{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T13:12:01Z","timestamp":1771333921720,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,6,27]],"date-time":"2020-06-27T00:00:00Z","timestamp":1593216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007917","name":"Agricultural Research Service","doi-asserted-by":"publisher","award":["8042-42000-020-00D and 8042-12630-011-00D"],"award-info":[{"award-number":["8042-42000-020-00D and 8042-12630-011-00D"]}],"id":[{"id":"10.13039\/100007917","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study provides detailed information about the use of a hyperspectral imaging system mounted on a motor-driven multipurpose floating platform (MFP) for water quality sensing and water sampling, including the spatial and spectral calibration for the camera, image acquisition and correction procedures. To evaluate chlorophyll-a concentrations in an irrigation pond, visible\/near-infrared hyperspectral images of the water were acquired as the MFP traveled to ten water sampling locations along the length of the pond, and dimensionality reduction with correlation analysis was performed to relate the image data to the measured chlorophyll-a data. About 80,000 sample images were acquired by the line-scan method. Image processing was used to remove sun-glint areas present in the raw hyperspectral images before further analysis was conducted by principal component analysis (PCA) to extract three key wavelengths (662 nm, 702 nm, and 752 nm) for detecting chlorophyll-a in irrigation water. Spectral intensities at the key wavelengths were used as inputs to two near-infrared (NIR)-red models. The determination coefficients (R2) of the two models were found to be about 0.83 and 0.81. The results show that hyperspectral imagery from low heights can provide valuable information about water quality in a fresh water source.<\/jats:p>","DOI":"10.3390\/rs12132070","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T11:17:17Z","timestamp":1593429437000},"page":"2070","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water"],"prefix":"10.3390","volume":"12","author":[{"given":"Geonwoo","family":"Kim","sequence":"first","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1044-349X","authenticated-orcid":false,"given":"Insuck","family":"Baek","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA"},{"name":"Oak Ridge Institute for Science and Education, 1299 Bethel Valley Rd, Oak Ridge, TN 37830, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew D.","family":"Stocker","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA"},{"name":"Oak Ridge Institute for Science and Education, 1299 Bethel Valley Rd, Oak Ridge, TN 37830, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaclyn E.","family":"Smith","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA"},{"name":"Oak Ridge Institute for Science and Education, 1299 Bethel Valley Rd, Oak Ridge, TN 37830, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew L.","family":"Van Tassell","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Iowa State University, 2025 Black Engineering, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwei","family":"Qin","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diane E.","family":"Chan","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yakov","family":"Pachepsky","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moon S.","family":"Kim","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,27]]},"reference":[{"key":"ref_1","unstructured":"(2020, June 20). Irrigation Water Use, Available online: https:\/\/www.usgs.gov\/special-topic\/water-science-school\/science\/irrigation-water-use?qt-science_center_objects=0#qt-science_center_objects."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dieter, C.A., Maupin, M.A., Caldwell, R.R., Harris, M.A., Ivahnenko, T.I., Lovelace, J.K., Barber, N.L., and Linsey, K.S. (2018). Estimated Use of Water in the United States in 2015.","DOI":"10.3133\/cir1441"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Barber, N.L. (2018). Summary of estimated Water Use in the United States in 2005: U.S. Geological Survey Fact Sheet 2009-3098.","DOI":"10.3133\/fs20093098"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.2166\/ws.2016.087","article-title":"Estimating the ratio of pond size to irrigated soybean land in Mississippi: A case study","volume":"16","author":"Ouyang","year":"2016","journal-title":"Water Sci. Technol. Water Supply"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pettygrove, G.S., Dennis, W.W., and Robert, S.A. (2019). Irrigation Water Quality Criteria. Irrig. Reclaimed Munic. Wastewater\u2013A Guid. Man., 3-1\u20133-35.","DOI":"10.1201\/9781351073905-3"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1007\/s13762-018-1657-3","article-title":"Assessment of river water quality for agricultural irrigation","volume":"16","author":"Mandal","year":"2019","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kavurmaci, M., and Apaydin, A. (2019). Assessment of irrigation water quality by a Geographic Information System\u2013Multicriteria Decision Analysis-based model: A case study from Ankara, Turkey. Water Environ. Res., 1\u201313.","DOI":"10.1002\/wer.1133"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.rse.2012.11.023","article-title":"Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: The Mississippi River and its tributaries in Minnesota","volume":"130","author":"Olmanson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1017\/S2040470017000991","article-title":"Hyperspectral imagery as a supporting tool in precision irrigation of karst landscapes","volume":"8","author":"Zovko","year":"2017","journal-title":"Adv. Anim. Biosci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1109\/TGRS.2003.812907","article-title":"Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality","volume":"41","author":"Brando","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1080\/09603120701628743","article-title":"Microbial contamination in vegetables due to irrigation with partially treated municipal wastewater in a tropical city","volume":"17","author":"Rai","year":"2007","journal-title":"Int. J. Environ. Health Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11270-019-4263-1","article-title":"Seasonality of E. coli and Enterococci Concentrations in Creek Water, Sediment, and Periphyton","volume":"230","author":"Stocker","year":"2019","journal-title":"Water Air Soil Pollut."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7457","DOI":"10.3390\/ijerph120707457","article-title":"Irrigation water quality for leafy crops: A perspective of risks and potential solutions","volume":"12","author":"Allende","year":"2015","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.fm.2004.04.007","article-title":"Survival of Escherichia coli O157:H7 in soil and on carrots and onions grown in fields treated with contaminated manure composts or irrigation water","volume":"22","author":"Islam","year":"2005","journal-title":"Food Microbiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1016\/j.scitotenv.2019.03.121","article-title":"Intraseasonal variation of E. coli and environmental covariates in two irrigation ponds in Maryland, USA","volume":"670","author":"Stocker","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1016\/j.watres.2011.11.068","article-title":"Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data","volume":"46","author":"Moses","year":"2012","journal-title":"Water Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pyo, J.C., Pachepsky, Y., Baek, S.S., Kwon, Y.S., Kim, M.J., Lee, H., Park, S., Cha, Y.K., Ha, R., and Nam, G. (2017). Optimizing semi-analytical algorithms for estimating chlorophyll-a and phycocyanin concentrations in inland waters in Korea. Remote Sens., 9.","DOI":"10.3390\/rs9060542"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"609","DOI":"10.5194\/isprs-annals-IV-2-W5-609-2019","article-title":"Estimating chlorophyll a concentrations of several inland waters with hyperspectral data and machine learning models","volume":"4","author":"Maier","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.watres.2012.10.027","article-title":"Escherichia coli survival in waters: Temperature dependence","volume":"47","author":"Blaustein","year":"2013","journal-title":"Water Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/B978-0-12-386473-4.00002-6","article-title":"Irrigation Waters as a Source of Pathogenic Microorganisms in Produce: A Review","volume":"113","author":"Pachepsky","year":"2011","journal-title":"Adv. Agron."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.4315\/0362-028X.JFP-17-122","article-title":"Evaluating the U.S. Food Safety Modernization Act Produce Safety Rule Standard for Microbial Quality of Agricultural Water for Growing Produce","volume":"80","author":"Havelaar","year":"2017","journal-title":"J. Food Prot."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, C.H., and Landgrebe, D. (1999). Information Extraction Principles and Methods for Multispectral and Hyperspectral Image Data. Inf. Process. Remote Sens., 3\u201337.","DOI":"10.1142\/9789812815705_0001"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1080\/01431169608948770","article-title":"Review article hyperspectral geological remote sensing: Evaluation of analytical techniques","volume":"17","author":"Cloutis","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14118","DOI":"10.1109\/ACCESS.2018.2812999","article-title":"Modern Trends in Hyperspectral Image Analysis: A Review","volume":"6","author":"Khan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_26","first-page":"145","article-title":"A review of hyperspectral remote sensing and its application in vegetation and water resource studies","volume":"33","author":"Govender","year":"2007","journal-title":"Water SA"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"118","DOI":"10.2166\/wpt.2018.119","article-title":"Monitoring water quality in Singapore reservoirs with hyperspectral remote sensing technology","volume":"14","author":"Liew","year":"2019","journal-title":"Water Pract. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, Z., Kawamura, K., Sakuno, Y., Fan, X., Gong, Z., and Lim, J. (2017). Retrieval of chlorophyll-a and total suspended solids using iterative stepwise elimination partial least squares (ISE-PLS) regression based on field hyperspectral measurements in irrigation ponds in Higashihiroshima, Japan. Remote Sens., 9.","DOI":"10.3390\/rs9030264"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.compag.2016.11.008","article-title":"Estimating Swiss chard foliar macro- and micronutrient concentrations under different irrigation water sources using ground-based hyperspectral data and four partial least squares (PLS)-based (PLS1, PLS2, SPLS1 and SPLS2) regression algorithms","volume":"132","author":"Mutanga","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3582","DOI":"10.1016\/j.rse.2008.04.015","article-title":"A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation","volume":"112","author":"Gitelson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3342","DOI":"10.1364\/AO.44.003342","article-title":"Erratum: Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: Experimental results","volume":"44","author":"Gitelson","year":"2005","journal-title":"Appl. Opt."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3367","DOI":"10.1080\/01431169208904125","article-title":"The peak near 700 nm on radiance spectra of algae and water: Relationships of its magnitude and position with chlorophyll","volume":"13","author":"Blaustein","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4009","DOI":"10.1016\/j.rse.2008.06.002","article-title":"Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin","volume":"112","author":"Randolph","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1128\/AEM.01876-17","article-title":"Temporal stability of Escherichia coli concentrations in waters of two irrigation ponds in Maryland","volume":"84","author":"Pachepsky","year":"2018","journal-title":"Appl. Environ. Microbiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"703","DOI":"10.13031\/2013.36473","article-title":"Line-Scan Hyperspectral Imaging Platform for Agro-Food Safety and Quality Evaluation: System Enhancement and Characterization","volume":"54","author":"Kim","year":"2011","journal-title":"Trans. ASABE"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jfoodeng.2013.04.001","article-title":"Hyperspectral and multispectral imaging for evaluating food safety and quality","volume":"118","author":"Qin","year":"2013","journal-title":"J. Food Eng."},{"key":"ref_37","first-page":"721","article-title":"Hyperspectral reflectance and fluorescence imaging system for food quality and safety","volume":"44","author":"Kim","year":"2001","journal-title":"Trans. Am. Soc. Agric. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Baek, I., Kim, M.S., Cho, B.K., Mo, C., Barnaby, J.Y., McClung, A.M., and Oh, M. (2019). Selection of optimal hyperspectral wavebands for detection of discolored, diseased rice seeds. Appl. Sci., 9.","DOI":"10.3390\/app9051027"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s11273-013-9290-x","article-title":"The influence of seasonality in estimating mangrove leaf chlorophyll-a content from hyperspectral data","volume":"21","author":"Kovacs","year":"2013","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.scienta.2012.02.024","article-title":"Nondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imaging","volume":"138","author":"Holmes","year":"2012","journal-title":"Sci. Hortic. (Amsterdam)."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4403","DOI":"10.1016\/j.proeng.2011.08.827","article-title":"Monitoring leaf chlorophyll fluorescence with spectral reflectance in Rice (Oryza sativa L.)","volume":"15","author":"Zhang","year":"2011","journal-title":"Procedia Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/S0034-4257(97)00106-5","article-title":"Comparison of NIR\/RED ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: A case study in a turbid reservoir","volume":"62","author":"Luoheng","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1021\/es9809657","article-title":"Optical teledetection of chlorophyll a in turbid inland waters","volume":"33","author":"Gons","year":"1999","journal-title":"Environ. Sci. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.postharvbio.2012.09.002","article-title":"Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imagery","volume":"76","author":"Cho","year":"2013","journal-title":"Postharvest Biol. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"8200","DOI":"10.1039\/C4CS00062E","article-title":"Near-infrared spectroscopy and hyperspectral imaging: Non-destructive analysis of biological materials","volume":"43","author":"Manley","year":"2014","journal-title":"Chem. Soc. Rev."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4758","DOI":"10.1109\/TGRS.2019.2892899","article-title":"Regionally and Locally Adaptive Models for Retrieving Chlorophyll-a Concentration in Inland Waters from Remotely Sensed Multispectral and Hyperspectral Imagery","volume":"57","author":"Xu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.rse.2014.04.034","article-title":"Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake","volume":"157","author":"Giardino","year":"2015","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/13\/2070\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:43:43Z","timestamp":1760175823000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/13\/2070"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,27]]},"references-count":47,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["rs12132070"],"URL":"https:\/\/doi.org\/10.3390\/rs12132070","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,27]]}}}