Table of Contents

1. Integrating NASA Earth Science Data into Global Agricultural Decision Support Systems:Data Analysis and Visualization to Ensure Optimal UseJoint Workshop on NASA Biodiversity, Terrestrial Ecology, and Related Applied SciencesAugust 22, 2006Steve Kempler, PISteven.J.Kempler@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 • Develop agriculture-oriented hydrologic products based on TRMM and other satellites • Generate MODIS 250-m, 10-Day composite surface reflectance product • Develop agriculture-oriented land products based on MODIS and TRMM • Develop Agricultural Information System (AIS) based on GES DISCs Giovanni data exploration and analysis tool • Integrate NASA products into USDA/FAS Decision Support System • 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.gsfc.nasa.gov/agriculture/index.shtml NASA GES DISC Agricultural Information System http://disc.gsfc.nasa.gov/agriculture/ais_sum.shtml Agriculture Online Visualization and Analysis System (AOVAS) http://agdisc.gsfc. nasa.gov/ Giovanni/aovas/ Map Guide to Analysis of Current Precipitation Conditions http://disc.gsfc.nasa. gov/agriculture/ ais_sup/current_ conditions.shtml 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)

26. AOVAS Analysis

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

32. Current Condition Analysis

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)

39. Crop Explorer users would link to the AIS data through the Crop Explorer home page: http://www.pecad.fas.usda.gov/cropexplorer/

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