{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T22:31:03Z","timestamp":1771885863896,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:00:00Z","timestamp":1694217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Russian Hydrometeorological Service","award":["169-15-2023-002"],"award-info":[{"award-number":["169-15-2023-002"]}]},{"name":"the Russian Hydrometeorological Service","award":["FFUF-2021-0006"],"award-info":[{"award-number":["FFUF-2021-0006"]}]},{"name":"the Russian Hydrometeorological Service","award":["N-468-99_2021\u20132023"],"award-info":[{"award-number":["N-468-99_2021\u20132023"]}]},{"name":"the Ministry of Education and Science of the Russian Federation","award":["169-15-2023-002"],"award-info":[{"award-number":["169-15-2023-002"]}]},{"name":"the Ministry of Education and Science of the Russian Federation","award":["FFUF-2021-0006"],"award-info":[{"award-number":["FFUF-2021-0006"]}]},{"name":"the Ministry of Education and Science of the Russian Federation","award":["N-468-99_2021\u20132023"],"award-info":[{"award-number":["N-468-99_2021\u20132023"]}]},{"name":"the Federal Academic Leadership Program \u201cPriority-2030\u201d of Lobachevsky State University of Nizhny Novgorod","award":["169-15-2023-002"],"award-info":[{"award-number":["169-15-2023-002"]}]},{"name":"the Federal Academic Leadership Program \u201cPriority-2030\u201d of Lobachevsky State University of Nizhny Novgorod","award":["FFUF-2021-0006"],"award-info":[{"award-number":["FFUF-2021-0006"]}]},{"name":"the Federal Academic Leadership Program \u201cPriority-2030\u201d of Lobachevsky State University of Nizhny Novgorod","award":["N-468-99_2021\u20132023"],"award-info":[{"award-number":["N-468-99_2021\u20132023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The development of regional satellite bio-optical models for natural waters with high temporal and spatial variability, such as inland seas, reservoirs, and coastal ocean waters, requires the implementation of an intermediate measuring link in the chain, \u201cwater sampling\u2014bio-optical models\u201d, and this link must have certain intermediate characteristics. The most crucial of them are the high-precision measurements of the main water quality parameters, such as the concentration of chlorophyll a (Chl a), colored dissolved organic matter (CDOM), and total suspended sediments (TSS) in the upper water layer, together with a high operational rate and the ability to cover a large water area in a short time, which corresponds to a satellite overpass. A possible solution is to utilize laser-induced fluorescence (LIF) of water constituents measured by a marine LiDAR in situ with a high sampling rate from a high-speed vessel. This allows obtaining a large ground-truth dataset of the main water quality parameters simultaneously with the satellite overpass within the time interval determined by NASA protocols. This method was successfully applied to the oligotrophic Issyk-Kul Lake in Kyrgyzstan, where we obtained more than 4000 and 1000 matchups for the Chl a and TSS, respectively. New preliminary regional bio-optical models were developed on the basis of a one-day survey and tested for archive Sentinel-2A data for 2022. This approach can be applied for regular monitoring and further correction in accordance with seasonal variability. The obtained results, together with previously published similar studies for eutrophic coastal and productive inland waters, emphasize the applicability of the presented method for the development or adjustment of regional bio-optical models for water bodies of a wide trophic range.<\/jats:p>","DOI":"10.3390\/rs15184443","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T09:09:21Z","timestamp":1694423361000},"page":"4443","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Regional Models for Sentinel-2\/MSI Imagery of Chlorophyll a and TSS, Obtained for Oligotrophic Issyk-Kul Lake Using High-Resolution LIF LiDAR Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-8970","authenticated-orcid":false,"given":"Vadim","family":"Pelevin","sequence":"first","affiliation":[{"name":"Shirshov Institute of Oceanology, 36 Nakhimovsky Prospekt, Moscow 117997, Russia"}]},{"given":"Ekaterina","family":"Koltsova","sequence":"additional","affiliation":[{"name":"Moscow Institute of Physics and Technology, 9 Institutskiy Per., Dolgoprudny 141700, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8550-2418","authenticated-orcid":false,"given":"Aleksandr","family":"Molkov","sequence":"additional","affiliation":[{"name":"Laboratory of Hydrology and Ecology of Inland Waters, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, Nizhny Novgorod 603022, Russia"},{"name":"Institute of Applied Physics of the Russian Academy of Sciences, 46 Ulyanov St., Nizhny Novgorod 603950, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7716-7456","authenticated-orcid":false,"given":"Sergei","family":"Fedorov","sequence":"additional","affiliation":[{"name":"Marine Hydrophysical Institute of the Russian Academy of Sciences, 2 Kapitanskaya St., Sevastopol 299011, Russia"}]},{"given":"Salmor","family":"Alymkulov","sequence":"additional","affiliation":[{"name":"Department of Informatics and Computer Science, Razzakov Kyrgyz State Technical University, Str. Aitmatova 66, Bishkek 720044, Kyrgyzstan"}]},{"given":"Boris","family":"Konovalov","sequence":"additional","affiliation":[{"name":"Shirshov Institute of Oceanology, 36 Nakhimovsky Prospekt, Moscow 117997, Russia"}]},{"given":"Mairam","family":"Alymkulova","sequence":"additional","affiliation":[{"name":"Department of Informatics and Computer Science, Razzakov Kyrgyz State Technical University, Str. Aitmatova 66, Bishkek 720044, Kyrgyzstan"}]},{"given":"Kubanychbek","family":"Jumaliev","sequence":"additional","affiliation":[{"name":"Department of Natural Sciences, International Medical University, Str. Ankara 1\/17, Bishkek 720048, Kyrgyzstan"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, B., and N\u00f5ges, T. (2016). First Experiences in Mapping Lake Water Quality Parameters with Sentinel-2 MSI Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8080640"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Molkov, A.A., Fedorov, S.V., Pelevin, V.V., and Korchemkina, E.N. (2019). Regional Models for High-Resolution Retrieval of Chlorophyll a and TSS Concentrations in the Gorky Reservoir by Sentinel-2 Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11101215"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"D\u00f6rnh\u00f6fer, K., G\u00f6ritz, A., Gege, P., Pflug, B., and Oppelt, N. (2016). Water constituents and water depth retrieval from Sentinel-2a\u2014A first evaluation in an oligotrophic lake. Remote Sens., 8.","DOI":"10.3390\/rs8110941"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111604","DOI":"10.1016\/j.rse.2019.111604","article-title":"Seamless Retrievals of Chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in Inland and Coastal Waters: A Machine-Learning Approach","volume":"240","author":"Pahlevan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_5","first-page":"157","article-title":"Validation of Envisat MERIS Algorithms for Chlorophyll Retrieval in a Large, Turbid and Optically Complex Shallow Lake","volume":"5","author":"Palmer","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1080\/01431161.2016.1274446","article-title":"Ground truth data on Chlorophyll-a, chromophoric dissolved organic constituents and suspended sediment concentrations in the upper water layer as obtained by LIF LiDARat high spatial resolution","volume":"38","author":"Pelevin","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6279","DOI":"10.5194\/hess-22-6279-2018","article-title":"New profiling and mooring records help to assess variability of Lake Issyk-Kul and reveal unknown features of its thermohaline structure","volume":"22","author":"Zavialov","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_8","unstructured":"Zavyalov, P.O., Zhumaliev, K.M., Alymkulov, S.A., Konovalov, B.V., Makkaveev, P.N., Pelevin, V.V., Rimsky-Korsakov, N.A., Izhitsky, A.S., and Izhitskaya, E.S. (2018). Comprehensive Studies of Lake Issyk-Kul: Part 1, Kyrgyz-Russian Slavic University. (In Russian)."},{"key":"ref_9","unstructured":"Romanovsky, V.V. (1991). Lake Issyk-Kul as a Natural Complex, Frunze. (In Russian)."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.ecss.2018.01.008","article-title":"High resolution LiDARmeasurements reveal fine internal structure and variability of sediment-carrying coastal plume","volume":"205","author":"Zavialov","year":"2018","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_11","unstructured":"Zavyalov, P.O., Alymkulov, S.A., Zhumaliev, K.M., Israilova, N.A., Konovalov, B.V., Sapozhnikov, F.V., Makkaveev, P.N., Khan, V.M., Pelevin, V.V., and Izhitsky, A.S. (2020). Comprehensive Studies of Lake Issyk-Kul: Part 2, Kyrgyz-Russian Slavic University. (In Russian)."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pereira-Sandoval, M., Ruescas, A.B., Garc\u00eda-Jimenez, J., Blix, K., Delegido, J., and Moreno, J. (2022). Supervised Classifications of Optical Water Types in Spanish Inland Waters. Remote Sens., 14.","DOI":"10.3390\/rs14215568"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"113498","DOI":"10.1016\/j.rse.2023.113498","article-title":"High-frequency time series comparison of Sentinel-1 and Sentinel-2 satellites for mapping open and vegetated water across the United States (2017\u20132021)","volume":"288","author":"Vanderhoof","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Many, G., Escoffier, N., Ferrari, M., Jacquet, P., Odermatt, D., Mariethoz, G., Perolo, P., and Perga, M.-E. (2022). Long-Term Spatiotemporal Variability of Whitings in Lake Geneva from Multispectral Remote Sensing and Machine Learning. Remote Sens., 14.","DOI":"10.1002\/essoar.10512504.1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111768","DOI":"10.1016\/j.rse.2020.111768","article-title":"Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters","volume":"246","author":"Balasubramanian","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ecolind.2014.01.006","article-title":"The seasonal and spatial variations of phytoplankton community and their correlation with environmental factors in a large eutrophic Chinese lake (Lake Chaohu)","volume":"40","author":"Jiang","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.3390\/toxins7051649","article-title":"Spatial and temporal patterns in the seasonal distribution of toxic cyanobacteria in western Lake Erie from 2002\u20132014","volume":"7","author":"Wynne","year":"2015","journal-title":"Toxins"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hansen, C.H., Burian, S.J., Dennison, P.E., and Williams, G.P. (2017). Spatiotemporal Variability of Lake Water Quality in the Context of Remote Sensing Models. Remote Sens., 9.","DOI":"10.3390\/rs9050409"},{"key":"ref_19","unstructured":"Pelevin, V., Zavialov, P., Konovalov, B., Zlinszky, A., Palmer, S., Toth, V., Goncharenko, I., Khymchenko, L., and Osokina, V. (2015, January 15\u201319). Measurements with high spatial resolution of Chlorophyll-a, CDOM and total suspended constituents in coastal zones and inland water basins by the portable UFL Lidar. Proceedings of the 35th EARSeL Symposium\u2014European Remote Sensing: Progress, Challenges and Opportunities, Stockholm, Sweden."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2767","DOI":"10.1364\/AO.42.002767","article-title":"Validation of Terra-MODIS phytoplankton chlorophyll fluorescence line height. I. Initial airborne LiDARresults","volume":"42","author":"Hoge","year":"2003","journal-title":"Appl. Opt."},{"key":"ref_21","first-page":"301","article-title":"Some Features of Self-Purification of Russian Black Sea Shoaling Waters near River Entries","volume":"4","author":"Aibulatov","year":"2008","journal-title":"Geosci. Ecol."},{"key":"ref_22","first-page":"721","article-title":"Remote estimation of fluorescence marine components distribution","volume":"61","author":"Vasilescu","year":"2009","journal-title":"Rom. Rep. Phys."},{"key":"ref_23","first-page":"1604","article-title":"Remote and local monitoring of dissolved and suspended fluorescent organic matter off the Svalbard","volume":"12","author":"Cisek","year":"2010","journal-title":"J. Optoelectron. Adv. Mater."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1134\/S0001437017010131","article-title":"Spatial variability of concentrations of chlorophyll a, dissolved organic matter and suspended particles in the surface layer of the Kara Sea in September 2011 from LiDAR data","volume":"57","author":"Pelevin","year":"2017","journal-title":"Oceanology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1140\/epjst\/e2007-00118-7","article-title":"The LiDAR investigation of the upper water layer fluorescence spectra of the Baltic Sea","volume":"144","author":"Drozdowska","year":"2007","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"ref_26","first-page":"817","article-title":"Remote sensing of the Southern Ocean: Techniques and results","volume":"3","author":"Barbini","year":"2001","journal-title":"J. Optoelectron. Adv. Mater."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1134\/S0001437010040181","article-title":"A joint Russian-Taiwanese expedition at the shelf of the South China Sea: Searching for manifestations of groundwater discharge to the ocean","volume":"50","author":"Zavialov","year":"2010","journal-title":"Oceanology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1134\/S0001437015060211","article-title":"Estimating the deposition of river-borne suspended matter from the joint analysis of suspension concentration and salinity","volume":"55","author":"Zavialov","year":"2015","journal-title":"Oceanology"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2018.05.032","article-title":"Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar","volume":"215","author":"Collister","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4405","DOI":"10.3390\/rs5094405","article-title":"Ultraviolet fluorescence LiDAR(UFL) as a measurement tool for water quality parameters in turbid lake conditions","volume":"5","author":"Palmer","year":"2013","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.marpolbul.2017.03.057","article-title":"Coastal and inland water monitoring using a portable hyperspectral laser fluorometer","volume":"119","author":"Chen","year":"2017","journal-title":"Mar. Pollut. Bull."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1007\/s00340-019-7215-y","article-title":"Aquatic environment monitoring using a drone-based fluorosensor","volume":"125","author":"Duan","year":"2019","journal-title":"Appl. Phys. B"},{"key":"ref_33","first-page":"8","article-title":"Combination of lidar, MODIS and SEAWIFS sensors for simultaneous chlorophyll monitoring","volume":"3","author":"Fiorani","year":"2004","journal-title":"EARSeL eProc."},{"key":"ref_34","first-page":"89","article-title":"LiDAR calibration of satellite sensed CDOM in the Southern Ocean","volume":"5","author":"Fiorani","year":"2006","journal-title":"EARSeL eProc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s11802-014-2098-3","article-title":"Field experiments of multi-channel oceanographic fluorescence LiDAR for oil spill and chlorophyll-a detection","volume":"13","author":"Li","year":"2014","journal-title":"J. Ocean. Univ. China"},{"key":"ref_36","first-page":"60","article-title":"Approach of non-station-based in situ measurements for high resolution satellite remote sensing of productive and highly changeable inland waters","volume":"13","author":"Molkov","year":"2020","journal-title":"Fundam. Appl. Hydrophys."},{"key":"ref_37","unstructured":"Mueller, J.L., Pietras, C., Hooker, S.B., Austin, R.W., Miller, M., Knobelspiesse, K.D., Frouin, R., Holben, B., and Voss, K. (2003). Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, Volume II: Instrument Specifications, Characterization and Calibration."},{"key":"ref_38","unstructured":"SCOR-UNESCO (1966). Monograph of Oceanography Methodology, UNESCO."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1134\/S0001437014040067","article-title":"Determination of the concentration of mineral particles and suspended organic substance based on their spectral absorption","volume":"54","author":"Konovalov","year":"2014","journal-title":"Oceanology"},{"key":"ref_40","unstructured":"Mueller, J.L., Bidigare, R.R., Trees, C., Balch, W.M., Dore, J., Drapeau, D.T., Karl, D., Van Heukelem, L., and Perl, J. (2003). Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 5, Volume 5: Biogeochemical and Bio-Optical Measurements and Data Analysis Protocols, Goddard Space Flight Space Center."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7442","DOI":"10.1364\/AO.38.007442","article-title":"Estimation of the remote sensing reflectance from above\u2013water methods","volume":"38","author":"Mobley","year":"1999","journal-title":"Appl. Opt."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"869611","DOI":"10.3389\/frsen.2022.869611","article-title":"QWIP: A Quantitative Metric for Quality Control of Aquatic Reflectance Spectral Shape Using the Apparent Visible Wavelength","volume":"3","author":"Dierssen","year":"2022","journal-title":"Front. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1002\/lno.10674","article-title":"Optical types of inland and coastal waters","volume":"63","author":"Spyrakos","year":"2018","journal-title":"Limnol. Oceanogr."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"096070","DOI":"10.1117\/1.JRS.9.096070","article-title":"Ocean color measurements with the Operational Land Imager on Landsat-8: Implementation and evaluation in SeaDAS","volume":"9","author":"Franz","year":"2015","journal-title":"J. Appl. Remote Sensing."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1016\/j.rse.2009.11.022","article-title":"Calibration and Validation of a Generic Multisensor Algorithm for Mapping of Total Suspended Matter in Turbid Waters","volume":"114","author":"Nechad","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2011.11.013","article-title":"Review of Constituent Retrieval in Optically Deep and Complex Waters from Satellite Imagery","volume":"118","author":"Odermatt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.rse.2007.03.012","article-title":"Examining the consistency of products derived from various ocean color sensors in open ocean (Case 1) waters in the perspective of a multi-sensor approach","volume":"111","author":"Morel","year":"2007","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4443\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:48:01Z","timestamp":1760129281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4443"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,9]]},"references-count":47,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184443"],"URL":"https:\/\/doi.org\/10.3390\/rs15184443","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,9]]}}}