OC3522Summer 2001 OC3522 - Remote Sensing of the Atmosphere and Ocean - Summer 2001 Ocean Color.

Slides:



Advertisements
Similar presentations
OTHER SATELLITE DATASETS 1.Ocean Biology 2.Vegetation Cover 3.Fire Counts.
Advertisements

Ecology, Climate, Physical Oceanography. Bering Sea, Alaska SeaWifs Image (Norman Kuring image, NASA, April 25, 1998) Turquoise = phytoplankton bloom.
Color and Spectral Signatures Steve Dutch University of Wisconsin-Green Bay.
Radiometric Corrections
Liang APEIS Capacity Building Workshop on Integrated Environmental Monitoring of Asia-Pacific Region September 2002, Beijing,, China Atmospheric.
REMOTE SENSING Presented by: Anniken Lydon. What is Remote Sensing? Remote sensing refers to different methods used for the collection of information.
Atmospheric effect in the solar spectrum
Retrieval of smoke aerosol loading from remote sensing data Sean Raffuse and Rudolf Husar Center for Air Pollution Impact and Trends Analysis Washington.
NRL09/21/2004_Davis.1 GOES-R HES-CW Atmospheric Correction Curtiss O. Davis Code 7203 Naval Research Laboratory Washington, DC 20375
2 Remote sensing applications in Oceanography: How much we can see using ocean color? Adapted from lectures by: Martin A Montes Rutgers University Institute.
Constraining aerosol sources using MODIS backscattered radiances Easan Drury - G2
Menghua Wang NOAA/NESDIS/ORA E/RA3, Room 102, 5200 Auth Rd.
Energy interactions in the atmosphere
Temporal and Spatial Variations of Sea Surface Temperature and Chlorophyll a in Coastal Waters of North Carolina Team Members: Brittany Maybin Yao Messan.
SIO RAS activities in O. Kopelevich, Lab. Of Ocean Optics SIO RAS, Moscow.
Quick Review of Remote Sensing Basic Theory Paolo Antonelli CIMSS University of Wisconsin-Madison Benevento, June 2007.
OC3522Summer 2001 OC Remote Sensing of the Atmosphere and Ocean - Summer 2001 Land/Ice Surface & Applications.
Ocean Color Observations and Their Applications to Climate Studies Alex Gilerson, Soe Hlaing, Ioannis Ioannou, Sam Ahmed Optical Remote Sensing Laboratory,
The IOCCG Atmospheric Correction Working Group Status Report The Eighth IOCCG Committee Meeting Department of Animal Biology and Genetics University.
Atmospheric Correction and Adjacency effect (a little) Ken Voss, Ocean Optics Class, Darling Center, Maine, Summer 2011.
Atmospheric Correction Algorithms for Remote Sensing of Open and Coastal Waters Zia Ahmad Ocean Biology Processing Group (OBPG) NASA- Goddard Space Flight.
The use of MOD09 product and in situ data in a reservoir Valério, A.M.; Kampel, M.; Stech, J.L. alineval, milton, stech COSPAR Training.
Chapter 7 Atmospheric correction and ocean color algorithm Remote Sensing of Ocean Color Instructor: Dr. Cheng-Chien LiuCheng-Chien Liu Department of Earth.
SeaDAS Training ~ NASA Ocean Biology Processing Group 1 Level-2 ocean color data processing basics NASA Ocean Biology Processing Group Goddard Space Flight.
Menghua Wang, NOAA/NESDIS/ORA Atmospheric Correction using the MODIS SWIR Bands (1240 and 2130 nm) Menghua Wang (PI, NASA NNG05HL35I) NOAA/NESDIS/ORA Camp.
GUIDELINES FOR (SHIP BORNE) AUTO-MONITORING OF COASTAL AND OCEAN COLOR. Wernand, M.R., Royal Netherlands Institute for Sea Research (Royal NIOZ), PO Box.
Retrieving Coastal Optical Properties from MERIS S. Ladner 1, P. Lyon 2, R. Arnone 2, R. Gould 2, T. Lawson 1, P. Martinolich 1 1) QinetiQ North America,
Spectral Characteristics
Remote Sensing & Satellite Research Group
1 Istanbul, November Joint Research Centre, European Commission, Ispra, Italy JRC Contribution to the NATO SfP Bio-Optical project Giuseppe.
Cal Val Telecon June 5, 2014 Stennis - Cal val Team Update on VIIRS Ocean Color Cal Val USM, NRL, QNA, SDSU 1.Tracking the Moby and WavCis 2. Sensitivity.
What are the four principal windows (by wavelength interval) open to effective remote sensing from above the atmosphere ? 1) Visible-Near IR ( );
Ocean Color Radiometer Measurements of Long Island Sound Coastal Observational platform (LISCO): Comparisons with Satellite Data & Assessments of Uncertainties.
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.
1 Applications of Remote Sensing: SeaWiFS and MODIS Ocean Color Outline  Physical principles behind the remote sensing of ocean color parameters  Satellite.
Estimating Water Optical Properties, Water Depth and Bottom Albedo Using High Resolution Satellite Imagery for Coastal Habitat Mapping S. C. Liew #, P.
Formerly Lecture 12 now Lecture 10: Introduction to Remote Sensing and Atmospheric Correction* Collin Roesler 11 July 2007 *A 30 min summary of the highlights.
Satellite-derived Sea Surface Temperatures Corey Farley Remote Sensing May 8, 2002.
IoE The Basics of Satellite Oceanography. 6. Oceanographic Applications: Ocean color observations Lecture 6 Oceanographic Applications: Ocean Color.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Image: MODIS Land Group,
Atmospheric Correction for Ocean Color Remote Sensing Geo 6011 Eric Kouba Oct 29, 2012.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Presented by Menghua Wang.
Ocean Color Remote Sensing Pete Strutton, COAS/OSU.
Optical Water Mass Classification for Interpretation of Coastal Carbon Flux Processes R.W. Gould, Jr. & R.A. Arnone Naval Research Laboratory, Code 7333,
Physics-Based Modeling of Coastal Waters Donald Z. Taylor RIT College of Imaging Science.
Menghua Wang, NOAA/NESDIS/ORA Refinement of MODIS Atmospheric Correction Algorithm Menghua Wang (PI, NASA NNG05HL35I) NOAA/NESDIS/ORA Camp Springs, MD.
Image Interpretation Color Composites Terra, July 6, 2002 Engel-Cox, J. et al Atmospheric Environment.
The effect of wind on the estimated plume extension of the La Plata River Erica Darken Summer 2004.
NASA’s Coastal and Ocean Airborne Science Testbed (COAST) L. Guild 1 *, J. Dungan 1, M. Edwards 1, P. Russell 1, S. Hooker 2, J. Myers 3, J. Morrow 4,
April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.
Co-Retrieval of Surface Color and Aerosols from SeaWiFS Satellite Data Outline of a Seminar Presentation at EPA May 2003 Sean Raffuse and Rudolf Husar.
NRL 7333 Rb = 1-  1+  1+  2 Non- Linear b1- b2q3 influences We developed improved SeaWIFS coastal ocean color algorithms to derived inherent optical.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Image: MODIS Land Group,
Polarization analysis in MODIS Gerhard Meister, Ewa Kwiatkowska, Bryan Franz, Chuck McClain Ocean Biology Processing Group 18 June 2008 Polarization Technology.
MODIS-Terra cross-calibration for ocean color bands Ewa Kwiatkowska Bryan Franz, Gerhard Meister, Gene Eplee OBPG 30 January 2008.
IRS-P4 OCM (Ocean Colour Monitor) Current Status of the Mission OCM is functioning normally and data is received at four ground stations.
SCM x330 Ocean Discovery through Technology Area F GE.
OC3522Summer 2001 OC Remote Sensing of the Atmosphere and Ocean - Summer 2001 Active Microwave Radar.
Comparison of MODIS and SeaWiFS Chlorophyll Products: Collection 4 Results Janet W. Campbell and Timothy S. Moore University of New Hampshire MODIS Science.
An Introduction to Marine Optics
The Dirty Truth of Coastal Ocean Color Remote Sensing Dave Siegel & St é phane Maritorena Institute for Computational Earth System Science University of.
Ocean color atmospheric correction – Using a bio-optical model to address the “black pixel assumption” Ocean Optics Class University of Maine July 2011.
Motivation: Help satellite studies of aerosol-cloud interactions Aerosol remote sensing near clouds is challenging Excluding areas near-cloud risks biases.
Remote Sensing of the Ocean and Coastal Waters
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.
Coastal Water Algorithms
Optical Oceanography and Ocean Color Remote Sensing
Energy Flow Concept Image Sensor Energy Source
Jian Wang, Ph.D IMCS Rutgers University
Simulation for Case 1 Water
Presentation transcript:

OC3522Summer 2001 OC Remote Sensing of the Atmosphere and Ocean - Summer 2001 Ocean Color

L t ( ) =  L w ( ) + L G ( ) + L 0 ( ) ) + L A ( )+ L r ( )

L t = L 0  0 + L rayleigh +L aerosal L 0  0 = radiance leaving surface = L reflectance & L water

Surface Reflectance Specular (mirror-like) ii rr Diffuse ii L G ( ) = glint radiance L W ( ) = water-leaving radiance

Sunglint Examples ftp://daac.gsfc.nasa.gov/data/czcs/oss_hires/97.tiff

L A ( ) = scattering due to aerosols (Mie) + molecular Raleigh scattering At a given wavelength; e.g.     70nm, L w = 0 Then L t ( ) = L r ( ) + L a ( ) ; measure L t ( ) ; estimate L a ( ); left with L r ( )

Bio Optical properties Suspended and dissolved substance Dominant compounds Chlorophyll dissolved organic compounds decayed organic matter “yellow substance” Maul, 1985

A ratio of the reflectance in one band to that in another can be used to determine chlorophyll concentrations Blue -443nm Yellow -550nm point near 500nm

Water Types Case I water Satellite Oceanography, Robinson (1983)

Case II waters: Suspended sediments

Case II waters: Gelbstoff (yellow substance)

 (  L w ( ) SeaWiFS Channels increased concentration decreased concentration Compute the chlorophyll in the water

 (  L w ( ): CASE I waters The bio-optical algoritms for L w ( ): in water or upwelled radiance L w ( )  backscatter coeff./ absorbtion coeff. blue green red

SeaWiFS Channels increased concentration decreased concentration

Case 2-Glebstoff Case 1

Are the differences detectable?

Again for the general solution… To derive ocean properties, we want the path radiance to have a small and removable contribution: Three sources: scatter by clouds (non-removable) scatter by molecules (calculable as Rayleigh scatter) scatter by aerosol particles (implied from red/NIR channels) Then the surface radiance, L 0 ( ), can be derived from measurements of L t ( ) L 0 ( ) = L G ( ) + L W ( ) so if sunglint can be avoided by geometry, L 0 ( ) = L W ( ) (containing information about ocean constituents)

Previous Ocean Color instruments: Coastal Zone Color Scanner (CZCS) on Nimbus-7 Ocean Color and Temperature Scanner (OCTS) on ADEOS 412 nm Channels 443 nm Channels

SeaWiFS on SeaStar (Sea-viewing Wide Field-of-view Sensor)

SeaWiFS channels: Band Center Wavelength (nm) Bandwidth Band Center Wavelength (nm) Bandwidth

BLUEGREEN

Shelf waters stirred by Hurricane Floyd Sept July 27, 2001 Seawifs

Jan 1986 June 1986

Feb 1983 Apr 1986