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Machine Learning Based Retrieval Algorithms: Application to Ocean Optics

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Springer Series in Light Scattering

Part of the book series: Springer Series in Light Scattering ((SSLS))

Abstract

Global ocean color measurements from satellites provide critical information about ocean ecology and biogeochemistry, this advances our knowledge of the carbon cycle and its impact on climate disturbances. The repeated synoptic observations of spectral leaving reflectance can be used to estimate marine inherent optical properties. The spatial, angular, and spectral distributions of underwater light field depend upon the absorption and scattering characteristics, the inherent optical properties (IOPs), of oceanic particles or hydrosols. The remote sensing algorithms or inverse methods typically rely on forward models to relate the satellite observed quantity, remote sensing reflectance, with the corresponding IOPs. The viewing angle dependence, spectral, and polarization states of reflected light contain rich information on the retrieval IOP parameters. The inverse methods depend on a reliable atmospheric correction algorithm. However, due to the presence of absorbing aerosols and complex optical properties of coastal waters, it is difficult to achieve successful atmospheric correction over coastal waters or when absorbing aerosols are involved. To overcome the limitations of atmospheric correction in coastal and inland waters involving absorbing aerosols, the use of multi-angle, multi-wavelength, polarized measurements to characterize aerosol and hydrosol properties would be beneficial. The polarized signal is measured by several multi-angular polarimeters like POLDER, the Research Scanning Polarimeter (RSP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Spectropolarimeter for Planetary EXploration (SPEX) and the HyperAngular Rainbow Polarimeter (HARP). Notably, NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission plans to carry SPEXone and HARP2 as well as the Ocean Color Instrument (OCI), which will acquire an abundance of unprecedented co-located datasets of polarimeter and ocean color measurements. To handle the huge volume of data it is critical to use a fast ocean reflectance model that can be used in the retrieval algorithms to achieve operational retrieval of aerosol and ocean color information. The retrieval algorithms need to call radiative transfer models iteratively in order to minimize the difference between measurements and model prediction, which is computationally expensive. In this chapter, available fast retrieval algorithms based on machine learning techniques used by the community are reviewed. Their scopes and limitations is discussed. Particularly this chapter summarizes various machine learning and optimization algorithms used in ocean optics studies.

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References

  • Brajard J, Jamet C, Moulin C, Thiria S (2006) Use of a neuro-variational inversion for retrieving oceanic and atmospheric constituents from satellite ocean colour sensor: application to absorbing aerosols. Neural Netw Off J Int Neural Netw Soc 19(2):178–185. https://doi.org/10.1016/j.neunet.2006.01.015

    Article  Google Scholar 

  • Branch MA, Coleman TF, Li Y (1995) A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems. Cornell University, Technical report

    MATH  Google Scholar 

  • Bricaud A, Babin M, Morel A, Claustre H (1995) Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: analysis and parameterization. J Geophys Res Oceans 100(C7):13321–13332

    Article  Google Scholar 

  • Bricaud A, Morel A, Babin M, Allali K, Claustre H (1998) Variations of light absorption by suspended particles with chlorophyll a concentration in oceanic (case 1) waters: analysis and implications for bio-optical models. J Geophys Res Oceans 103(C13):31033–31044

    Article  Google Scholar 

  • Cairns B, Russell EE, Travis LD (1999) Research scanning polarimeter: calibration and ground-based measurements. In: Polarization: measurement, analysis, and remote sensing II, vol 3754. International society for optics and photonics, pp 186–196

    Google Scholar 

  • Mobley CD (1994) Light and water: radiative transfer in natural waters. Academic Press

    Google Scholar 

  • Chandrasekhar S (1960a) Radiative transfer. Dover books on intermediate and advanced mathematics. Dover Publications. https://books.google.com/books?id=CK3HDRwCT5YC

  • Chandrasekhar S (1960) Radiative transfer. Dover Publications

    Google Scholar 

  • Chapman DV et al (1996) Water quality assessments: a guide to the use of biota, sediments, and water in environmental monitoring. World Health Organization

    Google Scholar 

  • Chomko RM, Gordon HR (1998) Atmospheric correction of ocean color imagery: use of the Junge power-law aerosol size distribution with variable refractive index to handle aerosol absorption. Appl Opt 37(24):5560–5572

    Article  ADS  Google Scholar 

  • Chowdhary J, Cairns B, Mishchenko MI, Hobbs PV, Cota GF, Redemann J, Rutledge K, Holben BN, Russell E (2005) Retrieval of aerosol scattering and absorption properties from photopolarimetric observations over the ocean during the clams experiment. J Atmos Sci 62(4):1093–1117

    Article  ADS  Google Scholar 

  • del Águila A, Efremenko D, Trautmann T (2019) A review of dimensionality reduction techniques for processing hyper-spectral optical signal. Light Eng 27(3):85–98

    Article  Google Scholar 

  • Deschamps P-Y, Bréon F-M, Leroy M, Podaire A, Bricaud A, Buriez J-C, Seze G (1994) The polder mission: instrument characteristics and scientific objectives. IEEE Trans Geosci Remote Sens 32(3):598–615

    Article  ADS  Google Scholar 

  • Deuflhard P (2012) A short history of Newton’s method. Documenta Mathematica, Optimization stories, pp 25–30

    Google Scholar 

  • Dierssen HM (2010) Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate. Proc Natl Acad Sci 107(40):17073–17078

    Article  ADS  Google Scholar 

  • Diner DJ, Xu F, Garay MJ, Martonchik JV, Rheingans BE, Geier S, Davis A, Hancock BR, Jovanovic VM, Bull MA, Capraro K, Chipman RA, McClain SC (2013) The airborne multiangle spectropolarimetric imager (AirMSPI): a new tool for aerosol and cloud remote sensing. Atmos Meas Tech 6(8):2007–2025

    Article  Google Scholar 

  • Doney SC (2010) The growing human footprint on coastal and open-ocean biogeochemistry. Science 328(5985):1512–1516

    Article  ADS  Google Scholar 

  • Dubovik O, Sinyuk A, Lapyonok T, Holben BN, Mishchenko M, Yang P, Eck TF, Volten H, Muñoz O, Veihelmann B, van der Zande WJ, Leon J-F, Sorokin M, Slutsker I (2006) Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust. J Geophys Res Atmos 111(D11). https://doi.org/10.1029/2005JD006619. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2005JD006619

  • Elena YL, Kadyshevich A, Rosenberrg G (1976) Light-scattering matrices of Pacific and Atlantic ocean waters. Izv Atmos Ocean Phys 12(2):186–195

    Google Scholar 

  • Fan Y, Li W, Gatebe CK, Jamet C, Zibordi G, Schroeder T, Stamnes K (2017) Atmospheric correction over coastal waters using multilayer neural networks. Remote Sens Environ 199:218–240

    Article  ADS  Google Scholar 

  • Fan C, Fu G, Di Noia A, Smit M, Rietjens JHH, Ferrare RA, Burton S, Li Z, Hasekamp OP (2019) Use of a neural network-based ocean body radiative transfer model for aerosol retrievals from multi-angle polarimetric measurements. Remote Sens 11(23):2877

    Article  ADS  Google Scholar 

  • Faure T, Isaka H, Guillemet B (2001) Neural network retrieval of cloud parameters of inhomogeneous and fractional clouds: feasibility study. Remote Sens Environ 77(2):123–138

    Article  ADS  Google Scholar 

  • Franz BA, Werdell PJ (2010) A generalized framework for modeling of inherent optical properties in ocean remote sensing applications. In: Proceedings of ocean optics, Anchorage, Alaska, 27 Sept 2010, pp 1–13

    Google Scholar 

  • Gao M, Zhai P-W, Franz B, Hu Y, Knobelspiesse K, Werdell PJ, Ibrahim A, Xu F, Cairns B (2018) Retrieval of aerosol properties and water-leaving reflectance from multi-angular polarimetric measurements over coastal waters. Opt Express 26(7):8968–8989

    Article  ADS  Google Scholar 

  • Gao M, Zhai P-W, Franz BA, Hu Y, Knobelspiesse K, Werdell PJ, Ibrahim A, Cairns B, Chase A (2019) Inversion of multiangular polarimetric measurements over open and coastal ocean waters: a joint retrieval algorithm for aerosol and water-leaving radiance properties. Atmos Meas Tech 12(7):3921–3941

    Article  Google Scholar 

  • Gao M, Franz BA, Knobelspiesse K, Zhai P-W, Martins V, Burton S, Cairns B, Ferrare R, Gales J, Hasekamp O, Hu Y, Ibrahim A, McBride B, Puthukkudy A, Werdell PJ, Xu X (2021) Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model. Atmos Meas Tech Discuss 2021:1–41

    Google Scholar 

  • Gordon HR (1973) Simple calculation of the diffuse reflectance of the ocean. Appl Opt 12(12):2803–2804

    Article  ADS  Google Scholar 

  • Gordon HR, Brown OB, Evans RH, Brown JW, Smith RC, Baker KS, Clark DK (1988) A semianalytic radiance model of ocean color. J Geophys Res Atmos 93(D9):10909–10924

    Article  ADS  Google Scholar 

  • Halton JH (1960) On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numer Math 2(1):84–90

    Article  MathSciNet  MATH  Google Scholar 

  • Han B, Vucetic S, Braverman A, Obradovic Z (2006) A statistical complement to deterministic algorithms for the retrieval of aerosol optical thickness from radiance data. Eng Appl Artif Intell 19(7):787–795, special issue on Engineering Applications of Neural Networks - Novel Applications of Neural Networks in Engineering. https://doi.org/10.1016/j.engappai.2006.05.009. https://www.sciencedirect.com/science/article/pii/S0952197606000947

  • Hasekamp OP, Litvinov P, Butz A (2011) Aerosol properties over the ocean from parasol multiangle photopolarimetric measurements. J Geophys Res Atmos 116(D14). https://doi.org/10.1029/2010JD015469. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2010JD015469

  • Hasekamp O, Landgraf J (2002) A linearized vector radiative transfer model for atmospheric trace gas retrieval. J Quant Spectrosc Radiat Transf 75(2):221–238

    Article  ADS  Google Scholar 

  • Hasekamp OP, Litvinov P, Butz A (2011) Aerosol properties over the ocean from parasol multiangle photopolarimetric measurements. J Geophys Res Atmos 116(D14):2156–2202

    Article  Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer, New York Inc., New York, NY, USA

    Google Scholar 

  • Hebb DO (1949) The organization of behavior: a neuropsychological theory. Wiley

    Google Scholar 

  • Hedley J, Roelfsema C, Phinn SR (2009) Efficient radiative transfer model inversion for remote sensing applications. Remote Sens Environ 113(11):2527–2532

    Article  ADS  Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366

    Article  MATH  Google Scholar 

  • Hou W, Wang J, Xu X, Reid JS, Han D (2016) An algorithm for hyperspectral remote sensing of aerosols: 1. Development of theoretical framework. J Quant Spectrosc Radiat Transf 178 (2016) 400–415, electromagnetic and light scattering by nonspherical particles XV: Celebrating 150 years of Maxwell’s electromagnetics. https://doi.org/10.1016/j.jqsrt.2016.01.019. https://www.sciencedirect.com/science/article/pii/S002240731530265X

  • Hu C, Lee Z, Franz B (2012) Chlorophyll algorithms for oligotrophic oceans: a novel approach based on three-band reflectance difference. J Geophys Res Oceans 117(C1):1–25

    Google Scholar 

  • Huot Y, Morel A, Twardowski MS, Stramski D, Reynolds RA (2008) Particle optical backscattering along a chlorophyll gradient in the upper layer of the eastern south pacific ocean. Biogeosciences 5(2):495–507

    Article  ADS  Google Scholar 

  • James G, Witten D, Hastie T, Tibshirani R (2014) An introduction to statistical learning: with applications in R. Springer Publishing Company, Incorporated

    Google Scholar 

  • Jamet C, Thiria S, Moulin C, Crépon M (2005) Use of a neurovariational inversion for retrieving oceanic and atmospheric constituents from ocean color imagery: a feasibility study. J Atmos Oceanic Tech 22:460–475

    Article  Google Scholar 

  • Jin Z, Stamnes K (1994) Radiative transfer in nonuniformly refracting layered media: atmosphere-ocean system. Appl Opt 33(3):431–442

    Article  ADS  Google Scholar 

  • Junge CE (1958) Atmospheric chemistry. Advances in geophysics, vol 4. Elsevier, pp 1–108. https://doi.org/10.1016/S0065-2687(08)60484-7. https://www.sciencedirect.com/science/article/pii/S0065268708604847

  • Kattawar GW, Adams CN (1989) Stokes vector calculations of the submarine light field in an atmosphere-ocean with scattering according to a Rayleigh phase matrix: effect of interface refractive index on radiance and polarization. Limnol Oceanogr 34(8):1453–1472

    Article  ADS  Google Scholar 

  • Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980

  • Kokhanovsky AA (2003) Parameterization of the mueller matrix of oceanic waters. J Geophys Res Oceans 108(C6). https://doi.org/10.1029/2001JC001222. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2001JC001222

  • Kokhanovsky AA, Budak VP, Cornet C, Duan M, Emde C, Katsev IL, Klyukov DA, Korkin SV, Labonnote LC, Mayer B et al (2010) Benchmark results in vector atmospheric radiative transfer. J Quant Spectrosc Radiat Transf 111(12):1931–1946

    Article  ADS  Google Scholar 

  • Kou L, Labrie D, Chylek P (1993) Refractive indices of water and ice in the 0.65 to 2.5-\(\upmu \)m spectral range. Appl Opt 32(19):3531–3540

    Google Scholar 

  • Landgraf J, Hasekamp O, Box M, Trautmann T (2001) A linearized radiative transfer model for ozone profile retrieval using the analytical forward-adjoint perturbation theory approach. J Geophys Res 106:27291–27305

    Article  ADS  Google Scholar 

  • Lawless R, Xie Y, Yang P, Kattawar GW, Laszlo I (2006) Polarization and effective Mueller matrix for multiple scattering of light by nonspherical ice crystals. Opt Express 14(14):6381–6393

    Article  ADS  Google Scholar 

  • Lee Z-P (ed) (2006) Remote sensing of inherent optical properties: fundamentals, tests of algorithms, and applications, vol 5. Reports of the International Ocean Colour Coordinating Group, IOCCG, Dartmouth, Canada

    Google Scholar 

  • Lee Z, Carder KL, Arnone RA (2002) Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Appl Opt 41(27):5755–5772

    Article  ADS  Google Scholar 

  • Ligi M, Kutser T, Kallio K, Attila J, Koponen S, Paavel B, Soomets T, Reinart A (2017) Testing the performance of empirical remote sensing algorithms in the Baltic sea waters with modelled and in situ reflectance data. Oceanologia 59(1):57–68

    Article  Google Scholar 

  • Liou KN (2002) An introduction to atmospheric radiation, vol 84, 2nd ed. International Geophysics, 2nd ed. Academic Press. http://amazon.com/o/ASIN/0124514510/

  • Liu C-C, Miller RL (2008) Spectrum matching method for estimating the chlorophyll-a concentration, CDOM ratio, and backscatter fraction from remote sensing of ocean color. Can J Remote Sens 34(4):343–355

    Article  Google Scholar 

  • Loisel H, Vantrepotte V, Jamet C, Dat DN (2013) Challenges and new advances in ocean color remote sensing of coastal waters. Top Oceanogr 1–38

    Google Scholar 

  • Loyola RDG, Pedergnana M, Gimeno García S (2016) Smart sampling and incremental function learning for very large high dimensional data. Neural Netw 78(C):75–87

    Google Scholar 

  • Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441

    Article  MathSciNet  MATH  Google Scholar 

  • Martins JV, Fernandez-Borda R, McBride B, Remer L, Barbosa HMJ (2018) The harp hype ran gular imaging polarimeter and the need for small satellite payloads with high science payoff for earth science remote sensing. In: 2018 IEEE international geoscience and remote sensing symposium, IGARSS 2018, pp 6304–6307. https://doi.org/10.1109/IGARSS.2018.8518823

  • Mobley C, Boss E, Roesler C (2010) Ocean optics web book. http://www.oceanopticsbook.info

  • Mobley CD, Sundman LK, Davis CO, Bowles JH, Downes TV, Leathers RA, Montes MJ, Bissett WP, Kohler DD, Reid RP et al (2005) Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables. Appl Opt 44(17):3576–3592

    Article  ADS  Google Scholar 

  • Morel A (1974) Optical properties of pure water and pure sea water. Opt Asp Oceanogr 1(1):1–24

    MathSciNet  Google Scholar 

  • Morel A, Gentili B (2009) A simple band ratio technique to quantify the colored dissolved and detrital organic material from ocean color remotely sensed data. Remote Sens Environ 113(5):998–1011

    Article  ADS  Google Scholar 

  • Mukherjee L (2020) Radiative properties of oceanic particles. PhD thesis, University of Maryland, Baltimore County (2020)

    Google Scholar 

  • Mukherjee L, Zhai P-W, Hu Y, Winker DM (2017) Equivalence of internal and external mixture schemes of single scattering properties in vector radiative transfer. Appl Opt 56(14)

    Google Scholar 

  • Mukherjee L, Zhai P-W, Gao M, Hu Y, Franz BA, Werdell PJ (2020) Neural network reflectance prediction model for both open ocean and coastal waters. Remote Sens 12(14)

    Google Scholar 

  • Nocedal J, Wright S (2006) Numerical optimization. Springer Science & Business Media

    Google Scholar 

  • Noia AD, Hasekamp O, Harten GV, Rietjens J, Smit J, Snik F, Henzing J, Boer JD, Keller C, Volten H (2015) Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations. Atmos Meas Tech 8(1):281–299

    Article  Google Scholar 

  • Noia AD, Hasekamp OP, Wu L, Diedenhoven BV, Cairns B, Yorks JE (2017) Combined neural network/Phillips–Tikhonov approach to aerosol retrievals over land from the NASA research scanning polarimeter. Atmos Meas Tech 10(11):4235–4252

    Article  Google Scholar 

  • Ota Y, Higurashi A, Nakajima T, Yokota T (2010) Matrix formulations of radiative transfer including the polarization effect in a coupled atmosphere-ocean system. J Quant Spectrosc Radiat Transf 111(6):878–894

    Article  ADS  Google Scholar 

  • Phillips DL (1962) A technique for the numerical solution of certain integral equations of the first kind. J ACM 9(1):84–97

    Article  MathSciNet  MATH  Google Scholar 

  • Pope RM, Fry ES (1997) Absorption spectrum (380–700 nm) of pure water. II. Integrating cavity measurements. Appl Opt 36(33):8710–8723

    Google Scholar 

  • Preisendorfer RW (2014) Radiative transfer on discrete spaces, vol 74. Elsevier

    Google Scholar 

  • Preisendorfer RW (1976) Hydrologic optics. Government Printing Office, U.S

    Google Scholar 

  • Rosenblatt F (1957) The perceptron: a perceiving and recognizing automaton. Technical report, Report 85-60-1, Cornell Aeronautical Laboratory, Buffalo, New York

    Google Scholar 

  • Sathyendranath S (ed) (2000) Remote sensing of ocean colour in coastal, and other optically-complex, waters, vol 3. Reports of the International Ocean Colour Coordinating Group, IOCCG, Dartmouth, Canada

    Google Scholar 

  • Shanmugam P, Ahn Y-H, Ryu J-H, Sundarabalan B (2010) An evaluation of inversion models for retrieval of inherent optical properties from ocean color in coastal and open sea waters around korea. J Oceanogr 66(6):815–830

    Article  Google Scholar 

  • Shi C, Hashimoto M, Shiomi K, Nakajima T (2020) Development of an algorithm to retrieve aerosol optical properties over water using an artificial neural network radiative transfer scheme: first result from GOSAT-2/CAI-2. IEEE Trans Geosci Remote Sens (2020)

    Google Scholar 

  • Smith JA (1993) Lai inversion using a back-propagation neural network trained with a multiple scattering model. IEEE Trans Geosci Remote Sens 31(5):1102–1106

    Article  ADS  Google Scholar 

  • Snik F, Rietjens JH, Van Harten G, Stam DM, Keller CU, Smit JM, Laan EC, Verlaan AL, Ter Horst R, Navarro R, Wielinga K, Moon SG, Voors R (2010) Spex: the spectropolarimeter for planetary exploration. In: Space telescopes and instrumentation 2010: optical, infrared, and millimeter wave. International society for optics and photonics, vol 7731, p 77311B

    Google Scholar 

  • Stokes GG (1851) On the composition and resolution of streams of polarized light from different sources. Trans Camb Philos Soc 9:399

    ADS  Google Scholar 

  • Tikhonov A (1963) Solution of incorrectly formulated problems and the regularization method

    Google Scholar 

  • van de Hulst HC (1981) Physics, light scattering by small particles (Dover Books on Physics). Reprint, Edition, Dover Publications. http://amazon.com/o/ASIN/0486642283/

  • Wang M (ed) (2010) Atmospheric correction for remotely-sensed ocean-colour products, vol 10. Reports of the International Ocean Colour Coordinating Group, IOCCG, Dartmouth, Canada

    Google Scholar 

  • Werdell PJ, Franz BA, Bailey SW, Feldman GC, Boss E, Brando VE, Dowell M, Hirata T, Lavender SJ, Lee Z et al (2013) Generalized ocean color inversion model for retrieving marine inherent optical properties. Appl Opt 52(10):2019–2037

    Article  ADS  Google Scholar 

  • Werdell PJ, McKinna LI, Boss E, Ackleson SG, Craig SE, Gregg WW, Lee Z, Maritorena S, Roesler CS, Rousseaux CS 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

    Article  ADS  Google Scholar 

  • Werdell PJ, Behrenfeld MJ, Bontempi PS, Boss E, Cairns B, Davis GT, Franz BA, Gliese UB, Gorman ET, Hasekamp O, Knobelspiesse KD, Mannino A, Martins JV, McClain CR, Meister G, Remer LA (2019) The plankton, aerosol, cloud, ocean ecosystem (pace) mission: status, science, advances. Bull Am Meteor Soc 100(9):1775–1794

    Article  ADS  Google Scholar 

  • Xu F, Dubovik O, Zhai P-W, Diner DJ, Kalashnikova OV, Seidel FC, Litvinov P, Bovchaliuk A, Garay MJ, Harten GV, Davis AB (2016) Joint retrieval of aerosol and water-leaving radiance from multispectral, multiangular and polarimetric measurements over ocean. Atmos Meas Tech 9(7):2877–2907

    Article  Google Scholar 

  • Yang P, Liou KN (1997) Light scattering by hexagonal ice crystals: solutions by a ray-by-ray integration algorithm. J Opt Soc Am 14:2278–2289

    Article  ADS  Google Scholar 

  • Zhai P-W, Hu Y, Trepte CR, Lucker PL (2009) A vector radiative transfer model for coupled atmosphere and ocean systems based on successive order of scattering method. Opt Express 17(4):2057–2079

    Article  ADS  Google Scholar 

  • Zhai P-W, Hu Y, Chowdhary J, Trepte CR, Lucker PL, Josset DB (2010) A vector radiative transfer model for coupled atmosphere and ocean systems with a rough interface. J Quant Spectrosc Radiat Transf 111(7–8):1025–1040

    Article  ADS  Google Scholar 

  • Zhang X, Hu L (2009) Scattering by pure seawater at high salinity. Opt Express 17(15):12685–12691

    Article  ADS  Google Scholar 

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    Lipi Mukherjee

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Mukherjee, L. (2021). Machine Learning Based Retrieval Algorithms: Application to Ocean Optics. In: Kokhanovsky, A. (eds) Springer Series in Light Scattering. Springer Series in Light Scattering. Springer, Cham. https://doi.org/10.1007/978-3-030-87683-8_2

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