Scanner Characteristics

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

Evaluating Calibration of MODIS Thermal Emissive Bands Using Infrared Atmospheric Sounding Interferometer Measurements Yonghong Li a, Aisheng Wu a, Xiaoxiong.
Summary of Terra and Aqua MODIS Long-term Performance Jack Xiong 1, Brian Wenny 2, Sri Madhaven 2, Amit Angal 3, William Barnes 4, and Vincent Salomonson.
VIIRS Reflective Solar On-orbit Calibration and Performance Jack Xiong and Jim Butler Code 618.0, NASA/GSFC, Greenbelt, MD CLARREO SDT Meeting, NASA.
Landsat Downloads & MODIS Downloads Data Sources for GIS in Water Resources by Ayse Kilic, David R. Maidment, and David G. Tarboton GIS in Water Resources.
VIIRS LST Uncertainty Estimation And Quality Assessment of Suomi NPP VIIRS Land Surface Temperature Product 1 CICS, University of Maryland, College Park;
Destriping of VIIRS and MODIS SST imagery Marouan Bouali and Alexander Ignatov NOAA/NESDIS/STAR and CSU/CIRA 1 Workshop: “SST from polar orbiters: use.
Reduction of stripe noise in ACSPO clear-sky radiances and SST Marouan Bouali and Alexander Ignatov NOAA/NESDIS/STAR and CSU/CIRA 1 SPIE Security, Defense.
MCST – MODLAND Eric F. Vermote –MODLAND representative.
VIIRS Day/Night Band (DNB) Stray Light Correction Approach
Radiometric and Geometric Errors
Remote Sensing of Mesoscale Vortices in Hurricane Eyewalls Presented by: Chris Castellano Brian Cerruti Stephen Garbarino.
Satellite Thermal Remote Sensing of Boiling Springs Lake Jeff Pedelty NASA Goddard Space Flight Center Goddard Center for Astrobiology.
Fundamentals of Satellite Remote Sensing NASA ARSET- AQ Introduction to Remote Sensing and Air Quality Applications Winter 2014 Webinar Series ARSET -
SeaDAS Training ~ NASA Ocean Biology Processing Group 1 Introduction to ocean color satellite calibration NASA Ocean Biology Processing Group Goddard Space.
Outline Further Reading: Chapter 04 of the text book - satellite orbits - satellite sensor measurements - remote sensing of land, atmosphere and oceans.
1 Image Pre-Processing. 2 Digital Image Processing The process of extracting information from digital images obtained from satellites Information regarding.
Reprojecting MODIS Images. Reasons why reprojection is desirable: 1.Removes Bowtie Artifacts 2.Allows geographic overlays (e.g. coastline, city locations)
MODIS: Moderate-resolution Imaging Spectroradiometer National-Scale Remote Sensing Imagery for Natural Resource Applications Mark Finco Remote Sensing.
MODIS/AIRS Workshop MODIS Level 2 Products 5 April 2006 Kathleen Strabala Cooperative Institute for Meteorological Satellite Studies University of Wisconsin-Madison.
NPP VIIRS M6 Saturation Study Update
Monitoring Bio-Optical Processes Using NPP-VIIRS And MODIS-Aqua Ocean Color Products Robert Arnone (1), Sherwin Ladner (2), Giulietta Fargion (3), Paul.
High Resolution MODIS Ocean Color Fred Patt 1, Bryan Franz 1, Gerhard Meister 2, P. Jeremy Werdell 3 NASA Ocean Biology Processing Group 1 Science Applications.
Sentinel: Dynamic Fire Location Mapping. Near- Real Time Emergency Mapping Environmental Remote Sensing Group CSIRO Land and Water Defence Imagery & Geospatial.
Orbit Characteristics and View Angle Effects on the Global Cloud Field
1 Applications of Remote Sensing: SeaWiFS and MODIS Ocean Color Outline  Physical principles behind the remote sensing of ocean color parameters  Satellite.
NASA, CGMS-41, July 2013 Coordination Group for Meteorological Satellites - CGMS Calibration/validation of Operational Instruments at NASA Langley Research.
MODIS Geolocation Status Robert Wolfe, Mash Nishihama, Al Fleig, James Kuyper MODIS Science Team Meeting December 19, 2001.
An Estimate of Contrail Coverage over the Contiguous United States David Duda, Konstantin Khlopenkov, Thad Chee SSAI, Hampton, VA Patrick Minnis NASA LaRC,
Terra Launched December 18, 1999
Evaluation of the Suomi NPP VIIRS Land Surface Temperature Product 1 CICS, University of Maryland, College Park; 2 STAR/NESDIS/NOAA Yuling Liu 1, Yunyue.
Facilitating Access to EOS Data at the NSIDC DAAC Siri Jodha Singh Khalsa ECS Science Coordinator for the National Snow and Ice Data Center, Distributed.
Cloud Mask: Results, Frequency, Bit Mapping, and Validation UW Cloud Mask Working Group.
MODIS Collection 6 MCST Proposed Changes to L1B. Page 2 Introduction MODIS Collection History –Collection 5 – Feb present –Collection 4 – Jan.
Robert Wolfe NASA Goddard Space Flight Center Code 614.5, Greenbelt, MD Robert Wolfe NASA Goddard Space Flight Center Code 614.5,
VIIRS Product Evaluation at the Ocean PEATE Frederick S. Patt Gene C. Feldman IGARSS 2010 July 27, 2010.
MODIS Preprocessing (before L1B) Changes over the Last Year (and looking forward) Chris Moeller and others CIMSS; Univ. Wisconsin July 13, 2004 Thanks.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
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.
Use of the Moon as a calibration reference for NPP VIIRS Frederick S. Patt, Robert E. Eplee, Robert A. Barnes, Gerhard Meister(*) and James J. Butler NASA.
MODIS Geolocation Status Mash Nishihama Raytheon NASA Goddard Space Flight Center, Code 922, Greenbelt, MD, USA Robert E. Wolfe Raytheon
Data acquisition From satellites with the MODIS instrument.
Collect 5 Calibration Issues Chris Moeller and others Univ. Wisconsin March 22, 2005 Presented at MCST Calibration breakout meeting, March 22, 2005.
Electro-optical systems Sensor Resolution
Collect 5 Calibration Issues Chris Moeller and others Univ. Wisconsin March 22, 2005.
High Resolution MODIS Ocean Color Bryan Franz NASA Ocean Biology Processing Group MODIS Science Team Meeting, 4-6 January 2006, Baltimore, MD.
Radiance Simulation System for OSSE  Objectives  To evaluate the impact of observing system data under the context of numerical weather analysis and.
Japan Meteorological Agency, June 2016 Coordination Group for Meteorological Satellites - CGMS Non-Meteorological Application for Himawari-8 Presented.
MODIS-Terra cross-calibration for ocean color bands Ewa Kwiatkowska Bryan Franz, Gerhard Meister Ocean Biology Processing Group 13 May 2008 MODIS Science.
SNPP data access for agricultural monitoring
Lunar observation data set preparation
NASA Aqua.
Extending DCC to other bands and DCC ray-matching
Deep Convective Clouds (DCC) BRDF Characterization Using PARASOL Bidirectional Observations Bertrand Fougnie CNES.
MODIS and VIIRS Reflective Solar Bands Calibration Methodologies and
An Overview of MODIS Reflective Solar Bands Calibration and Performance Jack Xiong NASA / GSFC GRWG Web Meeting on Reference Instruments and Their Traceability.
Aqua MODIS Reflective Solar Bands (RSB)
Calibration Activities of GCOM-W/AMSR2
AIRS (Atmospheric Infrared Sounder) Instrument Characteristics
Calibration and Performance MODIS Characterization Support Team (MCST)
MODIS Lunar Calibration Data Preparation and Results for GIRO Testing
MODIS Characterization and Support Team Presented By Truman Wilson
Data Preparation for ASTER
Deep Convective Cloud BRDF characterization using PARASOL
Using the Moon for Sensor Calibration Inter- comparisons
Satellite Sensors – Historical Perspectives
NASA alert as Russian and US satellites crash in space
MODIS L1B Data Product Uncertainty Index Jack Xiong (Xiaoxiong
Deep Convective Clouds (DCC) BRDF Characterization Using PARASOL Bidirectional Observations Bertrand Fougnie CNES.
Presentation transcript:

Scanner Characteristics

MODIS Orbit and Scan Geometry Terra: 10:30 am local descending Aqua: 1:30 pm local ascending Orbit period: 99 minutes Repeat cycle: 16 days (same as Landsat) Scan mirror: Double sided, 20.3 revs/minute Scan rate: 1.477 scans/sec Scan angle: +/- 55 degrees Swath width: 2330 km across track, 10 km along track

Image Acquisition Details Scan direction Image Acquisition Details Scan sequence: Solar diffuser Spectroradiometric Calibration Assembly Blackbody Space View Earth scan Flight direction

MODIS Bowtie Artifacts Consecutive “bowtie” shaped scans are contiguous at nadir, and overlap as scan angle increases…

MODIS bowtie artifacts at edge of swath Band 2 (0.87 micron) 250 meter resolution

Bowtie Artifacts Are not a ‘problem’: they are a consequence of the sensor design Can be removed for visualization purposes by reprojecting the image onto a map Do not affect science algorithms that run on a pixel-by-pixel basis or within one earth scan Will be present on next generation of operational polar orbiting imagers (VIIRS on NPP/NPOESS)

Inter-band Registration

Nominal MODIS inter-band registration First 1000 m pixel Second 1000 m pixel Geolocation is computed here

MODIS Geolocation Earth locations computed for every 1000 meter pixel (WGS84): Geodetic latitude (degrees, -90S to +90N) Geodetic longitude (degrees, -180W to +180E) Sensor zenith and azimuth (degrees, pixel to sensor) Solar zenith and azimuth (degrees, pixel to sun) Terrain height above geoid (meters) Land/Sea mask 0: Shallow Ocean 1: Land 2: Ocean Coastlines and Lake Shorelines 3: Shallow Inland Water 4: Ephemeral (intermittent) Water 5: Deep Inland Water 6: Moderate or Continental Ocean 7: Deep Ocean

Land-sea mask South America inland water From EOS DEM SWG – best available sources

MODIS geolocation includes terrain correction based on 1000 meter DEM Line of sight to sensor Earth location with terrain correction Earth location without terrain correction

Growth of MODIS 1 km pixel with scan angle

Terra Geolocation Measured RMS Error Update 2 – June ‘00 (Collection 1) Update 3 – Feb. ‘01 (Collection 3) Along Scan 58 m 56 m Along Track 57 m 74 m # Days 196 214 Match-ups/day 77 82 The images are of MODIS data (blue) and Simulated MODIS data (red) derived from TM Data using the MODIS geometric model. The top image shows the misalignment before bias removal. There is a strong red line off the coast showing the misalignment. The bottom image shows the images after the bias is removed. Three Geolocation LUT updates since launch Nadir equivalent meters Update 3 vs. 2: Scan direction error better Track direction error worse

Realtime Geolocation For realtime processing, ephemeris and attitude downlinked from spacecraft must be used. Post-processed ephemeris and attitude from NASA GSFC Flight Dynamics may be used for non realtime processing (delay of at least 24 hours after data acquisition) What is the impact on geolocation accuracy of realtime processing?

Figure courtesy of Stefan Maier, DOLA

Image Artifacts (other than Bowtie)

Mirror Side Striping (Band 8, 0.41 m) Reflectance, emissivity, or polarization of each scan mirror side not characterized correctly. Can be corrected.

Detector Difference Striping (Band 27, 6.7 m) Responsivity of each detector not characterized correctly. Can be corrected.

Noisy Detectors (Band 34, 13.6 m) Detectors are noisy on a per frame basis and unpredictable from scan to scan. Difficult to correct.

Saturation (Band 2, 0.87 m) Signal from earth scene is too large for 12 bit digitization with current gain settings. Work around available.

Handling Saturation in Bands 1-5 Problem: Bright cloud tops cause bands 1-5 to saturate, and the MODIS Cloud Mask cannot process these pixels correctly. It also makes true color image creation problematic (bands 1, 4, 3). Approach: Replace saturated pixels with maximum scaled integer. Method: Check for scaled integer values corresponding to “Detector is saturated” (65533) or “Aggregation algorithm failure” (65528). Replace these values with maximum allowed scaled integer (from valid_range attribute).

Response vs. Scan Angle (Band 35, 13.9 m) Scan mirror reflectance, emissivity, or polarization not characterized correctly as a function of scan angle. Correction is possible, but complicated.

Band 26 Optical Leak Photons intended for Band 5 detectors (1.24 m) leak into Band 26 (1.38 m) detectors. Correction is operational.

Band 26 Corrected Detector dependent correction factors remove the land surface contribution and reduce striping.

MODIS Performance

MODIS Performance cont.

MODIS Performance cont.

Destriping

MODIS LWIR Destriping Investigation LWIR L1B image artifacts are introduced by: Mirror sides not characterized perfectly Detectors calibrated independently Detectors "out of family” For all MOD021KM granules on 2001/06/04, extracted successive 100 x 100 boxes of pixels at nadir (5760 samples), and for each box computed (in radiance units): Overall mean and standard deviation Mean and standard deviation for each mirror side Mean and standard deviation for each detector For uniform boxes (low standard deviation): Selected reference detector Computed ratio of each detector to the reference

MODIS Band 27 (6.7 m), 2001-06-04 16:45 UTC Original L1B (V003) Destriped

MODIS Band 30 (9.6 m), 2001-06-04 16:45 UTC Original L1B (V003) Destriped

Terra MODIS with Destriping

Aqua MODIS without Destriping

MODIS LWIR Destriping Challenges Time dependence: Corrections for Jun. 2001 do not work as well in Dec. 2000 Need to analyze a series of months to ascertain time dependence Dealing with remaining artifacts: Bands dominated by noise (e.g. band 34) require image processing Some detectors must be replaced (replicate or interpolate?) Need to investigate scene dependence of detector ratios (currently assume same correction applies for all scene temperatures) Real-time monitoring: Implement destriping corrections on UW direct broadcast data, based on analysis on latest 7 days of overpasses Allows monitoring of changes in detector corrections over time