Integrating NASA Earth Science Enterprise Data into Global Agricultural Decision Support Systems A REASoN CAN Project Second Year Presentation and Demonstration July 21, 2005 Long Chiu, Paul Doraiswamy, Steven Kempler, Zhong Liu, William Teng, Robe - PowerPoint PPT Presentation

About This Presentation
Title:

Integrating NASA Earth Science Enterprise Data into Global Agricultural Decision Support Systems A REASoN CAN Project Second Year Presentation and Demonstration July 21, 2005 Long Chiu, Paul Doraiswamy, Steven Kempler, Zhong Liu, William Teng, Robe

Description:

Integrating NASA Earth Science Data into Global Agricultural Decision Support Systems: Data Analysis and Visualization to Ensure Optimal Use – PowerPoint PPT presentation

Number of Views:220
Avg rating:3.0/5.0

less

Transcript and Presenter's Notes

Title: Integrating NASA Earth Science Enterprise Data into Global Agricultural Decision Support Systems A REASoN CAN Project Second Year Presentation and Demonstration July 21, 2005 Long Chiu, Paul Doraiswamy, Steven Kempler, Zhong Liu, William Teng, Robe


1
Integrating NASA Earth Science Data into Global
Agricultural Decision Support Systems Data
Analysis and Visualization to Ensure Optimal Use
Joint Workshop on NASA Biodiversity,
Terrestrial Ecology, and Related Applied
SciencesAugust 22, 2006 Steve Kempler,
PISteven.J.Kempler_at_nasa.govNASA GSFC Earth
Science (GES) Data and Information Services
Center (DISC)withWilliam Teng (RSIS), Paul
Doraiswamy (USDA ARS), Zhong Liu (GMU), Long
Chiu (GMU), Dimitar Ouzounov (RSIS)Robert
Tetrault (USDA FAS), Leonard Milich (UN WFP)
2
Table of Contents
  • Project Synopsis
  • Project Objectives, Accomplishments, and Sample
    Products
  • Project Outreach
  • Conclusions - Impacts, Outcomes

3
Integrating NASA Earth Science Data into Global
Agricultural Decision Support SystemsObjectives
  • Integrate relevant NASA Earth Science data into
    modeling and operational systems to enhance the
    accuracy and timely assessments of global
    agricultural crop conditions
  • Provide NASA satellite data-based, operational
    solutions to the USDA FAS and UN WFP, by
    leveraging existing capabilities of these two
    user organizations and of the GES DISC

4
Integrating NASA Earth Science Data into Global
Agricultural Decision Support Systems
  • Partners
  • USDA Agricultural Research Service (ARS)
  • - Paul Doraiswamy
  • USDA Foreign Agricultural Service (FAS)
  • - Robert Tetrault
  • UN World Food Programme (WFP)
  • Leonard Milich
  • Other Particulars
  • This work is the result of funding from NASA
    REASoN Cooperative Agreement Notice (CAN)
    CAN-02-OES-01
  • Commenced 11/03
  • Program Manager Ed Sheffner

5
Collaborator Roles
  • NASA GSFC Earth Science (GES) Data and
    Information Services Center (DISC)
  • Develop the Agricultural Information System (AIS)
    to provide specific NASA remote sensing,
    agriculture related products of interest to its
    partners
  • USDA Agricultural Research Service (ARS)
  • Develop new/improved crop model outputs, based on
    FAS and WFP requirements, using NASA supplied
    data products
  • USDA Foreign Agricultural Service (FAS)
  • Operational user of remote sensing data for
    global crop monitoring, decision support systems.
  • UN World Food Programme (WFP)
  • Operational user of remote sensing data for
    global crop monitoring, decision support systems.

6
NASA Remote Sensing Data Requirements
  • Multi-Satellite Precipitation Product (TRMM based
    - 3B42RT) - 10 Day Composite, binned at 0.25
    degree
  • MODIS - 10 Day Composite, 250 m Surface
    Reflectance

7
Project Activities
  1. Develop agriculture-oriented hydrologic products
    based on TRMM and other satellites
  2. Generate MODIS 250-m, 10-Day composite surface
    reflectance product
  3. Develop agriculture-oriented land products based
    on MODIS and TRMM
  4. Develop Agricultural Information System (AIS)
    based on GES DISCs Giovanni data exploration and
    analysis tool
  5. Integrate NASA products into USDA/FAS Decision
    Support System
  6. Integrate NASA products into UN/WFP Decision
    Support System

8
Activity 1 Develop agriculture-oriented
hydrologic products
  • Objectives
  • Provide NASA precipitation products
  • Evaluate precipitation products bias and error
    with regards to AFWA (Agrimet, currently used by
    FAS) and mesonet gauge analysis
  • Evaluate and promote utility of new/potential
    products cumulative rainfall (departure,
    normalized departure) and 10 day rainfall for
    growing season

9
Accomplishments
  • Produced global 0.25 degree TRMM 3B42-V6, decadal
    accumulation, climatology, and percent-normal
  • Monthly TRMM compares well with GPCC and Climate
    Division Gauge Analysis over OK (bias, departure
    and percent normal)
  • Analysis over OK shows additional
    spatial/temporal information in TRMM to
    complement AFWA precipitation analysis,
    especially in other non-gauge areas

10
Time Series of TRMM, GPCC and Climate Division
(CD) Data over OK
11
Activity 2 Generate MODIS 250-m, 10-Day
composite surface reflectance product
  • Objectives
  • Generate MODIS 250-m surface reflectance product,
    as required, to be in phase with other FAS Crop
    Explorer products
  • Evaluate new surface reflectance product bias
    and error with regards to same 8-Day composite
    product
  • Facilitate on-line access to new products

12
Accomplishments
  • Completed development of 10-day MODIS Land
    Surface Reflectance product, based on a
    modification of the standard MODIS L3 8-day Land
    Surface Reflectance product (MOD_PR09A), written
    by Eric Vermote and Jim Ray of the MODIS Land
    Science Team.
  • Two crop seasons worth of files were generated
    for comparison by USDA-ARS.
  • NDVI was derived from the 10-day reflectance
    product and compared with the 8-day NDVI.
  • NDVI curves show a general similarity between the
    two products, but the reason for the temporal
    differences needs additional investigation.
  • 10-day NDVI curve tends to green up and senesce
    earlier than does the 8-day curve (See next
    slide)
  • 10-day NDVI curve shows less variability than
    does the 8-day curve. Investigations into the
    implications of these results are needed.

13
Comparison of 10-day and 8-day NDVI curves,
Oklahoma (USDA ARS)
Further analysis is needed for the proper use of
this 10-day product
14
Activity 3 Develop agriculture-oriented products
based NASA data inputs
  • Objectives
  • Conduct field studies to validate crop yield
    simulation models and scale simulation for
    regional assessment using MODIS 8-day composite
    data
  • Study areas Oklahoma, winter wheat
    (2003-04) Argentina, Corn (2004-2005)
  • Study disaggregation of TRMM rainfall data to 1
    km resolution using the MODIS Thermal data
  • Apply the TRMM rainfall data in crop yield
    simulation model and evaluate potential
    improvement in crop yield assessment
  • Evaluate a MODIS 10-day product for crop yield
    simulations
  • Provide FAS/PECAD validated models for their
    operational use

15
Accomplishments
  • Completed modeling of winter wheat yields for the
    Oklahoma study area and prepared a manuscript for
    submission to Photogrammetric Engineering and
    Remote Sensing.
  • Completed analyses of all field data collected in
    Argentina.
  • Developed algorithms to disaggregate TRMM
    0.25-degree grid data to a 1 km product using
    MODIS 1 km Thermal data
  • Acquired (from the GES DISC) MODIS 8-day
    composite bands 1 and 2 reflectance data over the
    200 x 200 km2 study area. Retrieved the
    reflectance for each of the study fields.
  • Used the SAIL radiative transfer model to derive
    leaf area index (LAI) from the MODIS data for
    each of the study fields. Completed model
    simulations of corn crop yields using the
    MODIS-derived LAI.
  • Evaluated the use of TRMM derived data products
    and MODIS 10-day composite data in the crop yield
    model

16
For Validation Only
17
Results of Winter Wheat Studies in Oklahoma
Parameter Optimization using Modis data
Model
Flowchart
Flowchart
Wheat Mask
Soil Polygons
Mesonet Stations
Canadian and Kingfisher counties in Oklahoma
18
Activity 4 Develop the Agricultural Information
System (AIS)
  • Objectives
  • Develop an information system (i.e., AIS) that
    easily locates desired data and provides quick
    visualizations of and access to the data for
    further analysis
  • Ensure that the AIS serves general agricultural
    information users, operational users, and
    advanced users (through community input).
  • Enhance GES DISC Giovanni data exploration and
    analysis tool to include NASA data relevant to
    agricultural applications

19
Enhancements to Giovanni for AIS
  • Precipitation anomalies generation
  • Inter-comparison of precipitation products
  • Customized plot features User-selectable
    features color bar, contour intervals,
    minimum/maximum, and ASCII output.
  • Customized scripts - For operational users
  • Additional precipitation and other
    agriculture-oriented data products (e.g., model
    prediction data).
  • Integration with existing Open Geospatial
    Consortium (OGC)-compliant client To enable
    remote access of distributed data, thus
    potentially thus potentially greatly increasing
    the number of data products available to AIS
    users.

20
Accomplishments
NASA GES DISC Agriculture Web Portalhttp//disc.g
sfc.nasa.gov/agriculture/index.shtml
NASA GES DISC Agricultural Information
System http//disc.gsfc.nasa.gov/agriculture/ais_s
um.shtml
Map Guide to Analysis of Current Precipitation
Conditions http//disc.gsfc.nasa. gov/agriculture/
ais_sup/current_ conditions.shtml
Agriculture Online Visualization and Analysis
System (AOVAS) http//agdisc.gsfc. nasa.gov/ Giova
nni/aovas/
Link to USDA FAS Crop Explorer http//www.pecad.
fas.usda.gov/ cropexplorer/ mpa_maps.cfm
21
NASA GES DISC Agriculture Web Portal (page top)
22
NASA GES DISC Agriculture Web Portal (page
bottom)
23
(No Transcript)
24
(No Transcript)
25
(No Transcript)
26
AOVAS Analysis
27
(No Transcript)
28
(No Transcript)
29
(No Transcript)
30
Accomplishments
  • Newest feature of AIS -
  • Current Precipitation Conditions
  • Provides analyses of current conditions, based on
    the experimental near-real-time TRMM
    Multi-Satellite Precipitation Analysis (TMPA or
    3B42RT).
  • Users can access continually updated maps of
    accumulated rainfall, rainfall anomaly, and
    percent of normal
  • For various regions of the world
  • For time periods ranging from 3-hourly to 90-day

31
(No Transcript)
32
Current Condition Analysis
33
(No Transcript)
34
Activity 5 Integrate NASA products into
USDA/FAS Decision Support System
  • Objectives
  • Provide NASA products that support the USDA/FAS
    Crop Explorer Decision Support System and
    analysis
  • Provide easy, seamless access to NASA data
    through web interfaces familiar to FAS analysts
  • Present NASA products to the FAS analysts,
    addressing product definitions, accuracy,
    relevance, and usability

35
Accomplishments
  • Completed the machine-to-machine, web service
    connection between the FAS Crop Explorer and
    Giovanni-Agriculture (AOVAS) in the FAS
    operational baseline.
  • Paradigm Shift!
  • Taking advantage of evolving technology, more
    efficient interactive data access directly from
    GES DISC archives was implemented, minimizing
    large data transfers to FAS (original concept).
  • This significantly reduces cost of data transfer,
    and maintenance.
  • FAS would thus ned to be concerned about data
    version changes, reprocessings, etc.
  • Data is, indeed, just a click away
  • Project products are made publicly visible,
    seamlessly, from within Crop Explorer.
  • User clicking on a region of the world will
    access and retrieve from AOVAS the latest 10-day
    rainfall map
  • Data derived from the TRMM Multi-Satellite
    Precipitation Analysis (TMPA) data produced by
    Dr. Robert Adler, TRMM Project Scientist.
  • From any Crop Explorer Web page of a given
    region, a user can access and retrieve NASA TMPA
    maps for the same spatial region/time period as
    those of other Crop Explorer rainfall maps (e.g.,
    WMO, Air Force Weather Agency).

36
NASA GES DISC Agriculture Web Portal (page
bottom)
37
(No Transcript)
38
(No Transcript)
39
  • Crop Explorer users would link to the AIS data
    through the Crop Explorer home page
  • http//www.pecad.fas.usda.gov/cropexplorer/

40
(No Transcript)
41
(No Transcript)
42
Activity 6 Integrate NASA products for UN/WFP
Crop Monitoring
  • Objective
  • Provide NASA products that supports UN/WFP crop
    monitoring and analysis

43
Accomplishments
  • Generated and delivered 504 maps (31 MB) for
    post-season summary, evaluation, and uncertainty
    analysis. These include
  • Climatology (individual months and growing
    season) maps from GPCC, TRMM, and Willmott
  • Difference maps of GPCC, TRMM, and Willmott
    climatology baseline products
  • Percent of normal maps derived from TRMM and the
    three baseline climatology products
  • Gini (index to measure rainfall evenness) and
    z-score (measuring statistical departure) maps
    derived from TRMM and the three baseline
    climatology products.
  • Received from WFP long-term station observations
    from Asia and Africa to better estimate
    anomalies.
  • WFP ENSO reports, based in large part on project
    results, have been sent in to WFP HQ, as well as
    used in presentations for donors.
  • AOVAS has also been used by WFP operations.

44
Supporting UN World Food Programme
  • Provided customized maps and data for UN WFP El
    Nino Bulletins
  • Post-event evaluation (e.g., data, methods, and
    strategies)
  • Summary of operation for journal publication

45
Project Outreach
  • Participated in and/or presented project results
    at (FY06)
  • CCSP Workshop, Nov. 2005
  • AGU Fall Meeting, Dec. 2005
  • ESIP Federation Winter Meeting, Jan. 2006
  • AMS 2006 Conference
  • ASPRS Annual Conference, May 2006
  • ESIP Federation Summer Meeting, July 2006.
  • Participated in SEEDS Reuse Working Group
    telecons.
  • Discussed potential extension/adaptation of
    project results with other USDA organizations and
    government agencies, in support of their decision
    support systems.

46
Related Publications
  • Teng, W., et al. 2004 Integrating NASA Earth
    Science Enterprise (ESE) data into global
    agricultural decision support systems, ASPRS
    annual conference, May 23-28, 2004, Denver, CO
  • Chiu, L., C. Lim, W. Teng, 2004 AIS development
    TRMM and Oklahoma Climate Division rain rates,
    Second TRMM International Conference, September
    2004, Nara, Japan.
  • Chiu, L., Z. Liu, H. Rui, and W. Teng, 2006
    Tropical Rainfall Measuring Mission (TRMM) data
    and access tools, in Earth System Science Remote
    Sensing, J. Qu et al. (Eds.), Springer-Tsinghua
    University Pub.
  • Chiu, L., D-B. Shin, J. Kwiatkowski, 2006
    Surface rain rate from TRMM satellite, in Earth
    System Science Remote Sensing, J. Qu et al.,
    (Eds.) Springer-Tsinghua University Pub.
  • Chiu, L., Z. Liu, J. Vongsaard, S. Morain, A.
    Budge, P. Neville, and S. Bales., 2006
    Comparison of TRMM and Water District Rain Rates
    over New Mexico, Advances in Atmospheric
    Sciences, 23 (1), 1-13
  • Chiu, L., C. Lim, Z. Liu, W. Teng, P. Doraiswamy,
    B. Akhmedov 2005 Comparison of daily rainfall
    from Multi-Satellite Precipitation and Air Force
    Weather Agency analyses over parts of Oklahoma
    and Argentina region for crop yield monitoring,
    IAMAS, August 1-11, 2005, Beijing, PRC

47
Conclusions Impacts
  • Developed required 10-day products (evaluation
    ongoing)
  • Precipitation, bias analysis
  • MODIS surface reflectance
  • Completed validation of improved climate-based
    crop model for Oklahoma and Argentina
  • Enhanced ARS crop model with NASA remote sensing
    products
  • Announced NASA Agriculture portal for access to
    NASA agriculture-related data products
  • Announced operational tools that allow decision
    makers (and all other users) quick data
    exploration, discovery, visualization, and access
    capabilities, not previously available.
  • Integrated NASA products for operational use into
    FAS and WFP decision support systems
  • Advanced information science by developing
    technology that makes data availability seamless,
    regardless of its actual physical location.
    Data is only a click away.

48
Conclusions Outcomes - 1
  • More accurate decisions can be made with the
    arrival of additional precipitation data inputs
  • At USDA/FAS - Precipitation maps available to FAS
    analysts, through their Crop Explorer decision
    support system
  • At UN/WFP - Precipitation maps have greatly
    increased WFP crop monitoring and analysis
    abilities
  • Soliciting feedback from FAS analysts will be
    valuable for further collaboration
  • Field analysis proves valuable on two fronts
  • USDA/ARS - Validates and improves crop models
  • NASA - In situ data, further validates remote
    sensing data
  • Additional field data analysis is needed to
    better understand regional biases on global
    remote sensing datasets

49
Conclusions Outcomes - 2
  • Data validation valuable to ensuring NASA product
    precision
  • Precipitation Products (NASA GES DISC)- Data
    comparisons lead to valuable bias analysis
  • MODIS Surface Reflectance - 8 day/10 day
    comparisons valuable in understanding data
    binning behavior
  • Further analysis needed to more accurately
    characterize biases.
  • Further analysis needed to understand the effects
    of varying multi-day composites
  • Implementing advanced information technology
  • Made operational, quick and easy exploration
    tools for very fast data analysis and
    visualization Takes the burden away from each
    user having to implement their own
  • Made operational, lastest NASA precipitation
    maps, gaining great usage
  • Implemented seamless operational access to
    remote data
  • Technology can be applied to, and otherwise
    reused by, other science and application users
  • Technology can be reused by other data management
    systems

50
Parting Thought
  • The usage of NASA data for specific applications
    can be best understood through close
    coordination.
  • How will the data be used e.g., strictly visual,
    for modeling?)
  • How precise must the data be (i.e., science
    quality?)
  • For some applications, global datasets need to be
    validated locally
  • Thank you,
  • The Integrated Team

51
BACKUP SLIDES
52
MPA Continuity
  • Operational SSM/I on board DMSP (F13, F14, F15) ?
    Conical scanning Microwave Imager/Sounder (CMIS)
    on board NPOESS
  • Aqua Advanced Microwave Scanning Radiometer
    (AMSR)
  • Operational NOAA Advanced Microwave Sounding Unit
    (AMSU)
  • Operational GOES IR
  • TRMM ? possible extension to 2010
  • Additional Research Satellite microwave Sensors
  • MPA ? prototype GPM core product

53
MODIS 10-Day Surface Reflectance Product
Development Description
  • Minor modifications were introduced into the PCF
    file in order to accept 10/11 MODIS tiles as
    inputs. There was no change in the order of
    compositing of the pixels across days and orbits,
    i.e., compositing within orbits according to
    orbital coverage of the pixel and the priority of
    the pixel (the pixel's score), then compositing
    across orbits according to channel 3 reflectance.
  • Input data are 10 days' worth of 250m, 500m, and
    1 km compact L2G data MODMGGAD, MOD09GQK,
    MOD09GHK, MOD09GST, MODPTHKM, MODPTQKM.
  • Output files are MOD09A1 500m Land surface
    reflectance, MOD09Q1 250m Land surface
    reflectance, and MOD09A1C 5km Land surface
    reflectance.

54
(No Transcript)
Write a Comment
User Comments (0)
About PowerShow.com