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 Sciences August 22, 2006 Steve Kempler, PI NASA GSFC Earth Science (GES) Data and Information Services Center (DISC) with William 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 Synopsis Project Objectives, Accomplishments, and Sample Products Project Objectives, Accomplishments, and Sample Products Project Outreach Project Outreach Conclusions - Impacts, Outcomes Conclusions - Impacts, Outcomes

3 Integrating NASA Earth Science Data into Global Agricultural Decision Support Systems Objectives Integrate relevant NASA Earth Science data into modeling and operational systems to enhance the accuracy and timely assessments of global agricultural crop conditions 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 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 Partners USDA Agricultural Research Service (ARS)USDA Agricultural Research Service (ARS) - Paul Doraiswamy USDA Foreign Agricultural Service (FAS)USDA Foreign Agricultural Service (FAS) - Robert Tetrault UN World Food Programme (WFP)UN World Food Programme (WFP) - Leonard Milich Other Particulars Other Particulars This work is the result of funding from NASA REASoN Cooperative Agreement Notice (CAN) CAN- 02-OES-01This work is the result of funding from NASA REASoN Cooperative Agreement Notice (CAN) CAN- 02-OES-01 Commenced: 11/03Commenced: 11/03 Program Manager: Ed SheffnerProgram Manager: Ed Sheffner

5 Collaborator Roles NASA GSFC Earth Science (GES) Data and Information Services Center (DISC) 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 partnersDevelop the Agricultural Information System (AIS) to provide specific NASA remote 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 requirements, using NASA supplied data productsDevelop new/improved crop model outputs, based on FAS and WFP requirements, 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, decision support systems.Operational user of remote sensing data for global crop monitoring, decision support systems. UN World Food Programme (WFP) UN World Food Programme (WFP) Operational user of remote sensing data for global crop monitoring, decision support systems.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 Multi-Satellite Precipitation Product (TRMM based - 3B42RT) - 10 Day Composite, binned at 0.25 degree MODIS - 10 Day Composite, 250 m Surface Reflectance MODIS - 10 Day Composite, 250 m Surface Reflectance

7 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 Project Activities

8 Activity 1: Develop agriculture-oriented hydrologic products Objectives Provide NASA precipitation products Provide NASA precipitation products Evaluate precipitation products: bias and error with regards to AFWA (Agrimet, currently used by FAS) and mesonet gauge analysis 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 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 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) 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 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 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 Evaluate new surface reflectance product: bias and error with regards to same 8-Day composite product Facilitate on-line access to new products 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. 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. 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 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.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 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.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 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 ( ) Argentina, Corn ( )  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

15Accomplishments  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 km 2 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 modelEvaluated the use of TRMM derived data products and MODIS 10- day composite data in the crop yield model

16 For Validation Only

17 Flow chart 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

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 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). 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 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 Precipitation anomalies generation Inter-comparison of precipitation products Inter-comparison of precipitation products Customized plot features – User-selectable features: color bar, contour intervals, minimum/maximum, and ASCII output. Customized plot features – User-selectable features: color bar, 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 (e.g., model prediction data). 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. 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 Map Guide to Analysis of Current Precipitation Conditions gov/gov/agriculture/ gov/ais_sup/current_conditions.shtml NASA GES DISC Agriculture Web Portal NASA GES DISC Agricultural Information System Agriculture Online Visualization and Analysis System (AOVAS) nasa.gov/ Giovanni/aovas/ Link to USDA FAS Crop Explorer

21 NASA GES DISC Agriculture Web Portal (page top)

22 NASA GES DISC Agriculture Web Portal (page bottom)

23

24

25

26 AOVAS Analysis

27

28

29

30 Accomplishments Newest feature of AIS - 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).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 normalUsers can access continually updated maps of accumulated rainfall, rainfall anomaly, and percent of 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

31

32 Current Condition Analysis

33

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

35Accomplishments Completed the machine-to-machine, web service connection between the FAS Crop Explorer and Giovanni-Agriculture (AOVAS) in the FAS operational baseline. Completed the machine-to-machine, web service connection between the FAS 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 directly from GES DISC archives was implemented, minimizing large data transfers to FAS (original concept).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.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 10-day rainfall mapUser 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.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). 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

38

39 Crop Explorer users would link to the AIS data through the Crop Explorer home page:

40

41

42 Activity 6: Integrate NASA products for UN/WFP Crop Monitoring Objective Provide NASA products that supports UN/WFP crop monitoring and analysis 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: 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 WillmottClimatology (individual months and growing season) maps from GPCC, TRMM, 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 climatology productsPercent 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.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. 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. 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. 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 Provided customized maps and data for UN WFP El Nino Bulletins Post-event evaluation (e.g., data, methods, and strategies) Post-event evaluation (e.g., data, methods, and strategies) Summary of operation for journal publication Summary of operation for journal publication

45 Project 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 AGU Fall Meeting, Dec. 2005AGU Fall Meeting, Dec ESIP Federation Winter Meeting, Jan. 2006ESIP Federation Winter Meeting, Jan 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 Participated in SEEDS Reuse Working Group telecons. 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. 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 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., 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., 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., 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., 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 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 will the data be used e.g., strictly visual, for 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 validated locallyFor 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 Operational SSM/I on board DMSP (F13, F14, F15)  Conical scanning Microwave Imager/Sounder (CMIS) on board NPOESS Aqua Advanced Microwave Scanning Radiometer (AMSR) Aqua Advanced Microwave Scanning Radiometer (AMSR) Operational NOAA Advanced Microwave Sounding Unit (AMSU) Operational NOAA Advanced Microwave Sounding Unit (AMSU) Operational GOES IR Operational GOES IR TRMM  possible extension to 2010 TRMM  possible extension to 2010 Additional Research Satellite microwave Sensors Additional Research Satellite microwave Sensors MPA  prototype GPM core product 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. 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. 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. Output files are MOD09A1 500m Land surface reflectance, MOD09Q1 250m Land surface reflectance, and MOD09A1C 5km Land surface reflectance.

54