Diffuse reflection coefficient or diffuse reflectance of light from water body is an informative part of remote sensing reflectance of light from the ocean.

Slides:



Advertisements
Similar presentations
Atmospheric Correction Algorithm for the GOCI Jae Hyun Ahn* Joo-Hyung Ryu* Young Jae Park* Yu-Hwan Ahn* Im Sang Oh** Korea Ocean Research & Development.
Advertisements

UPRM Lidar lab for atmospheric research 1- Cross validation of solar radiation using remote sensing equipment & GOES Lidar and Ceilometer validation.
A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.
II Escuela de Optica Biomedica, Puebla, 2011 Modeling of polarized light transfer in scattering media Monte Carlo.
Sherwin D. Ladner 1, Robert A. Arnone 2, Richard W. Gould, Jr. 2, Alan Weidemann 2, Vladimir I. Haltrin 2, Zhongping Lee 2, Paul M. Martinolich 3, and.
Sensitivity Analysis In deterministic analysis, single fixed values (typically, mean values) of representative samples or strength parameters or slope.
By: Derrick Griffin. It is a well-known fact that the Dead Sea located in Jordan, Israel is one of the saltiest lakes in the world, whose salinity is.
Overview of PROSPECT and SAIL Model 2nd IR/Microwave emissivity group meeting NOAA/NESDIS/STAR Bo Qian
A Dictionary of Aerosol Remote Sensing Terms Richard Kleidman SSAI/NASA Goddard Lorraine Remer UMBC / JCET Short.
A novel concept for measuring seawater inherent optical properties in and out of the water Alina Gainusa Bogdan and Emmanuel Boss School of Marine Sciences,
Active Calibration of Cameras: Theory and Implementation Anup Basu Sung Huh CPSC 643 Individual Presentation II March 4 th,
2 Remote sensing applications in Oceanography: How much we can see using ocean color? Adapted from lectures by: Martin A Montes Rutgers University Institute.
Menghua Wang NOAA/NESDIS/ORA E/RA3, Room 102, 5200 Auth Rd.
(a) (b) (c) (d) (e) (a)(b) (c)(d) OPTICAL IMPACTS ON SOLAR TRANSMISSION IN COASTAL WATERS Grace C. Chang and Tommy D. Dickey 1 Ocean Physics Laboratory,
OS12A-141 Comparison of Monte Carlo Model Predictions with Tank Beam Spread Experiments Using a Maalox Phase Function Obtained with Volume Scattering Function.
1 Remote sensing applications in Oceanography: How much we can see using ocean color? Martin A Montes Ph.D Rutgers University Institute of Marine and Coastal.
METO 621 Lesson 13. Separation of the radiation field into orders of scattering If the source function is known then we may integrate the radiative transfer.
Lecture 12 Monte Carlo Simulations Useful web sites:
UNH Coastal Observing Center NASA GEO-CAPE workshop August 19, 2008 Ocean Biological Properties Ru Morrison.
Rrs Modeling and BRDF Correction ZhongPing Lee 1, Bertrand Lubac 1, Deric Gray 2, Alan Weidemann 2, Ken Voss 3, Malik Chami 4 1 Northern Gulf Institute,
Interval Estimation of System Dynamics Model Parameters Spivak S.I. – prof., Bashkir State University Kantor O.G. – senior staff scientist, Institute of.
Institute of Problems of Chemical Physics Remote Recognition of Aerosol Chemicals B. Bravy, V.Agroskin, G.Vasiliev Laser Chemistry Laboratories.
Saratov State University ______________________________________________ Department of Optics & Biophotonics __________________________________________________.
Marine inherent optical properties (IOPs) from MODIS Aqua & Terra Marine inherent optical properties (IOPs) from MODIS Aqua & Terra Jeremy Werdell NASA.
“Fractal” optical anisotropy in clouds and Monte Carlo simulation of relative radiation effects Sergei M. Prigarin supported by INTAS ( ), RFBR ( ,
02/25/05© 2005 University of Wisconsin Last Time Meshing Volume Scattering Radiometry (Adsorption and Emission)
1 EE 543 Theory and Principles of Remote Sensing Derivation of the Transport Equation.
SeaDAS Training ~ NASA Ocean Biology Processing Group 1 Level-2 ocean color data processing basics NASA Ocean Biology Processing Group Goddard Space Flight.
The problem of reliable detection of coccolitophore blooms in the Black Sea from satellite ocean color data O. Kopelevich. Shirshov Institute of Oceanology.
The Limits of Light Diffusion Approximation Robert Scott Brock(1), Jun Qing Lu(1), Xin-Hua Hu(1), David W. Pravica(2) Department of Physics,(1) Department.
Determination of the optical thickness and effective radius from reflected solar radiation measurements David Painemal MPO531.
Chapter 5 Errors In Chemical Analyses Mean, arithmetic mean, and average (x) are synonyms for the quantity obtained by dividing the sum of replicate measurements.
Stochastic Linear Programming by Series of Monte-Carlo Estimators Leonidas SAKALAUSKAS Institute of Mathematics&Informatics Vilnius, Lithuania
Flow cytometry: characterizing particles towards the submicron scale
Soe Hlaing *, Alex Gilerson, Samir Ahmed Optical Remote Sensing Laboratory, NOAA-CREST The City College of the City University of New York 1 A Bidirectional.
Estimating Water Optical Properties, Water Depth and Bottom Albedo Using High Resolution Satellite Imagery for Coastal Habitat Mapping S. C. Liew #, P.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Image: MODIS Land Group,
1 Atmospheric Radiation – Lecture 9 PHY Lecture 10 Infrared radiation in a cloudy atmosphere: approximations.
ASSESSMENT OF OPTICAL CLOSURE USING THE PLUMES AND BLOOMS IN-SITU OPTICAL DATASET, SANTA BARBARA CHANNEL, CALIFORNIA Tihomir S. Kostadinov, David A. Siegel,
Optical Water Mass Classification for Interpretation of Coastal Carbon Flux Processes R.W. Gould, Jr. & R.A. Arnone Naval Research Laboratory, Code 7333,
Definition and assessment of a regional Mediterranean Sea ocean colour algorithm for surface chlorophyll Gianluca Volpe National Oceanography Centre, Southampton.
Physics-Based Modeling of Coastal Waters Donald Z. Taylor RIT College of Imaging Science.
Naval Research Laboratory, Ocean Optics Section, Code 7333, Stennis Space Center, MS , USA, webpage:
A model for predicting spectral signature of suspended sediments Vijay Garg & Indrajeet Chaubey † ECOLOGICAL ENGINEERING GROUP † Respectively, Graduate.
Estimating the uncertainties in the products of inversion algorithms or, how do we set the error bars for our inversion results? Emmanuel Boss, U. of Maine.
Developement of exact radiative transfer methods Andreas Macke, Lüder von Bremen, Mario Schewski Institut für Meereskunde, Uni Kiel.
The radiance received by a spaceborne or airborne instrument looking at nadir at an altitude of Z (km) is described by the RTE for plane parallel atmospheres.
Chapter 3 Radiative transfer processes in the aquatic medium Remote Sensing of Ocean Color Instructor: Dr. Cheng-Chien LiuCheng-Chien Liu Department of.
NRL 7333 Rb = 1-  1+  1+  2 Non- Linear b1- b2q3 influences We developed improved SeaWIFS coastal ocean color algorithms to derived inherent optical.
1 Retrieval of ocean properties using multispectral methods S. Ahmed, A. Gilerson, B. Gross, F. Moshary Students: J. Zhou, M. Vargas, A. Gill, B. Elmaanaoui,
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Image: MODIS Land Group,
Counter-current flows in liquid-liquid boundary layers II. Mass transfer kinetics E. Horvath 1, E. Nagy 1, Chr. Boyadjiev 2, J. Gyenis 1 1 University.
Chapter 4 Numerical models of radiative transfer Remote Sensing of Ocean Color Instructor: Dr. Cheng-Chien LiuCheng-Chien Liu Department of Earth Science.
The Orbiting Carbon Observatory Mission: Fast Polarization Calculations Using the R-2OS Radiative Transfer Model Vijay Natraj 1, Hartmut Bösch 2, Robert.
OC3522Summer 2001 OC Remote Sensing of the Atmosphere and Ocean - Summer 2001 Ocean Color.
Assessment on Phytoplankton Quantity in Coastal Area by Using Remote Sensing Data RI Songgun Marine Environment Monitoring and Forecasting Division State.
CHLTSSCDO M Suspended Sediment Only Lake Ontario Long Pond Lake Conesus Genesee River Plume APPROACH.
The Dirty Truth of Coastal Ocean Color Remote Sensing Dave Siegel & St é phane Maritorena Institute for Computational Earth System Science University of.
Lecture 2 Introduction to Inherent Optical Properties (IOPs) and Radiative Transfer Apparent Optical Properties (AOPs) C. Roesler 3 July 2007.
D e v e l o p m e n t o f t h e M N I R-S W I R a n d AA a t m o s p h e r I c c o r r e c t I o n a n d s u s p e n d e d s e d I m e n t c.
Dr. Amr Khairat Radi Department of Physics Faculty of Science
Vladimir I. Haltrin and Alex I. Chepyzhenko *
Timor Wienrib Itai Friedland
Optical Oceanography and Ocean Color Remote Sensing
Energy Flow Concept Image Sensor Energy Source
Jian Wang, Ph.D IMCS Rutgers University
The Apparent Absorptivity of the Infinite V-groove
Simulation for Case 1 Water
Wei Yang Center for Environmental Remote Sensing
Presentation transcript:

Diffuse reflection coefficient or diffuse reflectance of light from water body is an informative part of remote sensing reflectance of light from the ocean. Diffuse reflectance contains information on content of dissolved and suspended substances in seawater. Diffuse reflectance is an apparent optical property that depends not only on inherent optical properties of the seawater, but also on the parameters of illumination. The dependence on inherent optical properties is expressed as a dependence on a ratio of backscattering coefficient b b to absorption coefficient a. In the open ocean under diffuse illumination of the sky diffuse reflectance R is linearly proportional to the ratio of b b to a, i. e. R=k b b /a, with k=0.33 according to Morel and Prieur. The abovementioned linear equation is very good for the Type I open ocean waters. It is also acceptable for certain types of coastal waters. In fact, it is valid for all types of waters when the ratio of b b to a is less than 0.1. From physical considerations R should always lie between zero and one for any ratio b b /a between zero and infinity. The linear equation fails to pass this criterion, i. e. it exceeds unity when b b /a becomes greater than 1/k, or a < k b b (highly scattering water with a lot of very small particles). It means that indiscrete use of the linear equation for coastal waters, when parameter b b /a exceeds limitations of smallness, can cause unacceptable errors in processing of in situ and remote sensing optical information. In order to estimate possible errors in determining diffuse reflectance we used different approaches to generate diffuse reflectance as a function of b b /a, or g = b b /(a+b b ). One approach is based on numerical calculations using Monte Carlo simulation, and other approaches were theoretical. The input values of b b /a have been varied from very small to very large numbers. It was found that numerically and theoretically generated results for all varieties of input parameters satisfactory correspond to the available experimental data. It was found both theoretically and using Monte Carlo that diffuse reflectance strongly depends on backscattering coefficient and has very weak dependence on the shape of the phase function used. The widely used linear model is very good for b b /(a+b b ) 0.2. The majority of coastal water and almost all open ocean water cases fall in the range of applicability of linear model. But the linear model may be very inadequate in some important and interesting coastal water conditions like hazardous blooms, spills, etc. For the reasons to avoid possible unacceptable errors and missing interesting optical events it is advisable to avoid using linear model to process information related to coastal (Type II and III) waters. All presented non-linear equations (except the Kubelka- Munk equation that is not acceptable for seawater at small values of b b /(a+b b ) 0.2, and at b b /(a+b b ) < ) are capable to produce values of R that are correct for all possible values of b b /(a+b b ). In order to detect special optical cases the non-linear equations should be used in automatic processing of in-situ and remotely obtained optical information. The author thanks continuing support at the Naval Research Laboratory through the Spectral Signatures A1 program. This article represents NRL contribution AB/ (Morel-Prieur, 1977): Exact (Haltrin, 1988) Self-Consistent Asymptotic (Haltrin, 1985, 1993, 1997) Self-Consistent Diffuse (Haltrin, 1985) Two-Stream: (Gamburtsev, 1924; Kubelka-Munk, 1931; Sagan and Pollack, 1967) Monte Carlo (Gordon, Brown, and Jackobs, 1975) Semi-Empirical (Haltrin and Weidemann, 1996) Direct Diffuse e-m: e-m: ; nrlssc.navy.mil Because we do not have reliable in situ measurements of diffuse reflectances that represent the whole range of variability of inherent optical properties, 0 < b b /(a+b b ) < 1, we have to choose a dependence which can be regarded as sufficiently “precise one” in order to be a basis for error estimation. Such dependence exists in literature (Haltrin, 1988) and represents an exact solution of radiative transfer for diffuse reflection of light in a medium with delta-hyperbolic phase function. This solution lies exactly in the middle of two Monte Carlo and two theoretical solutions for diffuse reflections for small values of b b /(a+b b ) < 0.2, and it gives precise and asymptotically correct values for 1 - b b /(a+b b ) < < 1 (see first two figures). 1. G. A. Gamburtsev, “On the problem of the sea color,” Zh. RFKO, Ser. Fiz. (Journal of Russian Physical and Chemical Society, Physics Series), 56, (1924). 2. P. Kubelka and F. Munk. “Ein Beitrag zur Optik der Farbanstriche,” Zeit. Techn. Phys., 12, (1931). 3. C. Sagan and J. B. Pollack, “Anisotropic nonconservative scattering and the clouds of Venus,” J. Geophys. Res., 72, (1967). 4. H. R. Gordon, O. B. Brown, and M. M. Jacobs, “Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean,” Appl. Optics, 14, (1975). 5. A. Morel, L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr., 22, (1977). 6. V. I. Haltrin (a.k.a. V. I. Khalturin), “Propagation of light in sea depth,” in Remote Sensing of the Sea and the Influence of the Atmosphere (in Russian), V. A. Urdenko and G. Zimmermann, eds. (Academy of Sciences of the German Democratic Republic Institute for Space Research, Moscow-Berlin-Sevastopol, 1985), pp V. I. Haltrin, “Exact solution of the characteristic equation for transfer in the anisotropically scattering and absorbing medium,” Appl. Optics, 27, (1988). 8. V. I. Haltrin and G. W. Kattawar “Self-consistent solutions to the equation of transfer with elastic and inelastic scattering in oceanic optics: I. Model,” Appl. Optics, 32, (1993). 9. V. I. Haltrin, and A. D. Weidemann, “A Method and Algorithm of Computing Apparent Optical Properties of Coastal Sea Waters”, in Remote Sensing for a Sustainable Future: Proceedings of 1996 International Geoscience and Remote Sensing Symposium: IGARSS’96, Vol. 1, Lincoln, Nebraska, USA, p , V. I. Haltrin, “Self-consistent approach to the solution of the light transfer problem for irradiances in marine waters with arbitrary turbidity, depth and surface illumination,” Appl. Optics, 37, (1998).