Articles | Volume 18, issue 2
https://doi.org/10.5194/essd-18-779-2026
Copyright waived. This work has been dedicated to the public domain (Creative Commons Public Domain Dedication).
https://doi.org/10.5194/essd-18-779-2026
Copyright waived. This work has been dedicated to the public domain (Creative Commons Public Domain Dedication).
Origins, evolutions, and future directions of Landsat science products for advancing global inland water and coastal ocean observations
Benjamin Page
CORRESPONDING AUTHOR
Earth Space Technology Services (ESTS), Contractor to the USGS EROS Center, Sioux Falls, SD, 57198, USA
Christopher J. Crawford
U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, SD, 57198, USA
Saeed Arab
KBR, Inc., Contractor to the USGS EROS Center, Sioux Falls, SD, 57198, USA
Gail Schmidt
KBR, Inc., Contractor to the USGS EROS Center, Sioux Falls, SD, 57198, USA
Christopher Barnes
KBR, Inc., Contractor to the USGS EROS Center, Sioux Falls, SD, 57198, USA
Danika Wellington
KBR, Inc., Contractor to the USGS EROS Center, Sioux Falls, SD, 57198, USA
Related authors
No articles found.
Edward H. Bair, Dar A. Roberts, David R. Thompson, Philip G. Brodrick, Brenton A. Wilder, Niklas Bohn, Christopher J. Crawford, Nimrod Carmon, Carrie M. Vuyovich, and Jeff Dozier
The Cryosphere, 19, 2315–2320, https://doi.org/10.5194/tc-19-2315-2025, https://doi.org/10.5194/tc-19-2315-2025, 2025
Short summary
Short summary
Key to the success of future satellite missions is understanding snowmelt in our warming climate, as this has implications for nearly 2 billion people. An obstacle is that an artifact, called the hook, is often mistaken for soot or dust. Instead, it is caused by three amplifying effects: (1) background reflectance that is too dark, (2) an assumption of level terrain, and (3) differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are presented.
George Z. Xian, Kelcy Smith, Danika Wellington, Josephine Horton, Qiang Zhou, Congcong Li, Roger Auch, Jesslyn F. Brown, Zhe Zhu, and Ryan R. Reker
Earth Syst. Sci. Data, 14, 143–162, https://doi.org/10.5194/essd-14-143-2022, https://doi.org/10.5194/essd-14-143-2022, 2022
Short summary
Short summary
Continuous change detection algorithms were implemented with time series satellite records to produce annual land surface change products for the conterminous United States. The land change products are in 30 m spatial resolution and represent land cover and change from 1985 to 2017 across the country. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems.
Cited articles
Arena, M., Pratolongo, P., Loisel, H., Tran, M. D., Jorge, D. S. F., and Delgado, A. L.: Optical water characterization and atmospheric correction assessment of estuarine and coastal waters around the AERONET-OC Bahia Blanca, Front. Remote Sens., 5, 1305787, https://doi.org/10.3389/frsen.2024.1305787, 2024.
Ashapure, A., Smith, B., O'Shea, R., Maciel, D. A., Saranathan, A., Balasubramanian, S. V., and Zhai, P. W.: Aquaverse: A Machine Learning-Based Atmospheric Correction Framework for Inland and Coastal Waters, SSRN, https://doi.org/10.2139/ssrn.5078832, 2025.
Bailey, S. W., Franz, B. A., and Werdell, P. J.: Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing, Opt. Express, 18, 7521–7527, https://doi.org/10.1364/OE.18.007521, 2010.
Bassani, C., Cazzaniga, I., Manzo, C., Bresciani, M., Braga, F., Giardino, C., and Brando, V.: Atmospheric and adjacency correction of Landsat-8 imagery over inland and coastal waters near Aeronet-OC sites, ESA SP., https://hdl.handle.net/10281/129518 (last access: 1 January 2026), 2016.
Bates, J. J. and Privette, J. L.: A maturity model for assessing the completeness of climate data records, Eos Trans. AGU, 93, 441–441, https://doi.org/10.1029/2012EO440006, 2012
Bi, S. and Hieronymi, M.: Holistic optical water type classification for ocean, coastal, and inland waters, Limnol. Oceanogr., https://doi.org/10.1002/lno.12606, 2024.
Bramich, J. M., Bolch, C. J., and Fischer, A. M.: Evaluation of atmospheric correction and high-resolution processing on SeaDAS-derived chlorophyll-a: An example from mid-latitude mesotrophic waters, Int. J. Remote Sens., https://doi.org/10.1080/01431161.2017.1420930, 2018.
Brockmann, C., Doerffer, R., Peters, M., Kerstin, S., Embacher, S., and Ruescas, A.: Evolution of the C2RCC neural network for Sentinel 2 and 3 for the retrieval of ocean color products in normal and extreme optically complex waters, Living Planet Symposium, 740, 54 pp., https://www.brockmann-consult.de/wp-content/uploads/2017/11/sco1_12brockmann.pdf (last access: 1 January 2026), 2016.
Coddington, O. M., Richard, E. C., Harber, D., Pilewskie, P., Woods, T. N., Chance, K., Liu, X., and Sun, K.: The TSIS-1 hybrid solar reference spectrum, Geophys. Res. Lett., 48, e2020GL091709, https://doi.org/10.1029/2020GL091709, 2021.
Crawford, C. J., Roy, D. P., Arab, S., Barnes, C., Vermote, E., Hulley, G., Gerace, A., Choate, M., Engebretson, C., Micijevic, E., and Schmidt, G.: The 50-year Landsat collection 2 archive, Sci. Remote Sens., 8, 100103, https://doi.org/10.1016/j.srs.2023.100103, 2023.
Crawford, C. J., Page, B. P., Arab, S., Schmidt, G., Barnes, C., and Wellington, D.: In situ Radiometric Validation Data Record for Landsat 8/9 Operational Land Imager (OLI) Level 2 Aquatic Reflectance Products Version 1.0, ScienceBase [data set], https://doi.org/10.5066/P14RSMQD, 2025a.
Crawford, C. J., Page, B. P., Arab, S., Schmidt, G., Barnes, C., and Wellington, D.: Landsat 8–9 Operational Land Imager (OLI) Level 2 Provisional Aquatic Reflectance Products, Collection 2 Validation Subset, ScienceBase [data set], https://doi.org/10.5066/P14MBBRM, 2025b.
Concha, J. A. and Schott, J. R.: Retrieval of color producing agents in Case 2 waters using Landsat 8, Remote Sens. Environ., 185, 95–107, https://doi.org/10.1016/j.rse.2016.03.018, 2016.
Concha, J. A., Bracaglia, M., and Brando, V.E.: Assessing the influence of different validation protocols on Ocean Colour match-up analyses, Remote Sensing of Environment, 259, 112415, https://doi.org/10.1016/j.rse.2021.112415, 2021.
Dash, P., Walker, N., Mishra, D., D'Sa, E., and Ladner, S.: Atmospheric correction and vicarious calibration of Oceansat-1 Ocean Color Monitor (OCM) data in coastal case 2 waters, Remote Sens., 4, 1716–1740, https://doi.org/10.3390/rs4061716, 2012.
De Keukelaere, L., Sterckx, S., Adriaensen, S., Knaeps, E., Reusen, I., Giardino, C., Bresciani, M., Hunter, P., Neil, C., Van der Zande, D., and Vaiciute, D.: Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters, Eur. J. Remote Sens., 51, 525–542, https://doi.org/10.1080/22797254.2018.1457937, 2018.
Dekker, A., Evers-King, H., Bulgarelli, B., Gurlin, D., Gege, P., Pinnel, N., Brockmann, C., Costa, M., Strobl, P., Shukla, T., and Steventon, M.: The CEOS ARD for Aquatic Reflectance – Evolving From Inland and Near-Coastal Waters to Include Oceans, https://elib.dlr.de/216714/ (last access: 1 January 2026), 2025.
Dierssen, H. M., Zimmerman, R. C., Drake, L. A., and Burdige, D.: Benthic ecology from space: optics and net primary production in seagrass and benthic algae across the Great Bahama Bank, Mar. Ecol. Prog. Ser., 411, 1–15, https://doi.org/10.3354/meps08665, 2010.
Dierssen, H. M., Ackleson, S. G., Joyce, K. E., Hestir, E. L., Castagna, A., Lavender, S., and McManus, M. A.: Living up to the hype of hyperspectral aquatic remote sensing: science, resources and outlook, Front. Environ. Sci., 9, 649528, https://doi.org/10.3389/fenvs.2021.649528, 2021.
Dierssen, H. M., Vandermeulen, R. A., Barnes, B. B., Castagna, A., Knaeps, E., and Vanhellemont, Q.: QWIP: A quantitative metric for quality control of aquatic reflectance spectral shape using the apparent visible wavelength, Frontiers in Remote Sensing, 3, 869611, https://doi.org/10.3389/frsen.2022.869611, 2022.
Dogliotti, A. I., Ruddick, K. G., Nechad, B., Doxaran, D., and Knaeps, E.: A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters, Remote Sens. Environ., 156, 157–168, https://doi.org/10.1016/j.rse.2014.09.020, 2015.
Dwyer, J. L., Roy, D. P., Sauer, B., Jenkerson, C. B., Zhang, H. K., and Lymburner, L.: Analysis ready data: enabling analysis of the Landsat archive, Remote Sens., 10, 1363, https://doi.org/10.3390/rs10091363, 2018.
Fan, Y., Li, W., Chen, N., Ahn, J. H., Park, Y. J., Kratzer, S., Schroeder, T., Ishizaka, J., Chang, R., and Stamnes, K.: OC-SMART: A machine learning based data analysis platform for satellite ocean color sensors, Remote Sens. Environ., 253, 112236, https://doi.org/10.1016/j.rse.2020.112236, 2021.
Fickas, K. C., O'Shea, R. E., Pahlevan, N., Smith, B., Bartlett, S. L., and Wolny, J. L.: Leveraging multimission satellite data for spatiotemporally coherent cyanoHAB monitoring, Front. Remote Sens., 4, 1157609, https://doi.org/10.3389/frsen.2023.1157609, 2023.
Franz, B. A., Bailey, S. W., Werdell, P. J., and McClain, C. R.: Sensor-independent approach to the vicarious calibration of satellite ocean color radiometry, Appl. Opt., 46, 5068–5082, https://doi.org/10.1364/AO.46.005068, 2007.
Franz, B. A., Bailey, S. W., Kuring, N., and Werdell, P. J.: Ocean color measurements with the Operational Land Imager on Landsat-8: implementation and evaluation in SeaDAS, J. Appl. Remote Sens., 9, 096070, https://doi.org/10.1117/1.JRS.9.096070, 2015.
GCOS: The 2022 GCOS ECVs Requirements, https://library.wmo.int/idurl/4/58111 (last access: 1 January 2026), 2025.
Giardino, C., Kõks, K. L., Bolpagni, R., Luciani, G., Candiani, G., Lehmann, M. K., Van der Woerd, H. J., and Bresciani, M.: The color of water from space: a case study for Italian lakes from Sentinel-2, Geospatial Anal. Earth Obs. Data, https://doi.org/10.5772/intechopen.86596, 2019.
Gordon, H. R. and Wang, M.: Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm, Appl. Opt., 33, 443–452, https://doi.org/10.1364/AO.33.000443, 1994.
He, Q. and Chen, C.: A new approach for atmospheric correction of MODIS imagery in turbid coastal waters: a case study for the Pearl River Estuary, Remote Sens. Lett., 5, 249–257, https://doi.org/10.1080/2150704X.2014.898192, 2014.
He, X., Bai, Y., Pan, D., Tang, J., and Wang, D.: Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters, Opt. Express, 20, 20754–20770, https://doi.org/10.1364/OE.20.020754, 2012.
Ibrahim, A., Franz, B. A., Ahmad, Z., and Bailey, S. W.: Multiband atmospheric correction algorithm for ocean color retrievals, Front. Earth Sci., 7, 116, https://doi.org/10.3389/feart.2019.00116, 2019.
Ilori, C. O., Pahlevan, N., and Knudby, A.: Analyzing performances of different atmospheric correction techniques for Landsat 8: Application for coastal remote sensing, Remote Sens., 11, 469, https://doi.org/10.3390/rs11040469, 2019.
IOCCG: Evaluation of Atmospheric Correction Algorithms over Turbid Waters, edited by: Jamet, C. and Balasubramanian, S. V., IOCCG Report Series, No. 21, International Ocean Colour Coordinating Group, Dartmouth, Canada, https://ioccg.org/wp-content/uploads/2025/12/report_21_atm_corr_rr.pdf (last access: 1 January 2026), 2025.
IOCCG Technical Series and Jamet, C. (Ed.): Atmospheric Correction over turbid waters, IOCCG, Vol. 1.0, Dartmouth, NS, Canada, https://ioccg.org/wp-content/uploads/2019/12/ioccg_atm-corr-report21nov2019.pdf (last access: 1 January 2026), 2019.
Johnson, B. C., Zibordi, G., Brown, S. W., Feinholz, M. E., Sorokin, M. G., Slutsker, I., Woodward, J. T., and Yoon, H. W.: Characterization and absolute calibration of an AERONET-OC radiometer, Appl. Opt., 60, 3380–3392, https://doi.org/10.1364/AO.419766, 2021.
Joshi, I. D. and D'Sa, E. J.: Optical properties using adaptive selection of NIR/SWIR reflectance correction and quasi-analytic algorithms for the MODIS-Aqua in estuarine-ocean continuum: application to the northern Gulf of Mexico, IEEE Trans. Geosci. Remote Sens., 58, 6088–6105, https://doi.org/10.1109/TGRS.2020.2973157, 2020.
Korkin, S. and Lyapustin, A.: Radiative interaction of atmosphere and surface: Write-up with elements of code, J. Quant. Spectrosc. Radiat. Transfer, 309, 108663, https://doi.org/10.1016/j.jqsrt.2023.108663, 2023.
Kuhn, C., de Matos Valerio, A., Ward, N., Loken, L., Sawakuchi, H. O., Kampel, M., Richey, J., Stadler, P., Crawford, J., Striegl, R., and Vermote, E.: Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity, Remote Sens. Environ., 224, 104–118, https://doi.org/10.1016/j.rse.2019.01.023, 2019.
Lee, Z., Carder, K. L., Steward, R. G., Peacock, T. G., Davis, C. O., and Mueller, J. L.: Remote sensing reflectance and inherent optical properties of oceanic waters derived from above-water measurements, Ocean Opt. XIII, 2963, 160–166, https://doi.org/10.1117/12.266436, 1997.
Lee, Z., Carder, K. L., Chen, R. F., and Peacock, T. G.: Properties of the water column and bottom derived from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data, J. Geophys. Res., 106, 11639–11651, https://doi.org/10.1029/2000JC000554, 2001.
Lehmann, M. K., Nguyen, U., Allan, M., and Van der Woerd, H. J.: Colour classification of 1486 lakes across a wide range of optical water types, Remote Sens., 10, 1273, https://doi.org/10.3390/rs10081273, 2018.
Lehmann, M. K., Gurlin, D., Pahlevan, N., Alikas, K., Conroy, T., Anstee, J., Balasubramanian, S. V., Barbosa, C. C., Binding, C., Bracher, A., and Bresciani, M.: GLORIA – A globally representative hyperspectral in situ dataset for optical sensing of water quality, Sci. Data, 10, 100, https://doi.org/10.1038/s41597-023-01973-y, 2023.
Lekki, J., Deutsch, E., Sayers, M., Bosse, K., Anderson, R., Tokars, R., and Sawtell, R.: Determining remote sensing spatial resolution requirements for the monitoring of harmful algal blooms in the Great Lakes, J. Great Lakes Res., 45, 434–443, https://doi.org/10.1016/j.jglr.2019.03.014, 2019.
Liu, H., Zhou, Q., Li, Q., Hu, S., Shi, T., and Wu, G.: Determining switching threshold for NIR-SWIR combined atmospheric correction algorithm of ocean color remote sensing, ISPRS J. Photogramm. Remote Sens., 153, 59–73, https://doi.org/10.1016/j.isprsjprs.2019.04.013, 2019.
Louchard, E. M., Reid, R. P., Stephens, F. C., Davis, C. O., Leathers, R. A., and Valerie, T. D.: Optical remote sensing of benthic habitats and bathymetry in coastal environments at Lee Stocking Island, Bahamas: A comparative spectral classification approach, Limnol. Oceanogr., 48, 511–521, https://doi.org/10.4319/lo.2003.48.1_part_2.0511, 2003.
Mannino, A.: Landsat 8's Atmospheric Correction in SeaDAS: Comparison with AERONET-OC, J. Sci. Res., https://www.researchgate.net/profile/Javier-Concha-3/publication/310497423_Landsat_8's_atmospheric_correction_ in_SeaDAS_comparison_with_AERONET-OC_Conference_Presentation/links/5b609e320f7e9bc79a72b9 15/Landsat-8s-atmospheric-correction-in-SeaDAS-comparison-with-AERONET-OC-Conference-Presentation.pdf (last access: 1 January 2026), 2016.
Mao, Z., Chen, J., Hao, Z., Pan, D., Tao, B., and Zhu, Q.: A new approach to estimate the aerosol scattering ratios for the atmospheric correction of satellite remote sensing data in coastal regions, Remote Sens. Environ., 132, 186–194, https://doi.org/10.1016/j.rse.2013.01.015, 2013.
Mélin, F., Zibordi, G., Berthon, J. F., Bailey, S., Franz, B., Voss, K., Flora, S., and Grant, M.: Assessment of MERIS reflectance data as processed with SeaDAS over the European seas, Opt. Express, 19, 25657–25671, https://doi.org/10.1364/OE.19.025657, 2011.
Meyer, M. F., Topp, S. N., King, T. V., Ladwig, R., Pilla, R. M., Dugan, H. A., Eggleston, J. R., Hampton, S. E., Leech, D. M., Oleksy, I. A., and Ross, J. C.: National-scale remotely sensed lake trophic state from 1984 through 2020, Sci. Data, 11, 77, https://doi.org/10.6073/pasta/212a3172ac36e8dc6e1862f9c 2522fa4, 2024.
Mishra, S. and Mishra, D. R.: Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters, Remote Sens. Environ., 117, 394–406, https://doi.org/10.1016/j.rse.2011.10.016, 2012.
Mobley, C. D.: Estimation of the remote-sensing reflectance from above-surface measurements, Appl. Opt., 38, 7442–7455, https://doi.org/10.1364/AO.38.007442, 1999.
Mobley, C. D., Werdell, J., Franz, B., Ahmad, Z., and Bailey, S.: Atmospheric correction for satellite ocean color radiometry, NASA GSFC-E-DAA-TN35509, https://ntrs.nasa.gov/citations/20160011399 (last access: 1 January 2026), 2016.
Moore, T. S., Feng, H., Ruberg, S. A., Beadle, K., Constant, S. A., Miller, R., Muzzi, R. W., Johengen, T. H., DiGiacomo, P. M., Lance, V. P., and Holben, B. N.: SeaPRISM observations in the western basin of Lake Erie in the summer of 2016, J. Great Lakes Res., 45, 547–555, https://doi.org/10.1016/j.jglr.2018.10.008, 2019.
Moses, W. J., Sterckx, S., Montes, M. J., De Keukelaere, L., and Knaeps, E.: Atmospheric correction for inland waters, Bio-optical Model, Remote Sens. Inland Waters, Elsevier, 69–100, https://doi.org/10.1016/B978-0-12-804644-9.00003-3, 2017.
Nazeer, M., Bilal, M., Nichol, J. E., Wu, W., Alsahli, M. M., Shahzad, M. I., and Gayen, B. K.: First experiences with the Landsat-8 aquatic reflectance product: evaluation of the regional and ocean color algorithms in a coastal environment, Remote Sens., 12, 1938, https://doi.org/10.3390/rs12121938, 2020.
Niroumand-Jadidi, M., Bovolo, F., Bresciani, M., Gege, P., and Giardino, C.: Water quality retrieval from Landsat-9 (OLI-2) imagery and comparison to Sentinel-2, Remote Sens., 14, 4596, https://doi.org/10.3390/rs14184596, 2022.
Ogashawara, I., Jechow, A., Kiel, C., Kohnert, K., Berger, S. A., and Wollrab, S.: Performance of the Landsat 8 provisional aquatic reflectance product for inland waters, Remote Sens., 12, 2410, https://doi.org/10.3390/rs12152410, 2020.
Ogashawara, I., Wollrab, S., Berger, S. A., Kiel, C., Jechow, A., Guislain, A. L., Gege, P., Ruhtz, T., Hieronymi, M., Schneider, T., and Lischeid, G.: Unleashing the power of remote sensing data in aquatic research: Guidelines for optimal utilization, Limnol. Oceanogr. Lett., 9, 667–673, https://doi.org/10.1002/lol2.10427, 2024.
Olmanson, L. G., Brezonik, P. L., Finlay, J. C., and Bauer, M. E.: Comparison of Landsat 8 and Landsat 7 for regional measurements of CDOM and water clarity in lakes, Remote Sens. Environ., 185, 119–128, https://doi.org/10.1016/j.rse.2016.01.007, 2016.
O'Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder, K. L., Garver, S. A., Kahru, M., and McClain, C.: Ocean color chlorophyll algorithms for SeaWiFS, J. Geophys. Res. Oceans, 103, 24937–24953, https://doi.org/10.1029/98JC02160, 1998.
Pahlevan, N. and Schott, J. R.: Characterizing the relative calibration of Landsat-7 (ETM+) visible bands with Terra (MODIS) over clear waters: The implications for monitoring water resources, Remote Sens. Environ., 125, 167–180, https://doi.org/10.1016/j.rse.2012.07.013, 2012.
Pahlevan, N., Lee, Z., Wei, J., Schaaf, C. B., Schott, J. R., and Berk, A.: On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing, Remote Sens. Environ., 154, 272–284, https://doi.org/10.1016/j.rse.2014.08.001, 2014.
Pahlevan, N., Balasubramanian, S. V., Sarkar, S., and Franz, B. A.: Toward long-term aquatic science products from heritage Landsat missions, Remote Sens., 10, 1337, https://doi.org/10.3390/rs10091337, 2018.
Pahlevan, N., Schott, J. R., Franz, B. A., Zibordi, G., Markham, B., Bailey, S., Schaaf, C. B., Ondrusek, M., Greb, S., and Strait, C. M.: Landsat 8 remote sensing reflectance (Rrs) products: Evaluations, intercomparisons, and enhancements, Remote Sens. Environ., 190, 289–301, https://doi.org/10.1016/j.rse.2016.12.030, 2017.
Pahlevan, N., Chittimalli, S. K., Balasubramanian, S. V., and Vellucci, V.: Sentinel-2/Landsat-8 product consistency and implications for monitoring aquatic systems, Remote Sens. Environ., 220, 19–29, https://doi.org/10.1016/j.rse.2018.10.027, 2019.
Pahlevan, N., Mangin, A., Balasubramanian, S. V., Smith, B., Alikas, K., Arai, K., Barbosa, C., Bélanger, S., Binding, C., Bresciani, M., and Giardino, C.: ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters, Remote Sens. Environ., 258, 112366, https://doi.org/10.1016/j.rse.2021.112366, 2021.
Pellegrino, A., Fabbretto, A., Bresciani, M., de Lima, T. M. A., Braga, F., Pahlevan, N., Brando, V.E., Kratzer, S., Gianinetto, M., and Giardino, C.: Assessing the accuracy of PRISMA standard reflectance products in globally distributed aquatic sites, Remote Sensing, 15, 2163, https://doi.org/10.3390/rs15082163, 2023.
Pinnel, N., Langheinrich, M., Soppa, M. A., Randrianalisoa, A. N., Alvarado, L., Gege, P., de los Reyes, R., Heege, T., Bracher, A., Pato, M., and Habermeyer, M.: Hyperspectral EnMAP Data Processing for aquatic science and applications, J. Sci. Res., https://elib.dlr.de/206662/ (last access: 1 January 2026), 2024.
Poppenga, S. K. and Danielson, J. J.: A comparison of Landsat 8 Operational Land Imager and Provisional Aquatic Reflectance science product, Sentinel–2B, and WorldView – 3 imagery for empirical satellite-derived bathymetry, Unalakleet, Alaska, US Geological Survey, no. 2021–5097, https://doi.org/10.3133/sir20215097, 2021.
Radeloff, V. C., Roy, D. P., Wulder, M. A., Anderson, M., Cook, B., Crawford, C. J., Friedl, M., Gao, F., Gorelick, N., Hansen, M., and Healey, S.: Need and vision for global medium-resolution Landsat and Sentinel-2 data products, Remote Sens. Environ., 300, 113918, https://doi.org/10.1016/j.rse.2023.113918, 2024.
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., and Scambos, T. A.: Landsat-8: Science and product vision for terrestrial global change research, Remote Sens. Environ., 145, 154–172, https://doi.org/10.1016/j.rse.2014.02.001, 2014.
Ruddick, K. G., Ovidio, F., and Rijkeboer, M.: Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters, Appl. Opt., 39, 897–912, https://doi.org/10.1364/AO.39.000897, 2000.
Seegers, B. N., Stumpf, R. P., Schaeffer, B. A., Loftin, K. A., and Werdell, P. J.: Performance metrics for the assessment of satellite data products: an ocean color case study, Opt. Express, 26, 7404–7422, https://doi.org/10.1364/OE.26.007404, 2018.
Schott, J. R., Gerace, A., Woodcock, C. E., Wang, S., Zhu, Z., Wynne, R. H., and Blinn, C. E.: The impact of improved signal-to-noise ratios on algorithm performance: Case studies for Landsat class instruments, Remote Sens. Environ., 185, 37–45, https://doi.org/10.1016/j.rse.2016.04.015, 2016.
Singh, R. K. and Shanmugam, P.: A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters, Remote Sensing of Environment, 142, 188–206, https://doi.org/10.1016/j.rse.2013.12.008, 2014.
Steinmetz, F. and Ramon, D.: Sentinel-2 MSI and Sentinel-3 OLCI consistent ocean colour products using POLYMER, Remote Sens. Open Coastal Ocean Inland Waters, 10778, 46–55, https://doi.org/10.1117/12.2500232, 2018.
Steinmetz, F., Deschamps, P. Y., and Ramon, D.: Atmospheric correction in presence of sun glint: application to MERIS, Opt. Express, 19, 9783–9800, https://doi.org/10.1364/OE.19.009783, 2011.
Stengel, V. G., Trevino, J. M., King, T. V., Ducar, S. D., Hundt, S. A., Hafen, K. C., and Churchill, C. J.: Near real-time satellite detection and monitoring of aquatic algae and cyanobacteria: how a combination of chlorophyll-a indices and water-quality sampling was applied to north Texas reservoirs, J. Appl. Remote Sens., 17, 044514, https://doi.org/10.1117/1.JRS.17.044514, 2023.
Spyrakos, E., O'Donnell, R., Hunter, P. D., Miller, C., Scott, M., Simis, S. G., Neil, C., Barbosa, C. C., Binding, C. E., Bradt, S., and Bresciani, M.: Optical types of inland and coastal waters, Limnol. Oceanogr., 63, 846–870, https://doi.org/10.1002/lno.10674, 2018.
Tavora, J., Jiang, B., Kiffney, T., Bourdin, G., Gray, P. C., de Carvalho, L. S., Hesketh, G., Schild, K.M., Faria de Sousa, L., Brady, D. C., and Boss, E.: Recipes for the derivation of water quality parameters using the high-spatial-resolution data from sensors on board Sentinel-2A, Sentinel-2B, Landsat-5, Landsat-7, Landsat-8, and Landsat-9 satellites, J. Remote Sens., 3, 49, https://doi.org/10.34133/remotesensing.0049, 2023.
Thompson, D. R., Guanter, L., Berk, A., Gao, B. C., Richter, R., Schläpfer, D., and Thome, K. J.: Retrieval of atmospheric parameters and surface reflectance from visible and shortwave infrared imaging spectroscopy data, Surv. Geophys., 40, 333–360, https://doi.org/10.1007/s10712-018-9488-9, 2019a.
Thompson, D. R., Cawse-Nicholson, K., Erickson, Z., Fichot, C. G., Frankenberg, C., Gao, B. C., and Thompson, A.: A unified approach to estimate land and water reflectances with uncertainties for coastal imaging spectroscopy, Remote Sens. Environ., 231, 111198, https://doi.org/10.1016/j.rse.2019.05.017, 2019b.
Thompson, D. R., Bohn, N., Brodrick, P. G., Carmon, N., Eastwood, M. L., Eckert, R., Fichot, C. G., Harringmeyer, J. P., Nguyen, H. M., Simard, M., and Thorpe, A. K.: Atmospheric lengthscales for global VSWIR imaging spectroscopy, J. Geophys. Res. Biogeosci., 127, e2021JG006711, https://doi.org/10.1029/2021JG006711, 2022.
Thuillier, G., Hersé, M., Labs, D., Foujols, T., Peetermans, W., Gillotay, D., Simon, P. C., and Mandel, H.: The solar spectral irradiance from 200 to 2400 nm as measured by the SOLSPEC spectrometer from the ATLAS and EURECA missions, Sol. Phys., 214, 1–22, https://doi.org/10.1023/A:1024048429145, 2003.
Tyler, A., Hunter, P., De Keukelaere, L., Ogashawara, I., and Spyrakos, E.: Remote sensing of inland water quality, Encycl. Inl. Waters Second Ed., 4, 570–584, https://doi.org/10.1016/B978-0-12-819166-8.00213-9, 2022.
Vanhellemont, Q., Bailey, S., Franz, B., and Shea, D.: Atmospheric correction of Landsat-8 imagery using SeaDAS, ESA Spec. Publ., 726, https://odnature.naturalsciences.be/downloads/publications/vanhellemont_2014_landsat_seadas_web.pdf (last access: 1 January 2026), 2014.
USGS: Landsat 8–9 Collection 2 Level-2 Provisional Aquatic Reflectance Algorithm Description Document, https://www.usgs.gov/media/files/landsat-8-9-collection-2-level-2-provisional-aquatic-reflectance-algorithm-description (last access: 1 January 2026), 2024.
USGS: Landsat 8–9 Collection 2 Level-2 Provisional Aquatic Reflectance Product Guide, https://www.usgs.gov/media/files/landsat-8-9-collection-2-level-2-provisional-aquatic-reflectance-product-guide (last access: 1 January 2026), 2025.
Vanhellemont, Q.: Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives, Remote Sens. Environ., 225, 175–192, https://doi.org/10.1016/j.rse.2019.03.010, 2019.
Vanhellemont, Q. and Ruddick, K.: Advantages of high-quality SWIR bands for ocean colour processing: Examples from Landsat-8, Remote Sens. Environ., 161, 89–106, https://doi.org/10.1016/j.rse.2015.02.007, 2015.
Vanhellemont, Q. and Ruddick, K.: Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications, Remote Sens. Environ., 216, 586–597, https://doi.org/10.1016/j.rse.2018.07.015, 2018.
Vermote, E. F. and Kotchenova, S.: Atmospheric correction for the monitoring of land surfaces, J. Geophys. Res. Atmos., 113, https://doi.org/10.1029/2007JD009662, 2008.
Wang, M.: Atmospheric correction for remotely-sensed ocean-colour products, Reports and Monographs of the International Ocean-Colour Coordinating Group (IOCCG), International Ocean Colour Coordinating Group (IOCCG), https://ioccg.org/wp-content/uploads/2015/10/ioccg-report-10.pdf (last access: 1 January 2026), 2010.
Wang, M. and Gordon, H. R.: Sensor performance requirements for atmospheric correction of satellite ocean color remote sensing, Opt. Express, 26, 7390–7403, https://doi.org/10.1364/OE.26.007390, 2018.
Wang, M. and Shi, W.: The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing, Opt. Express, 15, 15722–15733, https://doi.org/10.1364/OE.15.015722, 2007.
Wang, J., Wang, Y., Lee, Z., Wang, D., Chen, S., and Lai, W.: A revision of NASA SeaDAS atmospheric correction algorithm over turbid waters with artificial Neural Networks estimated remote-sensing reflectance in the near-infrared, ISPRS J. Photogramm. Remote Sens., 194, 235–249, https://doi.org/10.1016/j.isprsjprs.2022.10.014, 2022.
Wei, J., Lee, Z., Garcia, R., Zoffoli, L., Armstrong, R. A., Shang, Z., Sheldon, P., and Chen, R. F.: An assessment of Landsat-8 atmospheric correction schemes and remote sensing reflectance products in coral reefs and coastal turbid waters, Remote Sens. Environ., 215, 18–32, https://doi.org/10.1016/j.rse.2018.05.033, 2018.
Wei, J., Wang, M., Ondrusek, M., Gilerson, A., Goes, J., Hu, C., Lee, Z., Voss, K. J., Ladner, S., Lance, V. P., and Tufillaro, N.: Satellite ocean color validation, Field Meas. Passive Environ. Remote Sens., Elsevier, 351–374, https://doi.org/10.1016/B978-0-12-823953-7.00006-X, 2023.
Wei, J., Wang, M., Jiang, L., Lee, Z., Kirby, R., Mikelsons, K., and Lin, G.: Satellite observations of water transparency from VIIRS in global aquatic ecosystems, Remote Sensing of Environment, 330, 114981, https://doi.org/10.1016/j.rse.2025.114981, 2025.
Werdell, P. J. and Bailey, S. W.: An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation, Remote Sens. Environ., 98, 122–140, https://doi.org/10.1016/j.rse.2005.07.001, 2005.
Werdell, P. J., Franz, B. A., Bailey, S. W., Harding Jr., L. W., and Feldman, G. C.: Approach for the long-term spatial and temporal evaluation of ocean color satellite data products in a coastal environment, Coastal Ocean Remote Sens., SPIE, 6680, 115–126, https://doi.org/10.1117/12.732489, 2007.
Werdell, P. J., Franz, B. A., and Bailey, S. W.: Evaluation of shortwave infrared atmospheric correction for ocean color remote sensing of Chesapeake Bay, Remote Sens. Environ., 114, 2238–2247, https://doi.org/10.1016/j.rse.2010.04.027, 2010.
Wu, Y., Knudby, A., Pahlevan, N., Lapen, D., and Zeng, C.: Sensor-generic adjacency-effect correction for remote sensing of coastal and inland waters, Remote Sens. Environ., 315, 114433, https://doi.org/10.1016/j.rse.2024.114433, 2024.
Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G., Woodcock, C. E., Allen, R. G., Anderson, M. C., Belward, A. S., Cohen, W. B., and Dwyer, J.: Current status of Landsat program, science, and applications, Remote Sens. Environ., 225, 127–147, https://doi.org/10.1016/j.rse.2010.04.027, 2019.
Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., and Hansen, M.: Fifty years of Landsat science and impacts, Remote Sens. Environ., 280, 113195, https://doi.org/10.1016/j.rse.2022.113195, 2022.
Xu, Y., Feng, L., Zhao, D., and Lu, J.: Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data, Int. J. Appl. Earth Obs. Geoinf., 93, 102192, https://doi.org/10.1016/j.rse.2022.113195, 2020.
Yan, N., Sun, Z., Huang, W., Jun, Z., and Sun, S.: Assessing Landsat-8 atmospheric correction schemes in low to moderate turbidity waters from a global perspective, Int. J. Digit. Earth, 16, 66–92, https://doi.org/10.1080/17538947.2022.2161651, 2023.
Zhu, Z., Wang, S., and Woodcock, C. E.: Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel-2 images, Remote Sens. Environ., 159, 269–277, https://doi.org/10.1016/j.rse.2014.12.014, 2015.
Zhu, Z., Wulder, M. A., Roy, D. P., Woodcock, C. E., Hansen, M. C., Radeloff, V. C., Healey, S. P., Schaaf, C., Hostert, P., Strobl, P., and Pekel, J. F.: Benefits of the free and open Landsat data policy, Remote Sens. Environ., 224, 382–385, https://doi.org/10.1016/j.rse.2019.02.016, 2019.
Zibordi, G., Holben, B., Hooker, S. B., Mélin, F., Berthon, J. F., Slutsker, I., Giles, D., Vandemark, D., Feng, H., Rutledge, K., and Schuster, G.: A network for standardized ocean color validation measurements, Eos Trans. Am. Geophys. Union, 87, 293–297, https://doi.org/10.1029/2006EO300001, 2006.
Zibordi, G., Mélin, F., Berthon, J. F., Holben, B., Slutsker, I., Giles, D., D'Alimonte, D., Vandemark, D., Feng, H., Schuster, G., and Fabbri, B. E.: AERONET-OC: a network for the validation of ocean color primary products, J. Atmos. Oceanic Technol., 26, 1634–1651, https://doi.org/10.1175/2009JTECHO654.1, 2009.
Short summary
The purpose of this paper is to communicate United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center’s origins, objectives, and future directions for operationalizing global Level 2 Aquatic Reflectance (AR) science products for Landsat satellite missions.
The purpose of this paper is to communicate United States Geological Survey (USGS) Earth...
Altmetrics
Final-revised paper
Preprint