. Román et al. 10.1016/j.rse.2023.113963
78. Matrix exponential in C/C++ version of vector radiative transfer code IPOL S. Korkin & A. Lyapustin 10.1016/j.jqsrt.2019.02.009
79. Himawari-8/AHI Aerosol Optical Depth Detection Based on Machine Learning Algorithm Y. Chen et al. 10.3390/rs14132967
80. Spatio-temporal variations of aerosol optical depth over Ukraine under the Russia-Ukraine war D. Jiadan et al. 10.1016/j.atmosenv.2023.120114
81. The Impact of the Control Measures during the COVID-19 Outbreak on Air Pollution in China C. Fan et al. 10.3390/rs12101613
82. Retrievals of Aerosol Optical Depth and Spectral Absorption From DSCOVR EPIC A. Lyapustin et al. 10.3389/frsen.2021.645794
83. Analysis of a severe dust storm and its impact on air quality conditions using WRF-Chem modeling, satellite imagery, and ground observations F. Karagulian et al. 10.1007/s11869-019-00674-z
84. The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2 A. Sinyuk et al. 10.5194/amt-13-3375-2020
85. Merging regional and global aerosol optical depth records from major available satellite products L. Sogacheva et al. 10.5194/acp-20-2031-2020
86. A Erosol S  Characteristics, Sources, and Drive Factors Analysis  In Typical Megacities, Nw China z. zhang 10.2139/ssrn.4111394
87. Meteorological controls on daily variations of nighttime surface urban heat islands J. Lai et al. 10.1016/j.rse.2020.112198
88. Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth X. Jin et al. 10.5194/acp-19-295-2019
89. Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China X. Chen et al. 10.5194/essd-14-5233-2022
90. UV Reflectance of the Ocean from DSCOVR/EPIC: Comparisons with a Theoretical Model and Aura/OMI Observations A. Vasilkov et al. 10.1175/JTECH-D-18-0150.1
91. Recurring South Asian smog episodes: Call for regional cooperation and improved monitoring M. Khokhar et al. 10.1016/j.atmosenv.2022.119534
92. Multi-Criteria Assessment for City-Wide Rooftop Solar PV Deployment: A Case Study of Bandung, Indonesia A. Sakti et al. 10.3390/rs14122796
93. Himawari-8-Derived Aerosol Optical Depth Using an Improved Time Series Algorithm Over Eastern China D. Li et al. 10.3390/rs12060978
94. Multi-Decadal Trends in Aerosol Optical Depth of the Main Aerosol Species Based on MERRA-2 Reanalysis: A Case Study in the Baltic Sea Basin E. Mancinelli et al. 10.3390/rs16132421
95. 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
96. Impact of satellite AOD data on top-down estimation of biomass burning particulate matter emission X. Ye et al. 10.1016/j.scitotenv.2022.161055
97. City-Scale Aerosol Loading Changes in the Sichuan Basin from 2001 to 2020 as Revealed by MODIS 1 km Aerosol Product R. Wang & H. Cai 10.3390/atmos14121715
98. A new constant scattering angle solar geometry definition for normalization of GOES-R ABI reflectance times series to support land surface phenology studies S. Gao et al. 10.1016/j.rse.2024.114407
99. Limitations of cloud cover for optical remote sensing of agricultural areas across South America V. Prudente et al. 10.1016/j.rsase.2020.100414
100. 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
101. Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020 Q. He et al. 10.1016/j.jenvman.2023.118145
102. Estimation of PM2.5 Concentration across China Based on Multi-Source Remote Sensing Data and Machine Learning Methods Y. Yang et al. 10.3390/rs16030467
103. Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region W. Xue et al. 10.1016/j.jclepro.2020.123742
104. Study on Spatial Changes in PM2.5 before and after the COVID-19 Pandemic in Southwest China X. Li et al. 10.3390/atmos14040671
105. The incidence and magnitude of the hot-spot bidirectional reflectance distribution function (BRDF) signature in GOES-16 Advanced Baseline Imager (ABI) 10 and 15 minute reflectance over north America Z. Li et al. 10.1016/j.rse.2021.112638
106. Sub-continental-scale carbon stocks of individual trees in African drylands C. Tucker et al. 10.1038/s41586-022-05653-6
107. Intercomparison of Aerosol Types Reported as Part of Aerosol Product Retrieval over Diverse Geographic Regions S. Falah et al. 10.3390/rs14153667
108. Air Quality over China G. de Leeuw et al. 10.3390/rs13173542
109. Preliminary Retrieval and Validation of Aerosol Optical Depths from FY-4B Advanced Geostationary Radiation Imager Images D. Zhou et al. 10.3390/rs16020372
110. Generation and Evaluation of LAI and FPAR Products from Himawari-8 Advanced Himawari Imager (AHI) Data Y. Chen et al. 10.3390/rs11131517
111. A practical guide to writing a radiative transfer code S. Korkin et al. 10.1016/j.cpc.2021.108198
112. Gaussian Markov random fields improve ensemble predictions of daily 1 km PM2.5 and PM10 across France I. Hough et al. 10.1016/j.atmosenv.2021.118693
113. Estimating PM2.5 Concentrations Using the Machine Learning RF-XGBoost Model in Guanzhong Urban Agglomeration, China L. Lin et al. 10.3390/rs14205239
114. Influence of Spatial Resolution and Retrieval Frequency on Applicability of Satellite-Predicted PM2.5 in Northern China R. Li et al. 10.3390/rs12040736
115. Spatio-temporal air quality assessment in Tehran, Iran, during the COVID-19 lockdown periods M. Bagherinia et al. 10.1080/10106049.2023.2169374
116. Development of aerosol optical properties for improving the MESSy photolysis module in the GEM-MACH v2.4 air quality model and application for calculating photolysis rates in a biomass burning plume M. Majdzadeh et al. 10.5194/gmd-15-219-2022
117. Estimating PM2.5 in Southern California using satellite data: factors that affect model performance J. Stowell et al. 10.1088/1748-9326/ab9334
118. A comprehensive review delineates advancements in retrieving particulate matter utilising satellite aerosol optical depth: Parameter consideration, data processing, models development and future perspectives S. Padimala & C. Matli 10.1016/j.atmosres.2024.107514
119. Geographical and temporal encoding for improving the estimation of PM2.5 concentrations in China using end-to-end gradient boosting N. Yang et al. 10.1016/j.rse.2021.112828
120. Characterization of dust activation and their prevailing transport over East Asia based on multi-satellite observations M. Tao et al. 10.1016/j.atmosres.2021.105886
121. Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. 10.1029/2019JD031380
122. How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM2.5 Using Machine Learning? Z. Tian et al. 10.3390/rs15153780
123. 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
124. Satellite-Based Optimization and Planning of Urban Ventilation Corridors for a Healthy Microclimate Environment D. Gong et al. 10.3390/su152115653
125. 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
126. Environmental hazards posed by mine dust, and monitoring method of mine dust pollution using remote sensing technologies: An overview H. Yu & I. Zahidi 10.1016/j.scitotenv.2022.161135
127. Columnar and surface urban aerosol in the Moscow megacity according to measurements and simulations with the COSMO-ART model N. Chubarova et al. 10.5194/acp-22-10443-2022
128. Accuracy and error cause analysis, and recommendations for usage of Himawari-8 aerosol products over Asia and Oceania L. Feng et al. 10.1016/j.scitotenv.2021.148958
129. Assessment of the impact of waste fires on air quality and atmospheric aerosol optical depth: A case study in Poland R. Oleniacz et al. 10.1016/j.egyr.2023.03.087
130. An accurate and efficient forecast framework for fine PM2.5 maps using spatiotemporal recurrent neural networks N. Liu et al. 10.1016/j.jclepro.2024.143624
131. Evaluation and Comparison of Spatio-Temporal Relationship between Multiple Satellite Aerosol Optical Depth (AOD) and Near-Surface PM2.5 Concentration over China Q. Xu et al. 10.3390/rs14225841
132. A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations W. Wang et al. 10.3390/rs14040964
133. Importance of aerosol composition and aerosol vertical profiles in global spatial variation in the relationship between PM2.5 and aerosol optical depth H. Zhu et al. 10.5194/acp-24-11565-2024
134. Heat flux assumptions contribute to overestimation of wildfire smoke injection into the free troposphere L. Thapa et al. 10.1038/s43247-022-00563-x
135. Monitoring the Spatial Variation of Aerosol Optical Depth and Its Correlation with Land Use/Land Cover in Wuhan, China: A Perspective of Urban Planning Q. Xie & Q. Sun 10.3390/ijerph18031132
136. First Retrieval of AOD at Fine Resolution Over Shallow and Turbid Coastal Waters From MODIS Y. Wang et al. 10.1029/2021GL094344
137. Improvement of spatial-temporal resolution of aerosol profile by using multi-source satellite data over the Persian Gulf M. Pashayi et al. 10.1016/j.atmosenv.2022.119410
138. Quantifying urban heat island and pollutant nexus: A novel geospatial approach K. Arunab & A. Mathew 10.1016/j.scs.2023.105117
139. Data-driven estimates of evapotranspiration and its controls in the Congo Basin M. Burnett et al. 10.5194/hess-24-4189-2020
140. Maternal Exposure to PM 2.5 and the Risk of Congenital Heart Defects in 1.4 Million Births: A Nationwide Surveillance-Based Study X. Yuan et al. 10.1161/CIRCULATIONAHA.122.061245
141. Vertical distribution of smoke aerosols over upper Indo-Gangetic Plain K. Vinjamuri et al. 10.1016/j.envpol.2019.113377
142. Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives Y. Zhang et al. 10.1016/j.fmre.2021.04.007
143. Long-Term Exposure to Fine Particulate Matter and Cardiovascular Disease in China F. Liang et al. 10.1016/j.jacc.2019.12.031
144. LGHAP v2: a global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big Earth data analytics K. Bai et al. 10.5194/essd-16-2425-2024
145. Evaluation of Novel NASA Moderate Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite Aerosol Products and Assessment of Smoke Height Boundary Layer Ratio During Extreme Smoke Events in the Western USA S. Loría‐Salazar et al. 10.1029/2020JD034180
146. A high-precision aerosol retrieval algorithm for FY-3D MERSI-II images Q. Wang et al. 10.1016/j.envint.2023.107841
147. Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods M. Danesh Yazdi et al. 10.3390/rs12060914
148. Land surface temperature and transboundary air pollution: a case of Bangkok Metropolitan Region T. Sarker et al. 10.1038/s41598-024-61720-0
149. Algorithm for the Reconstruction of the Ground Surface Reflectance in the Visible and Near IR Ranges from MODIS Satellite Data with Allowance for the Influence of Ground Surface Inhomogeneity on the Adjacency Effect and of Multiple Radiation Reflection M. Tarasenkov et al. 10.3390/rs15102655
150. First retrieval of daily 160 m aerosol optical depth over urban areas using Gaofen-1/6 synergistic observations: Algorithm development and validation J. Dong et al. 10.1016/j.isprsjprs.2024.04.020
151. Long-term satellite-based estimates of air quality and premature mortality in Equatorial Asia through deep neural networks N. Bruni Zani et al. 10.1088/1748-9326/abb733
152. Effect of lockdown due to COVID-19 on environmental pollutant: a comparative study between top three countries of the world S. Kumar 10.1007/s12648-024-03323-z
153. Influence of trees on landscape temperature in semi-arid agro-ecosystems of East Africa L. Villani et al. 10.1016/j.biosystemseng.2021.10.007
154. Downwind Ozone Changes of the 2019 Williams Flats Wildfire: Insights From WRF‐Chem/DART Assimilation of OMI NO2, HCHO, and MODIS AOD Retrievals A. Pouyaei et al. 10.1029/2022JD038019
155. Temporal and spatial distribution mapping of particulate matter in southwest of Iran using remote sensing, GIS, and statistical techniques A. Soleimany et al. 10.1007/s11869-022-01179-y
156. Characterizing aerosols during forest fires over Uttarakhand region in India using multi-satellite remote sensing data S. Verma et al. 10.1016/j.asr.2022.05.051
157. Daily 1 km terrain resolving maps of surface fine particulate matter for the western United States 2003–2021 A. Swanson et al. 10.1038/s41597-022-01488-y
158. Himawari-8 Aerosol Optical Depth (AOD) Retrieval Using a Deep Neural Network Trained Using AERONET Observations L. She et al. 10.3390/rs12244125
159. Evaluation and improvement of MODIS aerosol optical depth products over China Y. Li et al. 10.1016/j.atmosenv.2019.117251
160. Measuring and Monitoring Urban Impacts on Climate Change from Space C. Milesi & G. Churkina 10.3390/rs12213494
161. The comparison of AOD-based and non-AOD prediction models for daily PM2.5 estimation in Guangdong province, China with poor AOD coverage G. Chen et al. 10.1016/j.envres.2021.110735
162. Evaluation and comparison of MODIS aerosol optical depth retrieval algorithms over Brazil A. Rudke et al. 10.1016/j.atmosenv.2023.120130
163. Retrieving High-Resolution Aerosol Optical Depth from GF-4 PMS Imagery in Eastern China Z. Sun et al. 10.3390/rs13183752
164. Predicting annual PM2.5 in mainland China from 2014 to 2020 using multi temporal satellite product: An improved deep learning approach with spatial generalization ability Z. Wang et al. 10.1016/j.isprsjprs.2022.03.002
165. Satellite Monitoring for Air Quality and Health T. Holloway et al. 10.1146/annurev-biodatasci-110920-093120
166. Impact of aerosols on atmospheric processes and climate variability: A synthesis of recent research findings S. Perumpully & S. Gautam 10.1016/j.geogeo.2024.100317
167. LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion K. Bai et al. 10.5194/essd-14-907-2022
168. Estimation of fine spatial resolution all-sky surface net shortwave radiation over mountainous terrain from Landsat 8 and Sentinel-2 data Y. Ma et al. 10.1016/j.rse.2022.113364
169. Scene invariants for quantifying radiative transfer uncertainty D. Thompson et al. 10.1016/j.rse.2021.112432
170. Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm for Himawari-8 Data L. She et al. 10.3390/rs11232771
171. Spatio-Temporal Dynamics of Aerosol Optical Thickness derived Using MODIS-MAIAC Algorithm at a High Spatial Resolution Along with the HYSPLIT Trajectory Model A. Chauhan et al. 10.1007/s41810-024-00217-9
172. Profiling of Dust and Urban Haze Mass Concentrations during the 2019 National Day Parade in Beijing by Polarization Raman Lidar Z. Wang et al. 10.3390/rs13163326
173. Influence of Vegetation Phenology on the Temporal Effect of Crop Fractional Vegetation Cover Derived from Moderate-Resolution Imaging Spectroradiometer Nadir Bidirectional Reflectance Distribution Function–Adjusted Reflectance Y. Lin et al. 10.3390/agriculture14101759
174. Estimation of Daily Seamless PM2.5 Concentrations with Climate Feature in Hubei Province, China W. Ni et al. 10.3390/rs15153822
175. 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
176. Uncertainty in Aqua-MODIS Aerosol Retrieval Algorithms During COVID-19 Lockdown M. Bilal et al. 10.1109/LGRS.2021.3077189
177. Consistency of Aerosol Optical Properties between MODIS Satellite Retrievals and AERONET over a 14-Year Period in Central–East Europe L. Deaconu et al. 10.3390/rs16101677
178. Surface Reflectance and Aerosol Retrieval from SPOT-VGT and PROBA-V in the Mission Exploitation Platform Environment M. Luffarelli et al. 10.3390/rs15215109
179. Predicting gestational personal exposure to PM2.5 from satellite-driven ambient concentrations in Shanghai Q. Zhu et al. 10.1016/j.chemosphere.2019.05.251
180. Estimating the Impact of COVID-19 on the PM2.5 Levels in China with a Satellite-Driven Machine Learning Model Q. Li et al. 10.3390/rs13071351
181. Parameterization of size of organic and secondary inorganic aerosol for efficient representation of global aerosol optical properties H. Zhu et al. 10.5194/acp-23-5023-2023
182. Advances in the estimation of high Spatio-temporal resolution pan-African top-down biomass burning emissions made using geostationary fire radiative power (FRP) and MAIAC aerosol optical depth (AOD) data H. Nguyen & M. Wooster 10.1016/j.rse.2020.111971
183. Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM2.5) using satellite data over large regions A. Just et al. 10.1016/j.atmosenv.2020.117649
184. An Optimization Approach for Estimating Multiple Land Surface and Atmospheric Variables From the Geostationary Advanced Himawari Imager Top-of-Atmosphere Observations H. Ma et al. 10.1109/TGRS.2020.3007118
185. Assessment of Adjacency Correction over Inland Waters Using Sentinel-2 MSI Images R. Paulino et al. 10.3390/rs14081829
186. Optimizing afforestation and reforestation strategies to enhance ecosystem services in critically degraded regions . Fahrudin et al. 10.1016/j.tfp.2024.100700
187. First validation of Earth Reflector Type Index (p) parameter from DSCOVR EPIC VESDR data product using Terrestrial Ecosystem Research Network observing sites in Australia J. Pisek et al. 10.1016/j.rse.2023.113511
188. Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing J. Li et al. 10.1016/j.envint.2021.106445
189. Continental-scale surface reflectance product from CBERS-4 MUX data: Assessment of atmospheric correction method using coincident Landsat observations V. Martins et al. 10.1016/j.rse.2018.09.017
190. 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
191. Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder L. Liang et al. 10.1038/s41597-023-02696-w
192. Operational Evaluation of a Wildfire Air Quality Model from a Forecaster Point of View B. Ainslie et al. 10.1175/WAF-D-21-0064.1
193. A Geometry-Discrete Minimum Reflectance Aerosol Retrieval Algorithm (GeoMRA) for Geostationary Meteorological Satellite Over Heterogeneous Surfaces T. Zhang et al. 10.1109/TGRS.2022.3200425
194. Analysis of the spatial and temporal distribution characteristics of AOD in typical industrial cities in northwest China and the influence of meteorological factors H. Meng et al. 10.1016/j.apr.2023.101957
195. Satellite-observed vegetation responses to aerosols variability Z. Zhang et al. 10.1016/j.agrformet.2022.109278
196. Validation and Analysis of MAIAC AOD Aerosol Products in East Asia from 2011 to 2020 P. Wang et al. 10.3390/rs14225735
197. Impacts of wildfire-season air quality on park and playground visitation in the Northwest United States K. Mullan et al. 10.1016/j.ecolecon.2024.108285
198. Remote sensing tracks daily radial wood growth of evergreen needleleaf trees J. Eitel et al. 10.1111/gcb.15112
199. Geographic Graph Network for Robust Inversion of Particulate Matters L. Li 10.3390/rs13214341
200. 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
201. Improved Himawari-8 10-minute scale aerosol optical depth product using deep neural network over Japan Y. Tan et al. 10.1016/j.apr.2023.102005
202. Aerosol characteristics at the three poles of the Earth as characterized by Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations Y. Yang et al. 10.5194/acp-21-4849-2021
203. Quasi‐Global Maps of Daily Aerosol Optical Depth From a Ring of Five Geostationary Meteorological Satellites Using AERUS‐GEO X. Ceamanos et al. 10.1029/2021JD034906
204. A MAIA-like modeling framework to estimate PM2.5 mass and speciation concentrations with uncertainty Z. Jin et al. 10.1016/j.rse.2024.113995
205. Time series retrieval of Multi-wavelength Aerosol optical depth by adapting Transformer (TMAT) using Himawari-8 AHI data L. She et al. 10.1016/j.rse.2024.114115
206. The spatiotemporal heterogeneity of the relationship between PM2.5 concentrations and the surface urban heat island effect in Beijing, China Z. Li et al. 10.1177/03091333211033209
207. 基于GF-5卫星遥感数据的大气CO2快速反演方法 孙. Sun Zhiqiang et al. 10.3788/AOS231995
208. Satellite mapping of PM<sub>2.5</sub> episodes in the wintertime San Joaquin Valley: a “static” model using column water vapor R. Chatfield et al. 10.5194/acp-20-4379-2020
209. Does improved tenure security reduce fires? Evidence from the Greece land registry L. Diao & H. Song 10.1016/j.jeem.2024.103002
210. Analysis and Variation of the Maiac Aerosol Optical Depth in Underexplored Urbanized Area of National Capital Region, India V. Sharma et al. 10.2478/jlecol-2022-0019
211. 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
212. New Approach for Temporal Stability Evaluation of Pseudo-Invariant Calibration Sites (PICS) F. Tuli et al. 10.3390/rs11121502
213. 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
214. Potential role of urban forest in removing PM2.5: A case study in Seoul by deep learning with satellite data A. Lee et al. 10.1016/j.uclim.2021.100795
215. Effect of Scattering Angle on Earth Reflectance A. Marshak et al. 10.3389/frsen.2021.719610
216. Relationship between respiratory diseases and environmental conditions: a time-series analysis in Eastern Amazon M. Moura et al. 10.5327/Z217694781020
217. The global spatial-temporal distribution and EOF analysis of AOD based on MODIS data during 2003–2021 X. Tian et al. 10.1016/j.atmosenv.2023.119722
218. NO2 emissions from oil refineries in the Mississippi Delta M. Filonchyk & M. Peterson 10.1016/j.scitotenv.2023.165569
219. Characterization of temporal and spatial variability of aerosols from ground-based climatology: towards evaluation of satellite mission requirements C. Chen et al. 10.1016/j.jqsrt.2021.107627
220. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China J. Wei et al. 10.1016/j.envint.2020.106290
221. Effects of aerosol on terrestrial gross primary productivity in Central Asia W. Ma et al. 10.1016/j.atmosenv.2022.119294
222. A Satellite-Based High-Resolution (1-km) Ambient PM2.5 Database for India over Two Decades (2000–2019): Applications for Air Quality Management S. Dey et al. 10.3390/rs12233872
223. Environmental factors modulate the diffuse fertilization effect on gross primary productivity across Chinese ecosystems X. Gui et al. 10.1016/j.scitotenv.2021.148443
224. Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models S. Park et al. 10.1016/j.scitotenv.2020.136516
225. Tracking ambient PM2.5 build-up in Delhi national capital region during the dry season over 15 years using a high-resolution (1 km) satellite aerosol dataset S. Chowdhury et al. 10.1016/j.atmosenv.2019.02.029
226. Spatiotemporal variation and provincial scale differences of the AOD across China during 2000–2021 G. de Leeuw et al. 10.1016/j.apr.2022.101359
227. Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data D. Lu et al. 10.3390/rs13081423
228. Light-absorbing black carbon and brown carbon components of smoke aerosol from DSCOVR EPIC measurements over North America and central Africa M. Choi et al. 10.5194/acp-24-10543-2024
229. Estimating PM2.5 with high-resolution 1-km AOD data and an improved machine learning model over Shenzhen, China W. Chen et al. 10.1016/j.scitotenv.2020.141093
230. PM2.5 and PM10 during COVID-19 lockdown in Kuwait: Mixed effect of dust and meteorological covariates A. Al-Hemoud et al. 10.1016/j.envc.2021.100215
231. Nationwide estimation of daily ambient PM2.5 from 2008 to 2020 at 1 km2 in India using an ensemble approach S. Mandal et al. 10.1093/pnasnexus/pgae088
232. 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
233. Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity A. Gumber et al. 10.5194/amt-16-2547-2023
234. High-resolution estimation of PM2.5 concentrations across China using multiple machine learning approaches and model fusion L. Meng et al. 10.1016/j.apr.2024.102110
235. Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods M. Kim et al. 10.5194/amt-17-4317-2024
236. MODIS-Derived Arctic Melt Season Fog and Low Stratus over East Greenland Glaciers and the Ice Sheet H. Jiskoot et al. 10.1080/07038992.2019.1635878
237. Snow-corrected vegetation indices for improved gross primary productivity assessment in North American evergreen forests R. Wang et al. 10.1016/j.agrformet.2023.109600
238. Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China Q. Wang et al. 10.3390/rs12203425
239. Calibration of the SNPP and NOAA 20 VIIRS sensors for continuity of the MODIS climate data records A. Lyapustin et al. 10.1016/j.rse.2023.113717
240. Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis S. Falah et al. 10.1016/j.atmosenv.2021.118659
241. An intercomparison of SEMARA high-resolution AOD and MODIS operational AODs M. Bagherinia et al. 10.1016/j.apr.2023.102023
242. Quality Assessment and Application Scenario Analysis of AGRI Land Aerosol Product from the Geostationary Satellite Fengyun-4B in China N. Wang et al. 10.3390/s24165309
243. Long-term changes in aerosol loading over the ‘BIHAR’ State of India using nineteen years (2001–2019) of high-resolution satellite data (1 × 1 km2) M. Nair et al. 10.1016/j.apr.2021.101259
244. Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks L. Li & J. Wu 10.1016/j.rse.2020.112257
245. Spatial integration framework of solar, wind, and hydropower energy potential in Southeast Asia A. Sakti et al. 10.1038/s41598-022-25570-y
246. Synergistic data fusion of multimodal AOD and air quality data for near real-time full coverage air pollution assessment K. Li et al. 10.1016/j.jenvman.2021.114121
247. Absorbing Aerosol Optical Depth From OMI/TROPOMI Based on the GBRT Algorithm and AERONET Data in Asia D. Li et al. 10.1109/TGRS.2022.3231699
248. Remote sensing of air pollution due to forest fires and dust storm over Balochistan (Pakistan) S. Tariq et al. 10.1016/j.apr.2023.101674
249. Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US M. Childs et al. 10.1021/acs.est.2c02934
250. Assessing PM2.5 concentrations in Tehran, Iran, from space using MAIAC, deep blue, and dark target AOD and machine learning algorithms S. Nabavi et al. 10.1016/j.apr.2018.12.017
251. Exploring the Potential of DSCOVR EPIC Data to Retrieve Clumping Index in Australian Terrestrial Ecosystem Research Network Observing Sites J. Pisek et al. 10.3389/frsen.2021.652436
252. Accounting for 3D radiative effects in MODIS aerosol retrievals near clouds using CALIPSO observations G. Wen et al. 10.3389/frsen.2023.1333814
253. Long-term MAX-DOAS measurements of NO<sub>2</sub>, HCHO, and aerosols and evaluation of corresponding satellite data products over Mohali in the Indo-Gangetic Plain V. Kumar et al. 10.5194/acp-20-14183-2020
254. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications J. Wei et al. 10.1016/j.rse.2020.112136
255. Atmospheric Correction of DSCOVR EPIC: Version 2 MAIAC Algorithm A. Lyapustin et al. 10.3389/frsen.2021.748362
256. High aerosol loading over the Bohai Sea: Long-term trend, potential sources, and impacts on surrounding cities L. Li et al. 10.1016/j.envint.2023.108387
257. Evaluation and comparison of MODIS and MISR aerosol products with ground-based monitoring stations in the Amazon Basin V. Schumacher et al. 10.1016/j.atmosenv.2024.120597
258. High-accuracy full-coverage PM 2.5 retrieval from 2014 to 2023 over China based on satellite remote sensing and hierarchical deep learning model Y. Fan et al. 10.1080/17538947.2024.2392850
259. Extended aerosol and surface characterization from S5P/TROPOMI with GRASP algorithm. Part I: Conditions, approaches, performance and new possibilities P. Litvinov et al. 10.1016/j.rse.2024.114355
260. Atmospheric pollutants in Rosario, Argentina analysed through remote sensing: Wildfires and COVID-19 lockdown effects M. Valle Seijo et al. 10.1016/j.rsase.2024.101326
261. Grid-independent high-resolution dust emissions (v1.0) for chemical transport models: application to GEOS-Chem (12.5.0) J. Meng et al. 10.5194/gmd-14-4249-2021
262. The delayed effect of wildfire season particulate matter on subsequent influenza season in a mountain west region of the USA E. Landguth et al. 10.1016/j.envint.2020.105668
263. A Dark Target research aerosol algorithm for MODIS observations over eastern China: increasing coverage while maintaining accuracy at high aerosol loading Y. Shi et al. 10.5194/amt-14-3449-2021
264. Calibration of Maxar Constellation Over Libya-4 Site Using MAIAC Technique M. Choi et al. 10.1109/JSTARS.2024.3367250
265. Satellite-Derived PM2.5 Composition and Its Differential Effect on Children’s Lung Function K. Chau et al. 10.3390/rs12061028
266. An Ensemble Mean Method for Remote Sensing of Actual Evapotranspiration to Estimate Water Budget Response across a Restoration Landscape R. Petrakis et al. 10.3390/rs16122122
267. MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm J. Limbacher et al. 10.5194/amt-17-471-2024
268. Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale J. Bi et al. 10.1021/acs.est.9b06046
269. Aerosol profiling using radiometric and polarimetric spectral measurements in the O2 near infrared bands: Estimation of information content and measurement uncertainties M. Choi et al. 10.1016/j.rse.2020.112179
270. 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
271. Investigating the impact of drought and dust on oak trees decline in the West of Iran S. Sarab et al. 10.1007/s12517-022-10759-2
272. Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2.5 J. Li et al. 10.1016/j.atmosenv.2020.117293
273. Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation M. Tao et al. 10.1016/j.atmosenv.2019.06.004
274. Performance evaluation for retrieving aerosol optical depth from the Directional Polarimetric Camera (DPC) based on the GRASP algorithm S. Jin et al. 10.5194/amt-15-4323-2022
275. Impact of environmental pollution on the retrieval of hourly aerosol products from Advanced Himawari Imager (AHI) over Beijing Q. Xu et al. 10.1016/j.apr.2020.03.007
276. Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe S. Ibrahim et al. 10.3390/rs13153027
277. Application of remote sensing data to assess environmental situation in Krasnoyarsk K. Krasnoshchekov et al. 10.1051/e3sconf/202133302004
278. Retrieval of UV–visible aerosol absorption using AERONET and OMI–MODIS synergy: spatial and temporal variability across major aerosol environments V. Kayetha et al. 10.5194/amt-15-845-2022
279. Spatiotemporal Distributions of PM2.5 Concentrations in the Beijing–Tianjin–Hebei Region From 2013 to 2020 X. Yang et al. 10.3389/fenvs.2022.842237
280. Estimating High-Resolution PM2.5 Concentrations by Fusing Satellite AOD and Smartphone Photographs Using a Convolutional Neural Network and Ensemble Learning F. Wang et al. 10.3390/rs14061515
281. Aerosol characteristics from earth observation systems: A comprehensive investigation over South Asia (2000–2019) A. Mhawish et al. 10.1016/j.rse.2021.112410
282. Improvement of aerosol optical depth data for localized solar resource assessment C. Lin et al. 10.1016/j.solener.2022.11.047
283. Analyzing GOES-R ABI BRDF-adjusted EVI2 time series by comparing with VIIRS observations over the CONUS Y. Shen et al. 10.1016/j.rse.2023.113972
284. Climatology of aerosol component concentrations derived from multi-angular polarimetric POLDER-3 observations using GRASP algorithm L. Li et al. 10.5194/essd-14-3439-2022
285. Spatial validation reveals poor predictive performance of large-scale ecological mapping models P. Ploton et al. 10.1038/s41467-020-18321-y
286. Estimation of monthly 1 km resolution PM2.5 concentrations using a random forest model over “2 + 26” cities, China J. Lu et al. 10.1016/j.uclim.2020.100734
287. Revisiting dry season vegetation dynamics in the Amazon rainforest using different satellite vegetation datasets X. Xie et al. 10.1016/j.agrformet.2021.108704
288. On the Interplay between Desert Dust and Meteorology Based on WRF-Chem Simulations and Remote Sensing Observations in the Mediterranean Basin U. Rizza et al. 10.3390/rs15020435
289. A robust and flexible satellite aerosol retrieval algorithm for multi-angle polarimetric measurements with physics-informed deep learning method M. Tao et al. 10.1016/j.rse.2023.113763
290. Detecting intra- and inter-annual variability in gross primary productivity of a North American grassland using MODIS MAIAC data R. Wang et al. 10.1016/j.agrformet.2019.107859
291. 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
292. A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires S. O’Neill et al. 10.1080/10962247.2021.1891994
293. A novel big data mining framework for reconstructing large-scale daily MAIAC AOD data across China from 2000 to 2020 B. Chen et al. 10.1080/15481603.2022.2051382
294. Impacts of abiotic and biotic factors on tundra productivity near Utqiaġvik, Alaska Q. Zhang et al. 10.1088/1748-9326/acf7d6
295. Addressing Biases in Ambient PM2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India V. Katoch et al. 10.1021/acs.est.3c03355
296. Improving the ability of solar-induced chlorophyll fluorescence to track gross primary production through differentiating sunlit and shaded leaves Z. Zhang et al. 10.1016/j.agrformet.2023.109658
297. Synergistic monitoring of PM2.5 and CO2 based on active and passive remote sensing fusion during the 2022 Beijing Winter Olympics S. Wang et al. 10.1364/AO.505271
298. Satellite-based evaluation of AeroCom model bias in biomass burning regions Q. Zhong et al. 10.5194/acp-22-11009-2022
299. Modelling ambient PM2.5 exposure at an ultra-high resolution and associated health burden in megacity Delhi: exposure reduction target for 2030 S. Tiwari et al. 10.1088/1748-9326/acc261
300. Opinion: Aerosol remote sensing over the next 20 years L. Remer et al. 10.5194/acp-24-2113-2024
301. Chronic Effects of High Fine Particulate Matter Exposure on Lung Cancer in China J. Li et al. 10.1164/rccm.202001-0002OC
302. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact J. Wei et al. 10.1038/s41467-023-43862-3
303. Tracking hourly PM2.5 using geostationary satellite sensor images and multiscale spatiotemporal deep learning Z. Wang et al. 10.1016/j.jag.2024.104145
304. Influence of industrial sustainability transition on air quality in a typical resource-exhausted city J. Wang & X. Li 10.1016/j.heliyon.2024.e25138
305. 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
306. 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
307. A CatBoost approach with wavelet decomposition to improve satellite-derived high-resolution PM2.5 estimates in Beijing-Tianjin-Hebei Y. Ding et al. 10.1016/j.atmosenv.2021.118212
308. Features of the Extreme Fire Season of 2021 in Yakutia (Eastern Siberia) and Heavy Air Pollution Caused by Biomass Burning O. Tomshin & V. Solovyev 10.3390/rs14194980
309. An episode of transboundary air pollution in the central Himalayas during agricultural residue burning season in North India S. Khanal et al. 10.1016/j.apr.2021.101270
310. An improved method for retrieving aerosol optical depth over Ebinur Lake Basin from Gaofen-1 F. Liu & Z. Zhang 10.1016/j.atmosenv.2023.119699
311. 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
312. 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. 10.1016/j.rse.2023.113514
313. Advances in sunphotometer-measured aerosol optical properties and related topics in China: Impetus and perspectives X. Xia et al. 10.1016/j.atmosres.2020.105286
314. Air quality improvements can strengthen China’s food security X. Liu et al. 10.1038/s43016-023-00882-y
315. Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels Z. Chen et al. 10.3390/rs12183008
316. Estimation of High-Resolution PM2.5 over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and Land Use Variables A. Mhawish et al. 10.1021/acs.est.0c01769
317. Influence of the Indian Summer Monsoon on Inter-Annual Variability of the Tibetan-Plateau NDVI in Its Main Growing Season X. Mao et al. 10.3390/rs15143612
318. A Study of Air Quality in the Coalfields of NSW, Australia and Telangana, India H. Kamath et al. 10.1007/s12524-022-01557-0
319. Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application Z. Zhang et al. 10.1016/j.rse.2023.113699
320. Separating Daily 1 km PM2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data J. Wei et al. 10.1021/acs.est.3c00272
321. Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach T. Zheng et al. 10.1016/j.atmosenv.2020.117451
322. 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
323. Surface water and aerosol spatiotemporal dynamics and influence mechanisms over drylands X. Chen et al. 10.1016/j.gsf.2022.101524
324. Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India R. Singh et al. 10.1007/s10661-024-12631-0
325. Evaluating the Utility of High-Resolution Spatiotemporal Air Pollution Data in Estimating Local PM2.5 Exposures in California from 2015–2018 L. Gladson et al. 10.3390/atmos13010085
326. Generating Hourly Fine Seamless Aerosol Optical Depth Products by Fusing Multiple Satellite and Numerical Model Data B. Zou et al. 10.1109/TGRS.2024.3385397
327. Changes in leaf functional traits of rainforest canopy trees associated with an El Niño event in Borneo M. Nunes et al. 10.1088/1748-9326/ab2eae
328. 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. 10.3390/rs15123072
329. Assessment of natural and anthropogenic aerosol air pollution in the Middle East using MERRA-2, CAMS data assimilation products, and high-resolution WRF-Chem model simulations A. Ukhov et al. 10.5194/acp-20-9281-2020
330. A pigment ratio index based on remotely sensed reflectance provides the potential for universal gross primary production estimation W. Wu et al. 10.1088/1748-9326/abf3dc
331. Seasonality of Tropical Photosynthesis: A Pantropical Map of Correlations With Precipitation and Radiation and Comparison to Model Outputs M. Uribe et al. 10.1029/2020JG006123
332. Spatiotemporal PM2.5 estimations in China from 2015 to 2020 using an improved gradient boosting decision tree W. He et al. 10.1016/j.chemosphere.2022.134003
333. Monitoring atmospheric particulate matters using vertically resolved measurements of a polarization lidar, in-situ recordings and satellite data over Tehran, Iran H. Panahifar et al. 10.1038/s41598-020-76947-w
334. Improved 1-km-Resolution Hourly Estimates of Aerosol Optical Depth Using Conditional Generative Adversarial Networks L. Zhang et al. 10.3390/rs13193834
335. Spatiotemporal Evolution Disparities of Vegetation Trends over the Tibetan Plateau under Climate Change J. Ma et al. 10.3390/rs16142585
336. Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring Z. Li et al. 10.3390/rs11020122
337. Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud Á. Moreno-Martínez et al. 10.1016/j.rse.2020.111901
338. An improved method for retrieving aerosol optical depth using the ground-level meteorological data over the South-central Plain of Hebei Province, China F. Li et al. 10.1016/j.apr.2022.101334
339. Record-breaking aerosol levels explained by smoke injection into the stratosphere E. Hirsch & I. Koren 10.1126/science.abe1415
340. The effect of national protest in Ecuador on PM pollution R. Zalakeviciute et al. 10.1038/s41598-021-96868-6
341. Revised and extended benchmark results for Rayleigh scattering of sunlight in spherical atmospheres S. Korkin et al. 10.1016/j.jqsrt.2020.107181
342. Reprint of: Influence of trees on landscape temperature in semi-arid agro-ecosystems of East Africa L. Villani et al. 10.1016/j.biosystemseng.2022.10.004
343. Improved estimation of particulate matter in China based on multisource data fusion S. Wang et al. 10.1016/j.scitotenv.2023.161552
344. MAIAC Thermal Technique for Smoke Injection Height From MODIS A. Lyapustin et al. 10.1109/LGRS.2019.2936332
345. Decade-low aerosol levels over the Bohai and Yellow Seas amid the COVID-19 lockdown . RunaA et al. 10.1016/j.jag.2022.102905
346. Improving the south America wildfires smoke estimates: Integration of polar-orbiting and geostationary satellite fire products in the Brazilian biomass burning emission model (3BEM) G. Pereira et al. 10.1016/j.atmosenv.2022.118954
347. A Spatio-Temporal Weighted Filling Method for Missing AOD Values R. Gao et al. 10.3390/atmos13071080
348. Accounting for the aerosol type and additional satellite-borne aerosol products improves the prediction of PM2.5 concentrations S. Falah et al. 10.1016/j.envpol.2023.121119
349. Prediction of daily mean and one-hour maximum PM2.5 concentrations and applications in Central Mexico using satellite-based machine-learning models I. Gutiérrez-Avila et al. 10.1038/s41370-022-00471-4
350. COVID-19 lockdowns cause global air pollution declines Z. Venter et al. 10.1073/pnas.2006853117
351. Mapping Seasonal High-Resolution PM2.5 Concentrations with Spatiotemporal Bagged-Tree Model across China J. He et al. 10.3390/ijgi10100676
352. A gap-filling hybrid approach for hourly PM2.5 prediction at high spatial resolution from multi-sourced AOD data Q. Pu & E. Yoo 10.1016/j.envpol.2022.120419
353. Satellite-based estimation of the impacts of summertime wildfires on PM<sub>2.5</sub> concentration in the United States Z. Xue et al. 10.5194/acp-21-11243-2021
354. Three-dimensional spatiotemporal evolution of wildfire-induced smoke aerosols: A case study from Liangshan, Southwest China X. Zhang et al. 10.1016/j.scitotenv.2020.144586
355. Long-term variations of aerosol optical depth according to satellite data and its effects on radiation and temperature in the Moscow megacity A. Poliukhov et al. 10.1016/j.atmosres.2024.107398
356. Impact of Urban built-up volume on Urban environment: A Case of Jakarta T. Sarker et al. 10.1016/j.scs.2024.105346
357. Handling Missing Data in Large-Scale MODIS AOD Products Using a Two-Step Model Y. Chi et al. 10.3390/rs12223786
358. Effects of increasing spatial resolution on the spatial information content and accuracy of downward surface shortwave radiation Q. Lang et al. 10.1016/j.jag.2024.104128
359. An improved dark target method for aerosol optical depth retrieval over China from Himawari-8 L. Gao et al. 10.1016/j.atmosres.2020.105399
360. A Regionally Robust High-Spatial-Resolution Aerosol Retrieval Algorithm for MODIS Images Over Eastern China J. Wei et al. 10.1109/TGRS.2019.2892813
361. Deriving PM2.5 from satellite observations with spatiotemporally weighted tree-based algorithms: enhancing modeling accuracy and interpretability T. Li et al. 10.1038/s41612-024-00692-4
362. A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data H. Bagheri 10.1016/j.asr.2022.02.032
363. A study of the impact of spatial resolution on the estimation of particle matter concentration from the aerosol optical depth retrieved from satellite observations L. Mei et al. 10.1080/01431161.2019.1601279
364. Deep learning algorithms for prediction of PM10 dynamics in urban and rural areas of Korea H. Choi et al. 10.1007/s12145-022-00771-1
365. Extended aerosol and surface characterization from S5P/TROPOMI with GRASP algorithm. Part II: Global validation and Intercomparison C. Chen et al. 10.1016/j.rse.2024.114374
366. Impact of acute exposure to ambient PM2.5 on non-trauma all-cause mortality in the megacity Delhi P. Joshi et al. 10.1016/j.atmosenv.2021.118548
367. Where and why do conifer forests persist in refugia through multiple fire events? W. Downing et al. 10.1111/gcb.15655
368. Characterization of a seasonally snow-covered evergreen forest ecosystem Q. Zhang 10.1016/j.jag.2021.102464
369. Evaluating impacts of snow, surface water, soil and vegetation on empirical vegetation and snow indices for the Utqiaġvik tundra ecosystem in Alaska with the LVS3 model Q. Zhang et al. 10.1016/j.rse.2020.111677
370. The extreme forest fires in California/Oregon in 2020: Aerosol optical and physical properties and comparisons of aged versus fresh smoke T. Eck et al. 10.1016/j.atmosenv.2023.119798
371. Solar angle matters: Diurnal pattern of solar-induced chlorophyll fluorescence from OCO-3 and TROPOMI Z. Zhang & Y. Zhang 10.1016/j.rse.2022.113380
372. Temporal and Spatial Autocorrelation as Determinants of Regional AOD-PM2.5 Model Performance in the Middle East K. Chau et al. 10.3390/rs13183790
373. Validation, Stability, and Consistency of MODIS Collection 6.1 and VIIRS Version 1 Deep Blue Aerosol Data Over Land A. Sayer et al. 10.1029/2018JD029598
374. Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product A. Lipponen et al. 10.5194/amt-15-895-2022
375. Estimation of high-resolution PM2.5 concentrations based on gap-filling aerosol optical depth using gradient boosting model M. Han et al. 10.1007/s11869-021-01149-w
376. Spatial Variation and Relation of Aerosol Optical Depth with LULC and Spectral Indices V. Sharma et al. 10.3390/atmos13121992
377. Advanced algorithms on monitoring diurnal variations in dust aerosol properties using geostationary satellite imagery J. Li et al. 10.1016/j.rse.2024.113996
378. The impact of different aerosol layering conditions on the high-resolution MODIS/MAIAC AOD retrieval bias: The uncertainty analysis I. Rogozovsky et al. 10.1016/j.atmosenv.2023.119930
379. Estimation of Aerosol Optical Depth at 30 m Resolution Using Landsat Imagery and Machine Learning T. Liang et al. 10.3390/rs14051053
380. The new MISR research aerosol retrieval algorithm: a multi-angle, multi-spectral, bounded-variable least squares retrieval of aerosol particle properties over both land and water J. Limbacher et al. 10.5194/amt-15-6865-2022
381. Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model M. Stafoggia et al. 10.1016/j.envint.2019.01.016
382. Vegetation Angular Signatures of Equatorial Forests From DSCOVR EPIC and Terra MISR Observations X. Ni et al. 10.3389/frsen.2021.766805
383. Assessment of urban aerosol pollution over the Moscow megacity by the MAIAC aerosol product E. Zhdanova et al. 10.5194/amt-13-877-2020
384. Bayesian Aerosol Retrieval-Based PM2.5 Estimation through Hierarchical Gaussian Process Models J. Zhang et al. 10.3390/math10162878
385. An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors J. Gu et al. 10.3390/rs14071617
386. An alternative cloud index for estimating downwelling surface solar irradiance from various satellite imagers in the framework of a Heliosat-V method B. Tournadre et al. 10.5194/amt-15-3683-2022
387. Simplified and Fast Atmospheric Radiative Transfer model for satellite-based aerosol optical depth retrieval X. Yan et al. 10.1016/j.atmosenv.2020.117362
388. Spatiotemporal Weighted for Improving the Satellite-Based High-Resolution Ground PM2.5 Estimation Using the Light Gradient Boosting Machine X. Yu et al. 10.3390/rs15164104
389. Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–2020 Australian bushfires D. Robbins et al. 10.5194/amt-17-3279-2024
390. A Spatial Neighborhood Deep Neural Network Model for PM2.5 Estimation Across China D. Chen et al. 10.1109/TGRS.2023.3317905
391. Deep Ensemble Machine Learning Framework for the Estimation of PM2.5 Concentrations W. Yu et al. 10.1289/EHP9752
392. An Efficient and Accurate Model Coupled With Spatiotemporal Kalman Filter and Linear Mixed Effect for Hourly PM2.5 Mapping N. Liu et al. 10.1109/TGRS.2023.3324393
393. On the added value of satellite AOD for the investigation of ground-level PM2.5 variability J. Handschuh et al. 10.1016/j.atmosenv.2024.120601
394. Monitoring multiple satellite aerosol optical depth (AOD) products within the Copernicus Atmosphere Monitoring Service (CAMS) data assimilation system S. Garrigues et al. 10.5194/acp-22-14657-2022
395. Development of the Ames Global Hyperspectral Synthetic Data Set: Surface Bidirectional Reflectance Distribution Function W. Wang et al. 10.1029/2022JG007363
396. Vegetation net primary productivity in urban areas of China responded positively to the COVID-19 lockdown in spring 2020 Y. Li et al. 10.1016/j.scitotenv.2024.169998
397. Strong Local Evaporative Cooling Over Land Due to Atmospheric Aerosols T. Chakraborty et al. 10.1029/2021MS002491
398. Global evaluation of Fengyun-3 MERSI dark target aerosol retrievals over land L. Yang et al. 10.1080/17538947.2024.2344580
399. Spatial-Temporal Dust Fusion Model for Integration of MODIS and WRF-Chem M. Rezvani et al. 10.3103/S1068373921110078
400. Need and vision for global medium-resolution Landsat and Sentinel-2 data products V. Radeloff et al. 10.1016/j.rse.2023.113918
401. Retrieving aerosols single scattering albedo from MODIS reflectances Q. Wang et al. 10.1016/j.atmosres.2022.106381
402. Integrating Fixed Monitoring Systems with Low-Cost Sensors to Create High-Resolution Air Quality Maps for the Northern China Plain Region C. Chao et al. 10.1021/acsearthspacechem.1c00174
403. Window-Based Filtering Aerosol Retrieval Algorithm of Fine-Scale Remote Sensing Images: A Case Using Sentinel-2 Data in Beijing Region J. Zhou et al. 10.3390/rs15082172
404. 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
405. MODIS high-resolution MAIAC aerosol product: Global validation and analysis W. Qin et al. 10.1016/j.atmosenv.2021.118684
406. AnisoVeg: anisotropy and nadir-normalized MODIS multi-angle implementation atmospheric correction (MAIAC) datasets for satellite vegetation studies in South America R. Dalagnol et al. 10.5194/essd-15-345-2023
407. PM2.5 concentration estimation with 1-km resolution at high coverage over urban agglomerations in China using the BPNN-KED approach and potential application Y. Huang et al. 10.1016/j.atmosres.2021.105628
408. Air quality simulation with WRF-Chem over southeastern Brazil, part I: Model description and evaluation using ground-based and satellite data N. Benavente et al. 10.1016/j.uclim.2023.101703
409. Long-Range Transport of Aerosols and Regional Sources Using MODIS and NASA MERRA Reanalysis Over South Asia B. Sanatan et al. 10.1007/s41748-024-00478-x
410. Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification Q. Pu & E. Yoo 10.1016/j.envpol.2021.116574
411. Global modeling diurnal gross primary production from OCO-3 solar-induced chlorophyll fluorescence Z. Zhang et al. 10.1016/j.rse.2022.113383
412. Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China N. Luo et al. 10.3390/atmos15050564
413. Investigation of an Intense Dust Outbreak in the Mediterranean Using XMed-Dry Network, Multiplatform Observations, and Numerical Modeling U. Rizza et al. 10.3390/app11041566
414. Advancing Exposure Assessment of PM2.5 Using Satellite Remote Sensing: A Review H. Lee 10.5572/ajae.2020.14.4.319
415. Uncertainty of spatial averages and totals of natural resource maps A. Wadoux & G. Heuvelink 10.1111/2041-210X.14106
416. Performance of DSCOVR/EPIC diurnal aerosol products over China: Ground validation and intercomparison L. Gui et al. 10.1016/j.atmosres.2024.107268
417. A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD F. Yao & P. Palmer 10.1016/j.atmosenv.2021.118217
418. Constraining Aerosol Phase Function Using Dual‐View Geostationary Satellites Q. Bian et al. 10.1029/2021JD035209
419. Evaluation of minerals being deposited in the Red Sea using gravimetric, size distribution, and mineralogical analysis of dust deposition samples collected along the Red Sea coastal plain I. Shevchenko et al. 10.1016/j.aeolia.2021.100717
420. The impact of PM2.5 on children’s blood pressure growth curves: A prospective cohort study X. Liang et al. 10.1016/j.envint.2021.107012
421. 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
422. Spatial-Temporal Variation of AOD Based on MAIAC AOD in East Asia from 2011 to 2020 P. Wang et al. 10.3390/atmos13121983
423. The (mis)identification of high-latitude dust events using remote sensing methods in the Yukon, Canada: a sub-daily variability analysis R. Huck et al. 10.5194/acp-23-6299-2023
424. Remote sensing of large reservoir in the drought years: Implications on surface water change and turbidity variability of Sobradinho reservoir (Northeast Brazil) V. Martins et al. 10.1016/j.rsase.2018.11.006
425. Spatio-temporal modelling of PM10 daily concentrations in Italy using the SPDE approach G. Fioravanti et al. 10.1016/j.atmosenv.2021.118192
426. Aerosol pattern changes over the dead sea from west to east - Using high-resolution satellite data S. Lee et al. 10.1016/j.atmosenv.2020.117737
427. Constrained Retrievals of Aerosol Optical Properties Using Combined Lidar and Imager Measurements During the FIREX-AQ Campaign N. Midzak et al. 10.3389/frsen.2022.818605
428. Assessing spatiotemporal variations of AOD in Japan based on Himawari-8 L3 V31 aerosol products: Validations and applications Y. Tan et al. 10.1016/j.apr.2022.101439
429. A Simple and Effective Random Forest Refit to Map the Spatial Distribution of NO2 Concentrations Y. Chi & Y. Zhan 10.3390/atmos13111832
430. Retrospective assessment of pregnancy exposure to particulate matter from desert dust on a Caribbean island: could satellite-based aerosol optical thickness be used as an alternative to ground PM10 concentration? S. Tuffier et al. 10.1007/s11356-020-12204-x
431. Assessing the Nonlinear Relationship between Land Cover Change and PM10 Concentration Change in China X. Xu et al. 10.3390/land13060766
432. Regional monitoring of forests using the Vega-Les system: case study for Tungussko-Chunskoye forest management unit and Tunguska reserve in the Russian Krasnoyarsk region A. Kashnitskii et al. 10.1051/e3sconf/202022301003
433. Establishment of aerosol optical depth dataset in the Sichuan Basin by the random forest approach M. Jiang et al. 10.1016/j.apr.2022.101394
434. A dark target Kalman filter algorithm for aerosol property retrievals in urban environment using multispectral images G. Vivone et al. 10.1016/j.uclim.2022.101135
435. Longitudinal associations between ambient PM2.5 exposure and lipid levels in two Indian cities K. Anand et al. 10.1097/EE9.0000000000000295
436. Prediction of PM2.5 concentrations at unsampled points using multiscale geographically and temporally weighted regression N. Liu et al. 10.1016/j.envpol.2021.117116
437. Spatiotemporal dynamics and exposure analysis of daily PM2.5 using a remote sensing-based machine learning model and multi-time meteorological parameters B. Chen et al. 10.1016/j.apr.2020.10.005
438. Sensitivity of Estimated Total Canopy SIF Emission to Remotely Sensed LAI and BRDF Products Z. Zhang et al. 10.34133/2021/9795837
439. Biomass burning CO, PM and fuel consumption per unit burned area estimates derived across Africa using geostationary SEVIRI fire radiative power and Sentinel-5P CO data H. Nguyen et al. 10.5194/acp-23-2089-2023
440. 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
441. The Spectral Nature of Earth’s Reflected Radiation: Measurement and Science Applications G. Stephens et al. 10.3389/frsen.2021.664291
442. Aerosol Optical Depth Retrieval Over South Asia Using FY-4A/AGRI Data Y. Xie et al. 10.1109/TGRS.2021.3124421
443. Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh A. Gupta et al. 10.1007/s42452-020-03829-1
444. GOCI-II geostationary satellite hourly aerosol optical depth obtained by data-driven methods: Validation and comparison Y. Fan et al. 10.1016/j.atmosenv.2023.119965
445. 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
446. Full Coverage Estimation of the PM Concentration Across China Based on an Adaptive Spatiotemporal Approach C. Lei et al. 10.1109/TGRS.2022.3213797
447. High-Resolution Satellite-Based PM2.5 Concentration Data Acquired During the COVID-19 Outbreak Throughout China: Model, Variations, and Reasons H. Guo et al. 10.1109/JSTARS.2021.3119383
448. Spatiotemporally continuous PM2.5 dataset in the Mekong River Basin from 2015 to 2022 using a stacking model D. Chen et al. 10.1016/j.scitotenv.2023.169801
449. Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia A. Mhawish et al. 10.1016/j.rse.2019.01.033
450. Connecting Crop Productivity, Residue Fires, and Air Quality over Northern India H. Jethva et al. 10.1038/s41598-019-52799-x
451. A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model C. Jung et al. 10.3390/rs13183657
452. Data Integration for ML-CNPM₂.₅: A Public Sample Dataset Based on Machine Learning Models and Remote Sensing Technology Applied for Estimating Ground-Level PM₂.₅ in China Y. Fan et al. 10.1109/TGRS.2024.3436006
453. Impact of aerosol layering, complex aerosol mixing, and cloud coverage on high-resolution MAIAC aerosol optical depth measurements: Fusion of lidar, AERONET, satellite, and ground-based measurements I. Rogozovsky et al. 10.1016/j.atmosenv.2020.118163
454. First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia Y. Cho et al. 10.5194/amt-17-4369-2024
455. Meteorological and anthropogenic contributions to changes in the Aerosol Optical Depth (AOD) over China during the last decade G. de Leeuw et al. 10.1016/j.atmosenv.2023.119676
456. A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets Y. Shi et al. 10.3390/ijerph19010321
457. Direct estimates of biomass burning NO<sub><i>x</i></sub> emissions and lifetimes using daily observations from TROPOMI X. Jin et al. 10.5194/acp-21-15569-2021
458. Inversion of Aerosol Optical Depth: Incorporating Multimodel Approach X. Sun et al. 10.1109/TGRS.2024.3397315
459. Spatiotemporal estimates of daily PM2.5 concentrations based on 1-km resolution MAIAC AOD in the Beijing–Tianjin–Hebei, China X. Yang et al. 10.1016/j.envc.2022.100548
460. Which model to choose? Performance comparison of statistical and machine learning models in predicting PM2.5 from high-resolution satellite aerosol optical depth P. Kulkarni et al. 10.1016/j.atmosenv.2022.119164
461. Assessing Vertical Allocation of Wildfire Smoke Emissions Using Observational Constraints From Airborne Lidar in the Western U.S. X. Ye et al. 10.1029/2022JD036808
462. Spatiotemporal modeling of PM10 via committee method with in-situ and large scale information: Coupling of machine learning and statistical methods Y. Mohammadi et al. 10.1016/j.uclim.2023.101494
463. Automated Low-Cost LED-Based Sun Photometer for City Scale Distributed Measurements C. Garrido et al. 10.3390/rs13224585
464. Spatial heterogeneity and driving factors of aerosol in Western China: Analysis on multiangle implementation of atmospheric correction–aerosol optical depth in Xinjiang over 2001–2019 W. Ma et al. 10.1002/joc.7958
465. 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
466. An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM2.5 concentrations across China B. Chen et al. 10.1016/j.scitotenv.2020.144724
467. Evaluation of MERRA-2 and CAMS reanalysis for black carbon aerosol in China W. Li et al. 10.1016/j.envpol.2023.123182
468. Research on PM_2.5 Concentration Estimation Based on MAIAC AOD Spatiotemporal Supplement Data 英. 熊 10.12677/pm.2024.146263
469. Exploring the Use of PlanetScope Data for Particulate Matter Air Quality Research J. le Roux et al. 10.3390/rs13152981
470. Improving the accuracy of AOD by using multi-sensors data over the Red Sea and the Persian Gulf M. Pashayi et al. 10.1016/j.apr.2023.101948
471. Estimation of Net Surface Shortwave Radiation From Remotely Sensed Data Under Dust Aerosol Conditions Y. Sun & B. Tang 10.1109/ACCESS.2021.3069791
472. Estimating Daily PM2.5 and PM10 over Italy Using an Ensemble Model A. Shtein et al. 10.1021/acs.est.9b04279
473. Improving discrimination between clouds and optically thick aerosol plumes in geostationary satellite data D. Robbins et al. 10.5194/amt-15-3031-2022
474. Analysis of Surface Water Trends for the Conterminous United States Using MODIS Satellite Data, 2003–2019 R. Petrakis et al. 10.1029/2021WR031399
475. Sensitivity analysis of Look-up table for satellite-based aerosol optical depth retrieval S. Amini et al. 10.1016/j.jaerosci.2021.105842
476. Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke L. Li et al. 10.1016/j.envint.2020.106143
477. A Comparison of Multi-Angle Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance Products From MODIS A. Lyapustin et al. 10.3389/frsen.2021.712093
478. Satellite-based ground PM2.5 estimation using a gradient boosting decision tree T. Zhang et al. 10.1016/j.chemosphere.2020.128801
479. Developing an Advanced PM2.5 Exposure Model in Lima, Peru B. Vu et al. 10.3390/rs11060641
480. Observational evidence of elevated smoke layers during crop residue burning season over Delhi: Potential implications on associated heterogeneous PM2.5 enhancements A. Mhawish et al. 10.1016/j.rse.2022.113167
481. Predicting tropospheric nitrogen dioxide column density in South African municipalities using socio-environmental variables and Multiscale Geographically Weighted Regression S. Hlatshwayo et al. 10.1371/journal.pone.0308484
482. Aerosol spatiotemporal dynamics, source analysis and influence mechanisms over typical drylands Y. Zhang et al. 10.1016/j.gsf.2024.101958
483. Self-adaptive bandwidth eigenvector spatial filtering model for estimating PM2.5 concentrations in the Yangtze River Delta region of China H. Tan et al. 10.1007/s11356-021-15196-4
484. Global and Regional Variations and Main Drivers of Aerosol Loadings over Land during 1980–2018 J. Sun et al. 10.3390/rs14040859
485. 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
486. An Evaluation of Two Decades of Aerosol Optical Depth Retrievals from MODIS over Australia M. Shaylor et al. 10.3390/rs14112664
487. MAIAC AOD profiling over the Persian Gulf: A seasonal-independent machine learning approach M. Pashayi et al. 10.1016/j.apr.2024.102128
488. PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China N. Chen et al. 10.3390/ijgi10010031
489. A fast and accurate radiative transfer model for aerosol remote sensing L. Mei et al. 10.1016/j.jqsrt.2020.107270
490. Predicting ambient PM2.5 concentrations in Ulaanbaatar, Mongolia with machine learning approaches T. Enebish et al. 10.1038/s41370-020-0257-8
491. Variation of Aerosol Optical Depth Measured by Sun Photometer at a Rural Site near Beijing during the 2017–2019 Period X. Wu et al. 10.3390/rs14122908
492. Spatial Particulate Fields during High Winds in the Imperial Valley, California F. Freedman et al. 10.3390/atmos11010088
493. Aerosol optical depth retrieval using scaled digital number (DN) values of multi-spectral satellite and a generating adversarial model based on deep learning application Y. Fan et al. 10.1080/01431161.2024.2398821
494. 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. 10.3390/rs14010085
495. The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images F. Bao et al. 10.1016/j.rse.2022.113426
496. High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model Z. Wang et al. 10.1016/j.jclepro.2021.126493
497. Smoke‐Driven Changes in Photosynthetically Active Radiation During the U.S. Agricultural Growing Season K. Corwin et al. 10.1029/2022JD037446
498. Aerosols characteristics, sources, and drive factors analysis in typical megacities, NW China Z. Zhang et al. 10.1016/j.jclepro.2023.136879
499. Systematic Evaluation of Four Satellite AOD Datasets for Estimating PM2.5 Using a Random Forest Approach J. Handschuh et al. 10.3390/rs15082064
500. The spatiotemporal relationship between PM<sub>2.5</sub> and aerosol optical depth in China: influencing factors and implications for satellite PM<sub>2.5</sub> estimations using MAIAC aerosol optical depth Q. He et al. 10.5194/acp-21-18375-2021
501. A Spatiotemporal Interpolation Graph Convolutional Network for Estimating PM₂.₅ Concentrations Based on Urban Functional Zones X. Chen et al. 10.1109/TGRS.2022.3231968
502. Trends and classification of aerosol observed from MODIS sensor over Northern Europe and the Arctic K. Han et al. 10.1016/j.apr.2024.102329
503. Long term observations of biomass burning aerosol over Warsaw by means of multiwavelength lidar L. Janicka et al. 10.1364/OE.496794
504. New global aerosol fine-mode fraction data over land derived from MODIS satellite retrievals X. Yan et al. 10.1016/j.envpol.2021.116707
505. Enhancing the reliability of hindcast modeling for air pollution using history-informed machine learning and satellite remote sensing in China Q. He et al. 10.1016/j.atmosenv.2023.119994
506. Application of low-cost fine particulate mass monitors to convert satellite aerosol optical depth to surface concentrations in North America and Africa C. Malings et al. 10.5194/amt-13-3873-2020
507. 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. 10.1109/TGRS.2019.2944949
508. Spatiotemporal Variations of Aerosol Optical Depth and the Spatial Heterogeneity Relationship of Potential Factors Based on the Multi-Scale Geographically Weighted Regression Model in Chinese National-Level Urban Agglomerations J. Yuan et al. 10.3390/rs15184613
509. Introducing the VIIRS-based Fire Emission Inventory version 0 (VFEIv0) G. Ferrada et al. 10.5194/gmd-15-8085-2022
510. A generalized land surface reflectance reconstruction method for aerosol retrieval: Application to the Particulate Observing Scanning Polarimeter (POSP) onboard GaoFen-5 (02) satellite Z. Shi et al. 10.1016/j.rse.2023.113683
511. Impact of COVID-19 lockdown upon the air quality and surface urban heat island intensity over the United Arab Emirates A. Alqasemi et al. 10.1016/j.scitotenv.2020.144330
512. An Analysis of Factors Influencing the Relationship between Satellite-Derived AOD and Ground-Level PM10 R. Stirnberg et al. 10.3390/rs10091353
513. Simulating Multi-Directional Narrowband Reflectance of the Earth’s Surface Using ADAM (A Surface Reflectance Database for ESA’s Earth Observation Missions) C. Bacour et al. 10.3390/rs12101679
514. Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. 10.1080/10643389.2019.1665944
515. MODIS-based smoke detection shows that daily smoke cover dampens fire severity in initial burns but not reburns in complex terrain L. Harris & A. Taylor 10.1071/WF22061
516. Wildfire Smoke Cools Summer River and Stream Water Temperatures A. David et al. 10.1029/2018WR022964
517. Constraining chemical transport PM<sub>2.5</sub> modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley M. Friberg et al. 10.5194/acp-18-12891-2018
518. Global validation of columnar water vapor derived from EOS MODIS-MAIAC algorithm against the ground-based AERONET observations V. Martins et al. 10.1016/j.atmosres.2019.04.005