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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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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
Who We Are Open Community made up of interna5onal and na5onal agencies
concerned with agricultural monitoring including ministries of Ag, space agencies, universi5es, and industry
Interna5onal recogni5on of cri5cal need for improved real 5me, reliable, open informa5on on global agricultural produc5on prospects
Cri5cal for agricultural policies, stabilizing markets, aver5ng food crises
Need to increase food produc5on by 50%-‐70% by 2050 to meet demands
Nominal wheat price in US $/metric Ton
2010/11 Price hikes Drought: Russia
Landsat 1 Launched (1972)
‘grain robbery’ 1971/2’s price hike
2008 Price hikes Droughts:
Australia & Ukraine
Context
Monthly Wheat Prices 1960-2011($/Metric Ton) Source: World Bank
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
The GEOGLAM Components 1. GLOBAL/ REGIONAL SYSTEM OF SYSTEMS
Main producer countries, main crops
2. NATIONAL CAPACITY DEVELOPMENT
for agricultural monitoring using Earth Observation
3. MONITORING COUNTRIES AT RISK
Food security assessment
6. Data, products and INFORMATION DISSEMINATION
5. METHOD IMPROVEMENT through R&D coordinaBon (JECAM)
4. EO DATA COORDINATION
Crop NDVI Anomaly, August 15 2012
Becker-‐Reshef et al.
Corn Monthly Prices $/MT 2002-‐2012
Wheat Monthly Price$/MT 2002-‐2012
Soybeans Monthly Price $/MT 2002-‐2012
Monthly Market Prices of Corn, Soybeans and Wheat Highligh5ng 2012 Prices
GEOGLAM Crop Monitor Partners Developing Monthly Crop Condi5on Assessments
(>25 partners & growing)
-‐ USDA FAS, NASS -‐ NASA -‐ UMD -‐ EC JRC -‐ Canada (Agriculture Canada)
-‐ FAO -‐ China CropWatch -‐ Russia (IKI) -‐ Ukraine (Hydromet, NASU-‐NSAU)
-‐ Kazakhstan (ISR)
-‐ Australia (ABARES, CSIRO) -‐ South Africa (NRC) -‐ JAXA/Asia Rice -‐ AFSIS -‐ Indonesia (LAPAN) -‐ Thailand (GISTDA) -‐ Vietnam (VAST,VIMHE) -‐ IRRI -‐ Argen5na (INTA) -‐ Brazil (CONAB, INPE) -‐ India (ISRO) -‐ Mexico (SIAP) -‐ GEO SEC
Growing Degree Day Anomaly
Examples of Input Data Na5onal –Global: EO indices, weather,
model outputs etc
Synthesize and dis5l a range of data & informa5on from mul5ple sources while preserving the wealth of underlying data within suppor5ng materials document
Crop Assessment Interface
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
Data include: NDVI, Precip and Temperature Anomalies from NASA/UMD and JRC
Crop Type Distribu5on & Crop Calendars are Cri5cal!
Adap5ng to User Needs: November Synthesis Crop Condi5on Maps
October
November
December
September
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
LACIE Wheat Monitoring
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
Strong Correla5on Between NDVI Peak and Wheat Yield
2.35 2.54 2.21
3.36 2.49 2.69
1.61 1.48 2.49
Year
Winter Wheat emergence NDVI peak
Winter Wheat seasonal NDVI peak
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
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
Spa5al Resolu5on: Approach to mi5gate effects of crop rota5ons
Wheat Distribu5on In Kansas 2007
High Rate of Crop Rota5on
Low Rate of Crop Rota5on
High Rate of Crop RotaWon Low Rate of Crop RotaWon (wheat monoculture)
High Rate of Crop RotaWon Low Rate of Crop RotaWon (wheat monoculture)
High Rate of Crop RotaWon Low Rate of Crop RotaWon (wheat monoculture)
High Rate of Crop RotaWon Low Rate of Crop RotaWon (wheat monoculture)
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)
Harper County: Wheat mono-‐culture
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
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
Wheat Yield Model Development
Peak Seasonal Vegeta5on Index is posi5vely & linearly correlated with yield
% wheat per grid cell is posi5vely and linearly correlated with peak seasonal Vegeta5on Index
Regression-‐based model developed as a func5on of: • a seasonal maximum NDVI (adjusted for background noise) • Per grid cell percent wheat
Adjusted Max NDVI vs. Yield Regression Slopes Stratified by Percent Wheat in 0.05
degree pixels Percent Wheat: Slope:
Percent Wheat: Slope:
Percent Wheat: Slope:
Percent Wheat: Slope:
Lower Percent wheat à Higher regression slope
Yield (M
T/Ha
)
Adjusted Max NDVI
Generalized relaWonship of Yield-‐Max VI as a funcWon of % Wheat
Model Approach: Generaliza5on of VI to Yield Rela5onship
Percent Wheat
Y=9.61+(-‐0.05*X)
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.
% Error of Yield Es5mates by Resolu5on for 2 Scenarios of Data Availability
Minimized Error Tradeoff at 4-‐5Km
Error Trade off 1.2% rela5ve to Case 1 !!
Model Extendibility
Wheat Classifica5on (Decision Tree) Three Landsat scenes chosen for training: before
peak, peak, and aser peak
Early season Peak senescence
Model Results in Ukraine: Model es5mated produc5on vs. Ukrainian State Sta5s5cal Commitee Crop Sta5s5cs
The model forecasts are within 8% of final reported produc5on 6 weeks prior to beginning of harvest
RMSE= 9% R2= 0.88
2011
2012
Exploring Adaptability
Australia
Russia
Pakistan
Field Size Distribu5on: Guiding Spa5al Resolu5on Requirements
Source: Fritz et al., (IIASA)
Based on interpola5on of 50,000 GEOWIKI valida5on points
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
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
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
Thank You!
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