1 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World November 16, 2011 (A little dated, but still relevant) Presented By: Eugene.

Slides:



Advertisements
Similar presentations
NOAA National Geophysical Data Center
Advertisements

U.S. Department of the Interior U.S. Geological Survey Agency Report, WGISS #22 September 15, 2006 Lyndon R. Oleson U.S. Geological Survey Center for Earth.
Pathfinder –> MODIS -> VIIRS Evolution of a CDR Robert Evans, Peter Minnett, Guillermo Podesta Kay Kilpatrick (retired), Sue Walsh, Vicki Halliwell, Liz.
Scaling Biomass Measurements for Examining MODIS Derived Vegetation Products Matthew C. Reeves and Maosheng Zhao Numerical Terradynamic Simulation Group.
ASTER Operation Scenario and Status Y. Yamaguchi (Nagoya Univ., Japan) M. Fujita, T. Tachikawa, M. Kato, H. Tsu ( ERSDAC, Japan), M.J. Abrams, L. Maldonado.
Extension and application of an AMSR global land parameter data record for ecosystem studies Jinyang Du, John S. Kimball, Lucas A. Jones, Youngwook Kim,
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.
SDCG-6 Oslo, Norway October 22-24, 2014 SDCG/USGS: Landsat 7 & 8 SDCG-6 Session 5: Roles and Responsibilities Data flows from CEOS agencies.
Algorithm Development for Vegetation Change Detection and Environmental Monitoring Louis A. Scuderi 1, Amy Ellwein 2, Enrique Montano 3 and Richard P.
Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.
20 January 2010 Lazaros Oreopoulos Landsat Science Team NASA-GSFC John Gasch Landsat Mission Operations Emalico LLC.
April 23, 2009 Geography 414 Group 3 1 Boone, NC Laura Beth Adams- Average Temperature Alec Hoffman – Daily Temperature Range Jill Simmerman- Maximum Temperature.
S. N. Goward, D. L. Williams and E. Denning Landsat Science Team Meeting Sioux Falls, SD August 16-18, 2011.
Green Vegetation Fraction (GVF) derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the SNPP satellite Zhangyan Jiang 1,2,
History and Features of Landsat 7 By: Andy Vogelsberg Photo of Landsat 7 taken from tures/litho/landsat/land.jpg.
VENUS (Vegetation and Environment New µ-Spacecraft) A demonstration space mission dedicated to land surface environment (Vegetation and Environment New.
Satellite Thermal Remote Sensing of Boiling Springs Lake Jeff Pedelty NASA Goddard Space Flight Center Goddard Center for Astrobiology.
Geosynchronous Orbit A satellite in geosynchronous orbit circles the earth once each day. The time it takes for a satellite to orbit the earth is called.
Outline Further Reading: Chapter 04 of the text book - satellite orbits - satellite sensor measurements - remote sensing of land, atmosphere and oceans.
Cooperative Institute for Meteorological Satellite Studies University of Wisconsin - Madison Steve Ackerman Director, Cooperative Institute for Meteorological.
World Renewable Energy Forum May 15-17, 2012 Dr. James Hall.
Outline Further Reading: Chapter 04 of the text book - satellite orbits - satellite sensor measurements - remote sensing of land, atmosphere and oceans.
SDCG-4 Pasadena, CA, USA September 4-6, Brian Killough (NASA, SEO) Paul Kessler, Shaun Deacon, Kim Keith September 4-6, 2013 SDCG-4 Meeting Pasadena,
Geography 121 Lab #4 Finding Landsat Data November 8, 2006 Dave Alleman Pat Clancy Sarah Gustafson.
Vrije Universiteit amsterdamGlobal evaluation of four AVHRR–NDVI data sets: Intercomparison and assessment against Landsat imagery Hylke E. Beck a, *,
SDCG-5 ESA/ESRIN, Frascati, Italy February 24-26, 2014 SDCG-5 Session 2 Landsat 7/8 status and 2013 Implementation.
Soil Moisture Active/Passive Field Campaign 2012 (SMAPVEx): Carmen-Portage la Prairie A Partnership between Agriculture Canada, NASA, US Department of.
Assessment of Regional Vegetation Productivity: Using NDVI Temporal Profile Metrics Background NOAA satellite AVHRR data archive NDVI temporal profile.
A METHODOLOGY TO SELECT PHENOLOGICALLY SUITABLE LANDSAT SCENES FOR FOREST CHANGE DETECTION IGARSS 2011, Jul, 27, 2011 Do-Hyung Kim, Raghuram Narashiman,
Getting Ready for the Future Woody Turner Earth Science Division NASA Headquarters May 7, 2014 Biodiversity and Ecological Forecasting Team Meeting Sheraton.
Remote sensing and in situ measurements in the Global Earth Observing System of Systems Curtis Woodcock, Boston University.
MODIS Workshop An Introduction to NASA’s Earth Observing System (EOS), Terra, and the MODIS Instrument Michele Thornton
LANDSAT Program Update
GEO Forest Carbon Tracking Outcomes in 2010 Per-Erik Skrøvseth (NSC) Osamu Ochiai (JAXA) Frank Martin Seifert (ESA) Ake Rosenqvist (for JAXA) CEOS Plenary,
AN ENHANCED SST COMPOSITE FOR WEATHER FORECASTING AND REGIONAL CLIMATE STUDIES Gary Jedlovec 1, Jorge Vazquez 2, and Ed Armstrong 2 1NASA/MSFC Earth Science.
U.S. Department of the Interior U.S. Geological Survey Entering A New Landsat Era – The Future is Now Tom Loveland U.S. Geological Survey Earth Resources.
Terra Launched December 18, 1999
EG2234: Earth Observation Interactions - Land Dr Mark Cresswell.
14 ARM Science Team Meeting, Albuquerque, NM, March 21-26, 2004 Canada Centre for Remote Sensing - Centre canadien de télédétection Geomatics Canada Natural.
Assessing the Phenological Suitability of Global Landsat Data Sets for Forest Change Analysis The Global Land Cover Facility What does.
Cloud Mask: Results, Frequency, Bit Mapping, and Validation UW Cloud Mask Working Group.
Baseline Global Observation Scenario SDCG-8, Session 3 Gene Fosnight (USGS); Frank Martin Seifert (ESA) 24 Sep 2015, Bonn.
Soil Moisture Active/Passive Field Campaign 2012 (SMAPVEx): Carmen-Portage la Prairie A Partnership between Agriculture Canada, NASA, US Department of.
Environmental Remote Sensing GEOG 2021 Lecture 8 Observing platforms & systems and revision.
Introduction GOES-R ABI will be the first GOES imaging instrument providing observations in both the visible and the near infrared spectral bands. Therefore.
Recent Solar Irradiance Data From SBUV/2 and OMI Matthew DeLand and Sergey Marchenko Science Systems and Applications, Inc. (SSAI) SOLID WP-2 Workshop.
Interactions of EMR with the Earth’s Surface
1 J. Ranson NASA’s Earth Observing System Terra Mission Update Jon Ranson, Terra Project Scientist Si-Chee Tsay, Terra Deputy Project Scientist NASA’s.
Alignment of Growth Seasons from Satellite Data Ragnar Bang Huseby, Lars Aurdal, Dagrun Vikhamar, Line Eikvil, Anne Solberg, Rune Solberg.
SeaWiFS Highlights July 2002 SeaWiFS Celebrates 5th Anniversary with the Fourth Global Reprocessing The SeaWiFS Project has just completed the reprocessing.
References: 1)Ganguly, S., Samanta, A., Schull, M. A., Shabanov, N. V., Milesi, C., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B., Generating vegetation.
CGMS-43-NOAA-WP-24 Coordination Group for Meteorological Satellites - CGMS NOAA Report on Ocean Parameters – Coral Reef Watch Presented to CGMS-43 Working.
1) Global Coefficients: Derived from all available cloud-free ASTER scenes for a given network SURFRAD: 246 scenesUSCRN: 371 scenes 2) Day/Night-Specific.
Temporal Classification and Change Detection
Geog 121 Project 4: Finding LandSat Data
Cornelius Holmes, Derek Morris Jr.
VEGA-GEOGLAM Web-based GIS for crop monitoring and decision support in agriculture Evgeniya Elkina, Russian Space Research Institute The GEO-XIII Plenary.
STUDY ON THE PHENOLOGY OF ASPEN
KLAUS KDA4 – Solar Irradiance Monitoring Application
Jeremy Fisher & John Mustard Geological Sciences - Brown University
USGS Status Frank Kelly, USGS EROS CEOS Plenary 2017 Agenda Item #4.14
National seminar on ENVIRONMENT AND DEVELOPMENT IN EASTERN INDIA
Landsat Program The World’s Most Sophisticated Optical Observatories of the Earth The World’s Model for International Collaboration in Earth Observation.
Satellite Sensors – Historical Perspectives
COVE Tool Updates Joint SDCG/GEOGLAM/LSI-VC Meeting September 6, 2018
USGS Agency Status Landsat Operations Jenn Lacey 21 July 2016
Geography 121 Lab #4 Finding Landsat Data
Igor Appel Alexander Kokhanovsky
Geog 121 Project 4: Finding LandSat Data
Phenology Images (5-Year Long Term Average)
Presentation transcript:

1 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World November 16, 2011 (A little dated, but still relevant) Presented By: Eugene A. Fosnight Landsat Data Acquisition Manager Co-Authors: John Gasch, Terry Arvidson Landsat’s Long-Term Acquisition Plan

2 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World Science Goals The Landsat mission is driven by the requirement to create a long-term environmental record guided by the Long-Term Acquisition Plan Individual user requests and science campaigns are accepted, but only so far as they do not perturb the Long- Term Acquisition Plan. Individual user requests are most likely to be accepted if they have associated field campaigns or are for emergency response. Night acquisitions are only acquired by special request. Science campaigns tend to target areas associated with large mapping projects for which the temporal period is well defined, occasionally repeating, and tend to have a low priority boost leading to a moderated increase in the probability of acquisition. To meet these goals the Landsat Long-Term Acquisition Plan continues to evolve through lessons learned in response to new inputs, aging satellites, and new satellite capabilities

3 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World Long-Term Acquisition Plan controls Vegetation phenology quantified by seasonality files or NDVI Cloud predictions avoid acquisitions of “relatively” cloudy data Cloud climatology quantify “relatively” cloudy data Automatic Cloud Cover Assessments of acquired images identify successful acquisitions Missed opportunity boost Thematic Campaigns – requirements not well represented by seasonality: reefs, agriculture, volcanoes, glaciers,…

4 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World Long-Term Acquisition Plan controls Landsat 5  Has no onboard recorder, so can image only within line of site of ground stations  Acquired all images ( depending on season) within line of sight of a reception station (up to 20 active) prior to the failure of the redundant transmitter  Acquired using a simplified LTAP following the failure of the redundant transmitter in December 2009, which has now evolved to be the most advanced of the LTAPs  Acquires on average images/day or 65% of opportunities after the primary transmitter was restarted  The number of images/day acquired is constrained by health and safety of the mission – new constraints as of last week limit acquisitions to about 150 images/day Landsat 7  Acquires using a formal LTAP since launch in 1999  Has a solid state recorder that facilitates global acquisition of images  Images about 250 – 400 images per day out of opportunities limited by duty cycle and storage capacity constraints and the amount of sun lit land available on a given day  Since the Scan Line Corrector failure, the LTAP pursues image pairs to facilitate compositing Landsat 8  Will acquire using a formal LTAP  Has a solid state recorder that facilitates global acquisition of images  Will acquire about 400 images per day X

5 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World Landsat Reception Network

6 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World Landsat 1- and 16-day acquisition maps

7 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World L5 Sun Angle Constrained Coverage Solar cycle moving strongly southward. Imaging presently allowed (sun angle constraint) between the red horizontal lines

8 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World L7 Sun Angle Constrained Coverage Solar cycle moving strongly southward. Imaging presently allowed (sun angle constraint) between the red horizontal lines

9 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World Landsat 7 Seasonality Plots for Path 199 Graphs show relationship between NDVI, seasonality records, and acquisitions from Norway to Liberia. Black circles are images acquired with circles proportional to cloud cover (small – high, large – low) Yellow vertical lines are sun elevation cut off dates Red (acquire once) and blue (acquire always) lines represent the discrete seasonality records NDVI Max upper dotted line Mean + SD Mean green circles Mean – SD Min lower dotted line Upper left text – land database records Lower left text – ecosystems

10 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World LTAP in Summary Path 199 example – lessons observed  Acquire too frequently over arid areas  Acquire too seldom over persistently-cloudy vegetated areas  The need to balance the cost of acquiring a high proportion of cloudy data with the need for finding the rare cloud-free sub-images is compounded by the low confidence in the cloud predictions in the tropics.  The use of priorities derived from NDVI (which is attenuated by clouds…) and the incorporation of cloud confidence should help us find a better balance. LTAP similarities  Acquire 60-70% of opportunities  Always over the conterminous US  Somewhat less over deserts and snow  In general about images per year for a typical vegetated path/row  High latitudes are sun limited, but acquire at same rate as other vegetated sites during their growing season.  Boreal, tropical and Antarctica campaigns help compensate for lack of opportunity caused by clouds or very short season.

11 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World LTAP differences Landsat 5  Interval-based acquisitions and many other instrument constraints!  NDVI-based phenology  MODIS cloud climatology  Cloud prediction confidence Landsat 7  Discrete seasonality record phenology derived from AVHRR NDVI migrating in 2012 to probabilities derived directly from MODIS NDVI  ISCCP cloud climatology migrating to MODIS cloud climatology  Migration to cloud prediction confidence  Increased acquisitions from 300 to 380 images/day in 2011 Landsat 8  LTAP is frozen at Landsat 7 LTAP circa September 2008  Modeling to evaluate and tune Landsat 8 LTAP is planned for 2012  Migration to Landsat 7 logic will begin following launch  Acquire 400 images/day X

12 Pecora 18 Forty Years of Earth Observations… Understanding a Changing World The future of the Landsat LTAP The way forward  Parameter tuning will continue as new data, such as NDVI, Cloud Climatology and confidence estimates, are evaluated and implemented.  A land ACCA score will improve the acquisition of island/coastal scenes.  Increase focus on QA/QC of archive. Do the images acquired meet the expectations of the LTAP requirements?  Unlimited access to data has established a new paradigm where partially cloudy data have increased value. LTAP evolution will provide  a direct measurement of vegetative phenology augmented by permanent campaigns,  higher resolution cloud climatology, and  an estimate of cloud prediction confidence Permitting a more nuanced balance between the need for  additional coverage of persistently cloudy areas, and  less coverage of slow changing persistently clear areas,  while accommodating requirements distinct from scene-based phenology. The Landsat acquisition strategy remains focused on maintaining a long- term global environmental record.