Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands.
Open accessAbstract
Remote sensing algorithms that use red and NIR bands for the estimation of chlorophyll-a concentration [Chl] can be more effective in inland and coastal waters than algorithms that use blue and green bands. We tested such two-band and three-band red-NIR algorithms using comprehensive synthetic data sets of reflectance spectra and inherent optical properties related to various water parameters and a very consistent in situ data set from several lakes in Nebraska, USA. The two-band algorithms tested with MERIS bands were Rrs(708)/Rrs(665) and Rrs(753)/Rrs(665). The three-band algorithm with MERIS bands was in the form R3=[Rrs(-1)(665)-Rrs(-1)(708)]×Rrs(753). It is shown that the relationships of both Rrs(708)/Rrs(665) and R3 with [Chl] do not depend much on the absorption by CDOM and non-algal particles, or the backscattering properties of water constituents, and can be defined in terms of water absorption coefficients at the respective bands as well as the phytoplankton specific absorption coefficient at 665 nm. The relationship of the latter with [Chl] was established for [Chl]>1 mg/m3 and then further used to develop algorithms which showed a very good match with field data and should not require regional tuning.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Algorithms such as the ratio of 704-665 nm and a form of the MCI can be applied to Sentinel-2/MSI data and have the potential for identifying high biomass blooms in Chesapeake Bay.
“Algorithms, such as the ratio of 704-665 nm (Gilerson et al., 2010) and a form of the MCI from Gower et al. (2008), can be applied to Sentinel-2/MSI data and have the potential for identifying high biomass blooms in Chesapeake Bay.”
From Current and Future Remote Sensing of Harmful Algal Blooms in the Chesapeake Bay to Support the Shellfish Industry (2020) · cites this paper as Gilerson et al., 2010
Cites
Optical properties of the clearest natural waters (200–800 nm)
Smith, R. C., & Baker, K. S. (1981) · Applied Optics · doi:10.1364/ao.20.000177 · Cited by 1,767 · see citing works
A semianalytic radiance model of ocean color
Gordon, H. R., Brown, O. B., Evans, R. H., Brown, J. W., Smith, R. C., Baker, K. S., & Clark, D. K. (1988) · Journal of Geophysical Research Atmospheres · doi:10.1029/jd093id09p10909 · Cited by 1,308 · see citing works
Bricaud, A., Babin, M., Morel, A., & Claustre, H. (1995) · Journal of Geophysical Research Atmospheres · doi:10.1029/95jc00463 · Cited by 1,121 · see citing works
A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation
Gitelson, A. A., Dall’Olmo, G., Moses, W. J., Rundquist, D. C., Barrow, T. M., Fisher, T. R., Gurlin, D., & Holz, J. C. (2008) · Remote Sensing of Environment · doi:10.1016/j.rse.2008.04.015 · Cited by 663 · see citing works
Self-citationGitelson, A. A. (1992) · International Journal of Remote Sensing · doi:10.1080/01431169208904125 · Cited by 637 · see citing works
Self-citationThe NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing
Wang, M., & Shi, W. (2007) · Optics Express · doi:10.1364/oe.15.015722 · Cited by 537 · see citing works
Ciotti, Á. M., Lewis, M. R., & Cullen, J. J. (2002) · Limnology and Oceanography · doi:10.4319/lo.2002.47.2.0404 · Cited by 531 · see citing works
Seawifs Postlaunch Calibration and Validation Analyses
Hooker, S. B., Firestone, E. R., McClain, C. R., Ainsworth, E. J., Barnes, R. A., Eplee, R. E., Patt, F. S., Robinson, W. D., Wang, M., & Bailey, S. W. (2013) · NASA Technical Reports Server (NASA) · Cited by 423 · see citing works
Remote chlorophyll-a retrieval in turbid, productive estuaries: Chesapeake Bay case study
Gitelson, A. A., Schalles, J. F., & Hladik, C. M. (2007) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.01.016 · Cited by 422 · see citing works
Self-citationDall’Olmo, G., & Gitelson, A. A. (2005) · Applied Optics · doi:10.1364/ao.44.000412 · Cited by 405 · see citing works
Self-citationDetection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer
Gower, J. F. R., King, S., Borstad, G. A., & Brown, L. (2005) · International Journal of Remote Sensing · doi:10.1080/01431160500075857 · Cited by 355 · see citing works
Le, C., Li, Y., Zha, Y., Sun, D., Huang, C., & Lü, H. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.02.005 · Cited by 313 · see citing works
Moses, W. J., Gitelson, A. A., Berdnikov, S. V., & Povazhnyy, V. (2009) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2009.2026657 · Cited by 267 · see citing works
Self-citationDall’Olmo, G., Gitelson, A. A., & Rundquist, D. C. (2003) · Geophysical Research Letters · doi:10.1029/2003gl018065 · Cited by 265 · see citing works
Self-citationWang, M., & Shi, W. (2005) · Geophysical Research Letters · doi:10.1029/2005gl022917 · Cited by 258 · see citing works
Moore, G., Aiken, J., & Lavender, S. (1999) · International Journal of Remote Sensing · doi:10.1080/014311699212434 · Cited by 255 · see citing works
Gons, H. J. (2002) · Journal of Plankton Research · doi:10.1093/plankt/24.9.947 · Cited by 223 · see citing works
Gitelson, A. A., Gurlin, D., Moses, W. J., & Barrow, T. M. (2009) · Environmental Research Letters · doi:10.1088/1748-9326/4/4/045003 · Cited by 221 · see citing works
Self-citationGordon, H. R. (1979) · Applied Optics · doi:10.1364/ao.18.001161 · Cited by 183 · see citing works
Han, L., & Rundquist, D. C. (1997) · Remote Sensing of Environment · doi:10.1016/s0034-4257(97)00106-5 · Cited by 180 · see citing works
Werdell, P. J., Bailey, S. W., Franz, B. A., Harding, L. W., Feldman, G. C., & McClain, C. R. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.02.012 · Cited by 158 · see citing works
Ruddick, K. G., Gons, H. J., Rijkeboer, M., & Tilstone, G. H. (2001) · Applied Optics · doi:10.1364/ao.40.003575 · Cited by 157 · see citing works
Dall’Olmo, G., & Gitelson, A. A. (2006) · Applied Optics · doi:10.1364/ao.45.003577 · Cited by 142 · see citing works
Self-citationSnape, W. J., Chang, L., & Bortz, D. L. (1997) · The American Journal of Managed Care · Cited by 117 · see citing works
Gilerson, A., Zhou, J., Hlaing, S., Ioannou, I., Schalles, J. F., Gross, B. M., Moshary, F., & Ahmed, S. J. (2007) · Optics Express · doi:10.1364/oe.15.015702 · Cited by 115 · see citing works
Self-citationSatellite detection of bloom and pigment distributions in estuaries
Stumpf, R. P., & Tyler, M. A. (1988) · Remote Sensing of Environment · doi:10.1016/0034-4257(88)90014-4 · Cited by 106 · see citing works
Vasilkov, A. P., & Kopelevich, O. V. (1982) · Oceanology · Cited by 31 · see citing works
A TOOL FOR INVERSE MODELING OF SPECTRAL MEASUREMENTS IN DEEP AND SHALLOW WATERS
Gege, P., & Albert, A. (2006) · Remote sensing and digital image processing · doi:10.1007/1-4020-3968-9_4 · Cited by 30 · see citing works
Zhou, J., Gilerson, A., Ioannou, I., Hlaing, S., Schalles, J. F., Gross, B. M., Moshary, F., & Ahmed, S. J. (2008) · Optics Express · doi:10.1364/oe.16.017468 · Cited by 18 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Pahlevan, N., Smith, B., Schalles, J. F., Binding, C. E., Cao, Z., Ma, R., Alikas, K., Kangro, K., Gurlin, D., Hà, N. T. T., et al. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111604 · Cited by 579 · see citing works
Self-citationChlorophyll algorithms for ocean color sensors - OC4, OC5 & OC6
O’Reilly, J. E., & Werdell, P. J. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.04.021 · Cited by 526 · see citing works
A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes
Cao, Z., Ma, R., Duan, H., Pahlevan, N., Melack, J. M., Shen, M., & Xue, K. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111974 · Cited by 447 · see citing works
A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges
Yang, H., Kong, J., Hu, H., Du, Y., Gao, M., & Chen, F. (2022) · Remote Sensing · doi:10.3390/rs14081770 · Cited by 400 · see citing works
Pahlevan, N., Mangin, A., Balasubramanian, S. V., Smith, B., Alikas, K., Arai, K., Barbosa, C. C. F., Bélanger, S., Binding, C. E., Bresciani, M., et al. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112366 · Cited by 378 · see citing works
Self-citationCawse-Nicholson, K. A., Townsend, P. A., SCHIMEL, D. S., Assiri, A., Blake, P. L., Buongiorno, M. F., Campbell, P., Carmon, N., Casey, K. A., Correa-Pabón, R. E., et al. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112349 · Cited by 338 · see citing works
Self-citationGurlin, D., Gitelson, A. A., & Moses, W. J. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.08.011 · Cited by 297 · see citing works
Self-citationNeil, C., Spyrakos, E., Hunter, P., & Tyler, A. N. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.04.027 · Cited by 292 · see citing works
Factors affecting the measurement of CDOM by remote sensing of optically complex inland waters
Brezonik, P. L., Olmanson, L. G., Finlay, J. C., & Bauer, M. E. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.04.033 · Cited by 275 · see citing works
Moore, T. S., Dowell, M. D., Bradt, S., & Verdú, A. R. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.11.021 · Cited by 248 · see citing works
Pahlevan, N., Smith, B., Alikas, K., Anstee, J. M., Barbosa, C. C. F., Binding, C. E., Bresciani, M., Cremella, B., Giardino, C., Gurlin, D., et al. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112860 · Cited by 244 · see citing works
Self-citationGuan, Q., Feng, L., Hou, X., Schurgers, G., Zheng, Y., & Tang, J. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111890 · Cited by 207 · see citing works
Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges
Shi, K., Zhang, Y., Qin, B., & Zhou, B. (2019) · 中国科学通报：英文版 · doi:10.1016/j.scib.2019.07.002 · Cited by 196 · see citing works
Vanhellemont, Q., & Ruddick, K. G. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112284 · Cited by 196 · see citing works
Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria.
Stumpf, R. P., Davis, T. W., Wynne, T. T., Graham, J. L., Loftin, K. A., Johengen, T. H., Gossiaux, D., Palladino, D., & Burtner, A. (2016) · Harmful Algae · doi:10.1016/j.hal.2016.01.005 · Cited by 190 · see citing works
Watanabe, F. S. Y., Alcântara, E., Rodrigues, T., Imai, N. N., Barbosa, C. C. F., & Rotta, L. H. S. (2015) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph120910391 · Cited by 169 · see citing works
Saberioon, M., Brom, J., Nedbal, V., Souc̆ek, P., & Císař, P. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.106236 · Cited by 161 · see citing works
An analysis of satellite-derived chlorophyll and algal bloom indices on Lake Winnipeg
Binding, C. E., Greenberg, T. A., McCullough, G. K., Watson, S. B., & Page, E. (2018) · Journal of Great Lakes Research · doi:10.1016/j.jglr.2018.04.001 · Cited by 156 · see citing works
Sun, Y. J., Wang, D., Li, L., Ning, R., Yu, S., & Gao, N. (2024) · Water Research · doi:10.1016/j.watres.2024.122546 · Cited by 154 · see citing works
Moses, W. J., Gitelson, A. A., Berdnikov, S. V., Валерьевич, С. В., & Povazhnyi, V. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.01.024 · Cited by 148 · see citing works
Self-citationYacobi, Y. Z., Moses, W. J., Kaganovsky, S., Sulimani, B., Leavitt, B., & Gitelson, A. A. (2011) · Water Research · doi:10.1016/j.watres.2011.02.002 · Cited by 142 · see citing works
Self-citationAn optimized Chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters
Smith, M., Lain, L., & Bernard, S. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.06.002 · Cited by 139 · see citing works
Pahlevan, N., Smith, B., Binding, C. E., Gurlin, D., Li, L., Bresciani, M., & Giardino, C. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.112200 · Cited by 130 · see citing works
Self-citationRemote Sensing of Algal Blooms: An Overview with Case Studies
Klemas, V. (2011) · Journal of Coastal Research · doi:10.2112/jcoastres-d-11-00051.1 · Cited by 126 · see citing works
Fisheries applications of remote sensing: An overview
Klemas, V. (2013) · Fisheries Research · doi:10.1016/j.fishres.2012.02.027 · Cited by 119 · see citing works
Liu, G., Li, L., Song, K., Li, Y., Lyu, H., Wen, Z., Fang, C., Bi, S., Sun, X., Wang, Z., et al. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111648 · Cited by 124 · see citing works
Remote estimation of chlorophyll-a in turbid inland waters: Three-band model versus GA-PLS model
Song, K., Li, L., Tedesco, L. P., Li, S., Duan, H., Liu, D., Hall, B., Du, J., Li, Z., Shi, K., et al. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.05.017 · Cited by 121 · see citing works
Eutrophication state in the Eastern China based on Landsat 35-year observations
Hu, M., Ma, R., Xiong, J., Wang, M., Cao, Z., & Xue, K. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.113057 · Cited by 118 · see citing works
Binding, C. E., Greenberg, T. A., & Bukata, R. P. (2013) · Journal of Great Lakes Research · doi:10.1016/j.jglr.2013.04.005 · Cited by 117 · see citing works
Improved red-edge chlorophyll-a detection for Sentinel 2
Bramich, J. M., Bolch, C. J. S., & Fischer, A. M. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.106876 · Cited by 113 · see citing works
Kwon, Y. S., Pyo, J., Kwon, Y., Duan, H., Cho, K. H., & Park, Y. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111517 · Cited by 110 · see citing works
A low-cost autonomous optical sensor for water quality monitoring
Murphy, K., Heery, B., Sullivan, T., Zhang, D., Paludetti, L. F., Lau, K. T., Diamond, D., Costa, E. J. X., O'Connor, N. E., & Regan, F. B. (2014) · Talanta · doi:10.1016/j.talanta.2014.09.045 · Cited by 108 · see citing works
Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8
Bresciani, M., Cazzaniga, I., Austoni, M., Sforzi, T., Buzzi, F., Morabito, G., & Giardino, C. (2018) · Hydrobiologia · doi:10.1007/s10750-017-3462-2 · Cited by 107 · see citing works
Matsushita, B., Yang, W., Yu, G., Oyama, Y., Yoshimura, K., & Fukushima, T. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.12.022 · Cited by 106 · see citing works
Huang, C., Shi, K., Yang, H., Li, Y., Zhu, A., Sun, D., Xu, L., Zou, J., & Chen, X. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.12.016 · Cited by 105 · see citing works
Seegers, B. N., Werdell, P. J., Vandermeulen, R. A., Salls, W. B., Stumpf, R. P., Schaeffer, B. A., Owens, T., Bailey, S. W., Scott, J. P., & Loftin, K. A. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112685 · Cited by 107 · see citing works
Cui, T., Zhang, J., Wang, K., Wei, J., Mu, B., Ma, Y., Zhu, J., Liu, R. J., & Chen, X. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.02.017 · Cited by 100 · see citing works
Ocean water quality monitoring using remote sensing techniques: A review
Mohseni, F., Saba, F., Mirmazloumi, S. M., Amani, M., Mokhtarzade, M., Jamali, S., & Mahdavi, S. (2022) · Marine Environmental Research · doi:10.1016/j.marenvres.2022.105701 · Cited by 99 · see citing works
An analysis of MODIS-derived algal and mineral turbidity in Lake Erie
Binding, C. E., Greenberg, T. A., & Bukata, R. P. (2012) · Journal of Great Lakes Research · doi:10.1016/j.jglr.2011.12.003 · Cited by 99 · see citing works
Kravitz, J. A., Matthews, M. W., Bernard, S., & Griffith, D. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111562 · Cited by 95 · see citing works
A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms
Mishra, S., & Mishra, D. R. (2014) · Environmental Research Letters · doi:10.1088/1748-9326/9/11/114003 · Cited by 95 · see citing works
Mapping algal bloom dynamics in small reservoirs using Sentinel-2 imagery in Google Earth Engine
Kislik, C., Dronova, I., Grantham, T. E., & Kelly, M. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.109041 · Cited by 94 · see citing works
Werther, M., Odermatt, D., Simis, S. G. H., Gurlin, D., Lehmann, M. K., Kutser, T., Gupana, R. S., Varley, A., Hunter, P., Tyler, A. N., et al. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.113295 · Cited by 93 · see citing works
Self-citationChen, J., Zhang, M., Cui, T., & Zhenhe, W. (2013) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2013.2242845 · Cited by 87 · see citing works
Song, K., Li, L., Wang, Z., Liu, D., Zhang, B., Xu, J., Du, J., Li, L., Li, S., & Wang, Y. (2011) · Environmental Monitoring and Assessment · doi:10.1007/s10661-011-2053-3 · Cited by 87 · see citing works
Li, L., Li, L., Song, K., Li, Y., Tedesco, L. P., Shi, K., & Li, Z. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.03.031 · Cited by 86 · see citing works
Kim, Y. W., Kim, T., Shin, J., Lee, D., Park, Y., Kim, Y., & Cha, Y. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.108737 · Cited by 82 · see citing works
Application and recent progress of inland water monitoring using remote sensing techniques
Cao, Q., Yu, G., & Qiao, Z. (2022) · Environmental Monitoring and Assessment · doi:10.1007/s10661-022-10690-9 · Cited by 82 · see citing works
Wolny, J. L., Tomlinson, M. C., Schollaert Uz, S., Egerton, T. A., McKay, J. R., Meredith, A., Reece, K. S., Scott, G. P., & Stumpf, R. P. (2020) · Frontiers in Marine Science · doi:10.3389/fmars.2020.00337 · Cited by 80 · see citing works
“Algorithms, such as the ratio of 704–665 nm (Gilerson et al., 2010) and a form of the MCI from Gower et al.”
BackgroundOgashawara, I., Kiel, C., Jechow, A., Kohnert, K., Ruhtz, T., Grossart, H., Hölker, F., Nejstgaard, J. C., Berger, S. A., & Wollrab, S. (2021) · Remote Sensing · doi:10.3390/rs13081542 · Cited by 79 · see citing works
Warren, M. A., Simis, S. G. H., & Selmes, N. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112651 · Cited by 79 · see citing works
Remote chlorophyll-a estimates for inland waters based on a cluster-based classification
Shi, K., Li, Y., Li, L., Lü, H., Song, K., Liu, Z., Xu, Y., & Li, Z. (2012) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2012.11.058 · Cited by 79 · see citing works
Lavigne, H., der Zande, D. V., Ruddick, K. G., dos Santos, J. F. C., Gohin, F., Brotas, V., & Kratzer, S. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.112237 · Cited by 78 · see citing works
Landsat observations of chlorophyll-a variations in Lake Taihu from 1984 to 2019
Cao, Z., Ma, R., Melack, J. M., Duan, H., Liu, M., Kutser, T., Xue, K., Shen, M., Qi, T., & Yuan, H. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102642 · Cited by 78 · see citing works
Remote sensing for mapping algal blooms in freshwater lakes: a review
Rolim, S. B. A., Veettil, B. K., Vieiro, A. P., Kessler, A. B., & Gonzatti, C. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-25230-2 · Cited by 75 · see citing works
Xiong, J., Lin, C., Ma, R., & Cao, Z. (2019) · Remote Sensing · doi:10.3390/rs11172068 · Cited by 74 · see citing works
Liu, X., Steele, C., Simis, S. G. H., Warren, M. A., Tyler, A. N., Spyrakos, E., Selmes, N., & Hunter, P. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112710 · Cited by 74 · see citing works
Coffer, M. M., Schaeffer, B. A., Salls, W. B., Urquhart, E. A., Loftin, K. A., Stumpf, R. P., Werdell, P. J., & Darling, J. A. (2021) · Ecological Indicators · doi:10.1016/j.ecolind.2021.107822 · Cited by 71 · see citing works
Binding, C. E., Pizzolato, L., & Zeng, C. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.106999 · Cited by 72 · see citing works
Khattab, M. F. O., & Merkel, B. J. (2013) · Arabian Journal of Geosciences · doi:10.1007/s12517-013-1026-y · Cited by 71 · see citing works
Hà, N. T. T., Koike, K., Nhuận, M. T., Canh, B. D., Thảo, N. T. P., & Parsons, M. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2739184 · Cited by 71 · see citing works
Molkov, A. A., Fedorov, S. V., Pelevin, V. V., & Korchemkina, E. N. (2019) · Remote Sensing · doi:10.3390/rs11101215 · Cited by 70 · see citing works
A decade-long chlorophyll-a data record in lakes across China from VIIRS observations
Cao, Z., Wang, M., Ma, R., Zhang, Y., Duan, H., Jiang, L., Xue, K., Xiong, J., & Hu, M. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113953 · Cited by 68 · see citing works
Mission Requirements for Future Ocean-Colour Sensors.
McClain, C. R., Meister, G., & Ioccg (2012) · IOC of UNESCO (Intergovernmental Oceanographic Commission) · doi:10.25607/obp-104 · Cited by 67 · see citing works
Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies
Carrea, L., Crétaux, J., Liu, X., Wu, Y., Calmettes, B., Duguay, C., Merchant, C. J., Selmes, N., Simis, S. G. H., Warren, M. A., et al. (2023) · Scientific Data · doi:10.1038/s41597-022-01889-z · Cited by 74 · see citing works
Jiang, G., Loiselle, S., Yang, D., Ma, R., Su, W., & Gao, C. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111735 · Cited by 66 · see citing works
Distribution of hotspots of chub mackerel based on remote-sensing data in coastal waters of China
Li, G., Chen, X., Lin, L., & Guan, W. (2014) · International Journal of Remote Sensing · doi:10.1080/01431161.2014.916057 · Cited by 66 · see citing works
Devred, E., Turpie, K., Moses, W. J., Klemas, V., Moisan, T. A. H., Babin, M., Toro-Farmer, G., Forget, M., & Jo, Y. (2013) · Remote Sensing · doi:10.3390/rs5126812 · Cited by 64 · see citing works
Self-citationSong, K., Li, L., Tedesco, L. P., Li, S., Clercin, N., Hall, B., Li, Z., & Shi, K. (2012) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2012.03.058 · Cited by 64 · see citing works
Shi, K., Li, Y., Li, L., & Lü, H. (2013) · Journal of Geophysical Research Biogeosciences · doi:10.1002/jgrg.20071 · Cited by 62 · see citing works
Evaluation of Satellite Retrievals of Ocean Chlorophyll-a in the California Current
Kahru, M., Kudela, R. M., Anderson, C. R., Manzano‐Sarabia, M., & Mitchell, B. G. (2014) · Remote Sensing · doi:10.3390/rs6098524 · Cited by 61 · see citing works
Hà, N. T. T., Koike, K., & Nhuận, M. T. (2013) · Remote Sensing · doi:10.3390/rs6010421 · Cited by 61 · see citing works
Tran, M. D., Vantrepotte, V., Loisel, H., de Oliveira, E. N., Tran, T., Jorge, D. S. F., Mériaux, X., & Paranhos, R. (2023) · Remote Sensing · doi:10.3390/rs15061653 · Cited by 60 · see citing works
Abbas, M., Melesse, A. M., Scinto, L. J., & Rehage, J. S. (2019) · Water · doi:10.3390/w11081621 · Cited by 59 · see citing works
Xue, K., Ma, R., Shen, M., Li, Y., Duan, H., Cao, Z., Wang, D., & Xiong, J. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.137774 · Cited by 59 · see citing works
O’Shea, R. E., Pahlevan, N., Smith, B., Boss, E., Gurlin, D., Alikas, K., Kangro, K., Kudela, R. M., & Vaičiūtė, D. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113706 · Cited by 59 · see citing works
Self-citationKolluru, S., & Tiwari, S. P. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.157191 · Cited by 58 · see citing works
A simple method for distinguishing global Case-1 and Case-2 waters using SeaWiFS measurements
Matsushita, B., Yang, W., Chang, P., Yang, F., & Fukushima, T. (2012) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2012.02.008 · Cited by 58 · see citing works
Cairo, C. T., Barbosa, C. C. F., Lobo, F. D. L., Novo, E. M. L. D. M., Carlos, F. M., Maciel, D. A., Júnior, R. F., da Silva, E. F. F., & Curtarelli, V. P. (2019) · Remote Sensing · doi:10.3390/rs12010040 · Cited by 58 · see citing works
Inherent Optical Properties of the Baltic Sea in Comparison to Other Seas and Oceans
Kratzer, S., & Moore, G. (2018) · Remote Sensing · doi:10.3390/rs10030418 · Cited by 57 · see citing works
Challenges and New Advances in Ocean Color Remote Sensing of Coastal Waters
Loisel, H., Vantrepotte, V., Jamet, C., & Ngoc, D. D. (2013) · InTech eBooks · doi:10.5772/56414 · Cited by 56 · see citing works
Huang, C., Zou, J., Li, Y., Yang, H., Shi, K., Li, J., Wang, Y., Chena, X., & Zheng, F. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.03.012 · Cited by 56 · see citing works
Binding, C. E., Zastepa, A., & Zeng, C. (2018) · Journal of Great Lakes Research · doi:10.1016/j.jglr.2018.11.015 · Cited by 54 · see citing works
Dev, P. J., Sukenik, A., Mishra, D. R., & Ostrovsky, I. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.150423 · Cited by 54 · see citing works
Cao, Z., Shen, M., Kutser, T., Liu, M., Qi, T.-C., Ma, J., Ma, R., & Duan, H. (2022) · Earth-Science Reviews · doi:10.1016/j.earscirev.2022.104154 · Cited by 47 · see citing works
Cao, Z., Ma, R., Liu, M., Duan, H., Xiao, Q., Xue, K., & Shen, M. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3207345 · Cited by 53 · see citing works
Yuan, X., Wang, S., Fan, F., Dong, Y., Li, Y., Lin, W., & Zhou, C. (2022) · Environmental Research · doi:10.1016/j.envres.2022.114380 · Cited by 53 · see citing works
Fu, B., Li, S., Lao, Z., Yuan, B., Liang, Y., He, W., Sun, W., & He, H. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.165963 · Cited by 53 · see citing works
Bresciani, M., Pinardi, M., Free, G., Luciani, G., Ghebrehiwot, S., Laanen, M., Peters, S. W. M., Bella, V. D., Padula, R., & Giardino, C. (2020) · Water · doi:10.3390/w12010284 · Cited by 53 · see citing works
Coffer, M. M., Schaeffer, B. A., Foreman, K., Porteous, A., Loftin, K. A., Stumpf, R. P., Werdell, P. J., Urquhart, E. A., Albert, R. J., & Darling, J. A. (2021) · Water Research · doi:10.1016/j.watres.2021.117377 · Cited by 53 · see citing works
Spyrakos, E., Vilas, L. G., Palenzuela, J. M. T., & Barton, E. D. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.05.008 · Cited by 51 · see citing works
Li, J., Gao, M., Feng, L., Zhao, H., Shen, Q., Zhang, F., Wang, S., & Zhang, B. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2936403 · Cited by 50 · see citing works
Beal, M. R. W., Mutlu Ozdogan, A., & Block, P. (2024) · Water Resources Research · doi:10.1029/2023wr035744 · Cited by 50 · see citing works
Song, K., Lu, D., Li, L., Li, S., Wang, Z., & Du, J. (2011) · Ecological Informatics · doi:10.1016/j.ecoinf.2011.08.006 · Cited by 50 · see citing works
Phycocyanin‐specific absorption coefficient: Eliminating the effect of chlorophylls absorption
Yacobi, Y. Z., Köhler, J., Leunert, F., & Gitelson, A. A. (2015) · Limnology and Oceanography Methods · doi:10.1002/lom3.10015 · Cited by 50 · see citing works
Self-citationIntegrating Remote Sensing Methods for Monitoring Lake Water Quality: A Comprehensive Review
Batina, A., & Krtalić, A. (2024) · Hydrology · doi:10.3390/hydrology11070092 · Cited by 62 · see citing works
Mishra, D. R., Schaeffer, B. A., & Keith, D. J. (2014) · GIScience & Remote Sensing · doi:10.1080/15481603.2014.895581 · Cited by 49 · see citing works
Xiao, Y., Chen, J., Xu, Y., Guo, S., Nie, X., Guo, Y., Li, X., Hao, F., & Fu, Y. H. (2023) · Ecological Indicators · doi:10.1016/j.ecolind.2023.111041 · Cited by 48 · see citing works
Xue, K., Ma, R., Wang, D., & Shen, M. (2019) · Remote Sensing · doi:10.3390/rs11020184 · Cited by 48 · see citing works
Drone-borne sensing of major and accessory pigments in algae using deep learning modeling
Pyo, J., Hong, S. M., Jang, J., Park, S., Park, J., Noh, J. H., & Cho, K. H. (2022) · GIScience & Remote Sensing · doi:10.1080/15481603.2022.2027120 · Cited by 46 · see citing works
- Authors
- Gilerson, A. A., Gitelson, A. A., Zhou, J., Gurlin, D., Moses, W., Ioannou, I., & Ahmed, S. A.
- Affiliations
- City College of New York (US)
- City University of New York (US)
- University of Nebraska–Lincoln (US)
- Venue
- Optics Express
- Publisher
- Optica Publishing Group
- Published
- 2010-11-03
- Volume
- 18
- Issue
- 23
- Pages
- 24109
- ISSN
- 1094-4087 (ISSN-L)
- OpenAlex
- W2020972217
- Cited by
- 403
- References
- 30
- Citations by year
- 2026: 29 · 2025: 52 · 2024: 28 · 2023: 39 · 2022: 44 · 2021: 28 · 2020: 25 · 2019: 24 · 2018: 24 · 2017: 13 · 2016: 15 · 2015: 17 · 2014: 20 · 2013: 19 · 2012: 17
- FWCI
- 10.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Optics Express (publishedVersion)
- PubMed (publishedVersion)
- Lincoln (University of Nebraska) (submittedVersion)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Marine and coastal ecosystems
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Keywords
- chlorophyll-a estimation, remote sensing algorithms, red and near-infrared bands, MERIS, coastal waters, inland waters, lakes, spectral reflectance, inherent optical properties, CDOM absorption, non-algal particles, phytoplankton absorption coefficient, water absorption coefficient
- MeSH terms
- Absorption, Chlorophyll A, Algorithms, Chlorophyll, Computer Simulation, Models, Biological, Nebraska, Phytoplankton, Seawater, Water, Spectroscopy, Near-Infrared
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
