1932

Abstract

This article reviews the broad range of contemporary remote sensing technologies that can access the ocean, while emphasizing next-generation ones that might revolutionize the field. Significant challenges remain in studying the largest part of Earth's biosphere. As of 2022, less than 10% of the ocean has been imaged at a comparable resolution to the surface of the moon and Mars, despite comprising more than 90% of the habitable volume of our planet. Within the past five years, phenomena as modest as refractive ocean-wave distortion have finally been addressed, but steep technology maturation and challenges persist in remote sensing life in our oceans, hampering our understanding of rapidly changing ecosystems at a crucial inflection point in our history. We survey the field and share emerging technologies and trends, while motivating the case for a future Sustained Marine Imaging Program for the next decade in remote sensing the ocean biosphere.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-environ-112420-013219
2022-10-17
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/energy/47/1/annurev-environ-112420-013219.html?itemId=/content/journals/10.1146/annurev-environ-112420-013219&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Miloslavich P, Bax NJ, Simmons SE, Klein E, Appeltans W et al. 2018. Essential ocean variables for global sustained observations of biodiversity and ecosystem changes. Glob. Chang. Biol. 24:62416–33
    [Google Scholar]
  2. 2.
    Henson SA, Sarmiento JL, Dunne JP, Bopp L, Lima I et al. 2010. Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity. Biogeosciences 7:2621–40
    [Google Scholar]
  3. 3.
    Henson SA, Beaulieu C, Lampitt R. 2016. Observing climate change trends in ocean biogeochemistry: when and where. Glob. Chang. Biol. 22:41561–71
    [Google Scholar]
  4. 4.
    Beaulieu C, Henson SA, Sarmiento JL, Dunne JP, Doney SC et al. 2013. Factors challenging our ability to detect long-term trends in ocean chlorophyll. Biogeosciences 10:42711–24
    [Google Scholar]
  5. 5.
    Merchant CJ, Embury O, Bulgin CE, Block T, Corlett GK et al. 2019. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci. Data 6:1223
    [Google Scholar]
  6. 6.
    Wang M, Son S, Shi W 2009. Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data. Remote Sens. Environ. 113:3635–44
    [Google Scholar]
  7. 7.
    Purkis SJ, Gleason ACR, Purkis CR, Dempsey AC, Renaud PG et al. 2019. High-resolution habitat and bathymetry maps for 65,000 sq. km of Earth's remotest coral reefs. Coral Reefs 38:3467–88
    [Google Scholar]
  8. 8.
    Kikaki A, Karantzalos K, Power CA, Raitsos DE. 2020. Remotely sensing the source and transport of marine plastic debris in Bay Islands of Honduras (Caribbean Sea). Remote Sens. 12:111727
    [Google Scholar]
  9. 9.
    Roelfsema CM, Lyons M, Murray N, Kovacs EM, Kennedy EV et al. 2021. Workflow for the generation of expert-derived training and validation data: a view to global scale habitat mapping. Front. Mar. Sci. 8: https://doi.org/10.3389/fmars.2021.643381
    [Crossref] [Google Scholar]
  10. 10.
    Fladeland M, Schoenung S, Chirayath V, Podolske JR. 2019. Supporting NASA science with high-altitude long-endurance aircraft. Rep. ARC-E-DAA-TN68775 Natl. Aeronaut. Space Admin. Washington, DC:
  11. 11.
    Irons JR, Dwyer JL, Barsi JA. 2012. The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sens. Environ. 122:11–21
    [Google Scholar]
  12. 12.
    Chirayath V, Earle SA. 2016. Drones that see through waves - preliminary results from airborne fluid lensing for cm-scale aquatic conservation. Aquat. Conserv. Mar. Freshw. Ecosyst. 26:Suppl. 2237–50
    [Google Scholar]
  13. 13.
    Chirayath V, Li A. 2019. Next-generation optical sensing technologies for exploring ocean worlds—NASA FluidCam, MiDAR, and NeMO-Net. Front. . Mar. Sci. 6:521
    [Google Scholar]
  14. 14.
    Purkis S, Kenter JAM, Oikonomou EK, Robinson IS. 2002. High-resolution ground verification, cluster analysis and optical model of reef substrate coverage on Landsat TM imagery (Red Sea, Egypt). Int. J. Remote Sens. 23:81677–98
    [Google Scholar]
  15. 15.
    Capolsini P, Andréfouët S, Rion C, Payri C. 2003. A comparison of Landsat ETM+, SPOT HRV, Ikonos, ASTER, and airborne MASTER data for coral reef habitat mapping in South Pacific islands. Can. J. Remote Sens. 29:2187–200
    [Google Scholar]
  16. 16.
    Andréfouët S, Kramer P, Torres-Pulliza D, Joyce KE, Hochberg EJ et al. 2003. Multi-site evaluation of IKONOS data for classification of tropical coral reef environments. Remote Sens. Environ. 88:1128–43
    [Google Scholar]
  17. 17.
    Purkis SJ. 2005. A “Reef-Up” approach to classifying coral habitats from IKONOS imagery. IEEE Trans. Geosci. Remote Sens. 43:61375–90
    [Google Scholar]
  18. 18.
    Hernández-Cruz LR, Purkis SJ, Riegl B. 2006. Documenting decadal spatial changes in seagrass and Acropora palmata cover by aerial photography analysis in Vieques, Puerto Rico: 1937–2000. Bull. Mar. Sci. 79:401–14
    [Google Scholar]
  19. 19.
    Purkis SJ, Myint SW, Riegl BM. 2006. Enhanced detection of the coral Acropora cervicornis from satellite imagery using a textural operator. Remote Sens. Environ. 101:82–94
    [Google Scholar]
  20. 20.
    Hedley JD, Roelfsema CM, Chollett I, Harborne AR, Heron SF et al. 2016. Remote sensing of coral reefs for monitoring and management: a review. Remote Sens. 8:2118
    [Google Scholar]
  21. 21.
    Purkis SJ. 2018. Remote sensing tropical coral reefs: the view from above. Annu. Rev. Mar. Sci. 10:149–68
    [Google Scholar]
  22. 22.
    Bracher A, Bouman HA, Brewin RJW, Bricaud A, Brotas V et al. 2017. Obtaining phytoplankton diversity from ocean color: a scientific roadmap for future development. Front. Mar. Sci. 4:55
    [Google Scholar]
  23. 23.
    Organelli E, Nuccio C, Lazzara L, Uitz J, Bricaud A, Massi L. 2017. On the discrimination of multiple phytoplankton groups from light absorption spectra of assemblages with mixed taxonomic composition and variable light conditions. Appl. Opt. 56:143952–68
    [Google Scholar]
  24. 24.
    Kramer SJ, Siegel DA. 2019. How can phytoplankton pigments be best used to characterize surface ocean phytoplankton groups for ocean color remote sensing algorithms?. J. Geophys. Res. Oceans 124:117557–74
    [Google Scholar]
  25. 25.
    Behrenfeld MJ, Westberry TK, Boss ES, O'Malley RT, Siegel DA et al. 2009. Satellite-detected fluorescence reveals global physiology of ocean phytoplankton. Biogeosciences 6:5779–94
    [Google Scholar]
  26. 26.
    Dierssen H, McManus GB, Chlus A, Qiu D, Gao BC, Lin S 2015. Space station image captures a red tide ciliate bloom at high spectral and spatial resolution. PNAS 112:4814783–87
    [Google Scholar]
  27. 27.
    Werdell PJ, McKinna LIW, Boss E, Ackleson SG, Craig SE et al. 2018. An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing. Prog. Oceanogr. 160:186–212
    [Google Scholar]
  28. 28.
    Goodman J, Ustin SL. 2007. Classification of benthic composition in a coral reef environment using spectral unmixing. J. Appl. Remote Sens. 1:111501
    [Google Scholar]
  29. 29.
    Guillaume M, Minghelli A, Deville Y, Chami M, Juste L et al. 2020. Mapping benthic habitats by extending non-negative matrix factorization to address the water column and seabed adjacency effects. Remote Sens. 12:132072
    [Google Scholar]
  30. 30.
    Hedley JD, Mumby PJ, Joyce KE, Phinn SR. 2004. Spectral unmixing of coral reef benthos under ideal conditions. Coral Reefs 23:160–73
    [Google Scholar]
  31. 31.
    Alonso K, Bachmann M, Burch K, Carmona E, Cerra D et al. 2019. Data products, quality and validation of the DLR Earth Sensing Imaging Spectrometer (DESIS). Sensors 19:204471
    [Google Scholar]
  32. 32.
    Johnsen G, Volent Z, Dierssen H, Pettersen R, Van Ardelan M et al. 2013. Underwater hyperspectral imagery to create biogeochemical maps of seafloor properties. Subsea Optics and Imaging J Watson, O Zielinski 508–35 Sawston, UK: Woodhead Publ.
    [Google Scholar]
  33. 33.
    Dumke I, Purser A, Marcon Y, Nornes SM, Johnsen G et al. 2018. Underwater hyperspectral imaging as an in situ taxonomic tool for deep-sea megafauna. Sci. Rep. 8:12860
    [Google Scholar]
  34. 34.
    Chirayath V, Instrella R. 2019. Fluid lensing and machine learning for centimeter-resolution airborne assessment of coral reefs in American Samoa. Remote Sens. Environ. 235:111475
    [Google Scholar]
  35. 35.
    Chirayath V. 2018.. System and Method for Imaging Underwater Environments Using Fluid Lensing US Patent 62/634,803
  36. 36.
    Chirayath V. 2016. Fluid lensing and applications to remote sensing of aquatic environments. PhD Thesis Stanford Univ. Stanford, CA:
    [Google Scholar]
  37. 37.
    Chirayath V. 2021. Airborne fluid lensing for precision reef mapping—new results from Guam's priority coral reefs. OSA Optical Sensors and Sensing Congress 2021 (AIS, FTS, HISE, SENSORS, ES) S Buckley, F Vanier, S Shi, K Walker, I Coddington, et al Washington, DC: OSA Techn. Digest (Optica Publ. Group) https://doi.org/10.1364/HISE.2021.HTu2C.1
    [Crossref] [Google Scholar]
  38. 38.
    Brock JC, Purkis SJ. 2009. The emerging role of LiDAR remote sensing in coastal research and resource management. J. Coast. Res. 53:1–5
    [Google Scholar]
  39. 39.
    Brock J, Danielson JJ, Purkis S. 2013. Emerging methods for the study of coastal ecosystem landscape structure and change. Int. J. Remote Sens. 34:186283–85
    [Google Scholar]
  40. 40.
    Hostetler CA, Behrenfeld MJ, Hu Y, Hair JW, Schulien JA. 2018. Spaceborne lidar in the study of marine systems. Annu. Rev. Mar. Sci. 10:1121–47
    [Google Scholar]
  41. 41.
    Behrenfeld MJ, Hu Y, Hostetler CA, Dall'Olmo G, Rodier SD et al. 2013. Space-based lidar measurements of global ocean carbon stocks. Geophys. Res. Lett. 40:164355–60
    [Google Scholar]
  42. 42.
    Hoge FE, Lyon PE, Wright CW, Swift RN, Yungel JK. 2005. Chlorophyll biomass in the global oceans: airborne lidar retrieval using fluorescence of both chlorophyll and chromophoric dissolved organic matter. Appl. Opt. 44:142857–62
    [Google Scholar]
  43. 43.
    Wang C-K, Philpot WD. 2007. Using airborne bathymetric lidar to detect bottom type variation in shallow waters. Remote Sens. Environ. 106:1123–35
    [Google Scholar]
  44. 44.
    Jamet C, Ibrahim A, Ahmad Z, Angelini F, Babin M et al. 2019. Going beyond standard ocean color observations: lidar and polarimetry. Front. Mar. Sci. 8: https://doi.org/10.3389/fmars.2019.00251
    [Crossref] [Google Scholar]
  45. 45.
    Dionisi D, Brando VE, Volpe G, Colella S, Santoleri R. 2020. Seasonal distributions of ocean particulate optical properties from spaceborne lidar measurements in Mediterranean and Black sea. Remote Sens. Environ. 247:111889
    [Google Scholar]
  46. 46.
    Liu R, Ling Q, Zhang Q, Zhou Y, Le C et al. 2020. Detection of chlorophyll a and CDOM absorption coefficient with a dual-wavelength oceanic lidar: wavelength optimization method. Remote Sens. 12:183021
    [Google Scholar]
  47. 47.
    Briese C, Pfennigbauer M, Ullrich A, Doneus M. 2013. Multi-wavelength airborne laser scanning for archaeological prospection. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 40:119–24
    [Google Scholar]
  48. 48.
    Briese C, Pfennigbauer M, Lehner H, Ullrich A, Wagner W, Pfeifer N. 2012. Radiometric calibration of multi-wavelength airborne laser scanning data. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 1:335–40
    [Google Scholar]
  49. 49.
    Strait C, Twardowski M, Dalgleish F, Tonizzo A, Vuorenkoski A. 2018. Development and assessment of lidar modeling to retrieve IOPs. Proc. SPIE 10631, Ocean Sensing and Monitoring X, 106310U (25 May 2018) Bellingham, WA: Int. Soc. Optics Photon https://doi.org/10.1117/12.2309998
    [Crossref] [Google Scholar]
  50. 50.
    Rehm E, Dalgleish F, Huot M, Lagunas-Morales J, Lambert-Girard S et al. 2018. Comparing fluorescent and differential absorption LiDAR techniques for detecting algal biomass with applications to Arctic substrates. Proc. SPIE 10631, Ocean Sensing and Monitoring X, 106310Z (25 May 2018). Bellingham, WA: Int. Soc. Optics Photon https://doi.org/10.1117/12.2302381
    [Crossref] [Google Scholar]
  51. 51.
    Chen P, Jamet C, Zhang Z, He Y, Mao Z et al. 2021. Vertical distribution of subsurface phytoplankton layer in South China Sea using airborne lidar. Remote Sens. Environ. 263:112567
    [Google Scholar]
  52. 52.
    Churnside J, Marchbanks R, Marshall N. 2021. Airborne lidar observations of a spring phytoplankton bloom in the western Arctic Ocean. Remote Sens. 13:2512
    [Google Scholar]
  53. 53.
    Shakun S, Vermote E, Roger J-C, Franch B. 2017. Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale. AIMS Geosci 3:2163–86
    [Google Scholar]
  54. 54.
    Smith RC, Baker KS. 1978. The bio-optical state of ocean waters and remote sensing. Limnol. Oceanogr. 23:2247–59
    [Google Scholar]
  55. 55.
    Zimmerman RC, Sukenik CI, Hill VJ. 2013. Subsea LIDAR systems. Subsea Optics and Imaging J Watson, O Zielinski 471–88 Sawston, UK: Woodhead Publ.
    [Google Scholar]
  56. 56.
    Collister BL, Zimmerman RC, Sukenik CI, Hill VJ, Balch WM. 2018. Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar. Remote Sens. Environ. 215:85–96
    [Google Scholar]
  57. 57.
    Neumann TA, Martino AJ, Markus T, Bae S, Bock MR et al. 2019. The Ice, Cloud, and Land Elevation Satellite - 2 mission: a global geolocated photon product derived from the Advanced Topographic Laser Altimeter System. Remote Sens. Environ. 233:111325
    [Google Scholar]
  58. 58.
    Forfinski-Sarkozi NA, Parrish CE. 2016. Analysis of MABEL bathymetry in Keweenaw Bay and implications for ICESat-2 ATLAS. Remote Sens. 8:9772
    [Google Scholar]
  59. 59.
    Forfinski-Sarkozi NA, Parrish CE. 2019. Active-passive spaceborne data fusion for mapping nearshore bathymetry. Photogramm. Eng. Remote Sens. 85:4281–95
    [Google Scholar]
  60. 60.
    Li Y, Gao H, Jasinski MF, Zhang S, Stoll JD. 2019. Deriving high-resolution reservoir bathymetry from ICESat-2 prototype photon-counting lidar and Landsat imagery. IEEE Trans. Geosci. Remote Sens. 57:107883–93
    [Google Scholar]
  61. 61.
    Parrish CE, Magruder LA, Neuenschwander AL, Forfinski-Sarkozi N, Alonzo M, Jasinski M 2019. Validation of ICESat-2 ATLAS bathymetry and analysis of ATLAS's bathymetric mapping performance. Remote Sens. 11:141634
    [Google Scholar]
  62. 62.
    Martino AJ, Neumann TA, Kurtz NT, McLennan D. 2019. ICESat-2 mission overview and early performance. Proc. SPIE 11151, Sensors, Systems, and Next-Generation Satellites XXIII, 111510C (10 October 2019) Bellingham, WA: Int. Soc. Optics Photon https://doi.org/10.1117/12.2534938
    [Crossref] [Google Scholar]
  63. 63.
    Albright A, Glennie C 2021. Nearshore bathymetry from fusion of Sentinel-2 and ICESat-2 observations. IEEE Geosci. Remote Sens. Lett. 18:5900–904
    [Google Scholar]
  64. 64.
    Ma Y, Xu N, Liu Z, Yang B, Yang F et al. 2020. Satellite-derived bathymetry using the ICESat-2 lidar and Sentinel-2 imagery datasets. Remote Sens. Environ. 250:112047
    [Google Scholar]
  65. 65.
    Babbel B, Parrish C, Magruder L. 2021. ICESat-2 elevation retrievals in support of satellite-derived bathymetry for global science applications. Geophys. Res. Lett. 48:5e2020GL090629
    [Google Scholar]
  66. 66.
    Gleason ACR, Smith R, Purkis SJ, Goodrich K, Dempsey AC. 2021. The prospect of global coral reef bathymetry by combining Ice, Cloud, and Land Elevation Satellite-2 altimetry with multispectral satellite imagery. Front. Mar. Sci. 8:694783
    [Google Scholar]
  67. 67.
    Thomas N, Pertiwi AP, Traganos D, Lagomasino D, Poursanidis D et al. 2021. Space-borne cloud-native satellite-derived bathymetry (SDB) models using ICESat-2 and Sentinel-2. Geophys. Res. Lett. 48:6e2020GL092170
    [Google Scholar]
  68. 68.
    McGillivary PA, Chirayath V, Baghdady J. 2018. Use of multi-spectral high repetition rate LED systems for high bandwidth underwater optical communications, and communications to surface and aerial systems. 2018 Fourth Underwater Communications and Networking Conference (UComms)1–5 Piscataway, NJ: IEEE
    [Google Scholar]
  69. 69.
    Chirayath V. 2018. System and Method for Active Multispectral Imaging and Optical Communications US Patent 15/480,318
  70. 70.
    Henderson FM, Lewis AJ, eds. Principles and Applications of Imaging Radar: Manual of Remote Sensing, Vol. 2 Hoboken, NJ: Wiley. , 3rd ed..
  71. 71.
    Jones AT, Thankappan M, Logan GA, Kennard JM, Smith CJ et al. 2006. Coral spawn and bathymetric slicks in Synthetic Aperture Radar (SAR) data from the Timor Sea, north-west Australia. Int. J. Remote Sens. 27:102063–69
    [Google Scholar]
  72. 72.
    Cresswell A, Tildesley P, Cresswell G. 2019. Synthetic Aperture Radar scenes of the North West Shelf, Western Australia, suggest this is an underutilised method to remotely study mass coral spawning. J. R. Soc. West. Aust. 102:45–51
    [Google Scholar]
  73. 73.
    Fitzpatrick A, Singhvi A, Arbabian A. 2020. An airborne sonar system for underwater remote sensing and imaging. IEEE Access 8:189945–59
    [Google Scholar]
  74. 74.
    Basedow SL, McKee D, Lefering I, Gislason A, Daase M et al. 2019. Remote sensing of zooplankton swarms. Sci. Rep. 9:1686
    [Google Scholar]
  75. 75.
    Groom S, Sathyendranath S, Ban Y, Bernard S, Brewin R et al. 2019. Satellite ocean colour: current status and future perspective. Front. . Mar. Sci. 6:485
    [Google Scholar]
  76. 76.
    Wynne TT, Mishra S, Meredith A, Litaker RW, Stumpf RP. 2021. Intercalibration of MERIS, MODIS, and OLCI satellite imagers for construction of past, present, and future cyanobacterial biomass time series. Remote Sens. 13:122305
    [Google Scholar]
  77. 77.
    Ruddick K, Neukermans G, Vanhellemont Q, Jolivet D. 2014. Challenges and opportunities for geostationary ocean colour remote sensing of regional seas: a review of recent results. Remote Sens. Environ. 146:63–76
    [Google Scholar]
  78. 78.
    Lavigne H, Ruddick K. 2018. The potential use of geostationary MTG/FCI to retrieve chlorophyll-a concentration at high temporal resolution for the open oceans. Int. J. Remote Sens. 39:82399–2420
    [Google Scholar]
  79. 79.
    Smith WHF, Sandwell DT. 1997. Global sea floor topography from satellite altimetry and ship depth soundings. Science 277:53341956–62
    [Google Scholar]
  80. 80.
    Purkis SJ, Graham NAJ, Riegl BM. 2008. Predictability of reef fish diversity and abundance using remote sensing data in Diego Garcia (Chagos Archipelago). Coral Reefs 27:1167–78
    [Google Scholar]
  81. 81.
    Wedding L, Friedlander A, McGranaghan M, Yost R, Monaco M. 2008. Using bathymetric lidar to define nearshore benthic habitat complexity: implications for management of reef fish assemblages in Hawaii. Remote Sens. Environ. 112:4159–65
    [Google Scholar]
  82. 82.
    Asner GP, Vaughn N, Grady BW, Foo SA, Anand H et al. 2021. Regional reef fish survey design and scaling using high-resolution mapping and analysis. Front. Mar. Sci. 8:894
    [Google Scholar]
  83. 83.
    Ashphaq M, Srivastava PK, Mitra D 2021. Review of near-shore satellite derived bathymetry: classification and account of five decades of coastal bathymetry research. J. Ocean Eng. Sci. 6:4340–59
    [Google Scholar]
  84. 84.
    Kutser T, Hedley J, Giardino C, Roelfsema C, Brando VE. 2020. Remote sensing of shallow waters - a 50 year retrospective and future directions. Remote Sens. Environ. 240:111619
    [Google Scholar]
  85. 85.
    Lee Z, Carder K, Mobley C, Steward R, Patch J. 1999. Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization. Appl. Opt. 38:3831–43
    [Google Scholar]
  86. 86.
    Durand D, Bijaoui J, Cauneau F. 2000. Optical remote sensing of shallow-water environmental parameters: a feasibility study. Remote Sens. Environ. 73:2152–61
    [Google Scholar]
  87. 87.
    Stumpf RP, Holderied K, Sinclair M. 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol. Oceanogr. 48:547–56
    [Google Scholar]
  88. 88.
    Kerr JM, Purkis S. 2018. An algorithm for optically-deriving water depth from multispectral imagery in coral reef landscapes in the absence of ground-truth data. Remote Sens. Environ. 210:307–324
    [Google Scholar]
  89. 89.
    Geyman EC, Maloof AC. 2019. A simple method for extracting water depth from multispectral satellite imagery in regions of variable bottom type. Earth Sp. Sci. 6:3527–37
    [Google Scholar]
  90. 90.
    Li J, Knapp DE, Schill SR, Roelfsema C, Phinn S et al. 2019. Adaptive bathymetry estimation for shallow coastal waters using Planet Dove satellites. Remote Sens. Environ. 232:111302
    [Google Scholar]
  91. 91.
    Caballero I, Stumpf R. 2020. Towards routine mapping of shallow bathymetry in environments with variable turbidity: contribution of Sentinel-2A/B satellites mission. Remote Sens. 12:3451
    [Google Scholar]
  92. 92.
    Abileah R. 2013. Mapping near shore bathymetry using wave kinematics in a time series of WorldView-2 satellite images. 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS, pp. 2274–77 Piscataway, NJ: IEEE
    [Google Scholar]
  93. 93.
    Misra SK, Kennedy AB, Kirby JT. 2003. An approach to determining nearshore bathymetry using remotely sensed ocean surface dynamics. Coast. Eng. 47:265–93
    [Google Scholar]
  94. 94.
    Lawrence C, Trulsen K, Gramstad O. 2021. Statistical properties of wave kinematics in long-crested irregular waves propagating over non-uniform bathymetry. Phys. Fluids 33:446601
    [Google Scholar]
  95. 95.
    Wang M, Hu C, Barnes BB, Mitchum G, Lapointe B, Montoya JP. 2019. The great Atlantic Sargassum belt. Science 365:644883–87
    [Google Scholar]
  96. 96.
    Cuevas E, Uribe-Martínez A, de los Ángeles Liceaga-Correa M 2018. A satellite remote-sensing multi-index approach to discriminate pelagic Sargassum in the waters of the Yucatan Peninsula, Mexico. Int. J. Remote Sens. 39:113608–27
    [Google Scholar]
  97. 97.
    Dierssen HM, Chlus A, Russell B. 2015. 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. 167:247–58
    [Google Scholar]
  98. 98.
    Hu C. 2009. A novel ocean color index to detect floating algae in the global oceans. Remote Sens. Environ. 113:102118–29
    [Google Scholar]
  99. 99.
    Villa P, Laini A, Bresciani M, Bolpagni R. 2013. A remote sensing approach to monitor the conservation status of lacustrine Phragmites australis beds. Wetl. Ecol. Manag. 21:6399–416
    [Google Scholar]
  100. 100.
    Siddiqui MD, Zaidi AZ, Abdullah M. 2019. Performance evaluation of newly proposed seaweed enhancing index (SEI). Remote Sens. 11:121434
    [Google Scholar]
  101. 101.
    Mora-Soto A, Palacios M, Macaya EC, Gómez I, Huovinen P et al. 2020. A high-resolution global map of giant kelp (Macrocystis pyrifera) forests and intertidal green algae (Ulvophyceae) with Sentinel-2 imagery. Remote Sens. 12:4694
    [Google Scholar]
  102. 102.
    Marmorino G, Miller W, Smith G, Bowles J. 2011. Airborne imagery of a disintegrating Sargassum drift line. Deep Sea Res. Part I 58:316–21
    [Google Scholar]
  103. 103.
    Sacco A. 2020. Monitoring Atlantic Sargassum using EO-SAR fusion and convolutional neural networks. AGU Fall Meet. Abstr. 2020:B060–0013
    [Google Scholar]
  104. 104.
    Kudela RM, Palacios SL, Austerberry DC, Accorsi EK, Guild LS, Torres-Perez J. 2015. Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters. Remote Sens. Environ. 167:196–205
    [Google Scholar]
  105. 105.
    Remer LA, Davis AB, Mattoo S, Levy RC, Kalashnikova OV et al. 2019. Retrieving aerosol characteristics from the PACE mission, Part 1: Ocean Color Instrument. Front. Earth Sci. 7:152
    [Google Scholar]
  106. 106.
    Monaco ME, Anderson SM, Battista TA, Kendall MS, Rohmann SO et al. 2012. National summary of NOAA's shallow-water benthic habitat mapping of U.S. coral reef ecosystems. Tech. Memo. 122 Natl. Ocean. Atmos. Adm., Washington, DC
  107. 107.
    Andréfouët S, Muller-Karger FE, Robinson JA, Kranenburg CJ, Torres-Pulliza D et al. 2006. Global assessment of modern coral reef extent and diversity for regional science and management applications: a view from space. Proceedings of the 10th International Coral Reef Symposium, Vol. 21732–45 Okinawa: Jpn. Coral Reef Soc.
    [Google Scholar]
  108. 108.
    UNEP-WCMC (U. N. Environ. World Conserv. Monit. Cent.), Short FT 2017. Global distribution of seagrasses (version 6.0). Sixth update to the data layer used in Green and Short (2003) Cambridge, UK: UNEP-WCMC http://data.unepwcmc.org/datasets/7
  109. 109.
    McKenzie L, Nordlund L, Jones B, Cullen-Unsworth L, Roelfsema C, Unsworth R. 2020. The global distribution of seagrass meadows. Environ. Res. Lett. 15:7074041
    [Google Scholar]
  110. 110.
    Charry B, Tissier E, Iacozza J, Marcoux M, Watt CA. 2021. Mapping Arctic cetaceans from space: a case study for beluga and narwhal. PLOS ONE 16:8e0254380
    [Google Scholar]
  111. 111.
    Nakamoto K. 2009. Infrared and Raman Spectra of Inorganic and Coordination Compounds, Part B: Applications in Coordination, Organometallic, and Bioinorganic Chemistry Hoboken, NJ: Wiley
  112. 112.
    Bar-Cohen Y, Zacny K. 2020. Advances in Terrestrial and Extraterrestrial Drilling Boca Raton, FL: CRC Press. , 1st ed..
  113. 113.
    Sun AY, Scanlon BR, AghaKouchak A, Zhang Z. 2017. Using GRACE satellite gravimetry for assessing large-scale hydrologic extremes. Remote Sens. 9:121287
    [Google Scholar]
  114. 114.
    Nemani R, Votava P, Michaelis A, Melton F, Milesi C. 2011. Collaborative supercomputing for global change science. Eos 92:13109–10
    [Google Scholar]
  115. 115.
    Maximenko N, Arvesen J, Asner G, Carlton J, Castrence M et al. 2016. Remote sensing of marine debris to study dynamics, balances and trends White Pap. Decadal Survey Earth Sci. Appl. Space Washington, DC:
  116. 116.
    Biermann L, Clewley D, Martinez-Vicente V, Topouzelis K. 2020. Finding plastic patches in coastal waters using optical satellite data. Sci. Rep. 10:5364
    [Google Scholar]
  117. 117.
    Segal-Rozenhaimer M, Li A, Das K, Chirayath V. 2020. Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN). Remote Sens. Environ. 237:111446
    [Google Scholar]
  118. 118.
    Li AS, Chirayath V, Segal-Rozenhaimer M, Torres-Perez JL, van den Bergh J. 2020. NASA NeMO-net's convolutional neural network: mapping marine habitats with spectrally heterogeneous remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 13:5115–33
    [Google Scholar]
  119. 119.
    Pope RM, Fry ES. 1997. Absorption spectrum (380–700 nm) of pure water. II. Integrating cavity measurements. Appl. Opt. 36:8710–23
    [Google Scholar]
  120. 120.
    Pope RM. 1993. Optical absorption of pure water and sea water using the Integrating Cavity Absorption Meter PhD Thesis Texas A&M Univ. College Station, TX:
/content/journals/10.1146/annurev-environ-112420-013219
Loading
/content/journals/10.1146/annurev-environ-112420-013219
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error