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Empirical EO based approach to wheat yield forecasting and its adaptation within the GEOGLAM Framework
This document discusses an empirical Earth Observation (EO) based approach to wheat yield forecasting and its adaptation within the GEOGLAM framework. GEOGLAM is an international initiative that aims to inform agricultural decisions through coordinated Earth observations. The approach uses multi-temporal satellite data like NDVI to develop quantitative wheat yield forecasts at regional/national scales. A key challenge is obtaining annual crop type maps for spatially explicit time series. Aggregating high-resolution wheat masks to coarser resolutions can mitigate effects of crop rotations over time.
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Empirical EO based approach to wheat yield forecasting and its adaptation within the GEOGLAM Framework
- 1. Empirical EO based approach to wheat yield forecas5ng and its adapta5on within the GEOGLAM Framework Inbal Becker-‐Reshef1, Eric Vermote2, Mark Lindeman3 , Jan Dempewolf1, Joao Soares4, Chris Jus5ce1 1University of Maryland, 2NASA GSFC, 3USDA FAS, 4GEO Secretariat
- 2. Who We Are Interna5onal recogni5on of up of nterna5onal and na5onal agencies Open Community made cri5cal ineed for improved real 5me, reliable, open informa5on on g monitoring including ministries o concerned with agricultural lobal agricultural produc5on prospects f Ag, space agencies, universi5es, and industry Cri5cal for agricultural policies, stabilizing markets, aver5ng food crises Need to increase food produc5on by 50%-‐70% by 2050 to meet demands
- 3. Context Monthly Wheat Prices1960-2011($/Metric Ton) Source: World Bank 2008 Price hikes Droughts: Australia & Ukraine 2010/11 Price hikes Drought: Russia ‘grain robbery’ 1971/2’s price hike Landsat 1 Launched (1972) Nominal wheat price in US $/metric Ton
- 4. G-‐20 GEOGLAM: Interna5onal Framework & Scope • GEOGLAM- Group on Earth Observations (GEO) Global Agricultural Monitoring Initiative • Policy Mandate from G-20 2 related initiatives adopted as part of Action plan on Food Price Volatility and Agriculture: 1. AMIS (Agricultural Market Information System) 2. GEOGLAM • Vision: inform decisions and actions in agriculture through the use of coordinated and sustained Earth observations Ø building on existing agricultural monitoring systems
- 5. The GEOGLAM Components 1. GLOBAL/ REGIONAL SYSTEM OF SYSTEMS 2. NATIONAL CAPACITY DEVELOPMENT 3. MONITORING COUNTRIES AT RISK Main producer countries, main crops for agricultural monitoring using Earth Observation Food security assessment 4. EO DATA COORDINATION 5. METHOD IMPROVEMENT through R&D coordinaBon (JECAM) 6. Data, products and INFORMATION DISSEMINATION
- 6. Crop NDVI Anomaly, August 15 2012 Becker-‐Reshef et al.
- 7. Monthly Market Prices of Corn, Soybeans and Wheat Highligh5ng 2012 Prices Corn Monthly Prices $/MT 2002-‐2012 Soybeans Monthly Price $/MT 2002-‐2012 Wheat Monthly Price$/ MT 2002-‐2012
- 8. GEOGLAM Crop Monitor Partners Developing Monthly Crop Condi5on Assessments -‐ -‐ -‐ -‐ -‐ -‐ -‐ -‐ -‐ -‐ (>25 partners & growing) USDA FAS, NASS -‐ Australia (ABARES, CSIRO) NASA -‐ South Africa (NRC) UMD -‐ JAXA/Asia Rice EC JRC -‐ AFSIS -‐ Indonesia (LAPAN) Canada (Agriculture -‐ Thailand (GISTDA) Canada) -‐ Vietnam (VAST,VIMHE) FAO -‐ IRRI China CropWatch -‐ Argen5na (INTA) Russia (IKI) -‐ Brazil (CONAB, INPE) Ukraine (Hydromet, -‐ India (ISRO) NASU-‐NSAU) -‐ Mexico (SIAP) Kazakhstan (ISR) -‐ GEO SEC
- 9. Examples of Input Data Na5onal – Global: EO indices, weather, model outputs etc Synthesize ay Anomaly Growing Degree Dand dis5l a range of data & informa5on from mul5ple sources while preserving the wealth of underlying data within suppor5ng materials document
- 10. Crop Assessment Interface Data include: NDVI, Precip and Temperature Anomalies from NASA/UMD and JRC Enables comparison between relevant datasets (global, na5onal and regional), by crop type and accoun5ng for crop calendars and enables crop condi5on labeling and commen5ng to reflect na5onal expert assessments
- 11. Crop Type Distribu5on & Crop Calendars are Cri5cal!
- 12. Adap5ng to User Needs: November Synthesis Crop Condi5on Maps
- 13. October November December September
- 14. From Qualita5ve to Quan5ta5ve: Winter Wheat Yield Forecas5ng Overall ObjecWve: develop a prac5cal and robust approach to forecast wheat yields at regional/ na5onal scales using mul5-‐temporal and spa5al resolu5on earth observa5ons
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- 16. Strong Correla5on Between NDVI Peak and Wheat Yield Example of Daily Normalized Difference Vegeta5on Index (NDVI from MODIS) 2000-‐2008, Versus Crop Yields (Blue numbers are Yield (MT/Ha) ) in Harper County Kansas Winter Wheat emergence NDVI peak Winter Wheat seasonal NDVI peak 2.35 2.54 2.21 3.36 2.49 2.69 1.61 Year 1.48 2.49
- 17. Challenge: wheat specific EO 5me series • Need spa5ally explicit informa5on on crop type for yield forecas5ng (wheat mask) – Wheat field loca5ons vary between years due to crop rota5ons • Ideally, annual informa5on on crop type distribu5on at the start of the growing season – At present, this type of data is generally not readily available
- 18. Spa5al Resolu5on: Approach to mi5gate effects of crop rota5ons Hypothesis: if a year specific wheat map to coarser resolution is aggregated as a percent wheat mask the per grid cell percent wheat will become stable at a coarser resolution
- 19. Wheat Distribu5on In Kansas 2007 High Rate of Crop Rota5on Low Rate of Crop Rota5on
- 20. High Rate of Crop RotaWon Low Rate of Crop RotaWon (wheat monoculture)
- 21. High Rate of Crop RotaWon Low Rate of Crop RotaWon (wheat monoculture)
- 22. High Rate of Crop RotaWon Low Rate of Crop RotaWon (wheat monoculture)
- 23. High Rate of Crop RotaWon Low Rate of Crop RotaWon (wheat monoculture)
- 24. At What Spa5al Aggrega5on Level does Per Grid Cell % Wheat Stabilize? Kansas per Grid Cell Ranges of Percent Wheat Values over 5 years (2006-‐2010)
- 25. Maximum NDVI extracted for 2006 through 2011 using 6 seasonal wheat masks at increasing spa5al resolu5on Line colors are presented according to the year of the wheat mask Harper County: Wheat mono-‐culture
- 26. Maximum NDVI extracted for 2006 through 2011 using 6 seasonal wheat masks at increasing spa5al resolu5on Line colors are presented according to the year of the wheat mask Decatur County: High rate of crop rotaWon
- 27. Wheat Yield Model Development Regression-‐based model developed as a func5on of: • a seasonal maximum NDVI (adjusted for background noise) • Per grid cell percent wheat % wheat per grid cell is posi5vely Peak Seasonal Vegeta5on Index is posi5vely & linearly correlated with yield and linearly correlated with peak seasonal Vegeta5on Index
- 28. Model Approach: Generaliza5on of VI to Yield Rela5onship Adjusted Max NDVI vs. Yield Regression Slopes Stratified by Percent Wheat in 0.05 degree pixels Yield (MT/Ha) Percent Wheat: Slope: Percent Wheat: Slope: Percent Wheat: Slope: Generalized relaWonship of Yield-‐Max VI as a funcWon of % Wheat Percent Wheat: Slope: Adjusted Max NDVI Lower Percent wheat à Higher regression slope Y=9.61+(-‐0.05*X) Percent Wheat
- 29. Kansas Results: Kansas Model Es5mates vs. USDA NASS Crop Sta5s5cs Model EsWmates are within 7%, 6 weeks prior to harvest Becker-‐Reshef I, Vermote E, Lindeman M, Jus5ce C. 2010. In Remote Sensing of Environment, 114, 1312– 1323.
- 30. % Error of Yield Es5mates by Resolu5on for 2 Scenarios of Data Availability
- 31. Minimized Error Tradeoff at 4-‐5Km Error Trade off 1.2% rela5ve to Case 1 !!
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- 33. Wheat Classifica5on (Decision Tree) Three Landsat scenes chosen for training: before peak, peak, and aser peak Early season Peak senescence
- 34. Model Results in Ukraine: Model es5mated produc5on vs. Ukrainian State Sta5s5cal Commitee Crop Sta5s5cs RMSE= 9% R2= 0.88 2012 2011 The model forecasts are within 8% of final reported produc5on 6 weeks prior to beginning of harvest
- 35. Exploring Adaptability Australia Russia Pakistan
- 36. Field Size Distribu5on: Guiding Spa5al Resolu5on Requirements Source: Fritz et al., (IIASA) Based on interpola5on of 50,000 GEOWIKI valida5on points
- 37. JECAM: R&D Component of GEOGLAM • a network of study sites representa5ve of the world’s cropping systems • Support monitoring enhancements within opera5onal agricultural monitoring systems • JECAM Program Office is coordinated by AAFC, Canada and UCL Sites in development
- 38. Summary & Next Steps • Cri5cal need for improved 5mely, reliable forecasts • Fluctua5ons in produc5on-‐ primarily driven by weather events-‐ significant impact on market fluctua5ons • Developed a process for qualita5ve opera5onal assessments of crop condi5ons • Promising results for implemen5ng a simple empirical, generalized model for primary wheat producing countries • Explore feasibility of adapta5on of approach to more complex systems – Higher spa5al & temporal resolu5on
- 39. Challenges & Lessons Learned • Understand user needs • Developing awareness & demand for RS based informa5on • Opera5onal user community guiding the research agenda • Cross-‐fer5liza5on-‐ interna5onal partnerships are cri5cal • Improve base layers: crop type maps and calendars • Promise -‐ RS landscape is advancing rapidly – Resolu5on, temporal repeat, quality, processing capabili5es, distribu5on, data policy
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