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Empirical EO based approach to wheat yield forecasting and its adaptation within the GEOGLAM Framework

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Remote sensing –Beyond images …

Remote sensing –Beyond images
Mexico 14-15 December 2013

The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

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  • 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 Prices 1960-2011($/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    
  • 15. LACIE  Wheat  Monitoring  
  • 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  !!    
  • 32. Model  Extendibility  
  • 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  
  • 40. Thank  You!