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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)

withwithWilliam 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)

mailto:[email protected]

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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 Center NASA GSFC Earth Science (GES) Data and Information Services Center

(DISC)(DISC)• Develop the Agricultural Information System (AIS) to provide specific NASA Develop the Agricultural Information System (AIS) to provide specific NASA

remote sensing, agriculture related products of interest to its partnersremote sensing, agriculture related products of interest to its partners

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 prepared Completed modeling of winter wheat yields for the Oklahoma study area and prepared

a manuscript for submission to Photogrammetric Engineering and Remote Sensing.a manuscript for submission to Photogrammetric Engineering and Remote 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, contour Customized plot features – User-selectable features: color bar, contour intervals, minimum/maximum, and ASCII output.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)-compliant Integration with existing Open Geospatial Consortium (OGC)-compliant client – To enable remote access of distributed data, thus potentially client – To enable remote access of distributed data, thus potentially thus potentially greatly increasing the number of data products thus potentially greatly increasing the number of data products available to AIS users.available to AIS users.

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AccomplishmentsAccomplishments

Map Guide to Analysis of Map Guide to Analysis of Current Precipitation Current Precipitation

ConditionsConditionshttp://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 System

http://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



















http://www.pecad/

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NASA GES DISC Agriculture Web Portal NASA GES DISC Agriculture Web Portal (page top)(page top)

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

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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 the Provides analyses of current conditions, based on the

experimental near-real-time TRMM Multi-Satellite experimental near-real-time TRMM Multi-Satellite Precipitation Analysis (TMPA or 3B42RT).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 baseline.Crop Explorer and Giovanni-Agriculture (AOVAS) in the FAS operational 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 to directly from GES DISC archives was implemented, minimizing large data transfers to FAS (original concept). 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 Explorer. 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 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 summary, Generated and delivered 504 maps (~31 MB) for post-season summary,

evaluation, and uncertainty analysis. These include:evaluation, and uncertainty analysis. These include:• Climatology (individual months and growing season) maps from GPCC, TRMM, Climatology (individual months and growing season) maps from GPCC, TRMM,

and Willmottand 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 to Minor modifications were introduced into the PCF file in order to accept 10/11 MODIS tiles as inputs. There was no change in the 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., order of compositing of the pixels across days and orbits, i.e., compositing within orbits according to orbital coverage of the compositing within orbits according to orbital coverage of the pixel and the priority of the pixel (the pixel's score), then 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 5km MOD09Q1 250m Land surface reflectance, and MOD09A1C 5km Land surface reflectance.Land surface reflectance.

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