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Integrating NASA Earth Science Data into Global Integrating NASA Earth Science Data into Global
Agricultural Decision Support Systems:Agricultural Decision Support Systems: Data Analysis and Visualization to Ensure Optimal UseData Analysis and Visualization to Ensure Optimal Use
Joint Workshop on Joint Workshop on NASA Biodiversity, Terrestrial Ecology, and Related Applied SciencesNASA Biodiversity, Terrestrial Ecology, and Related Applied Sciences
August 22, 2006August 22, 2006
Steve Kempler, PISteve Kempler, [email protected]@nasa.gov
NASA GSFC Earth Science (GES) Data and Information Services Center (DISC)NASA GSFC Earth Science (GES) Data and Information Services Center (DISC)
withwith
William Teng (RSIS), Paul Doraiswamy (USDA ARS), Zhong Liu (GMU),William Teng (RSIS), Paul Doraiswamy (USDA ARS), Zhong Liu (GMU), Long Chiu (GMU), Dimitar Ouzounov (RSIS)Robert Tetrault (USDA FAS), Long Chiu (GMU), Dimitar Ouzounov (RSIS)Robert Tetrault (USDA FAS),
Leonard Milich (UN WFP)Leonard Milich (UN WFP)
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Table of ContentsTable of Contents
Project SynopsisProject Synopsis
Project Objectives, Accomplishments, and Project Objectives, Accomplishments, and Sample ProductsSample Products
Project OutreachProject Outreach
Conclusions - Impacts, OutcomesConclusions - Impacts, Outcomes
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Integrating NASA Earth Science Data into Global Integrating NASA Earth Science Data into Global Agricultural Decision Support SystemsAgricultural Decision Support Systems
ObjectivesObjectives
Integrate relevant NASA Earth Science data Integrate relevant NASA Earth Science data into modeling and operational systems to into modeling and operational systems to enhance the accuracy and timely assessments enhance the accuracy and timely assessments of global agricultural crop conditionsof global agricultural crop conditions
Provide NASA satellite data-based, operational Provide NASA satellite data-based, operational solutions to the USDA FAS and UN WFP, by solutions to the USDA FAS and UN WFP, by leveraging existing capabilities of these two leveraging existing capabilities of these two user organizations and of the GES DISCuser organizations and of the GES DISC
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Integrating NASA Earth Science Data into Integrating NASA Earth Science Data into Global Agricultural Decision Support SystemsGlobal Agricultural Decision Support Systems
PartnersPartners• USDA Agricultural Research Service (ARS) USDA Agricultural Research Service (ARS)
- Paul Doraiswamy- Paul Doraiswamy• USDA Foreign Agricultural Service (FAS) USDA Foreign Agricultural Service (FAS)
- Robert Tetrault- Robert Tetrault• UN World Food Programme (WFP) UN World Food Programme (WFP)
- Leonard MilichLeonard Milich
Other ParticularsOther Particulars• This work is the result of funding from NASA REASoN This work is the result of funding from NASA REASoN
Cooperative Agreement Notice (CAN) CAN-02-OES-01Cooperative Agreement Notice (CAN) CAN-02-OES-01• Commenced: 11/03Commenced: 11/03• Program Manager: Ed SheffnerProgram Manager: Ed Sheffner
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Collaborator RolesCollaborator Roles NASA GSFC Earth Science (GES) Data and Information Services NASA GSFC Earth Science (GES) Data and Information Services
Center (DISC)Center (DISC)• Develop the Agricultural Information System (AIS) to provide specific Develop the Agricultural Information System (AIS) to provide specific
NASA remote sensing, agriculture related products of interest to its NASA remote sensing, agriculture related products of interest to its partnerspartners
USDA Agricultural Research Service (ARS)USDA Agricultural Research Service (ARS)• Develop new/improved crop model outputs, based on FAS and WFP Develop new/improved crop model outputs, based on FAS and WFP
requirements, using NASA supplied data productsrequirements, using NASA supplied data products
USDA Foreign Agricultural Service (FAS)USDA Foreign Agricultural Service (FAS)• Operational user of remote sensing data for global crop monitoring, Operational user of remote sensing data for global crop monitoring,
decision support systems. decision support systems.
UN World Food Programme (WFP) UN World Food Programme (WFP) • Operational user of remote sensing data for global crop monitoring, Operational user of remote sensing data for global crop monitoring,
decision support systems. decision support systems.
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NASA Remote Sensing Data NASA Remote Sensing Data RequirementsRequirements
Multi-Satellite Precipitation Product Multi-Satellite Precipitation Product (TRMM based - 3B42RT) - 10 Day (TRMM based - 3B42RT) - 10 Day Composite, binned at 0.25 degree Composite, binned at 0.25 degree
MODIS - 10 Day Composite, 250 m MODIS - 10 Day Composite, 250 m Surface ReflectanceSurface Reflectance
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1.1. Develop agriculture-oriented hydrologic products based Develop agriculture-oriented hydrologic products based on TRMM and other satelliteson TRMM and other satellites
2.2. Generate MODIS 250-m, 10-Day composite surface Generate MODIS 250-m, 10-Day composite surface reflectance productreflectance product
3.3. Develop agriculture-oriented land products based on Develop agriculture-oriented land products based on MODIS and TRMMMODIS and TRMM
4.4. Develop Agricultural Information System (AIS) based on Develop Agricultural Information System (AIS) based on GES DISCs Giovanni data exploration and analysis toolGES DISCs Giovanni data exploration and analysis tool
5.5. Integrate NASA products into USDA/FAS Decision Integrate NASA products into USDA/FAS Decision Support SystemSupport System
6.6. Integrate NASA products into UN/WFP Decision Support Integrate NASA products into UN/WFP Decision Support SystemSystem
Project ActivitiesProject Activities
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Activity 1: Develop agriculture-oriented Activity 1: Develop agriculture-oriented hydrologic productshydrologic products
ObjectivesObjectives Provide NASA precipitation productsProvide NASA precipitation products
Evaluate precipitation products: bias and error Evaluate precipitation products: bias and error with regards to AFWA (Agrimet, currently used with regards to AFWA (Agrimet, currently used by FAS) and mesonet gauge analysis by FAS) and mesonet gauge analysis
Evaluate and promote utility of new/potential Evaluate and promote utility of new/potential products – cumulative rainfall (departure, products – cumulative rainfall (departure, normalized departure) and 10 day rainfall for normalized departure) and 10 day rainfall for growing seasongrowing season
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AccomplishmentsAccomplishments
Produced global 0.25 degree TRMM 3B42-V6, Produced global 0.25 degree TRMM 3B42-V6, decadal accumulation, climatology, and percent-decadal accumulation, climatology, and percent-normalnormal
Monthly TRMM compares well with GPCC and Monthly TRMM compares well with GPCC and Climate Division Gauge Analysis over OK (bias, Climate Division Gauge Analysis over OK (bias, departure and percent normal)departure and percent normal)
Analysis over OK shows additional Analysis over OK shows additional spatial/temporal information in TRMM to spatial/temporal information in TRMM to complement AFWA precipitation analysis, complement AFWA precipitation analysis, especially in other non-gauge areas especially in other non-gauge areas
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Time Series of TRMM, GPCC and Climate Time Series of TRMM, GPCC and Climate Division (CD) Data over OKDivision (CD) Data over OK
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Activity 2: Generate MODIS 250-m, 10-Day Activity 2: Generate MODIS 250-m, 10-Day composite surface reflectance productcomposite surface reflectance product
ObjectivesObjectives Generate MODIS 250-m surface reflectance Generate MODIS 250-m surface reflectance
product, as required, to be in phase with other product, as required, to be in phase with other FAS Crop Explorer productsFAS Crop Explorer products
Evaluate new surface reflectance product: bias Evaluate new surface reflectance product: bias and error with regards to same 8-Day composite and error with regards to same 8-Day composite productproduct
Facilitate on-line access to new productsFacilitate on-line access to new products
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AccomplishmentsAccomplishments
Completed development of 10-day MODIS Land Surface Completed development of 10-day MODIS Land Surface Reflectance product, based on a modification of the standard Reflectance product, based on a modification of the standard MODIS L3 8-day Land Surface Reflectance product (MOD_PR09A), MODIS L3 8-day Land Surface Reflectance product (MOD_PR09A), written by Eric Vermote and Jim Ray of the MODIS Land Science written by Eric Vermote and Jim Ray of the MODIS Land Science Team.Team.
Two crop seasons worth of files were generated for comparison by Two crop seasons worth of files were generated for comparison by USDA-ARS.USDA-ARS.• NDVI was derived from the 10-day reflectance product and compared NDVI was derived from the 10-day reflectance product and compared
with the 8-day NDVI. with the 8-day NDVI. • NDVI curves show a general similarity between the two products, but NDVI curves show a general similarity between the two products, but
the reason for the temporal differences needs additional investigation.the reason for the temporal differences needs additional investigation.• 10-day NDVI curve tends to green up and senesce earlier than does the 10-day NDVI curve tends to green up and senesce earlier than does the
8-day curve (See next slide)8-day curve (See next slide)• 10-day NDVI curve shows less variability than does the 8-day curve. 10-day NDVI curve shows less variability than does the 8-day curve.
Investigations into the implications of these results are needed.Investigations into the implications of these results are needed.
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Comparison of 10-day and 8-day NDVI curves, Oklahoma (USDA ARS)
Further analysis is needed for the proper use of thisFurther analysis is needed for the proper use of this 10-day product10-day product
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Activity 3: Develop agriculture-oriented Activity 3: Develop agriculture-oriented products based NASA data inputsproducts based NASA data inputs
ObjectivesObjectives Conduct field studies to validate crop yield simulation models Conduct field studies to validate crop yield simulation models
and scale simulation for regional assessment using MODIS 8-and scale simulation for regional assessment using MODIS 8-day composite dataday composite data
Study areas: Oklahoma, winter wheat (2003-04)Study areas: Oklahoma, winter wheat (2003-04)Argentina, Corn (2004-2005)Argentina, Corn (2004-2005)
Study disaggregation of TRMM rainfall data to 1 km resolution Study disaggregation of TRMM rainfall data to 1 km resolution using the MODIS Thermal datausing the MODIS Thermal data
Apply the TRMM rainfall data in crop yield simulation model Apply the TRMM rainfall data in crop yield simulation model and evaluate potential improvement in crop yield assessmentand evaluate potential improvement in crop yield assessment
Evaluate a MODIS 10-day product for crop yield simulationsEvaluate a MODIS 10-day product for crop yield simulations
Provide FAS/PECAD validated models for their operational useProvide FAS/PECAD validated models for their operational use
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AccomplishmentsAccomplishments Completed modeling of winter wheat yields for the Oklahoma study area and Completed modeling of winter wheat yields for the Oklahoma study area and
prepared a manuscript for submission to Photogrammetric Engineering and Remote prepared a manuscript for submission to Photogrammetric Engineering and Remote Sensing.Sensing.
Completed analyses of all field data collected in Argentina. Completed analyses of all field data collected in Argentina.
Developed algorithms to disaggregate TRMM 0.25-degree grid data to a 1 km product Developed algorithms to disaggregate TRMM 0.25-degree grid data to a 1 km product using MODIS 1 km Thermal datausing MODIS 1 km Thermal data
Acquired (from the GES DISC) MODIS 8-day composite bands 1 and 2 reflectance data Acquired (from the GES DISC) MODIS 8-day composite bands 1 and 2 reflectance data over the 200 x 200 kmover the 200 x 200 km22 study area. Retrieved the reflectance for each of the study study area. Retrieved the reflectance for each of the study fields.fields.
Used the SAIL radiative transfer model to derive leaf area index (LAI) from the MODIS 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 data for each of the study fields. Completed model simulations of corn crop yields using the MODIS-derived LAI. using the MODIS-derived LAI.
• Evaluated the use of TRMM derived data products and Evaluated the use of TRMM derived data products and MODIS 10-day composite data MODIS 10-day composite data in the crop yield modelin the crop yield model
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Operational Crop Condition and Yield Mapping
Landsat ETM
Crop Classification
Crop LAI Image
Soils &Climate Data Grids
Canopy LAI Model
Crop Model
Ground data of LAI , Crop Reflectance
Crop Yield Map
Validation
MODIS and TRMMProducts
Ground data of Crop Condition & Yield
Validation
Crop Condition Map
DisaggregatedRainfall ( 1 km)
Ground truth for Classification
Operational Crop Condition and Yield Mapping
Landsat ETM
Crop Classification
Crop LAI Image
Soils &Climate Data Grids
Canopy LAI Model
Crop Model
Ground data of LAI , Crop Reflectance
Crop Yield Map
Validation
MODIS and TRMMProducts
Ground data of Crop Condition & Yield
Validation
Crop Condition Map
DisaggregatedRainfall ( 1 km)
Ground truth for Classification
For Validation Only
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Flowchart
Flowchart
Soil Polygons
Mesonet Stations
Model
Wheat Mask
Results of Winter Wheat Studies in Oklahoma
Canadian and Kingfisher counties in Oklahoma
Parameter Optimization using Modis data
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Activity 4: Develop the Agricultural Activity 4: Develop the Agricultural Information System (AIS)Information System (AIS)
ObjectivesObjectives Develop an information system (i.e., AIS) that easily Develop an information system (i.e., AIS) that easily
locates desired data and provides quick visualizations locates desired data and provides quick visualizations of and access to the data for further analysisof and access to the data for further analysis
Ensure that the AIS serves general agricultural Ensure that the AIS serves general agricultural information users, operational users, and advanced information users, operational users, and advanced users (through community input).users (through community input).
Enhance GES DISC Giovanni data exploration and Enhance GES DISC Giovanni data exploration and analysis tool to include NASA data relevant to analysis tool to include NASA data relevant to agricultural applicationsagricultural applications
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Enhancements to Giovanni for AISEnhancements to Giovanni for AIS Precipitation anomalies generation Precipitation anomalies generation
Inter-comparison of precipitation products Inter-comparison of precipitation products
Customized plot features – User-selectable features: color bar, Customized plot features – User-selectable features: color bar, contour intervals, minimum/maximum, and ASCII output.contour intervals, minimum/maximum, and ASCII output.
Customized scripts - For operational users Customized scripts - For operational users
Additional precipitation and other agriculture-oriented data products Additional precipitation and other agriculture-oriented data products (e.g., model prediction data).(e.g., model prediction data).
Integration with existing Open Geospatial Consortium (OGC)-Integration with existing Open Geospatial Consortium (OGC)-compliant client – To enable remote access of distributed data, thus compliant client – To enable remote access of distributed data, thus potentially thus potentially greatly increasing the number of data potentially thus potentially greatly increasing the number of data products available to AIS users.products available to AIS users.
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AccomplishmentsAccomplishments
Map Guide to Analysis Map Guide to Analysis of of
Current Precipitation Current Precipitation ConditionsConditions
http://disc.gsfc.nasa.http://disc.gsfc.nasa.
gov/gov/agriculture/agriculture/
ais_sup/current_ais_sup/current_
conditions.shtmlconditions.shtml
NASA GES DISCNASA GES DISCAgriculture Web PortalAgriculture Web Portal
http://disc.gsfc.nasa.gov/agriculture/index.shtml
NASA GES DISC NASA GES DISC
Agricultural Information SystemAgricultural Information Systemhttp://disc.gsfc.nasa.gov/agriculture/ais_sum.shtml
Agriculture Online Agriculture Online Visualization and Visualization and Analysis System Analysis System
(AOVAS)(AOVAS)http://agdisc.gsfc.http://agdisc.gsfc.
nasa.gov/nasa.gov/
Giovanni/aovas/Giovanni/aovas/
Link to Link to
USDA FASUSDA FAS
Crop ExplorerCrop Explorer
http://www.pecadhttp://www.pecad..
fas.usda.gov/fas.usda.gov/
cropexplorer/cropexplorer/
mpa_maps.cfmmpa_maps.cfm
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NASA GES DISC Agriculture Web Portal NASA GES DISC Agriculture Web Portal (page top)(page top)
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NASA GES DISC Agriculture Web Portal NASA GES DISC Agriculture Web Portal (page bottom)(page bottom)
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AOVAS AnalysisAOVAS Analysis
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AccomplishmentsAccomplishments
Newest feature of AISNewest feature of AIS - -
Current Precipitation Conditions::• Provides analyses of current conditions, based on Provides analyses of current conditions, based on
the experimental near-real-time TRMM Multi-the experimental near-real-time TRMM Multi-Satellite Precipitation Analysis (TMPA or 3B42RT).Satellite Precipitation Analysis (TMPA or 3B42RT).
• Users can access continually updated maps of Users can access continually updated maps of accumulated rainfall, rainfall anomaly, and percent accumulated rainfall, rainfall anomaly, and percent of normalof normal
• For various regions of the worldFor various regions of the world
• For time periods ranging from 3-hourly to 90-dayFor time periods ranging from 3-hourly to 90-day
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Current Current Condition Condition AnalysisAnalysis
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Activity 5: Integrate NASA products into Activity 5: Integrate NASA products into USDA/FAS Decision Support SystemUSDA/FAS Decision Support System
ObjectivesObjectives Provide NASA products that support the Provide NASA products that support the
USDA/FAS Crop Explorer Decision Support System USDA/FAS Crop Explorer Decision Support System and analysisand analysis
Provide easy, seamless access to NASA data Provide easy, seamless access to NASA data through web interfaces familiar to FAS analyststhrough web interfaces familiar to FAS analysts
Present NASA products to the FAS analysts, Present NASA products to the FAS analysts, addressing product definitions, accuracy, addressing product definitions, accuracy, relevance, and usabilityrelevance, and usability
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AccomplishmentsAccomplishments Completed the machine-to-machine, web service connection between the FAS Completed the machine-to-machine, web service connection between the FAS
Crop Explorer and Giovanni-Agriculture (AOVAS) in the FAS operational Crop Explorer and Giovanni-Agriculture (AOVAS) in the FAS operational baseline.baseline.
Paradigm Shift!Paradigm Shift! • Taking advantage of evolving technology, more efficient interactive data access Taking advantage of evolving technology, more efficient interactive data access
directly from GES DISC archives was implemented, minimizing large data transfers directly from GES DISC archives was implemented, minimizing large data transfers to FAS (original concept). to FAS (original concept).
• This significantly reduces cost of data transfer, and maintenance.This significantly reduces cost of data transfer, and maintenance.• FAS would thus ned to be concerned about data version changes, reprocessings, etc.FAS would thus ned to be concerned about data version changes, reprocessings, etc.• Data is, indeed, just ‘a click away’Data is, indeed, just ‘a click away’
Project products are made publicly visible, seamlessly, from within Crop Project products are made publicly visible, seamlessly, from within Crop Explorer. Explorer. • User clicking on a region of the world will access and retrieve from AOVAS the latest User clicking on a region of the world will access and retrieve from AOVAS the latest
10-day rainfall map 10-day rainfall map • Data derived from the TRMM Multi-Satellite Precipitation Analysis (TMPA) data Data derived from the TRMM Multi-Satellite Precipitation Analysis (TMPA) data
produced by Dr. Robert Adler, TRMM Project Scientist.produced by Dr. Robert Adler, TRMM Project Scientist. From any Crop Explorer Web page of a given region, a user can access and 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 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). other Crop Explorer rainfall maps (e.g., WMO, Air Force Weather Agency).
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NASA GES DISC Agriculture Web Portal NASA GES DISC Agriculture Web Portal (page bottom)(page bottom)
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Crop Explorer users would link to the AIS data Crop Explorer users would link to the AIS data through the Crop Explorer home page:through the Crop Explorer home page:
http://www.pecad.fas.usda.gov/cropexplorer/http://www.pecad.fas.usda.gov/cropexplorer/
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Activity 6: Integrate NASA products for Activity 6: Integrate NASA products for UN/WFP Crop Monitoring UN/WFP Crop Monitoring
ObjectiveObjective Provide NASA products that supports Provide NASA products that supports
UN/WFP crop monitoring and analysis UN/WFP crop monitoring and analysis
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AccomplishmentsAccomplishments Generated and delivered 504 maps (~31 MB) for post-season Generated and delivered 504 maps (~31 MB) for post-season
summary, evaluation, and uncertainty analysis. These include:summary, evaluation, and uncertainty analysis. These include:• Climatology (individual months and growing season) maps from GPCC, Climatology (individual months and growing season) maps from GPCC,
TRMM, and WillmottTRMM, and Willmott• Difference maps of GPCC, TRMM, and Willmott climatology baseline productsDifference maps of GPCC, TRMM, and Willmott climatology baseline products• Percent of normal maps derived from TRMM and the three baseline Percent of normal maps derived from TRMM and the three baseline
climatology productsclimatology products• Gini (index to measure rainfall evenness) and z-score (measuring statistical Gini (index to measure rainfall evenness) and z-score (measuring statistical
departure) maps derived from TRMM and the three baseline climatology departure) maps derived from TRMM and the three baseline climatology products.products.
Received from WFP long-term station observations from Asia and Africa Received from WFP long-term station observations from Asia and Africa to better estimate anomalies.to better estimate anomalies.
WFP ENSO reports, based in large part on project results, have been 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. sent in to WFP HQ, as well as used in presentations for donors.
AOVAS has also been used by WFP operations.AOVAS has also been used by WFP operations.
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Supporting UN World Food ProgrammeSupporting UN World Food Programme
Provided Provided customized maps customized maps and data for UN and data for UN WFP El Nino WFP El Nino BulletinsBulletins
Post-event Post-event evaluation (e.g., evaluation (e.g., data, methods, data, methods, and strategies)and strategies)
Summary of Summary of operation for operation for journal publicationjournal publication
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Project OutreachProject Outreach
Participated in and/or presented project results at (FY06):Participated in and/or presented project results at (FY06):• CCSP Workshop, Nov. 2005CCSP Workshop, Nov. 2005• AGU Fall Meeting, Dec. 2005AGU Fall Meeting, Dec. 2005• ESIP Federation Winter Meeting, Jan. 2006ESIP Federation Winter Meeting, Jan. 2006• AMS 2006 ConferenceAMS 2006 Conference• ASPRS Annual Conference, May 2006ASPRS Annual Conference, May 2006• ESIP Federation Summer Meeting, July 2006.ESIP Federation Summer Meeting, July 2006.
Participated in SEEDS Reuse Working Group telecons.Participated in SEEDS Reuse Working Group telecons.
Discussed potential extension/adaptation of project results Discussed potential extension/adaptation of project results with other USDA organizations and government agencies, with other USDA organizations and government agencies, in support of their decision support systems.in support of their decision support systems.
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Related PublicationsRelated Publications Teng, W., et al. 2004: Integrating NASA Earth Science Enterprise (ESE) Teng, W., et al. 2004: Integrating NASA Earth Science Enterprise (ESE)
data into global agricultural decision support systems, ASPRS annual data into global agricultural decision support systems, ASPRS annual conference, May 23-28, 2004, Denver, CO conference, May 23-28, 2004, Denver, CO
Chiu, L., C. Lim, W. Teng, 2004: AIS development: TRMM and Chiu, L., C. Lim, W. Teng, 2004: AIS development: TRMM and Oklahoma Climate Division rain rates, Second TRMM International Oklahoma Climate Division rain rates, Second TRMM International Conference, September 2004, Nara, Japan.Conference, September 2004, Nara, Japan.
Chiu, L., Z. Liu, H. Rui, and W. Teng, 2006: Tropical Rainfall Measuring Chiu, L., Z. Liu, H. Rui, and W. Teng, 2006: Tropical Rainfall Measuring Mission (TRMM) data and access tools, in Mission (TRMM) data and access tools, in Earth System Science Earth System Science Remote SensingRemote Sensing, J. Qu et al. (Eds.), Springer-Tsinghua University Pub., J. Qu et al. (Eds.), Springer-Tsinghua University Pub.
Chiu, L., D-B. Shin, J. Kwiatkowski, 2006: Surface rain rate from TRMM Chiu, L., D-B. Shin, J. Kwiatkowski, 2006: Surface rain rate from TRMM satellite, in satellite, in Earth System Science Remote SensingEarth System Science Remote Sensing, J. Qu et al., (Eds.) , J. Qu et al., (Eds.) Springer-Tsinghua University Pub.Springer-Tsinghua University Pub.
Chiu, L., Z. Liu, J. Vongsaard, S. Morain, A. Budge, P. Neville, and S. 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 Bales., 2006: Comparison of TRMM and Water District Rain Rates over New Mexico, New Mexico, Advances in Atmospheric Sciences, 23 (1), 1-13Advances in Atmospheric Sciences, 23 (1), 1-13
Chiu, L., C. Lim, Z. Liu, W. Teng, P. Doraiswamy, B. Akhmedov: 2005: Chiu, L., C. Lim, Z. Liu, W. Teng, P. Doraiswamy, B. Akhmedov: 2005: Comparison of daily rainfall from Multi-Satellite Precipitation and Air Comparison of daily rainfall from Multi-Satellite Precipitation and Air Force Weather Agency analyses over parts of Oklahoma and Argentina Force Weather Agency analyses over parts of Oklahoma and Argentina region for crop yield monitoring, IAMAS, August 1-11, 2005, Beijing, region for crop yield monitoring, IAMAS, August 1-11, 2005, Beijing, PRCPRC
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Conclusions: ImpactsConclusions: Impacts Developed required 10-day products (evaluation ongoing):Developed required 10-day products (evaluation ongoing):
Precipitation, bias analysisPrecipitation, bias analysis MODIS surface reflectanceMODIS surface reflectance
Completed validation of improved climate-based crop model for Oklahoma and Completed validation of improved climate-based crop model for Oklahoma and ArgentinaArgentina
Enhanced ARS crop model with NASA remote sensing productsEnhanced ARS crop model with NASA remote sensing products
Announced NASA Agriculture portal for access to NASA agriculture-related data Announced NASA Agriculture portal for access to NASA agriculture-related data productsproducts
Announced operational tools that allow decision makers (and all other users) quick Announced operational tools that allow decision makers (and all other users) quick data exploration, discovery, visualization, and access capabilities, not previously data exploration, discovery, visualization, and access capabilities, not previously available.available.
Integrated NASA products for operational use into FAS and WFP decision support Integrated NASA products for operational use into FAS and WFP decision support systemssystems
Advanced information science by developing technology that makes data availability Advanced information science by developing technology that makes data availability seamless, regardless of its actual physical location. ‘Data is only a click away’.seamless, regardless of its actual physical location. ‘Data is only a click away’.
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Conclusions: Outcomes - 1Conclusions: Outcomes - 1
More accurate decisions can be made with the arrival of additional precipitation data inputs: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 systemAt 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 abilitiesAt UN/WFP - Precipitation maps have greatly increased WFP crop monitoring and analysis abilities
Soliciting feedback from FAS analysts will be valuable for further collaborationSoliciting feedback from FAS analysts will be valuable for further collaboration
Field analysis proves valuable on two fronts:Field analysis proves valuable on two fronts: USDA/ARS - Validates and improves crop modelsUSDA/ARS - Validates and improves crop models NASA - In situ data, further validates remote sensing dataNASA - In situ data, further validates remote sensing data
Additional field data analysis is needed to better understand regional biases on global remote sensing datasetsAdditional field data analysis is needed to better understand regional biases on global remote sensing datasets
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Conclusions: Outcomes - 2Conclusions: Outcomes - 2 Data validation valuable to ensuring NASA product precision:Data validation valuable to ensuring NASA product precision:
Precipitation Products (NASA GES DISC)- Data comparisons lead to valuable bias analysisPrecipitation Products (NASA GES DISC)- Data comparisons lead to valuable bias analysis MODIS Surface Reflectance - 8 day/10 day comparisons valuable in understanding data binning behaviorMODIS 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 more accurately characterize biases.Further analysis needed to understand the effects of varying multi-day composites Further analysis needed to understand the effects of varying multi-day composites
Implementing advanced information technologyImplementing 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 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 ownimplement their own Made ‘operational’, lastest NASA precipitation maps, gaining great usageMade ‘operational’, lastest NASA precipitation maps, gaining great usage Implemented seamless ‘operational’ access to remote dataImplemented seamless ‘operational’ access to remote data
Technology can be applied to, and otherwise reused by, other science and application usersTechnology can be applied to, and otherwise reused by, other science and application usersTechnology can be reused by other data management ‘systems’Technology can be reused by other data management ‘systems’
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Parting ThoughtParting Thought
The usage of NASA data for specific applications can be The usage of NASA data for specific applications can be best understood through close coordination.best understood through close coordination.
• How will the data be used e.g., strictly visual, for How will the data be used e.g., strictly visual, for modeling?)modeling?)
• How precise must the data be (i.e., science quality?)How precise must the data be (i.e., science quality?)
• For some applications, global datasets need to be For some applications, global datasets need to be validated locallyvalidated locally
Thank you,Thank you,
The ‘Integrated’ TeamThe ‘Integrated’ Team
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BACKUP SLIDESBACKUP SLIDES
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MPA ContinuityMPA Continuity Operational SSM/I on board DMSP (F13, F14, F15) Operational SSM/I on board DMSP (F13, F14, F15)
Conical scanning Microwave Imager/Sounder Conical scanning Microwave Imager/Sounder (CMIS) on board NPOESS(CMIS) on board NPOESS
Aqua Advanced Microwave Scanning Radiometer Aqua Advanced Microwave Scanning Radiometer (AMSR) (AMSR)
Operational NOAA Advanced Microwave Sounding Operational NOAA Advanced Microwave Sounding Unit (AMSU)Unit (AMSU)
Operational GOES IR Operational GOES IR TRMM TRMM possible extension to 2010 possible extension to 2010 Additional Research Satellite microwave SensorsAdditional Research Satellite microwave Sensors MPA MPA prototype GPM core product prototype GPM core product
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MODIS 10-Day Surface Reflectance Product MODIS 10-Day Surface Reflectance Product Development DescriptionDevelopment Description
Minor modifications were introduced into the PCF file in order Minor modifications were introduced into the PCF file in order to accept 10/11 MODIS tiles as inputs. There was no change in to accept 10/11 MODIS tiles as inputs. There was no change in the order of compositing of the pixels across days and orbits, the order of compositing of the pixels across days and orbits, i.e., compositing within orbits according to orbital coverage of i.e., compositing within orbits according to orbital coverage of the pixel and the priority of the pixel (the pixel's score), then the pixel and the priority of the pixel (the pixel's score), then compositing across orbits according to channel 3 reflectance.compositing across orbits according to channel 3 reflectance.
Input data are 10 days' worth of 250m, 500m, and 1 km Input data are 10 days' worth of 250m, 500m, and 1 km compact L2G data: compact L2G data: MODMGGAD, MOD09GQK, MOD09GHK, MODMGGAD, MOD09GQK, MOD09GHK, MOD09GST, MODPTHKM, MOD09GST, MODPTHKM, MODPTQKM.MODPTQKM.
Output files are MOD09A1 500m Land surface reflectance, Output files are MOD09A1 500m Land surface reflectance, MOD09Q1 250m Land surface reflectance, and MOD09A1C MOD09Q1 250m Land surface reflectance, and MOD09A1C 5km Land surface reflectance.5km Land surface reflectance.
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