{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T04:43:52Z","timestamp":1771562632365,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T00:00:00Z","timestamp":1719964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["2019135"],"award-info":[{"award-number":["2019135"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["OAC-2115094"],"award-info":[{"award-number":["OAC-2115094"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["84057001-0"],"award-info":[{"award-number":["84057001-0"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["2019135"],"award-info":[{"award-number":["2019135"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["OAC-2115094"],"award-info":[{"award-number":["OAC-2115094"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["84057001-0"],"award-info":[{"award-number":["84057001-0"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013031","name":"EPA","doi-asserted-by":"publisher","award":["2019135"],"award-info":[{"award-number":["2019135"]}],"id":[{"id":"10.13039\/100013031","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013031","name":"EPA","doi-asserted-by":"publisher","award":["OAC-2115094"],"award-info":[{"award-number":["OAC-2115094"]}],"id":[{"id":"10.13039\/100013031","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013031","name":"EPA","doi-asserted-by":"publisher","award":["84057001-0"],"award-info":[{"award-number":["84057001-0"]}],"id":[{"id":"10.13039\/100013031","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout the densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data were then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM\u00a02.5 levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional datasets can effectively model PM\u00a02.5 concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM\u00a02.5 surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM\u00a02.5 analyses. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM\u00a02.5 to 9 \u03bcg\/m\u00a03, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM\u00a02.5 distribution across the entire United States for our study period, we found that the entire nation encountered PM\u00a02.5 levels that exceeded 9 \u03bcg\/m\u00a03 for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 \u03bcg\/m\u00a03 for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM\u00a02.5 concentrations below 9 \u03bcg\/m\u00a03.<\/jats:p>","DOI":"10.3390\/rs16132454","type":"journal-article","created":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T03:52:58Z","timestamp":1720065178000},"page":"2454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2657-3416","authenticated-orcid":false,"given":"Prabuddha M. H.","family":"Dewage","sequence":"first","affiliation":[{"name":"Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2688-648X","authenticated-orcid":false,"given":"Lakitha O. H.","family":"Wijeratne","sequence":"additional","affiliation":[{"name":"Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"given":"Xiaohe","family":"Yu","sequence":"additional","affiliation":[{"name":"Geospatial Information Science, The University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"given":"Mazhar","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"given":"Gokul","family":"Balagopal","sequence":"additional","affiliation":[{"name":"Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5910-0183","authenticated-orcid":false,"given":"John","family":"Waczak","sequence":"additional","affiliation":[{"name":"Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0667-2345","authenticated-orcid":false,"given":"Ashen","family":"Fernando","sequence":"additional","affiliation":[{"name":"Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"given":"Matthew D.","family":"Lary","sequence":"additional","affiliation":[{"name":"Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"given":"Shisir","family":"Ruwali","sequence":"additional","affiliation":[{"name":"Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4265-9543","authenticated-orcid":false,"given":"David J.","family":"Lary","sequence":"additional","affiliation":[{"name":"Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Boucher, O. (2015). Atmospheric Aerosols: Properties and Climate Impacts, Springer.","DOI":"10.1007\/978-94-017-9649-1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4934","DOI":"10.1016\/j.scitotenv.2011.08.058","article-title":"Coarse particles and mortality in three Chinese cities: The China Air Pollution and Health Effects Study (CAPES)","volume":"409","author":"Chen","year":"2011","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"611","DOI":"10.4081\/gh.2014.292","article-title":"Estimating the global abundance of ground level presence of particulate matter (PM2.5)","volume":"8","author":"Lary","year":"2014","journal-title":"Geospat. Health"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1001\/jama.287.9.1132","article-title":"Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution","volume":"287","author":"Pope","year":"2002","journal-title":"Jama"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1175\/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2","article-title":"Variability of absorption and optical properties of key aerosol types observed in worldwide locations","volume":"59","author":"Dubovik","year":"2002","journal-title":"J. Atmos. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1126\/science.255.5043.423","article-title":"Climate forcing by anthropogenic aerosols","volume":"255","author":"Charlson","year":"1992","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7520","DOI":"10.1002\/anie.200501122","article-title":"Atmospheric aerosols: Composition, transformation, climate and health effects","volume":"44","year":"2005","journal-title":"Angew. Chem. Int. Ed."},{"key":"ref_8","unstructured":"National Research Council (1996). A Plan for a Research Program on Aerosol Radiative Forcing and Climate Change, National Academies Press."},{"key":"ref_9","unstructured":"Chin, M. (2023, March 26). Atmospheric Aerosol Properties and Climate Impacts, Available online: https:\/\/books.google.com\/books?id=IgJZXXgtHmQC."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yu, X., Lary, D.J., Simmons, C.S., and Wijeratne, L.O.H. (2022). High Spatial-Temporal PM2.5 Modeling Utilizing Next Generation Weather Radar (NEXRAD) as a Supplementary Weather Source. Remote Sens., 14.","DOI":"10.3390\/rs14030495"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yu, X., Lary, D.J., and Simmons, C.S. (2021). PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD. Remote Sens., 13.","DOI":"10.3390\/rs13234788"},{"key":"ref_12","unstructured":"Wijeratne, L.O.H. (2021). Coupling Physical Measurement with Machine Learning for Holistic Environmental Sensing. [Ph.D. Thesis, The University of Texas at Dallas]."},{"key":"ref_13","first-page":"41","article-title":"Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies","volume":"1","author":"Lary","year":"2015","journal-title":"Environ. Health Insights"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wijeratne, L.O., Kiv, D.R., Aker, A.R., Talebi, S., and Lary, D.J. (2019). Using Machine Learning for the Calibration of Airborne Particulate Sensors. Sensors, 20.","DOI":"10.3390\/s20010099"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.3155\/1047-3289.59.11.1358","article-title":"The Relation between Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth and PM2.5 over the United States: A Geographical Comparison by U.S. Environmental Protection Agency Regions","volume":"59","author":"Zhang","year":"2009","journal-title":"Air Waste Manag. Assoc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13473","DOI":"10.5194\/acp-17-13473-2017","article-title":"Analysis of influential factors for the relationship between PM2.5 and AOD in Beijing","volume":"17","author":"Zheng","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_17","unstructured":"Harrison, W.A. (2015). In-Situ Observation of Atmospheric Particulates, The University of Texas at Dallas."},{"key":"ref_18","unstructured":"Talebi, S. (2022). Physical Quantification of the Interactions Between Environment, Physiology, and Human Performance. [Ph.D. Thesis, The University of Texas at Dallas]."},{"key":"ref_19","unstructured":"Piera Systems (2022). IPS Series Sensor, Piera Systems Inc."},{"key":"ref_20","unstructured":"United States Environment Protection Agency EPA (2023, March 26). Air Quality System (AQS) API, Available online: https:\/\/aqs.epa.gov\/aqsweb\/documents\/data_api.html."},{"key":"ref_21","unstructured":"(2023). Standard No. Air Quality System (AQS) Data API. Available online: https:\/\/aqs.epa.gov\/aqsweb\/documents\/data_api.html."},{"key":"ref_22","unstructured":"OpenAQ-About (2023, March 26). OpenAQ. n.d. Available online: https:\/\/openaq.org\/about\/."},{"key":"ref_23","first-page":"2345","article-title":"Geostationary operational environmental satellite system\u2014R (GOES-R)","volume":"81","author":"Volz","year":"2000","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1175\/BAMS-D-15-00230.1","article-title":"Introducing the Next-Generation Advanced Baseline Imager on GOES-R","volume":"98","author":"Schmit","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_25","first-page":"1452","article-title":"Early results from GOES-16 and GOES-17 magnetometer and magnetometer inversion algorithm","volume":"17","author":"Mannucci","year":"2019","journal-title":"Space Weather"},{"key":"ref_26","first-page":"631","article-title":"The GOES-R Proving Ground: Accelerating User Readiness for the Next-Generation Geostationary Environmental Satellites","volume":"99","author":"Timothy","year":"2018","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_27","first-page":"1493","article-title":"Geostationary operational environmental satellite R-series: The next generation of geostationary weather satellites","volume":"55","author":"Wooten","year":"2016","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"709","DOI":"10.5194\/amt-11-709-2018","article-title":"Evaluation of a Low-cost Optical Particle Counter (Alphasense OPC-N2) for Ambient Air Monitoring","volume":"11","author":"Crilley","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Di Antonio, A., Popoola, O.A., Ouyang, B., Saffell, J., and Jones, R.L. (2018). Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter. Sensors, 18.","DOI":"10.3390\/s18092790"},{"key":"ref_30","first-page":"22","article-title":"Climate Service Develops User-Friendly Data Store","volume":"151","author":"Raoult","year":"2017","journal-title":"ECMWF Newsl."},{"key":"ref_31","unstructured":"Climate Data Store (CDS) (2023, March 26). ERA5-Land Hourly Data from 1950 to Present, Available online: https:\/\/cds.climate.copernicus.eu\/cdsapp#!\/dataset\/reanalysis-era5-land?tab=overview."},{"key":"ref_32","unstructured":"Bosilovich, M.G., Lucchesi, R., and Suarez, M. (2023, March 26). MERRA-2: File Specification. GMAO Office Note No. 9 (Version 1.1), Available online: http:\/\/gmao.gsfc.nasa.gov\/pubs\/office_notes."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"8217","DOI":"10.5194\/acp-15-8217-2015","article-title":"Particulate matter, air quality and climate: Lessons learned and future needs","volume":"15","author":"Fuzzi","year":"2015","journal-title":"Atmos. Chem. Phys."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2250","DOI":"10.1126\/science.1075159","article-title":"Climate effects of black carbon aerosols in China and India","volume":"297","author":"Menon","year":"2002","journal-title":"Science"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1289\/ehp.8979137","article-title":"Health effects of acid aerosols formed by atmospheric mixtures","volume":"79","author":"Kleinman","year":"1989","journal-title":"Environ. Health Perspect."},{"key":"ref_36","unstructured":"Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.M. (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press. Available online: https:\/\/www.ipcc.ch\/report\/ar5\/wg1\/."},{"key":"ref_37","unstructured":"EPA (2023, March 26). Air Quality Guide for Nitrogen Dioxide, Available online: https:\/\/www.epa.gov\/sites\/production\/files\/2015-08\/documents\/no2_aqg_summary.pdf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5955","DOI":"10.5194\/amt-13-5955-2020","article-title":"Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm","volume":"13","author":"Zhang","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"ref_39","unstructured":"Yu, X. (2021). Cloud Detection and PM2.5 Estimation Using Machine Learning. [Ph.D. Thesis, The University of Texas at Dallas]."},{"key":"ref_40","unstructured":"(2023, March 26). SEDAC GPW-v4 Population Density, Rev11, Available online: https:\/\/sedac.ciesin.columbia.edu\/data\/set\/gpw-v4-population-density-rev11."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.ufug.2017.10.019","article-title":"Air pollution removal by urban forests in Canada and its effect on air quality and human health","volume":"29","author":"Nowak","year":"2018","journal-title":"Urban For. Urban Green."},{"key":"ref_42","unstructured":"NRCS (2023, March 26). Web Soil Survey (WSS), Available online: https:\/\/websoilsurvey.sc.egov.usda.gov\/app\/."},{"key":"ref_43","unstructured":"Multi-Resolution Land Characteristics (MRLC)\u2014National Land Cover Database (NLCD) (2023, March 26). Multi-Resolution Land Characteristics (MRLC), Available online: https:\/\/www.mrlc.gov\/data?f%5B0%5D=year%3A2019."},{"key":"ref_44","unstructured":"National Land Cover Database Class Legend and Description (2023, March 26). Multi-Resolution Land Characteristics Consortium, Available online: https:\/\/www.mrlc.gov\/data\/legends\/national-land-cover-database-class-legend-and-description."},{"key":"ref_45","unstructured":"Gridded Bathymetry Data (2023, March 26). General Bathymetric Chart of the Oceans. n.d. Available online: https:\/\/www.gebco.net\/data_and_products\/gridded_bathymetry_data\/."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"Q12004","DOI":"10.1029\/2012GC004370","article-title":"The new global lithological map database GLiM: A representation of rock properties at the Earth surface","volume":"13","author":"Hartmann","year":"2012","journal-title":"Geochem. Geophys. Geosyst."},{"key":"ref_47","unstructured":"Pedro Camargo (2023, March 26). USBuildingFootprints. Available online: https:\/\/github.com\/microsoft\/USBuildingFootprints."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"180227","DOI":"10.1038\/sdata.2018.227","article-title":"Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010","volume":"5","author":"Gilbert","year":"2018","journal-title":"Sci. Data"},{"key":"ref_49","unstructured":"CIRC Systems (2023, March 26). CIRC Team at UT Dallas. Available online: https:\/\/docs.circ.utdallas.edu\/user-guide\/systems\/index.html."},{"key":"ref_50","unstructured":"(2011). \u201cStampede\u2019s\u201d Comprehensive Capabilities to Bolster U.S. Open Science Computational Resources, Texas Advanced Computing Center. Available online: https:\/\/www.tacc.utexas.edu\/-\/-stampede-s-comprehensive-capabilities-to-bolster-u-s-open-science-computational-resources."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"150338","DOI":"10.1016\/j.scitotenv.2021.150338","article-title":"Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms","volume":"805","author":"Chen","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_52","unstructured":"(2024, February 10). Particulate Matter (PM) Pollution, Available online: https:\/\/www.epa.gov\/pm-pollution\/final-reconsideration-national-ambient-air-quality-standards-particulate-matter-pm?emci=8c4af901-18c2-ee11-b660-002248223197&emdi=06d4332d-11c6-ee11-b660-002248223848&ceid=5660439."},{"key":"ref_53","unstructured":"Gewin, V., and Air Pollution Threatens Millions of Lives (2024, February 08). Now the Sources Are Shifting, Scientific American. Available online: https:\/\/www.scientificamerican.com\/article\/air-pollution-threatens-millions-of-lives-now-the-sources-are-shifting\/."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1016\/j.atmosenv.2020.117501","article-title":"Controlling factors analysis for the Himawari-8 aerosol optical depth accuracy from the standpoint of size distribution, solar zenith angles and scattering angles","volume":"233","author":"Zhang","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_55","first-page":"1","article-title":"Wildfire emissions, detection, and impacts on air quality","volume":"92","author":"Sharma","year":"2016","journal-title":"Environ. Int."},{"key":"ref_56","first-page":"229","article-title":"Wildfires and their impacts on air quality in the western US","volume":"5","author":"Jiang","year":"2019","journal-title":"Curr. Pollut. Rep."},{"key":"ref_57","first-page":"6106","article-title":"Increased heat, drought, and insect outbreaks have contributed to severe wildfires in the western United States","volume":"26","author":"Westrick","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_58","first-page":"160","article-title":"Effects of drought and insect outbreaks on epigaeic beetle communities in western USA deciduous forests","volume":"17","author":"Johnson","year":"2015","journal-title":"Agric. For. Entomol."},{"key":"ref_59","first-page":"1","article-title":"US wildfires, 1984\u20132012: A spatial temporal analysis of trends, drivers, and climatic associations","volume":"107","author":"Weiss","year":"2017","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_60","unstructured":"WHO Air Quality Guidelines (2023, March 26). Howpublished. Available online: https:\/\/www.c40knowledgehub.org\/s\/article\/WHO-Air-Quality-Guidelines?language=en_US."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2454\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:09:55Z","timestamp":1760108995000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2454"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,3]]},"references-count":60,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16132454"],"URL":"https:\/\/doi.org\/10.3390\/rs16132454","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,3]]}}}