1932

Abstract

Satellite passive ocean color instruments have provided an unbroken ∼20-year record of global ocean plankton properties, but this measurement approach has inherent limitations in terms of spatial-temporal sampling and ability to resolve vertical structure within the water column. These limitations can be addressed by coupling ocean color data with measurements from a spaceborne lidar. Airborne lidars have been used for decades to study ocean subsurface properties, but recent breakthroughs have now demonstrated that plankton properties can be measured with a satellite lidar. The satellite lidar era in oceanography has arrived. Here, we present a review of the lidar technique, its applications in marine systems, a perspective on what can be accomplished in the near future with an ocean- and atmosphere-optimized satellite lidar, and a vision for a multiplatform virtual constellation of observational assets that would enable a three-dimensional reconstruction of global ocean ecosystems.

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2018-01-03
2024-06-21
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Literature Cited

  1. Alvain S, Moulin C, Dandonneau Y, Breon F. 2005. Remote sensing of phytoplankton groups in case 1 waters for global SeaWiFS imagery. Deep-Sea Res. I 52:1989–2004 [Google Scholar]
  2. Antoine D, André J-M, Morel A. 1996. Oceanic primary production 2. Estimation at global scale from satellite (coastal zone color scanner) chlorophyll. Glob. Biogeochem. Cycles 10:57–69 [Google Scholar]
  3. Behrenfeld MJ, Boss E, Siegel DA, Shea DM. 2005. Carbon-based ocean productivity and phytoplankton physiology from space. Glob. Biogeochem. Cycles 19:GB1006 [Google Scholar]
  4. Behrenfeld MJ, Falkowski PG. 1997. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42:1–20 [Google Scholar]
  5. 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. 40:4355–60 [Google Scholar]
  6. Behrenfeld MJ, Hu Y, O'Malley RT, Boss ES, Hostetler CA. et al. 2017. Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar. Nat. Geosci. 10:118–22 [Google Scholar]
  7. Behrenfeld MJ, Milligan AJ. 2013. Photophysiological expressions of iron stress in phytoplankton. Annu. Rev. Mar. Sci. 5:217–46 [Google Scholar]
  8. Behrenfeld MJ, Randerson JT, McClain CR, Feldman GC, Los SO. et al. 2001. Biospheric primary production during an ENSO transition. Science 291:2594–97 [Google Scholar]
  9. Behrenfeld MJ, Westberry TK, Boss E, O'Malley RT, Siegel DA. et al. 2009. Satellite-detected fluorescence reveals global physiology of ocean phytoplankton. Biogeosciences 6:779–94 [Google Scholar]
  10. Behrenfeld MJ, Worthington K, Sherrell RM, Chavez FP, Strutton P. et al. 2006. Controls on tropical Pacific Ocean productivity revealed through nutrient stress diagnostics. Nature 442:1025–28 [Google Scholar]
  11. Billard B, Abbot RH, Penny MF. 1986. Airborne estimation of sea turbidity parameters from the WRELADS laser airborne depth sounder. Appl. Opt. 25:2080–88 [Google Scholar]
  12. Boss E, Pegau WS. 2001. Relationship of light scattering at an angle in the backward direction to the backscattering coefficient. Appl. Opt. 40:5503–7 [Google Scholar]
  13. Bracher A, Vountas M, Dinter T, Burrows JP, Röettgers R, Peeken I. 2009. Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data. Biogeosciences 6:751–64 [Google Scholar]
  14. Bristow M, Nielsen D, Bundy D, Furtek R. 1981. Use of water Raman emission to correct airborne laser fluorosensor data for effects of water optical attenuation. Appl. Opt. 20:2889–906 [Google Scholar]
  15. Bruneau D, Pelon J, Blouzon F, Spatazza J, Genau P. et al. 2015. 355-nm high spectral resolution airborne lidar LNG: system description and first results. Appl. Opt. 54:8776–85 [Google Scholar]
  16. Bunkin AF, Surovegin AL. 1992. Lidar-aided measurement of phytoplankton chlorophyll and underwater scattering layers. EARSeL Adv. Remote Sens. 1:101–5 [Google Scholar]
  17. Burton SP, Ferrare RA, Hostetler CA, Hair JW, Rogers RR. et al. 2012. Aerosol classification using airborne high spectral resolution lidar measurements-methodology and examples. Atmos. Meas. Tech. 5:73–98 [Google Scholar]
  18. Burton SP, Vaughan MA, Ferrare RA, Hostetler CA. 2014. Separating mixtures of aerosol types in airborne high spectral resolution lidar data. Atmos. Meas. Tech. 7:419–36 [Google Scholar]
  19. Churnside JH. 2008. Polarization effects on oceanographic lidar. Opt. Express 16:1196–207 [Google Scholar]
  20. Churnside JH. 2014. Review of profiling oceanographic lidar. Opt. Eng. 53:051405 [Google Scholar]
  21. Churnside JH. 2015. Bio-optical model to describe remote sensing signals from a stratified ocean. J. Appl. Remote Sens. 9:095989 [Google Scholar]
  22. Churnside JH. 2016. Airborne lidar estimates of photosynthesis profiles. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)3777–80 New York: IEEE [Google Scholar]
  23. Churnside JH, Demer DA, Mahmoudi B. 2003. A comparison of lidar and echosounder measurements of fish schools in the Gulf of Mexico. ICES J. Mar. Sci. 60:147–54 [Google Scholar]
  24. Churnside JH, Donaghay PL. 2009. Thin scattering layers observed by airborne lidar. ICES J. Mar. Sci. 66:778–89 [Google Scholar]
  25. Churnside JH, Marchbanks RD. 2015. Subsurface plankton layers in the Arctic Ocean. Geophys. Res. Lett. 42:4896–902 [Google Scholar]
  26. Churnside JH, Ostrovsky LA. 2005. Lidar observation of a strongly nonlinear internal wave train in the Gulf of Alaska. Int. J. Remote Sens. 26:167–77 [Google Scholar]
  27. Churnside JH, Sullivan JM, Twardowski MS. 2014. Lidar extinction-to-backscatter ratio of the ocean. Opt. Express 22:18698–706 [Google Scholar]
  28. Churnside JH, Wilson JJ, Tatarskii VV. 1997. Lidar profiles of fish schools. Appl. Opt. 36:6011–20 [Google Scholar]
  29. Churnside JH, Wilson JJ, Tatarskii VV. 2001. Airborne lidar for fisheries applications. Opt. Eng. 40:406–14 [Google Scholar]
  30. Clarke GL, Ewing GC, Lorenzen CJ. 1970. Spectra of backscattered light from the sea obtained from aircraft as a measure of chlorophyll concentration. Science 167:1119–21 [Google Scholar]
  31. Cullen JJ. 1982. The deep chlorophyll maximum: comparing vertical profiles of chlorophyll a. Can. J. Fish. Aquat. Sci. 39:791–803 [Google Scholar]
  32. Cullen JJ. 2015. Subsurface chlorophyll maximum layers: enduring enigma or mystery solved?. Annu. Rev. Mar. Sci. 7:207–39 [Google Scholar]
  33. DeVries T, Primeau F, Deutsch C. 2012. The sequestration efficiency of the biological pump. Geophys. Res. Lett. 39:L13601 [Google Scholar]
  34. Esselborn M, Wirth M, Fix A, Tesche M, Ehret G. 2008. Airborne high spectral resolution lidar for measuring aerosol extinction and backscatter coefficients. Appl. Opt. 47:346–58 [Google Scholar]
  35. Falkowski PG, Barber RT, Smetacek V. 1998. Biogeochemical controls and feedbacks on ocean primary production. Science 281:200–6 [Google Scholar]
  36. Fennel K, Boss E. 2003. Subsurface maxima of phytoplankton and chlorophyll: steady state solutions from a simple model. Limnol. Oceanogr. 48:1521–34 [Google Scholar]
  37. Fernald FG. 1984. Analysis of atmospheric lidar observations—some comments. Appl. Opt. 23:652–53 [Google Scholar]
  38. Field CB, Behrenfeld MJ, Randerson JT, Falkowski PG. 1998. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281:237–40 [Google Scholar]
  39. Gantt B, Meskhidze N. 2013. The physical and chemical characteristics of marine primary organic aerosol: a review. Atmos. Chem. Phys. 13:3979–96 [Google Scholar]
  40. Garver SA, Siegel DA. 1997. Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation: I. Time series from the Sargasso Sea. J. Geophys. Res. 102:18607–25 [Google Scholar]
  41. Gordon HR. 1982. Interpretation of airborne oceanic lidar: effects of multiple scattering. Appl. Optics 21:2996–3001 [Google Scholar]
  42. Hair JW, Hostetler CA, Cook AL, Harper DB, Ferrare RA. et al. 2008. Airborne high spectral resolution lidar for profiling aerosol optical properties. Appl. Opt. 47:6734–52 [Google Scholar]
  43. Hair JW, Hostetler CA, Hu Y, Behrenfeld MJ, Butler C. et al. 2016. Combined atmospheric and ocean profiling from an airborne high spectral resolution lidar. EPJ Web Conf 119:22001 [Google Scholar]
  44. Hickman GD, Harding JM, Carnes M, Pressman A, Kattawar GW, Fry ES. 1991. Aircraft laser sensing of sound velocity in water: Brillouin scattering. Remote Sens. Environ. 36:165–78 [Google Scholar]
  45. Hill VJ, Matrai PA, Olson E, Suttles S, Steele M. et al. 2013. Synthesis of integrated primary production in the Arctic Ocean: II. In situ and remotely sensed estimates. Prog. Oceanogr. 110:107–25 [Google Scholar]
  46. Hill VJ, Zimmerman RC. 2010. Estimates of primary production by remote sensing in the Arctic Ocean: assessment of accuracy with passive and active sensors. Deep-Sea Res. I 57:1243–54 [Google Scholar]
  47. Hoge FE, Lyon PE, Swift RN, Yungel JK, Abbott MR. et al. 2003. Validation of Terra-MODIS phytoplankton chlorophyll fluorescence line height. I. Initial airborne lidar results. Appl. Opt. 42:2767–71 [Google Scholar]
  48. 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. 44:2857–62 [Google Scholar]
  49. Hoge FE, Swift RN. 1981. Airborne simultaneous spectroscopic detection of laser-induced water Raman backscatter and fluorescence from chlorophyll a and other naturally occurring pigments. Appl. Opt. 20:3197–205 [Google Scholar]
  50. Hoge FE, Swift RN, Yungel JK. 1995. Oceanic radiance model development and validation: application of airborne active-passive ocean color spectral measurements. Appl. Opt. 34:3468–76 [Google Scholar]
  51. Hoge FE, Wright CW, Krabill WB, Buntzen RR, Gilbert GD. et al. 1988. Airborne lidar detection of subsurface oceanic scattering layers. Appl. Opt. 27:3969–77 [Google Scholar]
  52. Hu Y. 2007. Depolarization ratio-effective lidar ratio relation: theoretical basis for space lidar cloud phase discrimination. Geophys. Res. Lett. 34:L11812 [Google Scholar]
  53. Hu Y. 2009. Ocean, land and meteorology studies using space-based lidar measurements. Recent Advances in Remote Sensing: Proceedings of the 5th WSEAS International Conference on Remote Sensing (REMOTE’09) R Revetria, V Mladenov, N Mastorakis 47–50 Sofia, Bulg: WSEAS https://ntrs.nasa.gov/search.jsp?R=20090037431 [Google Scholar]
  54. Hu Y, Vaughan M, McClain C, Behrenfeld MJ, Maring H. et al. 2007. Global statistics of liquid water content and effective number concentration of water clouds over ocean derived from combined CALIPSO and MODIS measurements. Atmos. Chem. Phys. 7:3353–59 [Google Scholar]
  55. IPCC (Intergov. Panel Clim. Change). 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Ed. TF Stocker, D Qin, G-K Plattner, M Tignor, SK Allen et al. Cambridge, UK: Cambridge Univ. Press [Google Scholar]
  56. Jacox MG, Edwards CA, Kahru M, Rudnick DL, Kudela RM. 2015. The potential for improving remote primary productivity estimates through subsurface chlorophyll and irradiance measurement. Deep-Sea Res. II 112:107–16 [Google Scholar]
  57. Kim HH. 1973. New algae mapping technique by the use of an airborne laser fluorosensor. Appl. Opt. 12:1454–59 [Google Scholar]
  58. Kostadinov TS, Siegel DA, Maritorena S. 2010. Global variability of phytoplankton functional types from space: assessment via the particle size distribution. Biogeosciences 7:3239–57 [Google Scholar]
  59. Lee JH, Churnside JH, Marchbanks RD, Donaghay PL, Sullivan JM. 2013. Oceanographic lidar profiles compared with estimates from in situ optical measurements. Appl. Opt. 52:786–94 [Google Scholar]
  60. Lee ZP, Carder KL, Arnone RA. 2002. Deriving inherent optical properties from water color: a multi-band quasi-analytical algorithm for optically deep waters. Appl. Opt. 41:5755–72 [Google Scholar]
  61. Lee ZP, Darecki M, Carder KL, Davis CO, Stramski D, Rhea WJ. 2005. Diffuse attenuation coefficient of downwelling irradiance: an evaluation of remote sensing methods. J. Geophys. Res. 110:C02017 [Google Scholar]
  62. Lin H, Kuzminov FI, Park J, Lee S, Falkowski PG, Gorbunov MY. 2016. The fate of photons absorbed by phytoplankton in the global ocean. Science 351:264–67 [Google Scholar]
  63. Loisel H, Duforet L, Dessailly D, Chami M, Dubuisson P. 2008. Investigation of the variations in the water leaving polarized reflectance from the POLDER satellite data over two biogeochemical contrasted oceanic areas. Opt. Express 16:12905–18 [Google Scholar]
  64. Maritorena S, Siegel DA, Peterson AR. 2002. Optimization of a semianalytical ocean color model for global-scale applications. Appl. Opt. 41:2705–14 [Google Scholar]
  65. Martin JH, Coale KH, Johnson KS, Fitzwater SE, Gordon RM. et al. 1994. Testing the iron hypothesis in ecosystems of the equatorial Pacific Ocean. Nature 371:123–29 [Google Scholar]
  66. McClain CR. 2009. A decade of satellite ocean color observations. Annu. Rev. Mar. Sci. 1:19–42 [Google Scholar]
  67. McCoy DT, Burrows SM, Wood R, Grosvenor DP, Elliott SM. et al. 2015. Natural aerosols explain seasonal and spatial patterns of Southern Ocean cloud albedo. Sci. Adv. 1:e1500157 [Google Scholar]
  68. Meskhidze N, Nenes A. 2006. Phytoplankton and cloudiness in the Southern Ocean. Science 314:1419–23 [Google Scholar]
  69. Müller D, Hostetler CA, Ferrare RA, Burton SP, Chemyakin E. et al. 2014. Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US. Atmos. Meas. Tech. 7:3487–96 [Google Scholar]
  70. NASA. 2012. Pre-Aerosols, Clouds, and ocean Ecosystems (PACE) Mission Science Definition Team report Rep NASA, Washington, DC: https://pace.oceansciences.org/docs/pace_sdt_report_final.pdf [Google Scholar]
  71. O'Malley RT, Behrenfeld MJ, Westberry TK, Milligan AJ, Shang S, Yan J. 2014. Geostationary satellite observations of dynamic phytoplankton photophysiology. Geophys. Res. Lett. 41:5052–59 [Google Scholar]
  72. Platt T, Sathyendranath S. 1988. Oceanic primary production: estimation by remote sensing at local and regional scales. Science 241:1613–20 [Google Scholar]
  73. Poole LR, Esaias WE. 1982. Water Raman normalization of airborne laser fluorosensor measurements: a computer model study. Appl. Opt. 21:3756–61 [Google Scholar]
  74. Quinn PK, Bates TS. 2011. The case against climate regulation via oceanic phytoplankton sulphur emissions. Nature 480:51–56 [Google Scholar]
  75. Ryu JH, Han HJ, Cho S, Park YJ, Ahn YH. 2012. Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS). Ocean Sci. J. 47:223–33 [Google Scholar]
  76. Sadeghi A, Dinter T, Vountas M, Taylor B, Altenburg-Soppa M, Bracher A. 2012. Remote sensing of coccolithophore blooms in selected oceanic regions using the PhytoDOAS method applied to hyper-spectral satellite data. Biogeosciences 9:2127–43 [Google Scholar]
  77. Sathyendranath S, Aiken J, Alvain S, Barlow R, Bouman H. et al. 2014. Phytoplankton functional types from space Rep. 15 Int. Ocean-Colour Coord. Group, Dartmouth, Can. [Google Scholar]
  78. Sathyendranath S, Platt T. 1989. Remote sensing of ocean chlorophyll: consequence of nonuniform pigment profile. Appl. Opt. 28:490–95 [Google Scholar]
  79. Sawamura P, Moore RH, Burton SP, Chemyakin E, Müller D. et al. 2017. HSRL-2 aerosol optical measurements and microphysical retrievals vs. airborne in situ measurements during DISCOVER-AQ 2013: an intercomparison study. Atmos. Chem. Phys. 17:7229–43 [Google Scholar]
  80. Schrader PS, Milligan AJ, Behrenfeld MJ. 2011. Surplus photosynthetic antennae complexes underlie diagnostics of iron limitation in a cyanobacterium. PLOS ONE 6:e18753 [Google Scholar]
  81. Schulien JA, Behrenfeld MJ, Hair JW, Hostetler CA, Twardowski MS. 2017. Vertically-resolved phytoplankton carbon and net primary production from a high spectral resolution lidar. Opt. Express 25:13577–87 [Google Scholar]
  82. Shipley ST, Tracy DH, Eloranta EW, Trauger JT, Sroga JT. et al. 1983. High spectral resolution lidar to measure optical scattering properties of atmospheric aerosols. 1: theory and instrumentation. Appl. Opt. 22:3716–24 [Google Scholar]
  83. Siegel DA, Maritorena S, Nelson NB, Behrenfeld MJ. 2005. Independence and interdependencies of global ocean color properties: reassessing the bio-optical assumption. J. Geophys. Res. 110:C07011 [Google Scholar]
  84. Siegel DA, Maritorena S, Nelson NB, Hansell DA, Lorenzi-Kayser M. 2002. Global distribution and dynamics of colored dissolved and detrital organic materials. J. Geophys. Res. 107:3228 [Google Scholar]
  85. Silsbe GM, Behrenfeld MJ, Halsey KH, Milligan AJ, Westberry TK. 2016. The CAFE model: a net production model for global ocean phytoplankton. Glob. Biogeochem. Cycles 30:1756–77 [Google Scholar]
  86. Steinvall KO, Koppari KR, Karlsson UC. 1993. Experimental evaluation of an airborne depth-sounding lidar. Opt. Eng. 32:1307–21 [Google Scholar]
  87. Stier P. 2016. Limitations of passive remote sensing to constrain global cloud condensation nuclei. Atmos. Chem. Phys. 16:6595–607 [Google Scholar]
  88. Stramska M, Stramski D. 2005. Effects of a nonuniform vertical profile of chlorophyll concentration on remote-sensing reflectance of the ocean. Appl. Opt. 44:1735–47 [Google Scholar]
  89. Thorsen TJ, Ferrare RA, Hostetler CA, Vaughan MA, Fu Q. 2017. The impact of lidar detection sensitivity on assessing aerosol direct radiative effects. Geophys. Res. Lett. 44:9059–67 [Google Scholar]
  90. Werdell PJ, Franz BA, Bailey SW, Feldman GC, Boss E. et al. 2013. Generalized ocean color inversion model for retrieving marine inherent optical properties. Appl. Opt. 52:2019–37 [Google Scholar]
  91. Westberry TK, Behrenfeld MJ, Milligan AJ, Doney SC. 2013. Retrospective satellite ocean color analysis of purposeful and natural ocean iron fertilization. Deep-Sea Res. I 73:1–16 [Google Scholar]
  92. Westberry TK, Behrenfeld MJ, Siegel DA, Boss E. 2008. Carbon-based primary productivity modeling with vertically resolved photoacclimation. Glob. Biogeochem. Cycles 22:GB2024 [Google Scholar]
  93. Winker DM, Vaughan MA, Omar A, Hu Y, Powell KA. et al. 2009. Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Ocean. Technol. 26:2310–23 [Google Scholar]
  94. Yoder JA, Aiken J, Swift RN, Hoge FE, Stegmann PM. 1993. Spatial variability in near-surface chlorophyll a fluorescence measured by the Airborne Oceanographic Lidar (AOL). Deep-Sea Res. II 40:37–53 [Google Scholar]
  95. Zawada DG, Zaneveld J, Ronald V, Boss E, Gardner WD. et al. 2005. A comparison of hydrographically and optically derived mixed layer depths. J. Geophys. Res. 110:C11001 [Google Scholar]
  96. Zhai L, Gudmundsson K, Miller P, Peng W, Guðfinnsson H. et al. 2012. Phytoplankton phenology and production around Iceland and Faroes. Cont. Shelf Res. 37:15–25 [Google Scholar]
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