{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T13:23:10Z","timestamp":1769692990665,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,15]],"date-time":"2020-01-15T00:00:00Z","timestamp":1579046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accurate monitoring of water quality indicators, bathymetry and distribution of benthic habitats in vulnerable ecosystems is key to assessing the effects of climate change, the quality of natural areas and to guide appropriate biodiversity, tourism or fisheries policies. Coastal and inland water ecosystems are very complex but crucial due to their richness and primary production. In this context, remote sensing can be a reliable way to monitor these areas, mainly thanks to satellite sensors\u2019 improved spatial and spectral capabilities and airborne or drone instruments. In general, mapping bodies of water is challenging due to low signal-to-noise (SNR) at sensor level, due to the very low reflectance of water surfaces as well as atmospheric effects. Therefore, the main objective of this work is to provide a robust processing framework to estimate water quality parameters in inland shallow waters using multiplatform data. More specifically, we measured chlorophyll concentrations (Chl-a) from multispectral and hyperspectral sensors on board satellites, aircrafts and drones. The Natural Reserve of Maspalomas, Canary Island (Spain), was chosen for the study because of its complexity as well as being an inner lagoon with considerable organic and inorganic matter and chlorophyll concentration. This area can also be considered a well-known coastal-dune ecosystem attracting a large amount of tourists. The water quality parameter estimated by the remote sensing platforms has been validated using co-temporal in situ measurements collected during field campaigns, and quite satisfactory results have been achieved for this complex ecosystem. In particular, for the drone hyperspectral instrument, the root mean square error, computed to quantify the differences between the estimated and in situ chlorophyll-a concentrations, was 3.45 with a bias of 2.96.<\/jats:p>","DOI":"10.3390\/rs12020284","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T04:14:41Z","timestamp":1579234481000},"page":"284","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multiplatform Earth Observation Systems for Monitoring Water Quality in Vulnerable Inland Ecosystems: Maspalomas Water Lagoon"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0010-4024","authenticated-orcid":false,"given":"Francisco","family":"Eugenio","sequence":"first","affiliation":[{"name":"Instituto de Oceanograf\u00eda y Cambio Global, IOCAG, Universidad de las Palmas de Gran Canaria, ULPGC, Unidad Asociada ULPGC-CSIC, 35017 Las Palmas de Gran Canaria, Canary Islands, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9646-1017","authenticated-orcid":false,"given":"Javier","family":"Marcello","sequence":"additional","affiliation":[{"name":"Instituto de Oceanograf\u00eda y Cambio Global, IOCAG, Universidad de las Palmas de Gran Canaria, ULPGC, Unidad Asociada ULPGC-CSIC, 35017 Las Palmas de Gran Canaria, Canary Islands, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javier","family":"Mart\u00edn","sequence":"additional","affiliation":[{"name":"Departamento de F\u00edsica, Universidad de las Palmas de Gran Canaria, ULPGC, 35017 Las Palmas de Gran Canaria, Canary Islands, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Horning, E., Robinson, J., Sterling, E., Turner, W., and Spector, S. (2010). Remote Sensing for Ecology and Conservation, Oxford University Press.","DOI":"10.1093\/oso\/9780199219940.001.0001"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Purkis, S., and Klemas, V. (2011). Remote Sensing and Global Environmental Change, John Wiley & Sons Ltd.","DOI":"10.1002\/9781118687659"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (2013). Remote Sensing Digital Image Analysis, Springer. [5th ed.].","DOI":"10.1007\/978-3-642-30062-2"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gholizadeh, M.H., Melesse, A.M., and Reddi, L. (2016). A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors, 16.","DOI":"10.3390\/s16081298"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2014.09.021","article-title":"Remote sensing of inland waters: Challenges, progress and future directions","volume":"157","author":"Palmer","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.rse.2014.10.032","article-title":"Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in R\u00edo Tercero reservoir (Argentina)","volume":"158","author":"Bonanseaa","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Toming, K., Kutser, T., Uiboupin, R., Arikas, A., Vahter, K., and Paavel, B. (2017). Mapping water quality parameters with sentinel-3 ocean and land colour instrument imagery in the Baltic sea. Remote Sens., 9.","DOI":"10.3390\/rs9101070"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3539","DOI":"10.1109\/TGRS.2014.2377300","article-title":"High-resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery","volume":"53","author":"Eugenio","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Martin, J., Eugenio, F., Marcello, J., and Medina, A. (2016). Automatic sunglint removal of multispectral high-resolution worldview-2 imagery for retrieving coastal shallow water parameters. Remote Sens., 8.","DOI":"10.3390\/rs8010037"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Eugenio, F., Marcello, J., Martin, J., and Rodr\u00edguez-Esparrag\u00f3n, D. (2017). Benthic habitat mapping using multispectral high-resolution imagery: evaluation of shallow water atmospheric correction techniques. Sensors, 17.","DOI":"10.3390\/s17112639"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1021\/acs.est.5b03518","article-title":"High-resolution remote sensing of water quality in the San Francisco bay-delta estuary","volume":"50","author":"Fichot","year":"2016","journal-title":"Environ. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.pocean.2017.08.007","article-title":"Uncertainties and applications of satellite-derived coastal water quality products","volume":"159","author":"Zheng","year":"2017","journal-title":"Prog. Oceanogr."},{"key":"ref_13","first-page":"173","article-title":"Monitoring of chlorophyll-a and sea surface silicate concentrations in the south part of Cheju island in the East China sea using MODIS data","volume":"67","author":"Zhanga","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Koparan, C., Koc, A.B., Privette, C.V., and Sawyer, C.B. (2018). In situ water quality measurements using an Unmanned Aerial Vehicle (UAV) system. Water, 10.","DOI":"10.3390\/w10030264"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.ecss.2019.01.004","article-title":"Spatial and temporal variation in chlorophyll a concentration in the Eastern China Seas based on a locally modified satellite dataset","volume":"220","author":"Haoa","year":"2019","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qi, L., Lee, Z., Hu, C., and Wang, M. (2017). Requirement of minimal signal-to-noise ratios of ocean color sensors and uncertainties of ocean color products. J. Geophys. Res. Ocean., 122.","DOI":"10.1002\/2016JC012558"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Y. (2010). Remote Sensing of Coastal Environments, CRC Press.","DOI":"10.1201\/9781420094428"},{"key":"ref_18","first-page":"1","article-title":"Estudio de la din\u00e1mica del ecosistema del charco de Maspalomas (1992\u20131998), Gran Canaria","volume":"6","author":"Moreno","year":"1999","journal-title":"Inf. T\u00e9cnicos Inst. Canario Cienc. Mar."},{"key":"ref_19","unstructured":"Digital Globe (2019, February 02). Accuracy of Worldview Products. Available online: https:\/\/dg-cms-uploads-production.s3.amazonaws.com\/uploads\/document\/file\/38\/DG_ACCURACY_WP_V3.pdf."},{"key":"ref_20","first-page":"20","article-title":"A review of INTA AHS PAF","volume":"13","author":"Prado","year":"2014","journal-title":"EARSeL eProc."},{"key":"ref_21","unstructured":"Giulietta, S., Fargion, G.S., and Mueller, J.L. (2003). Above-water radiance and remote sensing reflectance measurement and analysis protocols, Chapter 10, Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 2."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.3390\/ijgi3031003","article-title":"Field spectroscopy metadata system based on ISO and OGC standards","volume":"3","author":"Amaro","year":"2014","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_23","first-page":"53","article-title":"Environmental monitoring of El Hierro Island submarine volcano, by combining low and high resolution satellite imagery","volume":"29","author":"Eugenio","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1016\/j.gsf.2016.06.004","article-title":"Evaluation of atmospheric corrections on hyperspectral data with special reference to mineral mapping","volume":"8","author":"Rani","year":"2017","journal-title":"Geosci. Front."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"361","DOI":"10.14358\/PERS.73.4.361","article-title":"A comparison of four common atmospheric correction methods","volume":"73","author":"Mahiny","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_26","first-page":"128","article-title":"An improved atmospheric correction algorithm for applying MERIS data to very turbid inland waters","volume":"39","author":"Jaelani","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","unstructured":"Richter, R., and Schl\u00e4pfer, D. (2016). Atmospheric\/Topographic Correction for Airborne Imagery: ATCOR-4 User Guide, DLR and ReSe Report. Version 7.0.3."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Manessa, M.D.M., Haidar, M., Budhiman, S., Winarso, G., Kanno, A., Sagawa, T., and Sekine, M. (2016, January 23\u201326). Evaluating the performance of Lyzenga\u2019s water column correction in case-1 coral reef water using a simulated Worldview-2 imagery. Proceedings of the IOP Conference Series: Earth and Environmental Science, Shanghai, China.","DOI":"10.1088\/1755-1315\/47\/1\/012018"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6329","DOI":"10.1364\/AO.37.006329","article-title":"Hyperspectral remote sensing for shallow waters: 1. A semi-analytical model","volume":"37","author":"Lee","year":"1998","journal-title":"Appl. Opt."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.1364\/AO.38.003831","article-title":"Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization","volume":"38","author":"Lee","year":"1999","journal-title":"Appl. Opt."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4112","DOI":"10.1109\/TGRS.2011.2155070","article-title":"Fully constrained least squares spectral unmixing by simplex projection","volume":"49","author":"Heylen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1364\/AO.14.000417","article-title":"Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean","volume":"14","author":"Gordon","year":"1975","journal-title":"Appl. Opt."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2873","DOI":"10.1364\/OE.11.002873","article-title":"An analytical model for subsurface irradiance and remote sensing reflectance in deep and shallow case-2 waters","volume":"11","author":"Albert","year":"2003","journal-title":"Opt. Express"},{"key":"ref_34","unstructured":"Gavin, H.P. (2011). The Levenberg-Marquardt Method for Nonlinear Least Squares Curve-Fitting Problems, Department of Civil and Environmental Engineering, Duke University."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Korosov, A., Moiseev, A., Shuchman, R.A., and Pozdnyakov, D. (2017). Modis-aqua and sentinel-2 data fusion: Application to optically shallow waters of Lake Michigan. Karelian Res. Cent. Russ. Acad. Sci.","DOI":"10.17076\/lim692"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/2\/284\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:26:29Z","timestamp":1760365589000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/2\/284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,15]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["rs12020284"],"URL":"https:\/\/doi.org\/10.3390\/rs12020284","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,15]]}}}