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.

Slides:



Advertisements
Similar presentations
 nm)  nm) PurposeSpatial Resolution (km) Ozone, SO 2, UV8 3251Ozone8 3403Aerosols, UV, and Volcanic Ash8 3883Aerosols, Clouds, UV and Volcanic.
Advertisements

Mapping of Fires Over North America Using Satellite Data Sean Raffuse CAPITA, Washington University September,
Development of a Simulated Synthetic Natural Color ABI Product for GOES-R AQPG Hai Zhang UMBC 1/12/2012 GOES-R AQPG workshop.
Aerosol Pattern over Southeastern Europe Rudolf B. Husar and Janja D. Husar CAPITA, Washington University, St. Louis, MO Conference on Visibility, Aerosols,
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
Liang APEIS Capacity Building Workshop on Integrated Environmental Monitoring of Asia-Pacific Region September 2002, Beijing,, China Atmospheric.
Satellite Haze Detection on July July 16-18,1999 Rudolf B. Husar CAPITA, Washington University October 1999.
October 25, 2004, 9:00 a.m. - 12:00 p.m. Asheville, NC
Retrieval of smoke aerosol loading from remote sensing data Sean Raffuse and Rudolf Husar Center for Air Pollution Impact and Trends Analysis Washington.
CLOUD DETECTION WITH OPERATIONAL IMAGERS
Fusion of SeaWIFS and TOMS Satellite Data with Surface Observations and Topographic Data During Extreme Aerosol Events Stefan Falke and Rudolf Husar Center.
Remote Sensing of Our Environment Using Satellite Digital Images to Analyze the Earth’s Surface.
Constraining aerosol sources using MODIS backscattered radiances Easan Drury - G2
Quantifying aerosol direct radiative effect with MISR observations Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,
Karthaus, September 2005 Wouter Greuell IMAU, Utrecht, NL -Why? -Cloud masking -Retrieval method -An application: estimate surface mass balance from satellite.
Characterizing and comparison of uncertainty in the AVHRR Pathfinder SST field, Versions 5 & 6 Robert Evans Guilllermo Podesta’ RSMAS Nov 8, 2010 with.
Energy interactions in the atmosphere
Satellite Imagery ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences Introduction to Remote Sensing and Air Quality Applications.
Visible Satellite Imagery Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied Sciences Week –
An Overview of Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences Originally presented as part.
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.
Alternative Approaches for PM2.5 Mapping: Visibility as a Surrogate Stefan Falke AAAS Science and Engineering Fellow U.S. EPA - Office of Environmental.
SeaDAS Training ~ NASA Ocean Biology Processing Group 1 Level-2 ocean color data processing basics NASA Ocean Biology Processing Group Goddard Space Flight.
Developing a High Spatial Resolution Aerosol Optical Depth Product Using MODIS Data to Evaluate Aerosol During Large Wildfire Events STI-5701 Jennifer.
Spectral Characteristics
Chapter 5 Remote Sensing Crop Science 6 Fall 2004 October 22, 2004.
West Hills College Farm of the Future. West Hills College Farm of the Future Precision Agriculture – Lesson 4 Remote Sensing A group of techniques for.
EUMETSAT METEOROLOGICAL SATELLITE CONFERENCE 15/09/2013 – 20/09/2013, VIENNA EUMETSAT METEOROLOGICAL SATELLITE CONFERENCE 15/09/2013 – 20/09/2013, VIENNA.
1 Applications of Remote Sensing: SeaWiFS and MODIS Ocean Color Outline  Physical principles behind the remote sensing of ocean color parameters  Satellite.
Electromagnetic Radiation Most remotely sensed data is derived from Electromagnetic Radiation (EMR). This includes: Visible light Infrared light (heat)
Satellite-derived Sea Surface Temperatures Corey Farley Remote Sensing May 8, 2002.
Using Satellite Imagery to Analyze Lake Quality Matthew J. Kucharski Under the direction of Stefan Falke And CAPITA Washington University in St. Louis.
GOES and GOES-R ABI Aerosol Optical Depth (AOD) Validation Shobha Kondragunta and Istvan Laszlo (NOAA/NESDIS/STAR), Chuanyu Xu (IMSG), Pubu Ciren (Riverside.
 Introduction  Surface Albedo  Albedo on different surfaces  Seasonal change in albedo  Aerosol radiative forcing  Spectrometer (measure the surface.
MODIS Retrievals for the Amazon Rainforest Dan Sauceda.
 Introduction to Remote Sensing Example Applications and Principles  Exploring Images with MultiSpec User Interface and Band Combinations  Questions…
NOAA/NESDIS Cooperative Research Program Second Annual Science Symposium SATELLITE CALIBRATION & VALIDATION July Barry Gross (CCNY) Brian Cairns.
1 of 26 Characterization of Atmospheric Aerosols using Integrated Multi-Sensor Earth Observations Presented by Ratish Menon (Roll Number ) PhD.
Spatial Pattern of PM2.5 over the US PM2.5 FRM Network Analysis for the First Year: July 1999-June 2000 Prepared for EPA OAQPS Richard Scheffe by Rudolf.
The Second TEMPO Science Team Meeting Physical Basis of the Near-UV Aerosol Algorithm Omar Torres NASA Goddard Space Flight Center Atmospheric Chemistry.
Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences NASA ARSET- AQ – EPA Training September 29,
Assessing the Phenological Suitability of Global Landsat Data Sets for Forest Change Analysis The Global Land Cover Facility What does.
The Asian Dust Events of April 1998 Prepared by: R. B. Husar, D. Tratt, B. A. Schichtel, S. R. Falke, F. Li D. Jaffe, S. Gassó, T. Gill, N. S. Laulainen,
Synergy of MODIS Deep Blue and Operational Aerosol Products with MISR and SeaWiFS N. Christina Hsu and S.-C. Tsay, M. D. King, M.-J. Jeong NASA Goddard.
Use of Solar Reflectance Hyperspectral Data for Cloud Base Retrieval Andrew Heidinger, NOAA/NESDIS/ORA Washington D.C, USA Outline " Physical basis for.
Under the direction of Rudolf Husar
Air and Waste Management Association Professional Development Course AIR-257: Satellite Detection of Aerosols Issues and Opportunities Fraction.
April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.
Introduction GOES-R ABI will be the first GOES imaging instrument providing observations in both the visible and the near infrared spectral bands. Therefore.
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.
Aerosol Characterization Using the SeaWiFS Sensor and Surface Data E. M. Robinson and R. B. Husar Washington University, St. Louis, MO
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Alternative Approaches for PM2.5 Mapping: Visibility as a Surrogate Stefan Falke AAAS Science and Engineering Fellow U.S. EPA - Office of Environmental.
CAPITA Center for Air Pollution Impact and Trend Analysis.
Aerosol Pattern over Southern North America Tropospheric Aerosols: Science and Decisions in an International Community A NARSTO Technical Symposium on.
OMI Satellite Data Analysis Weekly, Seasonal, Elevation Pattern of NO2 M. Kieffer, S. Kovacs, E. Robinson Advisor: R. B. Husar Washington University, St.
Preliminary Analysis of Relative MODIS Terra-Aqua Calibration Over Solar Village and Railroad Valley Sites Using ASRVN A. Lyapustin, Y. Wang, X. Xiong,
Fire, Smoke & Air Quality: Tools for Data Exploration & Analysis : Data Sharing/Processing Infrastructure This project integrates.
Orbits and Sensors Multispectral Sensors. Satellite Orbits Orbital parameters can be tuned to produce particular, useful orbits Geostationary Sun synchronous.
Electromagnetic Radiation
Fourth TEMPO Science Team Meeting
GEOGRAPHIC INFORMATION SYSTEMS & RS INTERVIEW QUESTIONS ANSWERS
GOES visible (or “sun-lit”) image
In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven.
Atmospheric Optics - I.
Atmospheric Optics - I.
Atmospheric Optics - I.
Presentation transcript:

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 CAPITA, Washington University, St. Louis, MO

SeaWiFS Satellite Platform and Sensors Satellite maps the world daily in 24 polar swaths The 8 sensors are in the transmission windows in the visible & near IR Designed for ocean color but also suitable for land color detection, particularly of vegetation Swath 2300 KM 24/day Polar Orbit: ~ 1000 km, 100 min. Equator Crossing: Local Noon Chlorophyll Absorption Designed for Vegetation Detection

Components of the Sensed Radiation 1.Air scattering depends on geometry and can be calculated (Rayleigh scattering) 2.Clouds completely obscure the surface and have to masked out 3.Aerosols redirect incoming radiation by scattering and also absorb a fraction 4.Surface reflectance is a property of the surface but it is modified by aerosols

Apparent Surface Reflectance, R R = (R 0 + (e -  – 1) P) e -  The surface reflectance R 0 is modified by aerosol scattering and absorption Aerosol acts as a filter of surface reflectance and as a reflector solar radiation The apparent reflectance, R, detected by the sensor is: R = (R 0 + R a ) T a Aerosol as Reflector: R a = (e -  – 1) P Aerosol as Filter: T a = e -  Surface reflectance R 0 Under cloud-free conditions, the sensor receives the reflected radiation from surface and aerosols Both surface and aerosol signal varies independently in time and space Challenge: Separate the total received radiation into surface and aerosol components

General Approach: Co-Retrieval of Surface and Aerosol Reflectance 1.Surface Reflectance Retrieval by Time Series Analysis –(Sean Raffuse, MS Thesis 2003) 2.Aerosol Retrieval over Land –Radiative transfer model + Surface data 3.Refined Surface Reflectance –Iteration back to 1., 2. …

Problem 1: Clouds and Haze are Highly variable in Space and Time Dominate reflectance wherever they occur; the cloud frequency very regional New England is cloudy much of the time Illinois is less cloudy San Joaquin Valley New Hampshire Illinois Farmland Surface Reflectance (0.67 um) x 1000 Surface Reflectance JunJulMayOctAprOctSepAug S. California nearly cloud-free but it is hazy Advantage: The temporal variability of clouds/haze means that occasionally the surface reflectance is un-obscured and can be extracted from the noisy data.

Cloud Shadows Cloud shadows result in dark pixels, well below the normal surface reflectance Shadows are eliminated by enlarging the cloud mask and by the ‘jump’ filter

Problem 2: Vegetated Surface Reflectance Can Change Rapidly Vegetated surfaces change reflectance color and intensity with season The shape of the seasonal reflectance pattern depends on the surface Advantage: The seasonal reflectance pattern can be used to identify surface types. April 29July 18October  m 0.41  m 0.67  m

Problem 3: Surface and Haze Reflectance Depends on Geometry sdhsdhg

Surface Retrieval Approach: Reflectance Time Series Analysis Surface reflectance is retrieved for individual pixels from time series data (e.g. year) The procedure first identifies a set of ‘preliminary clear anchor’ days in a 17- day moving window Next, a two-pass-two-directional ‘jump’ filter eliminates days with substantial haze or cloud shadows The remaining clear anchor days are interpolated to yield daily surface reflectance estimates

Surface Reflectance in Blue & Red, Illinois Haze perturbation of the surface reflectance is most pronounced at 0.41  m, ‘blue’ In some cases, haze is evident in blue, but not in red (0.67  m). Hence, the blue channel is used to identify the anchor days. For the selected days, the pixel’s reflectance is retained for each of the 8 channels (need better explanation) Clouds Haze

Spatial Variation: 9 pixel rectangle Adjacent pixels show similar pattern in some areas, more variable in others

US Surface Reflectance Map, April 1, 2000 The resulting data are 8-channel cloud/surface-free surface reflectance The test dataset consists of daily values (Apr-Nov 2000) at ~ 1 km resolution for the conterminous US.

Seasonal Surface Reflectance, Eastern US April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290

Seasonal Surface Reflectance, Western US April 29, 2000, Day 120July 18, 2000, Day 200October 16, 2000, Day 290

Surface Color Seasonality of Urban Pixels

Aerosol Retrieval See PPT – will be merged