Articles | Volume 18, issue 2
https://doi.org/10.5194/os-18-361-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-18-361-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Mar 2022
Research article |
| 24 Mar 2022
| 24 Mar 2022
The capabilities of Sentinel-MSI (2A/2B) and Landsat-OLI (8/9) in seagrass and algae species differentiation using spectral reflectance
Abderrazak Bannari
CORRESPONDING AUTHOR
Space Pix-Map International Inc., Gatineau, Québec, J8R 3R7,
Canada
Thamer Salim Ali
Department of Natural Resources and Environment, College of
Graduate Studies, Arabian Gulf University, P.O. Box 26671, Manama, Kingdom of Bahrain
Asma Abahussain
Department of Natural Resources and Environment, College of
Graduate Studies, Arabian Gulf University, P.O. Box 26671, Manama, Kingdom of Bahrain
Related authors
Abderrazak Bannari and Abdelgader Abuelgasim
SOIL Discuss., https://doi.org/10.5194/soil-2021-55, https://doi.org/10.5194/soil-2021-55, 2021
Manuscript not accepted for further review
Short summary
Short summary
The study aims to analyze the ability of vegetation indices (VI’s) to map soil salt contents compared to the potential of evaporite mineral indices (EMI). The method used is based on simulated and satellite data acquired over two study areas: Kuwait-State and Omongwa salt-pan in Namibia. The obtained results demonstrated that it is impossible for VI’s to discriminate or to predict soil salinity. While, the EMI performed very well for the salt-affected soil classes mapping.
Abderrazak Bannari and Abdelgader Abuelgasim
SOIL Discuss., https://doi.org/10.5194/soil-2021-55, https://doi.org/10.5194/soil-2021-55, 2021
Manuscript not accepted for further review
Short summary
Short summary
The study aims to analyze the ability of vegetation indices (VI’s) to map soil salt contents compared to the potential of evaporite mineral indices (EMI). The method used is based on simulated and satellite data acquired over two study areas: Kuwait-State and Omongwa salt-pan in Namibia. The obtained results demonstrated that it is impossible for VI’s to discriminate or to predict soil salinity. While, the EMI performed very well for the salt-affected soil classes mapping.
Cited articles
Anders, K. and Lina, N.: Remote sensing of seagrasses in a patchy
multi-species environment, Int. J. Remote Sens., 32,
2227–2244, 2011.
ASD: Analytical Spectral Devices. Technical Guide, 4th Edn., ASD Inc.:
Boulder, CO, USA, available at:
http://www.asdi.com/products-spectroradiometers.asp (last access: 30
September 2020), 2015.
Bakirman, T. and Gumusay, M. U.: Assessment of Machine Learning Methods for
Seagrass Classification in the Mediterranean, Baltic J. Modern Comput.,
8, 315–326, https://doi.org/10.22364/bjmc.2020.8.2.07, 2020.
Bannari, A.: Synergy between Sentinel-MSI and Landsat-OLI to Support High
Temporal Frequency for Soil Salinity Monitoring in an Arid Landscape, in:
Research Developments in Saline Agriculture, edited by:
Dagar, J. C., Yadav, R. K., and Sharma, P. C.,
Springer Nature Singapore Pte Ltd., 67–93, ISBN 978-981-13-5831-9,
https://doi.org/10.1007/978-981-13-5832-6_3, 2019.
Bannari, A. and Kadhem, G.: MBES-CARIS Data Validation for Bathymetric
Mapping of Shallow Water in the Kingdom of Bahrain on the Arabian Gulf,
Remote Sens., 9, 385–404, 2017.
Bannari, A. and Al-Ali, Z.: Ground Reflectance Factor Retrieval from Landsat
(MSS, TM, ETM+, and OLI) Time Series Data based on Semi-empirical Line
Approach and Pseudo-invariant Targets in Arid Landscape, International
Geoscience and Remote Sensing Symposium (IGARSS-2020), 19–24 July,
Waikoloa, Hawaii, USA, 5990–5993, 2020.
Bannari, A., Morin, D., Huete, A. R., and Bonn, F.: A Review of Vegetation
indices, Remote Sens. Rev., 13, 95–120, 1995.
Bannari, A., Asalhi, H., and Teillet, P. M.: Transformed Difference
Vegetation Index (TDVI) for Vegetation cover Mapping, International
Geoscience and Remote Sensing Symposium (IGARSS'2002), Toronto, Ontario,
9–13 July, 3053–3055, 2002.
Bannari, A., Teillet, P. M., and Landry, R.: Comparaison des
réflectances des surfaces naturelles dans les bandes spectrales
homologues des capteurs TM de Landsat-5 et TME+ de Landsat-7, Revue
Télédétection, 4, 263–275, 2004.
Bargain, A., Robin, M., Le-Men, E., Huete, A. R., and Barillé, L.:
Spectral response of the seagrass Zostera noltii with different sediment
backgrounds, Aquat. Bot., 98, 45–56, 2012.
Barillé, L., Robin, M., Harin, N., Bargain, A., and Launeau, P.: Increase
in seagrass distribution at Bourgneuf Bay (France) detected by spatial
remote sensing, Aquat. Bot., 92, 185–194, 2010.
Barillé, L., Mouget, J. L., Méléder, V., Rosa, P., and Jesus, B.:
Spectral response of benthic diatoms with different sediment backgrounds,
Remote Sens. Environ., 115, 1034–1042, 2011.
Bayyana, S., Pawar, S., Gole, S., Dudhat, S., Pande, A., Mitra, D., Johnson,
J. A., and Sivakumar, K.: Detection and mapping of seagrass meadows at
Ritchie's archipelago using Sentinel 2A satellite imagery, Curr. Sci.,
118, 1275–1282, https://doi.org/10.18520/cs/v118/i8/1275-1282, 2020.
Ben-Dor, E., Ong, C., and Lau, I. C.: Reflectance measurements of soils in
the laboratory: Standards and protocols, Geoderma, 245/246, 112–124, 2015.
Boström, C., Pittman, S., Kneib, R., and Simenstad, C.: Seascape ecology
of coastal biogenic habitats: advances, gaps and challenges, Mar. Ecol. Prog. Ser., 427, 191–217,
2011.
Burfeind, D. D. and Stunz, G. W.: The effects of boat propeller scarring
intensity on nekton abundance in subtropical seagrass meadows, Mar.
Biol., 148, 953–962, 2006.
Candra, E. D., Harjo, H., and Wicaksono, P.: Above Ground Carbon Stock
Estimates of Mangrove Forest Using Worldview-2 Imagery in Teluk Benoa, Bali,
IOP Conference Series: Earth and Environmental Science, Earth Environ. Sci., 47, 012014, https://doi.org/10.1088/1755-1315/47/1/012014, 2016.
Chen, C.-F., Lau, A.-K., Chang, N.-B., Son, N.-T., Tong, P.-H.-S., and Chiang,
S.-H.: Multi-temporal change detection of seagrass beds using integrated
Landsat TM/ETM+/OLI imageries in Cam Ranh Bay, Vietnam, Ecol.
Inform., 35, 43–54, 2016.
Clark, R. N., Swayze, G. A., Livo, K. E., Kokaly, R. F., Sutley, S. J., Dalton, J. B., McDougal, R. R., and Gent, C. A.: Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems, J. Geophys. Res., 108, 5131, https://aviris.jpl.nasa.gov/proceedings/workshops/95_docs/12.PDF (last accss: 30 September 2020), 2003.
Clark, R. N. and Roush, T. L.: Reflectance spectroscopy: Quantitative
analysis techniques for remote sensing applications, J. Geophys.
Res., 89, 6329–6340, 1984.
Clark, R. N. and Swayze, G. A.: Mapping minerals, amorphous materials,
environmental materials, vegetation, water, ice and snow, and other
materials. The USGS Tricorder algorithm, in: Summaries of the
fifth annual NASA Jet Propulsion Laboratory airborne earth science workshop:
Pasadena, edited by: Green, R. O., NASA Jet Propulsion Laboratory Publication, 95, 39–40, ID 19950027321, 1995.
Clark, R. N., King, T. V. V., and Gorelick, N. S.: Automatic continuum
analysis of reflectance spectra. In JPL Proceedings of the 3rd Airborne
Imaging Spectrometer Data Analysis Workshop, 138–142,
https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19880004388.pdf
(last access: 18 March 2021), 1987.
Clark, R. N., Gallagher, A. J., and Swayze, G. A.: Material absorption-band
depth mapping of imaging spectrometer data using the complete band shape
least-squares algorithm simultaneously fit to multiple spectral features
from multiple materials, in: Proceedings of the Third Airborne
Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, NASA – Jet
Propulsion Laboratory Publications, No. 90–54, 176–186, 1990.
Clark, R. N., Swayze, G. A., Carlson, R., Grundy, W., and Noll, K.:
Spectroscopy from Space, Rev. Mineral. Geochem., 78, 399–446, https://doi.org/10.2138/rmg.2014.78.10, 2014.
Crowley, J. K., Brickey, D. W., and Rowan, L. C.: Airborne imaging
spectrometer data of the Ruby Mountains, Montana: mineral discrimination
using relative absorption band-depth images, Remote Sens. Environ.,
29, 121–134, https://doi.org/10.1016/0034-4257(89)90021-7, 1989.
Cummings, M. E. and Zimmerman, R. C.: Light harvesting and the package
effect in the seagrasses Thalassia testudinum Banks ex König and Zostera marina L., Optical constraints on photo-acclimation, Aquat. Bot., 75,
261–274, 2003.
Dahdouh-Guebas, F., Coppejans, E., and Van-Speybroeck, D.: Remote sensing and
zonation of seagrasses and algae along the Kenyan coast, Hydrobiologia, 400,
63–73, 1999.
Dattola, L., Rende, S. F., Dominici, R., Lanera, P., Di-Mento, R., Scalise,
S., Cappa, P., Oranges, T., and Aramini, G.: Comparison of Sentinel-2 and
Landsat-8 OLI satellite images vs. high spatial resolution images (MIVIS and
WorldView-2) for mapping Posidonia oceanica meadows. Proceedings of SPIE
10784, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water
Regions, 10 October 2018, Vol. 10784, 1078419-1; https://doi.org/10.1117/12.2326798,
2018.
Davranche, A., Lefebvre, G., and Poulin, B.: Wetland monitoring using
classification trees and SPOT-5 seasonal time series, Remote Sens.
Environ., 114, 552–562, 2010.
Dean, A. and Salim, A.: Remote sensing for the sustainable development of
offshore mariculture, in: A global assessment of offshore mariculture potential from a spatial perspective, edited by: Kapetsky, J. M., Aguilar-Manjarrez,
J., and Jenness, J.: FAO Fisheries and Aquaculture Technical Paper N. 549,
Rome, Italy, 181 pp., 2013.
Dekker, A. G., Hestir, E. L., Malthus, T. J., and Thankappan, M.: Continental
Scale Aquatic Habitat Monitoring Using Sentinel-2, ESA-ESRIN, Frascati,
Italy, 23 to 27 April, 28 pp., 2012.
Den-Hartog, C.: The seagrasses of the world, North-Holland Publishing
Company, Amsterdame, the Netherland, 275 pp.,
https://doi.org/10.1002/iroh.19710560139, 1970.
Dierssen, H. M., Chlus, A., and Russell, B.: Hyperspectral discrimination of
floating mats of seagrass wrack and the macroalgae Sargassum in coastal
waters of Greater Florida Bay using airborne remote sensing, Remote Sens.
Environ., 167, 247–258, 2015.
Drusch, M., Del-Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon,
F., Hoersch, B., Isola, C. Laberinti, P., Martimort, P., Meygret, A., Spoto,
F., Sy, O., Marchese, F., and Bargellini, P.: Sentinel-2: ESA's optical
high-resolution mission for GMES operational services, Remote Sens.
Environ., 120, 25–36, https://doi.org/10.1016/j.rse.2011.11.026, 2012.
Duarte, C. M. and Gattuso, J.-P.: Seagrass meadows, in: Encyclopedia of
Earth, edited by: Cleveland, C. J., Environmental information coalition
National Council for Science and the Environment, Washington, DC, USA, https://www.odysseyexpedition.org/curriculum/Seagrass_meadows.pdf (last access: 30 September 2020), 2008.
Duarte, C. M., Losada, I. J., Hendriks, I. E., Mazarrasa, I., and Marbà,
N.: The role of coastal plant communities for climate change mitigation and
adaptation, Nat. Clim. Change, 3, 961–968, https://doi.org/10.1038/nclimate1970, 2013.
Duffy, J. P., Pratt, L., Anderson, K., Land, P. E., and Shutler, J. D.:
Spatial assessment of intertidal seagrass meadows using optical imaging
systems and a lightweight drone, Estuar. Coast. Shelf Sci., 200,
169–180, 2018.
Dunton, K. H. and Schonberg, S. V.: Assessment of propeller scarring in
seagrass beds of the south Texas coast, J. Coast. Res., 37,
100–110, 2002.
ENVI: Continuum Removal Tutorial, Boulder, Colorado, USA,
http://www.harrisgeospatial.com/docs/ContinuumRemoval.html (last access: 5 November 2020), 2017.
Erftemeijer, P. L. A. and Shuail, D. A.: Seagrass Habitats in Arabian Gulf:
Distribution, Tolerance Thresholds and Threats, Aquat. Ecosyst. Health, 15, 73–83, 2012.
Ferguson, R. L. and Wood, L. L.: Mapping Submerged Aquatic Vegetation in
North Carolina with Conventional Aerial Photography. Federal Coastal Wetland
Mapping Programs, National Ocean Pollution Policy Board's Habitat Loss and
Modification Working Group, Biological Report, 90, 125–132,
https://apps.dtic.mil/sti/pdfs/ADA322827.pdf{#}page=123 (last access: 5 November 2020), 1990.
Flood, N.: Comparing Sentinel-2A and Landsat 7 and 8 Using Surface
Reflectance over Australia, Remote Sens., 9, 659, https://doi.org/10.3390/rs9070659, 2017.
Fourqurean, J. W., Duarte, C. M., Kennedy, H., Marbà, N., Holmer, M.,
Mateo, M. A., Apostolaki, E. T., Kendrick, G. A., Krause-Jensen, D.,
McGlathery, K. D., and Serrano, O.: Seagrass ecosystems as a globally
significant carbon stock, Nat. Geosci., 5, 505–509, 2012.
Fyfe, S. K.: Spatial and temporal variation in spectral reflectance: Are
seagrass species spectrally distinct? Limnology and Oceanography, 48, 464–479, https://doi.org/10.4319/lo.2003.48.1_part_2.0464, 2003.
Gascon, F., Bouzinac, C., Thépaut, O., Jung, M., Francesconi, B., Louis,
J., Lonjou, V., Lafrance, B., Massera, S., Gaudel-Vacaresse, A., Languille,
F., Alhammoud, B., Viallefont, F., Pflug, B., Bieniarz, J., Clerc, S.,
Pessiot, L., Trémas, T., Cadau, E., De Bonis, R., Isola, C., Martimort,
P., and Fernandez, V.: Copernicus Sentinel-2A Calibration and Products
Validation Status, Remote Sens., 9, 584,
https://doi.org/10.3390/rs9060584, 2017.
Gitelson, A. A., Kaufman, Y. J., Stark, R., and Rundquist, D.: Novel
algorithms for remote estimation of vegetation fraction, Remote Sens.
Environ., 80, 76–87, 2002a.
Gitelson, A. A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y. J., and
Derry, D.: Vegetation and soil lines in visible spectral space: a concept
and technique for remote estimation of vegetation fraction, Int. J.
Remote Sens., 23, 2537–2562, 2002b.
Grech, A., Chartrand-Miller, K., Erftemeijer, P., Fonseca, M., McKenzie, L.,
Rasheed, M., and Coles, R.: A comparison of threats, vulnerabilities and
management approaches in global seagrass bioregions, Environ. Res.
Lett., 7, 024006, https://doi.org/10.1088/1748-9326/7/2/024006, 2012.
Green, E. P. and Short, F.: World atlas of seagrasses. Prepared by the UIMEP
World Conservation Monitoring Centre. University of California Press,
Berkeley, USA, Vol. 47, Berkeley, California, USA, University of
California,https://doi.org/10.1515/BOT.2004.029, 2003.
Hamel, M. A. and Andréfouët, S.: Using very high resolution remote
sensing for the management of coral reef fisheries: review and perspectives,
Mar. Pollut. Bull., 60, 1397–1405,
doi10.1016/j.marpolbul.2010.07.002, 2010.
Hashim, M., Misbari, S., Yahya, N. N., Ahmad, S., Reba, M. N., and Komatsu, T.:
An approach for quantification of submerged seagrass biomass in shallow
turbid coastal waters. In Proceedings of IGARSS, Quebec, Canada,
4439–4442, https://doi.org/10.1109/IGARSS.2014.6947476, 2014.
Hedley, J., Roelfsema, C., Koetz, B., and Phinn, S.: Capability of the
Sentinel 2 mission for tropical coral reef mapping and coral bleaching
detection, Remote Sens. Environ., 120, 145–155, 2012a.
Hedley, J. D., Roelfsema, C. M., Phinn, S. R., and Mumby, P. J.:
Environmental and sensor limitations in optical remote sensing of coral
reefs: implications for monitoring and sensor design, Remote Sens., 4, 271–302,
https://doi.org/10.3390/rs4010271, 2012b.
Hossain, M. S., Bujang, J. S., Zakaria, M. H., and Hashim, M.: The
application of remote sensing to seagrass ecosystems: An overview and future
research prospects, Int. J. Remote Sens., 36, 61–113, 2014.
Huang, Z., Turner, B. J., Dury, S. J., Wallis, I. R., and Foley, W. J.:
Estimating foliage nitrogen concentration from HYMAP data using continuum
removal analysis, Remote Sens. Environ., 93, 18–29, 2004.
Huete, A. R.: A soil-adjusted vegetation index (SAVI), Remote Sens.f
Environ., 25, 295–309, 1988.
Huete, A. R., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira,
L. G.: Overview of the radiometric and biophysical performance of the MODIS
vegetation indices, Remote Sens. Environ., 83, 195–213,
https://doi.org/10.1016/S0034-4257(02)00096-2, 2002.
Hunt Jr., E. R., Doraiswamy, P. C., McMurtrey, J. E., Daughtry, C. S. T.,
Perry, E. M., and Akhmedov, B.: A visible band index for remote sensing leaf
chlorophyll content at the canopy scale, Int. J. Appl. Earth
Observ. Geoinf., 21, 103–112, 2013.
Indayani, A. B., Danoedoro, P., Wicaksono, P., Winarso, G., and Setiawan, K.
T.: Spectral Analysis from Absorption and Reflectance of Seagrass Leaves
using Trios-Ramses Spectroradiometer in Nusa Lembongan and Pemuteran, Bali.
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital, 17, 103-113,
https://doi.org/10.30536/j.pjpdcd.2020.v17.a3384, 2020.
Irons, J. R. Dwyer, J. L., and Barsi, J. A.: The next Landsat satellite: the
Landsat data continuity mission, Remote Sens. Environ., 122, 11–21,
https://doi.org/10.1016/j.rse.2011.08.026, 2012.
Jackson, R. D, Pinter, P. J., Paul, J., Reginato, R. J., Robert, J., and
Idso, S. B.: Hand-Held Radiometry, Agricultural Reviews and Manuals,
ARM-W-19; U.S. Department of Agriculture Science and Education
Administration, Phoenix, AZ, USA, Agricultural Research (Western Region), Science and Education Administration, U.S. Department of Agriculture, Oakland, California 94612,
https://naldc.nal.usda.gov/download/50523/PDF (last access: 30 September 2020), 1980.
James, M. E. and Kalluri, S. N. V.: The Pathfinder AVHRR land data set: an
improved coarse resolution data set for terrestrial monitoring, Int. J. Remote Sens., 15, 3347–3363, 1994.
Johnsen, G. and Sakshaug, E.: Biooptical characteristics of PSII and PSI in
33 species (13 pigment groups) of marine phytoplankton, and the relevance
for pulse amplitude-modulated and fast-repetition-rate fluorometry, J. Phycol., 43, 1236–1251, 2007.
Kibele, J.: Submerged habitats from space: Increasing map production
capacity with new methods and software, PhD Thesis, Institute of Marine
Science, the University of Auckland, New-Zeeland, University of Auckland, 179 pp., https://doi.org/10.13140/RG.2.2.33652.55683, 2017.
Kirk, J. T. O.: Light and photosynthesis in aquatic ecosystems, 2nd
Edn., Cambridge university press, 509 pp.,
https://doi.org/10.1017/CBO9780511623370, 1994.
Knight, E. and Kvaran, G.: Landsat-8 operational land imager design,
characterization and performance, Remote Sens., 6, 10286–10305, 2014.
Knudby, A. and Nordlund, L.: Remote sensing of seagrasses in a patchy
multi-species environment, Int. J. Remote Sens., 32,
2227–2244, 2011.
Kokaly, R. F., Despain, D. G., Clark, R. N., and Livo, K. E.: Mapping
vegetation in Yellowstone National Park using spectral feature analysis of
AVIRIS data, Remote Sens. Environ., 84, 437–456, 2003.
Komatsu, T., Hashim, M., Nurdin, N., Noiraksar, T., Prathep, A., Stankovic,
M., Hoang-Son, T. P., Thu, P. M., Luong, C. V., Wouthyzen, S., Phauk, S.,
Muslim, A. M., Yahya, N. N., Terauchi, G., Sagawa, T., and Ken-ichi
Hayashizaki, K.-H.: Practical mapping methods of seagrass beds by satellite
remote sensing and ground trothing, Coast. Mar. Sci., 43, 1–25,
2020.
Konstantinos, T., Spyridon, C. S., Apostolos, P., and Nikolaos, S.: The use
of Sentinel-2 imagery for seagrass mapping: Kalloni Gulf (Lesvos Island,
Greece) case study. Proceedings of the SPIE, Vol. 9688, Fourth
International Conference on Remote Sensing and Geoinformation of the
Environment (RSCy2016), 96881F, https://doi.org/10.1117/12.2242887,
2016.
Kovacs, E., Roelfsema, C., Lyons, M., Zhao, S., and Phinn, S.: Seagrass
habitat mapping: how do Landsat 8 OLI, Sentinel-2, ZY-3A, and Worldview-3
perform?, Remote Sens. Lett., 9, 686–695, 2018.
Larkum, A. W. D., Orth, R. J., and Duarte, C. M.: Seagrasses: Biology,
ecology and conservation, Springer, Dordrecht, the Netherlands, 689 pp.,
ISBN 13 978-1-4020-2983-7,
https://doi.org/10.1007/978-1-4020-2983-7, 2006.
Leleu, K., Alban, F., Pelletier, D., Charbonnel, E., Letourneur, Y., and
Boudouresque, C. F.: Fishers' perceptions as indicators of the performance of
Marine Protected Areas (MPAs), Mar. Policy, 36, 414–422,
https://doi.org/10.1016/j.marpol.2011.06.002, 2012.
Li, J. and Chen, B.: Global Revisit Interval Analysis of Landsat-8 -9 and
Sentinel-2A-2B Data for Terrestrial Monitoring, Sensors, 20, 6631,
https://doi.org/10.3390/s20226631, 2020.
Li, J. and Roy, D. P.: A Global Analysis of Sentinel-2A, Sentinel-2B and
Landsat-8 Data Revisit Intervals and Implications for Terrestrial
Monitoring, Remote Sens., 9, 902, https://doi.org/10.3390/rs9090902, 2017.
Li, R., Liu, J.-K., Sukcharoenpong, A., Yuan, J., Zhu, H., and Zhang, S.: A
Systematic Approach toward Detection of Seagrass Patches from Hyperspectral
Imagery, Mar. Geod., 35, 271–286, 2012.
Li, S.: Seagrass Mapping and Human Impact Evaluation Using Remote Sensing
Imagery at Core Banks, North Carolina, Duke University, Master thesis, Nicholas School of the Environment,
Duke University,
29 pp.,
https://hdl.handle.net/10161/16596 (last access: 30 September 2020), 2018.
Li, S., Ganguly, S., Dungan, J. L., Wang, W. L., and Nemani, R. R.:
Sentinel-2 MSI Radiometric Characterization and Cross-Calibration with
Landsat-8 OLI, Adv. Remote Sens., 6, 147–159,
https://doi.org/10.4236/ars.2017.62011, 2017.
Lin, C., Gong, Z., and Zhao, W.: The extraction of wetland hydrophytes types
based on medium resolution TM data, Shengtai Xuebao/Acta Ecologica Sinica,
30, 6460–6469, 2010.
Loveland, T. R. and Dwyer, J. L.: Landsat: Building a strong future, Remote
Sens. Environ., 122, 22–29,
https://doi.org/10.1016/j.rse.2011.09.022, 2012.
Lyimo, L. D.: Carbon sequestration processes in tropical seagrass beds. PhD
Thesis, Department of Ecology, Environment and Plant Sciences, Stockholm
University, Sweden, Department of Ecology, Environment and Plant Sciences, Stockholm University,
49 pp., ISBN 978-91-7649-369-4, 2016.
Lyons, M. B., Phinn, S. R., and Roelfsema, C. M.: Integrating Quickbird
Multi-Spectral Satellite and Field Data: Mapping Bathymetry, Seagrass Cover,
Seagrass Species and Change in Moreton Bay, Australia in 2004 and 2007,
Remote Sens., 3, 42–64, https://doi.org/10.3390/rs3010042, 2011.
Lyons, M. B., Phinn, S. R., and Roelfsema, C. M.: Long term land cover and
seagrass mapping using Landsat and object-based image analysis from 1972 to
2010 in the coastal environment of South East Queensland, Australia, ISPRS
J. Photogramm. Remote Sens., 71, 34–46, 2012.
Lyons, M. B., Roelfsema, C. M., and Phinn, S. R.: Towards understanding
temporal and spatial dynamics of seagrass landscapes using time-series
remote sensing. Estuarine, Coast. Shelf Sci., 20, 42–53, 2013.
Mandanici, E. and Bitelli, G.: Preliminary Comparison of Sentinel-2 and
Landsat 8 Imagery for a Combined Use, Remote Sens., 8, 1014,
https://doi.org/10.3390/rs8121014, 2016.
Manevski, K., Manakos, I., Petropoulos, G. P., and Kalaitzidis, C.:
Discrimination of common Mediterranean plant species using field
Spectroradiometry, Int. J. Appl. Earth Observ. Geoinf.n,
13, 922–933, 2011.
Marcello, J., Eugenio, F., Martín, J., and Marqués, F.: Seabed
Mapping in Coastal Shallow Waters Using High Resolution Multispectral and
Hyperspectral Imagery, Remote Sens., 10, 1208, https://doi.org/10.3390/rs10081208,
2018.
Markham, B., Barsi, J., Kvaran, G., Ong, L., Kaita, E., Biggar, S.,
Czapla-Myers, J., Mishra, N., and Helder, D.: Landsat-8 Operational Land
Imager Radiometric Calibration and Stability, Remote Sens., 6,
12275–12308, https://doi.org/10.3390/rs61212275, 2014.
Markham, B., Jenstrom, D., Masek, J. G., Dabney, P., Pedelty, J. A., Barsi,
J. A., and Montanaro, M.: Landsat 9: Status and plans. In Earth Observing
Systems XXI, International Society for Optics and Photonics, San Diego, CA,
USA, 9972, 99720G, https://doi.org/10.1117/12.2238658, 2016.
Marshall, C. D., Cullen J. A., Al-Ansi M., Hamza S., and Abdel-Moati Mohamed A. R.: Environmental Drivers of Habitat Use by Hawksbill Turtles (Eretmochelys imbricata) in the Arabian Gulf (Qatar), Front. Mar. Sci., 7, 549575, https://doi.org/10.3389/fmars.2020.549575, 2020.
Mcfeeters, S. K.: The use of the normalized difference water index (NDWI) in
the delineation of open water features, Int. J. Remote Sens., 17,
1425–1432, 1996.
Meehan, A. J., Williams, R. J., and Watford, F. A.: Detecting Trends in
Seagrass Abundance Using Aerial Photograph Interpretation: Problems Arising
with the Evolution of Mapping Methods, Estuaries, 28, 462–472, 2005.
Morrison, M. A., Lowe, M. L., Grant, C. M., Smith, P. J., Carbines, G.,
Reed, J., Bury, S. J., and Brown, J.: Seagrass meadows as biodiversity
and productivity hotspots. New Zealand Aquatic Environment and Biodiversity,
Report No. 137, 151 pp.,
http://www.mpi.govt.nz/news-resources/publications.aspx (last access: 30 September 2020), 2014.
Mount, R. E.: Rapid monitoring of extent and condition of seagrass habitats
with aerial photography “mega-quadrats, J. Spat. Sci., 52,
105–119, 2007.
Mumby, P. J., Green, E. P., Edwards, A. J., and Clark, C. D.: Measurement of
Seagrass Standing Crop using Satellite and Digital Airborne Remote Sensing,
Mar. Ecol. Prog. Ser., 159, 51–60, 1997.
NASA: Landsat-8 Instruments, http://www.nasa.gov/mission_pages/landsat/spacecraft/index.html (last access: 30 September 2020), 2014.
NASA: Landsat-9 Mission Details, https://landsat.gsfc.nasa.gov/landsat-9/landsat-9-mission-details/ (last access: 30 September 2020),
2019.
NASA: Landsat-9 overview, continuity the legacy – 2021 and beyond,
https://landsat.gsfc.nasa.gov/landsat-9/landsat-9-overview (last access: 30 September 2020), 2021.
Naser, H.: Human Impacts on Marine Biodiversity: Macrobenthos in Bahrain,
Arabian Gulf, chap. 7, 109–126, in: The Importance of Biological
Interactions in the Study of Biodiversity, edited by:
López-Pujol, J.,
InTech, 390 pp., ISBN 978-953- 307-751-2, 2011.
Neckles, H. A., Kopp, B. S., Peterson, B. J., and Pooler, P. S.: Integrating
Scales of Seagrass Monitoring to Meet Conservation Needs, Estuar.
Coast., 35, 23–46, 2012.
Novak, A. B and Short, F. T.: Submerged Aquatic Vegetation: Seagrasses,
Encyclopedia of Natural Resources, in: Encyclopedia of Natural Resources: Water, Taylor and Francis, edited by: Wang, Y. Q., 9 pp., https://doi.org/10.1081/E-ENRW-120047540,
2014.
Onuf, C. P.: Seagrasses, dredging and light in Laguna Madre, Texas, USA,
Estuar. Coast. Shelf Sci., 39, 75–91, 1994.
Orth, R. J., Carruthers, T. J. B., Dennison, W. C., Duarte, C. M.,
Fourqurean, J. W., Heck, K. L., Hughes, A. R., Kendrick, G. A., Kenworthy,
W. J., Olyarnik, S., Short, F. T., Waycott, M., and Williams, S. L.: A Global
Crisis for Seagrass Ecosystems, Bioscience, 56, 987–996,
https://doi.org/10.1641/0006-3568(2006)56[987:AGCFSE]2.0.CO;2, 2006.
Pasqualini, V., Pergent-Martini, C., Pergent, G., Agreil, M., Skoufas, G.,
Sourbes, L., and Tsirika, A.: Use of SPOT 5 for mapping seagrasses: An
application to Posidonia oceanica, Remote Sens. Environ., 94,
39–45, 2005.
Peneva, E., Griffith, J. A., and Carter, G. A.: Seagrass mapping in the
northern Gulf of Mexico using airborne hyperspectral imagery: a comparison
of classification methods, J. Coast. Res., 24, 850–856, 2008.
Perez, D., Islam, K., Hill, V., Zimmerman, R., Schaeffer, B., Shen, Y., and
Li, J.: Quantifying Seagrass Distribution in Coastal Water with Deep
Learning Models, Remote Sens., 12, 1581, https://doi.org/10.3390/rs12101581, 2020.
Peterson, B. J. and Fourqurean, J. W.: Large-scale patterns in seagrass
(Thalassia testudinum) demographics in south Florida, Limnol.
Oceanogr., 46, 1077–1090, 2001.
Phinn, S., Roelfsema, C., Dekker, A., Brando, V., and Anstee, J.: Mapping
seagrass species, cover and biomass in shallow waters: An assessment of
satellite multispectral and airborne hyper-spectral imaging systems in
Moreton Bay (Australia), Remote Sens. Environ., 112, 3413–3425,
2008.
Preen, A.: Distribution, abundance and conservation status of dugongs and
dolphins in the southern and western Arabian Gulf, Biol. Conserv.,
118, 205–218, 2004.
Pu, R., Bell, S., Baggett, L., Meyer, C., and Zhao, Y.: Discrimination of
Seagrass Species and Cover Classes with in situ Hyperspectral Data, J.
Coast. Res., 28, 1330–1344, 2012.
Richardson, A. J. and Wiegand, C. L.: Distinguishing vegetation from soil
background information, Photogramm. Eng. Rem. S.,
43, 1541–1552, 1977.
Roelfsema, C. M., Phinn, S. R., Udy, N., and Maxwell, P.: An integrated field
and remote sensing approach for mapping seagrass cover, Moreton Bay,
Australia, J. Spat. Sci., 54, 45–62,
https://doi.org/10.1080/14498596.2009.9635166, 2009.
Roelfsema, C. M., Lyons, M., Kovacs, E. M., Maxwell, P., Saunders, M. I.,
Samper-Villarreal, J., and Phinn, S. R.: Multi-temporal mapping of seagrass
cover, species and biomass: A semi-automated object based image analysis
approach, Remote Sens. Environ., 150, 172–187, 2014.
Rouse, J. W., Haas, R. W., Schell, J. A., Deering, D. W., and Harlan, J. C.:
Monitoring the vernal advancement and retrogradation (Greenwave
effect) of natural vegetation, NASA/GSFC Type-III Final Report, Greenbelt,
Maryland, USA, NASA-GSFC; Report E75-10354-NASA-CR-144661-RSC-1978-4, 164 pp., 1974.
Roy, D., Zhang, H., Ju, J., Gomez-Dans, J., Lewis, P., Schaaf, C., Sun, Q.,
Li, J., Huang, H., and Kovalskyy, V.: A general method to normalize Landsat
reflectance data to nadir BRDF adjusted reflectance, Remote Sens.
Environ., 176, 255–271, https://doi.org/10.1016/j.rse.2016.01.023, 2016.
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.,
Scambos, T. A., Schaaf, C. B., Schott, J. R., Sheng, Y., Vermote, E. F.,
Belward, A. S., Bindschadler, R., Cohen, W. B., Gao, F., Hipple, J. D.,
Hostert, P., Huntington, J., Justice, C. O., Kilic, A., Kovalskyy, V., Lee,
Z. P., Lymburner, L., Masek, J. G., McCorkel, J., Shuai, Y., Trezza, R.,
Vogelmann, J., Wynne,R. H., and Zhu, Z.: 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.
Roy, D. P., Li, J., Zhang, H. K., Yan, L., Huang, H., and Li, Z.: Examination
of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and
the suitability of a general method to normalize MSI reflectance to nadir
BRDF adjusted reflectance, Remote Sens. Environ., 199, 25–38,
https://doi.org/10.1016/j.rse.2017.06.019, 2017.
Saarman, E., Gleason, M., Ugoretz, J., Airamé, S., Carr, M., Fox, E.,
Frimodig, A., Mason, T., and Vasques, J.: The role of science in supporting
marine protected area network planning and design in California, Ocean
Coast. Manag., 74, 45–56,
https://doi.org/10.1016/j.ocecoaman.2012.08.021, 2013.
Sandmeier, St., Muller, C., Hosgood, B., and Andreoli, G.: Sensitivity
Analysis and quality Assessment of Laboratory BRDF Data, Remote Sens.
Environ., 64, 176–191, 1998.
Short, F. T. and Wyllie-Echeverria, S.: Natural and humaninduced disturbance
of seagrasses, Environ. Conserv., 23, 17–27, 1996.
Short, F. T. and Coles, R.: Global Seagrass Research Methods, Elsevier
Publishing, the Netherlands, 482 pp., https://doi.org/10.1016/S0044-8486(02)00307-1, 2001.
Short, F. T., Polidoro, B., Livingstone, S. R., Carpenter, K. E., Bandeira,
S., Bujang, J. S., Calumpong, H. P., Carruthers, T. J. B., Coles, R. G.,
Dennison, W. C., Erftemeijer, P. L. A., Fortes, M. D., Freeman, A. S.,
Jagtap, T. G., Kamal-Abu-Hena, M., Kendrick, G. A., Kenworthy, W. J.,
La-Nafie, Y. A., Nasution, I. M., Orth, R. J., Prathep, A., Sanciangco, J.
C., Tussenbroek, B. V., Vergara, S. G., Waycott, M. W., and Zieman, J. C.:
Extinction risk assessment of the world's seagrass species, Biol.
Conserv., 144, 1961–1971,
https://doi.org/10.1016/j.biocon.2011.04.010, 2011.
Silva, T. S. F., Costa, M. P. F., Melack, J. M., and Novo, E. M. L. M.:
Remote sensing of aquatic vegetation: Theory and applications, Environ.
Monit. Assess., 140, 131–145,
https://doi.org/10.1007/s10661-007-9855-3, 2008.
Skakun, S., Roger, J.-C., Vermote, E. F., Masek, J. G., and Justice, C. O.:
Automatic sub-pixel co-registration of Landsat-8 Operational Land Imager and
Sentinel-2A Multi-Spectral Instrument images using phase correlation and
machine learning based mapping, Int. J. Digit. Earth, 10, 1253–1269,
https://doi.org/10.1080/17538947.2017.1304586, 2017.
Slater, P. N.: Remote Sensing – Optics and Optical System, Addison-Wesley,
reading, MA, 575 pp., ISBN 0201072505, 1980.
Teillet, P. M. and Santer, R.: Terrain Elevation and Sensor Altitude
Dependence in a Semi-Analytical Atmospheric Code, Can. J. Remote
Sens., 17, 36–44, 1991.
Thorhaug, A., Richardson, A. D., and Berlyn, G. P.: Spectral reflectance of
the seagrasses: Thalassia testudinum, Halodule wrightii, Syringodium
filiforme and five marine algae, Int. J. Remote Sens., 28,
1487–1501, 2007.
Traganos, D.: Development of seagrass monitoring techniques using remote
sensing data. PhD Thesis, Osnabrück University,
Osnabrück, Lower Saxony, Germany, 199 pp., https://elib.dlr.de/137795/1/thesis_traganos.pdf, last access: 30 September 2020.
Uhrin, A. V. and Townsend, P. H.: Improved Seagrass Mapping Using Linear
Spectral Unmixing of Aerial Photographs, Estuar. Coast. Shelf
Sci., 171, 11–22, 2016.
Umamaheswari, R., Ramachandran, S., and Nobi, E. P.: Mapping the extend of
seagrass meadows of Gulf of Mannar Biosphere Reserve, India using IRS ID
satellite imagery, Int. J. Biodiv. Conserv., 1,
187–193, 2009.
Van-Der-Meera, F.: Analysis of spectral absorption features in hyperspectral
imagery, Int. J. Appl. Earth Observ. Geoinf., 5, 55–68,
2004.
Van-der-Werff, H. and Van-der-Meer, F.: Sentinel-2A MSI and Landsat 8 OLI
Provide Data Continuity for Geological Remote Sensing, Remote Sens., 8,
883, https://doi.org/10.3390/rs8110883, 2016.
Vermote, E., Justice, C., Claverie, M., and Franch, B.: Preliminary analysis
of the performance of the Landsat 8/OLI land surface reflectance product,
Remote Sens. Environ., 185, 46–56,
https://doi.org/10.1016/j.rse.2016.04.008, 2016.
Villa, P., Mariano Bresciani, M., Braga, F., and Bolpagni, R.: Mapping
Aquatic Vegetation through Remote Sensing Data: A Comparison of Vegetation
Indices Performances, 6th EARSeL Workshop on Remote S. of the Coastal Zone,
7–8 June 2013, Matera, Italy, 10–15, European Association of Remote Sensing Laboratories,
ISBN 978-88-89693-34-6, 2013.
Villa, P., Bresciani, M., Braga, F., and Bolpagni, R.: Comparative Assessment
of Broadband Vegetation Indices over Aquatic Vegetation, IEEE J.
Sel. Top. Appl., 7,
3117–3127, 2014.
Wabnitz, C. C., Andréfouët, S., Torres-Pulliza, D., Muller-Karger,
F. E., and Kramer, P. A.: Regional-scale seagrass habitat mapping in the
Wider Caribbean region using Landsat sensors: Applications to conservation
and ecology, Remote Sens. Environ., 12, 3455–3467, 2008.
Warren, C., Dupont, J., Abdel-Moati, M., Hobeichi, S., Palandro, D., and
Purkis, S.: Remote sensing of Qatar nearshore habitats with perspectives for
coastal management, Mar. Pollut. Bull., 105, 641–653,
https://doi.org/10.1016/j.marpolbul.2015.11.036, 2016.
Waycott, M., Duarte, C. M., Carruthers, T. J. B., Orth, R. J., Dennison, W.
C., Olyarnik, S., Calladine, A., Fourqurean, J. W., Heck Jr., K. L.,
Hughes, A. R., Kendrick, G. A., Kenworthy, W. J., Short, F. T., and
Williams, S. L.: Accelerating loss of seagrasses across the globe threatens
coastal ecosystems, P. Natl. Acad, Sci. USA, 106, 12377–12381, 2009.
Wicaksono, P. and Hafizt, M.: Mapping Seagrass from Space: Addressing the
Complexity of Seagrass LAI Mapping, Europ. J. Remote Sens.,
46, 18–39, https://doi.org/10.5721/EuJRS20134602, 2013.
Wicaksono, P., Kumara, I. S., Kamal, M., Fauzan, A. M., Zhafarina, Z.,
Nurswantoro, D. A., and Yogyantoro, R. N.: Multispectral Resampling of
Seagrass Species Spectra: WorldView-2, Quickbird, Sentinel-2A, ASTER VNIR,
and Landsat 8 OLI, The 5th Geoinformation Science Symposium 2017 (GSS
2017), IOP Conf. Series: Earth and Environmental Science, 98, 012039,
https://doi.org/10.1088/1755-1315/98/1/012039, 2017.
Wicaksono, P., Fauzan, M. A., Kumara, I. S. W., Yogyantoro, R. N., Lazuardi,
W., and Zhafarina, Z.: Analysis of reflectance spectra of tropical seagrass
species and their value for mapping using multispectral satellite images,
Int. J. Remote Sens., 40, 8955–8977,
https://doi.org/10.1080/01431161.2019.1624866, 2019.
Willmott, C. J.: Some comments on the evaluation of model performance, Bull.
Am. Meteorol. Soc., 63, 1309–1313, 1982.
Wood, J. S.: Hyperspectral analysis of seagrass in Redfish Bay, Texas, Ph.D.
Thesis, Texas A&M University-Corpus Christi, Corpus Christi, Texas University, Texas (USA),
141 pp., 2012.
Wulder, M. A., Hilker, T., White, J. C., Coops, N. C., Masek, J. G.,
Pflugmacher, D., and Crevier, Y.: Virtual constellations for global
terrestrial monitoring, Remote Sens. Environ., 170, 62–76,
https://doi.org/10.1016/j.rse.2015.09.001, 2015.
Yan, L., Roy, D.P., Li, Z., Zhang, H. K., and Huang, H.: Sentinel-2A
multi-temporal misregistration characterization and an orbit-based sub-pixel
registration methodology, Remote Sens.Environ., 215, 495–506,
https://doi.org/10.1016/j.rse.2018.04.021, 2018.
Yang, D. and Yang, C.: Detection of seagrass distribution changes from 1991
to 2006 in Xincun Bay, Hainan, with satellite remote sensing, Sensors, 9,
830–844, 2009.
Yang, D. and Yang, C.: Seagrass Distribution in China with Satellite Remote
Sensing, chap. 4, in: Remote Sensing of Planet Earth, edited by: Chemin, Y.,
75–94, ISBN 978-953-307-919-6, InTech,
http://www.intechopen.com/books/remote-sensing-of-planet-earth/ (last access: 30 September 2020),
2012.
Zhang, H. K. and Roy, D. P.: Computationally inexpensive Landsat-8
operational land imager (OLI) pan-sharpening, Remote Sens., 8, 180,
https://doi.org/10.3390/rs8030180,
2016.
Zhang, H. K., Roy, D. P., Yan, L., Li, Z., Huang, H., Vermote, E., Skakun,
S., and Roger, J. C.: Characterization of Sentinel-2A and Landsat-8 top of
atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI
differences, Remote Sens. Environ., 215, 482–494,
https://doi.org/10.1016/j.rse.2018.04.031, 2018.
Zhao, D., Lv, M., Jiang, H., Cai, Y., Xu, D., and An, S.: Spatio-Temporal
Variability of Aquatic Vegetation in Taihu Lake over the Past 30 Years, PLoS
ONE, 8, 6–12,https://doi.org/10.1371/journal.pone.0066365, 2013.
Zoffoli, M. L., Gernez, P., Rosa, P., Le-Bris, A., Brando, V. E., Barille,
A.-L., Harin, N., Peters, S., Poser, K., Spaias, L., Peralta, G., and
Barille, L.: Sentinel-2 remote sensing of Zostera noltei-dominated intertidal seagrass
meadows, Remote Sens. Environ., 251, 112020, https://doi.org/10.1016/j.rse.2020.112020, 2020.
Zorrilla, N. A., Vantrepotte, V., Ngoc, D.-D., Huybrechts, N., and Gardel,
A.: Automated SWIR based empirical sun glint correction of Landsat 8-OLI
data over coastal turbid water, Optics Exp., 27, 25 pp.,
https://doi.org/10.1364/OE.27.00A294, 2019.
Short summary
Spectral analyses showed the importance of the blue, green, and NIR wavelengths for submerged aquatic vegetation (SAV) discrimination. Moreover, the integration of the blue or the green bands in water vegetation indices (WVIs) increases their discriminating power of SAV. Statistical fits between homologous bands of Sentinel-SMI and Landsat-OLI revealed excellent linear relationships (R2 of 0.999) with insignificant RMSD (≤ 0.0015). Accordingly, MSI and OLI sensors are spectrally similar.
Spectral analyses showed the importance of the blue, green, and NIR wavelengths for submerged...
