{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:33:49Z","timestamp":1775745229607,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea National University of Transportation Industry-Academy Cooperation Foundation","award":["2023"],"award-info":[{"award-number":["2023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Harmful algal blooms (HABs) threaten lake ecosystems and public health. Early HAB detection is possible by monitoring chlorophyll-a (Chl-a) concentration. Ground-based Chl-a data have limited spatial and temporal coverage but can be geo-registered with temporally coincident satellite imagery to calibrate a remote sensing-based predictive model for regional mapping over time. When matching ground and satellite data, positional and temporal discrepancies are unavoidable due particularly to dynamic lake surfaces, thereby biasing the model calibration. This limitation has long been recognized but so far has not been addressed explicitly. To mitigate such effects of data mismatching, we proposed an Akaike Information Criterion (AIC)-like weighted regression algorithm that relies on an error-based heuristic to automatically favor \u201cgood\u201d data points and downplay \u201cbad\u201d points. We evaluated the algorithm for estimating Chl-a over inland lakes in Ohio using Harmonized Landsat Sentinel-2. The AIC-like weighted regression estimates showed superior performance with an R2 of 0.91 and an error variance (\u03c3E2) of 0.29 \u03bcg\/L, outperforming linear regression (R2 = 0.34, \u03c3E2 = 2.34 \u03bcg\/L) and random forest (R2 = 0.82, \u03c3E2 = 0.92 \u03bcg\/L). We also noticed the poorest performance occurred in the spring due to low reflectance variation in clear water and low Chl-a concentration. Our weighted regression scheme is adaptive and generically applicable. Future studies may adopt our scheme to tackle other remote sensing estimation problems (e.g., terrestrial applications) for alleviating the adverse effects of geolocation errors and temporal discrepancies.<\/jats:p>","DOI":"10.3390\/rs16152761","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T09:50:05Z","timestamp":1722246605000},"page":"2761","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Remote Sensing of Chlorophyll-a and Water Quality over Inland Lakes: How to Alleviate Geo-Location Error and Temporal Discrepancy in Model Training"],"prefix":"10.3390","volume":"16","author":[{"given":"Jongmin","family":"Park","sequence":"first","affiliation":[{"name":"Department of Environmental Engineering, Korea National University of Transportation, Chungju 27469, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3875-4054","authenticated-orcid":false,"given":"Sami","family":"Khanal","sequence":"additional","affiliation":[{"name":"Department of Food, Agricultural, and Biological Engineering, The Ohio State University, Columbus, OH 43210, USA"}]},{"given":"Kaiguang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7645-2917","authenticated-orcid":false,"given":"Kyuhyun","family":"Byun","sequence":"additional","affiliation":[{"name":"Department of Environmental Engineering, Incheon National University, Incheon 22012, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101590","DOI":"10.1016\/j.hal.2019.03.008","article-title":"Harmful algal blooms: A climate change co-stressor in marine and freshwater ecosystems","volume":"91","author":"Griffith","year":"2020","journal-title":"Harmful Algae"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1038\/s43247-021-00178-8","article-title":"Perceived global increase in algal blooms is attributable to intensified monitoring and emerging bloom impacts","volume":"2","author":"Hallegraeff","year":"2021","journal-title":"Commun. Earth Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107481","DOI":"10.1016\/j.ecolecon.2022.107481","article-title":"Property values and cyanobacterial algal blooms: Evidence from satellite monitoring of Inland Lakes","volume":"199","author":"Zhang","year":"2022","journal-title":"Ecol. Econ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"118056","DOI":"10.1016\/j.envpol.2021.118056","article-title":"Cyanobacterial community succession and associated cyanotoxin production in hypereutrophic and eutrophic freshwaters","volume":"290","author":"Tanvir","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1021\/acs.chemrestox.0c00164","article-title":"Microcystins: Biogenesis, toxicity, analysis, and control","volume":"33","author":"Schreidah","year":"2020","journal-title":"Chem. Res. Toxicol."},{"key":"ref_6","first-page":"151","article-title":"Associations between chlorophyll a and various microcystin health advisory concentrations","volume":"5","author":"Hollister","year":"2016","journal-title":"F1000Research"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"115403","DOI":"10.1016\/j.watres.2019.115403","article-title":"Space-time chlorophyll-a retrieval in optically complex waters that accounts for remote sensing and modeling uncertainties and improves remote estimation accuracy","volume":"171","author":"He","year":"2020","journal-title":"Water Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ma, A.Q., Yan, X., and Wang, Y.X. (2022, January 22\u201324). Research on Remote Sensing Retrieval of Chl-a Concentration in the Jiaozhou Bay, Qingdao Based on Semi-analytical\/Semi-empirical Model. Proceedings of the 2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS), Zhoushan, China.","DOI":"10.1109\/ICGMRS55602.2022.9849236"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1002\/eap.1708","article-title":"Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring","volume":"28","author":"Boucher","year":"2018","journal-title":"Ecol. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3918","DOI":"10.1080\/01431161.2021.1875149","article-title":"Tracking historical chlorophyll-a change in the guanting reservoir, Northern China, based on landsat series inter-sensor normalization","volume":"42","author":"Zhang","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mamun, M., Ferdous, J., and An, K.G. (2021). Empirical estimation of nutrient, organic matter and algal chlorophyll in a drinking water reservoir using Landsat 5 TM data. Remote Sen., 13.","DOI":"10.3390\/rs13122256"},{"key":"ref_12","first-page":"100614","article-title":"Space-time monitoring of water quality in an eutrophic reservoir using Sentinel-2 data-A case study of San Roque, Argentina","volume":"24","author":"Shimoni","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_13","first-page":"103223","article-title":"Chlorophyll dynamics from Sentinel-3 using an optimized algorithm for enhanced ecological monitoring in complex urban estuarine waters","volume":"118","author":"Sherman","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tran, M.D., Vantrepotte, V., Loisel, H., Oliveira, E.N., Tran, K.T., Jorge, D., M\u00e9riaux, X., and Paranhos, R. (2023). Band Ratios Combination for Estimating Chlorophyll-a from Sentinel-2 and Sentinel-3 in Coastal Waters. Remote Sens., 15.","DOI":"10.3390\/rs15061653"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Joshi, N., Park, J., Zhao, K., Londo, A., and Khanal, S. (2024). Monitoring Harmful Algal Blooms and Water Quality Using Sentinel-3 OLCI Satellite Imagery with Machine Learning. Remote Sens., 16.","DOI":"10.3390\/rs16132444"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1016\/j.jglr.2021.05.005","article-title":"Empirical modeling of chlorophyll a from MODIS satellite imagery for trophic status monitoring of Lake Victoria in east Africa","volume":"47","author":"Gidudu","year":"2021","journal-title":"J. Gt. Lakes Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7381","DOI":"10.1080\/01431161.2021.1957513","article-title":"A machine learning approach for spatiotemporal imputation of MODIS chlorophyll-a","volume":"42","author":"Mohebzadeh","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102119","DOI":"10.1016\/j.ocemod.2022.102119","article-title":"Chlorophyll-a in Chesapeake Bay based on VIIRS satellite data: Spatiotemporal variability and prediction with machine learning","volume":"180","author":"Yu","year":"2022","journal-title":"Ocean. Model."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4211417","DOI":"10.1109\/TGRS.2022.3220529","article-title":"Evaluating and Optimizing VIIRS Retrievals of Chlorophyll-a and Suspended Particulate Matter in Turbid Lakes Using a Machine Learning Approach","volume":"60","author":"Cao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1677","DOI":"10.1016\/j.csr.2007.02.002","article-title":"Validation of SeaWiFS chlorophyll-a in Massachusetts Bay","volume":"27","author":"Hyde","year":"2007","journal-title":"Cont. Shelf Res"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2612","DOI":"10.1109\/TGRS.2011.2104966","article-title":"Resolving the subscale spatial variability of apparent and inherent optical properties in ocean color match-up sites","volume":"49","author":"Salama","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"34","article-title":"Using a spatial synoptic classification to analyze the weather-type dring the main soybean development period in northwest Ohio, 1999\u20132013","volume":"57","author":"Carmello","year":"2019","journal-title":"Pa. Geogr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.hal.2017.06.001","article-title":"A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing","volume":"67","author":"Urquhart","year":"2017","journal-title":"Harmful Algae"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S1364-8152(00)00089-X","article-title":"Modelling long-term C dynamics in croplands in the context of climate change: A case study from Ohio","volume":"16","author":"Evrendilek","year":"2001","journal-title":"Environ. Model. Softw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.hal.2017.04.013","article-title":"Ten-year survey of cyanobacterial blooms in Ohio\u2019s waterbodies using satellite remote sensing","volume":"66","author":"Gorham","year":"2017","journal-title":"Harmful Algae"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.ecolind.2017.04.046","article-title":"Satellite monitoring of cyanobacterial harmful algal bloom frequency in recreational waters and drinking water sources","volume":"80","author":"Clark","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.hal.2013.12.008","article-title":"Taxonomic assessment of a toxic cyanobacteria shift in hypereutrophic Grand Lake St. Marys (Ohio, USA)","volume":"33","author":"Steffen","year":"2014","journal-title":"Harmful Algae"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.ecoleng.2017.08.040","article-title":"Solving Lake Erie\u2019s harmful algal blooms by restoring the Great Black Swamp in Ohio","volume":"108","author":"Mitsch","year":"2017","journal-title":"Ecol. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1016\/j.ejrh.2015.06.017","article-title":"Modeling the effects of climate change on water, sediment, and nutrient yields from the Maumee River watershed","volume":"4","author":"Cousino","year":"2015","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_30","unstructured":"Philpott, T. (2024, May 05). The Big-Ag-Fueled Algae Bloom That Won\u2019t Leave Toledo\u2019s Water Supply Alone. Mother Jones, Available online: https:\/\/www.motherjones.com\/food\/2015\/08\/giant-toxic-algae-bloom-haunts-toledo\/#:~:text=The%20citizens%20of%20Toledo%2C%20Ohio,400%2C000%20draws%20its%20tap%20water."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e1373","DOI":"10.1002\/wat2.1373","article-title":"Nutrients, eutrophication and harmful algal blooms along the freshwater to marine continuum","volume":"6","author":"Wurtsbaugh","year":"2019","journal-title":"Wiley Inerdiscip. Rev. Water"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10012","DOI":"10.1029\/2019WR024883","article-title":"AquaSat: A data set to enable remote sensing of water quality for inland waters","volume":"55","author":"Ross","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"25175","DOI":"10.1007\/s11356-018-2612-z","article-title":"Accuracy of data buoys for measurement of cyanobacteria, chlorophyll, and turbidity in a large lake (Lake Erie, North America): Implications for estimation of cyanobacterial bloom parameters from water quality sonde measurements","volume":"25","author":"Chaffin","year":"2018","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_34","unstructured":"Cooperative Institute for Great Lakes Research, and University of Michigan and NOAA Great Lakes Environmental Research Laboratory (2019). Physical, Chemical, and Biological Water Quality Monitoring Data to Support Detection of Harmful Algal Blooms (HABs) in Western Lake Erie, Collected by the Great Lakes Environmental Research Laboratory and the Cooperative Institute for Great Lakes Research Since 2012, NOAA National Centers for Environmental Information. [2015\u20132017]."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2028","DOI":"10.1002\/lno.12185","article-title":"The role of internal nitrogen loading in supporting non-N-fixing harmful cyanobacterial blooms in the water column of a large eutrophic lake","volume":"67","author":"Hoffman","year":"2022","journal-title":"Limnol. Oceanogr."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_37","unstructured":"Claverie, M., Masek, J.G., Ju, J., and Dungan, J.L. (2017). Harmonized Landsat-8 Sentinel-2 (HLS) Product User\u2019s Guide, National Aeronautics and Space Administration (NASA)."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kayastha, P., Dzialowski, A.R., Stoodley, S.H., Wagner, K.L., and Mansaray, A.S. (2022). Effect of time window on satellite and ground-based data for estimating chlorophyll-a in reservoirs. Remote Sens., 14.","DOI":"10.3390\/rs14040846"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, X., Yang, Q., Wang, Y., and Zhang, Y. (2022). Evaluation of GOCI remote sensing reflectance spectral quality based on a quality assurance score system in the Bohai Sea. Remote Sens., 14.","DOI":"10.3390\/rs14051075"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"31415","DOI":"10.1364\/OE.460735","article-title":"Estimating pixel-level uncertainty in ocean color retrievals from MODIS","volume":"30","author":"Zhang","year":"2022","journal-title":"Opt. Express"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"113815","DOI":"10.1016\/j.marpolbul.2022.113815","article-title":"Monitoring multi-temporal and spatial variations of water transparency in the Jiaozhou Bay using GOCI data","volume":"180","author":"Zhou","year":"2022","journal-title":"Mar. Pollut. Bull."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2818","DOI":"10.1080\/01431161.2018.1430912","article-title":"Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager","volume":"39","author":"Keith","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2012.03.006","article-title":"Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity","volume":"123","author":"McCullough","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2005.09.003","article-title":"Characterizing the vertical diffuse attenuation coefficient for downwelling irradiance in coastal waters: Implications for water penetration by high resolution satellite data","volume":"60","author":"Mishra","year":"2005","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1007\/s11222-016-9696-4","article-title":"Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC","volume":"27","author":"Vehtari","year":"2017","journal-title":"STAT Comput."},{"key":"ref_46","first-page":"305","article-title":"A coordinator\u2019s guide to volunteer lake monitoring methods","volume":"96","author":"Carlson","year":"1996","journal-title":"N. Am. Lake Manag. Soc."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s11270-008-9692-1","article-title":"Agricultural impacts on lake and stream water quality in Grand Lake St. Marys, Western Ohio","volume":"193","author":"Hoorman","year":"2008","journal-title":"Water Air Soil Pollut."},{"key":"ref_48","unstructured":"Perry Soil and Water Conservation District (2020). Buckeye Lake HUC-12: Nine Element Nonpoint Source Implementation Strategic Plan (NPS-IS Plan), Perry Soil and Water Conservation District."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6448","DOI":"10.1073\/pnas.1216006110","article-title":"Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions","volume":"110","author":"Michalak","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"10762","DOI":"10.1038\/s41598-017-11167-3","article-title":"Reconciling the opposing effects of warming on phytoplankton biomass in 188 large lakes","volume":"7","author":"Kraemer","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"27A","DOI":"10.2489\/jswc.70.2.27A","article-title":"What Is Causing the Harmful Algal Blooms in Lake Erie?","volume":"70","author":"Smith","year":"2015","journal-title":"J. Soil Water Conserv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"112685","DOI":"10.1016\/j.rse.2021.112685","article-title":"Satellites for Long-Term Monitoring of Inland U.S. Lakes: The MERIS Time Series and Application for Chlorophyll-A","volume":"266","author":"Seegers","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.rse.2019.04.027","article-title":"A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types","volume":"229","author":"Neil","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.isprsjprs.2017.06.004","article-title":"The impacts of environmental variables on water reflectance measured using a lightweight unmanned aerial vehicle (UAV)-based spectrometer system","volume":"130","author":"Zeng","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","unstructured":"Timmons, J.S. (2021). Identifying the Isotopic Signature of Lake Effect Precipitation on Northeast Ohio Isocape. [Master\u2019s Thesis, Kent State University]."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.jglr.2019.03.011","article-title":"Spatial and temporal variability of inherent and apparent optical properties in western Lake Erie: Implications for water quality remote sensing","volume":"45","author":"Sayers","year":"2019","journal-title":"J. Gt. Lakes Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2756","DOI":"10.1139\/f94-276","article-title":"Effects of lake size on nutrient availability in the mixed layer during summer stratification","volume":"52","author":"Fee","year":"1994","journal-title":"Can. J. Fish. Aquat. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2006.01.015","article-title":"A multi-sensor approach for the on-orbit validation of ocean color satellite data products","volume":"102","author":"Bailey","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"146271","DOI":"10.1016\/j.scitotenv.2021.146271","article-title":"Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm","volume":"778","author":"Li","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"6223","DOI":"10.3390\/rs5126223","article-title":"Satellite regional cloud climatology over the Great Lakes","volume":"5","author":"Ackerman","year":"2013","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/15\/2761\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:25:34Z","timestamp":1760109934000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/15\/2761"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,29]]},"references-count":60,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["rs16152761"],"URL":"https:\/\/doi.org\/10.3390\/rs16152761","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,29]]}}}