Remote sensing technology for crop insurance –
Applications and limitations

CIMMYT Workshop: Remote Sensing - Beyond Imag...
Welcome address

Topic:
Remote sensing technology for crop insurance –
Applications and limitations

Dr. Joachim Herbold
A...
Agenda

1. Crop insurance - status and outlook
2. Remote sensing for crop insurance
3. Need for further research
4. Summar...
Crop insurance

Slide 4
Crop insurance
Drought in the USA in 2012

Slide 5
Crop insurance
Drought in the USA in 2012 - Financial consequences

• Total crop losses: around 20 billion US$

• Indemnif...
Crop insurance
Why do crops fail?
Losses per peril in MPCI programs

USA

Ontario, Canada

(1981–2005)

(1966–2009)
Diseas...
World Map of Agricultural Insurance

© Joachim Herbold, 2013

RS technology for crop insurance

Dr. Joachim Herbold

Slide...
Remote sensing technology

Slide 9
Initial situation – Dominance of NDVI

 Most agriculture-related RS applications are focused on monitoring the
vegetation...
Challenges applying RS technology to crop insurance

1. Insured area relatively small: 1 ha – approx. 100 ha:
→ relatively...
Radar sensors – a solution for the future?

 Advantage: data can be obtained even when there is a cloud cover
 Area of a...
Potential applications
Plot identification

Relevant topics:

• Agricultural use?
• Boundaries of plot
• Size of plot

© G...
Potential applications
Crop identification

Relevant topics:
• Which crop/crop type?

Auxiliary factors:
• Sowing/planting...
Potential applications
Monitoring of crop progress

Alternatively:

 Soil preparation
 Sowing
 Emergence

 Closing of ...
Potential applications
Yield estimations
Approaches for yield forecasts:

1. Satellite information as primary source: NDVI...
Potential applications
Yield estimations

Approaches for yield forecasts:
1. Satellite information as primary source: NDVI...
Potential applications
Loss event monitoring

Applications
 Crop insurance
 Disaster management (state authorities)
→ Ea...
Typhoon “Parma” in Northern Philippines on October 2 , 2009
Monitoring of flood

Max. monitoring
frequency: 6
images/day

...
Potential applications
Loss event monitoring – use of unmanned aircraft
• Unmanned system
• Aerial photos
• Partial losses...
Potential applications
Risk assessment and underwriting

1. Digital elevation models
→ to assess flood and frost risk

2. ...
Need for further research

Slide 22
Need for further research

1. Yield estimations: increase in accuracy
 In field:

o methodology for yield estimations (e....
Need for further research

2. Yield modeling

 Yield variability in the case of extreme weather events (actual
yield mode...
Need for further research

3. Susceptibility of crops in different vegetation periods to adverse
climatic conditions
 Hai...
Summary

Slide 26
Summary

1. Crop insurance currently in its infancy.
2. Potential to further develop and enhance crop insurance and
disast...
Thank you. For further information please see
Article: Herbold, J.: RS technology for crop insurance. Geospatial World, Se...
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Remote sensing technology for crop insurance – Applications and limitations

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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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Remote sensing technology for crop insurance – Applications and limitations

  1. 1. Remote sensing technology for crop insurance – Applications and limitations CIMMYT Workshop: Remote Sensing - Beyond Images December 14-15, 2013, Texcoco, Mexico Dr. Joachim Herbold Slide 1
  2. 2. Welcome address Topic: Remote sensing technology for crop insurance – Applications and limitations Dr. Joachim Herbold Agricultural Division Munich Reinsurance Company Contact: jherbold@munichre.com RS technology for crop insurance Dr. Joachim Herbold Slide 2
  3. 3. Agenda 1. Crop insurance - status and outlook 2. Remote sensing for crop insurance 3. Need for further research 4. Summary RS technology for crop insurance Dr. Joachim Herbold Slide 3
  4. 4. Crop insurance Slide 4
  5. 5. Crop insurance Drought in the USA in 2012 Slide 5
  6. 6. Crop insurance Drought in the USA in 2012 - Financial consequences • Total crop losses: around 20 billion US$ • Indemnifications to farmers: 17 billion US$ → Biggest insurance loss in the history of crop insurance in the USA and worldwide. RS technology for crop insurance Dr. Joachim Herbold Slide 6
  7. 7. Crop insurance Why do crops fail? Losses per peril in MPCI programs USA Ontario, Canada (1981–2005) (1966–2009) Disease 3% Winterkill 3% Other * 17% Other 13% Plant Disease 2% Drought 39% Heat 4% Drought 42% Hail 6% Frost 9% Hail 9% Freeze 4% Excess Moisture 24% Source: RAIN&HAIL, 2011 Excessive Rainfall 25% Source: AGRICORP, 2011 Estimate for developing countries: 70-80% of losses attributable to either a lack or excess of rain (incl. flood) RS technology for crop insurance Dr. Joachim Herbold Slide 7
  8. 8. World Map of Agricultural Insurance © Joachim Herbold, 2013 RS technology for crop insurance Dr. Joachim Herbold Slide 8
  9. 9. Remote sensing technology Slide 9
  10. 10. Initial situation – Dominance of NDVI  Most agriculture-related RS applications are focused on monitoring the vegetation status using the NDVI (Normalized Difference Vegetation Index)  Advantage of NDVI: daily recording by different sensors and time-series of up to 30 years.  Disadvantages of NDVI: inaccuracies as a consequence of such factors as background reflectance and the three-dimensional structure of the canopy, → alternative indices are currently being developed: FAPAR (Fraction of Absorbed Photosynthetically Active Radiation), VHI (Vegetation Health Index) and LAI (Leaf Area Index). For more information, see Meroni et. al., 2013 and Rojas et. al., 2013. RS technology for crop insurance Dr. Joachim Herbold Slide 10
  11. 11. Challenges applying RS technology to crop insurance 1. Insured area relatively small: 1 ha – approx. 100 ha: → relatively high spatial resolution required. 2. Vegetation period: 4 to 9 months → medium temporal resolution required, cloud cover in vegetation period can be a critical success factor for optical sensors. → high temporal resolution required if RS is to be used after a specific loss event 3. Biomass ≠ yield 4. Ground truth data essential for calibration and validation → reliable yield data RS technology for crop insurance Dr. Joachim Herbold Slide 11
  12. 12. Radar sensors – a solution for the future?  Advantage: data can be obtained even when there is a cloud cover  Area of application in agriculture restricted at present  Applications at present:  Information on cultivated area  Soil moisture analysis,  Flood monitoring → often in combination with optical sensors  With availability of new bands and more research, advances may be expected in future RS technology for crop insurance Dr. Joachim Herbold Slide 12
  13. 13. Potential applications Plot identification Relevant topics: • Agricultural use? • Boundaries of plot • Size of plot © GAF AG, 2008, Relin et al. 2005, Relin 2013 RS technology for crop insurance Dr. Joachim Herbold Slide 13
  14. 14. Potential applications Crop identification Relevant topics: • Which crop/crop type? Auxiliary factors: • Sowing/planting date • Ripening date • Harvest date • Phyllotaxy • Leaf area in relation to soil area in different growing stages RS technology for crop insurance Dr. Joachim Herbold Slide 14
  15. 15. Potential applications Monitoring of crop progress Alternatively:  Soil preparation  Sowing  Emergence  Closing of rows  Tasseling/Flowering  Ripening  Harvesting RS technology for crop insurance Dr. Joachim Herbold Slide 15
  16. 16. Potential applications Yield estimations Approaches for yield forecasts: 1. Satellite information as primary source: NDVI as classic example RS technology for crop insurance Dr. Joachim Herbold Slide 16
  17. 17. Potential applications Yield estimations Approaches for yield forecasts: 1. Satellite information as primary source: NDVI as classic example 2. Crop growth models as a tool, using satellite information on the biomass as input factor, as well as other factors such as soil type, weather conditions (e.g. precipitation, temperature) and management factors RS technology for crop insurance Dr. Joachim Herbold Slide 17
  18. 18. Potential applications Loss event monitoring Applications  Crop insurance  Disaster management (state authorities) → Early loss estimations and categorizing the regions according to degree affected Flood monitoring  Radar sensors in addition to optical sensors very efficient.  Crop loss assessment: duration and height of flood, crop types RS technology for crop insurance Dr. Joachim Herbold Slide 18
  19. 19. Typhoon “Parma” in Northern Philippines on October 2 , 2009 Monitoring of flood Max. monitoring frequency: 6 images/day Legend: Reference water, (Oct 2) Flooded (Oct 4) Flood draw-off between Oct. 3 and 4 RS technology for crop insurance Dr. Joachim Herbold Slide 19
  20. 20. Potential applications Loss event monitoring – use of unmanned aircraft • Unmanned system • Aerial photos • Partial losses in big plots RS technology for crop insurance Dr. Joachim Herbold Slide 20
  21. 21. Potential applications Risk assessment and underwriting 1. Digital elevation models → to assess flood and frost risk 2. Insurance products  based on NDVI → grassland USA and Spain  in future: area yield products? RS technology for crop insurance Dr. Joachim Herbold Slide 21
  22. 22. Need for further research Slide 22
  23. 23. Need for further research 1. Yield estimations: increase in accuracy  In field: o methodology for yield estimations (e.g. size of samples, methods used) o Automatic yield recording devices  Satellite-based technology o Integration of satellite data into crop growth models o Validation of results using ground truth data RS technology for crop insurance Dr. Joachim Herbold Slide 23
  24. 24. Need for further research 2. Yield modeling  Yield variability in the case of extreme weather events (actual yield models reproduce yields inaccurately for extreme weather events)  Yield models with few input parameters  Variability between regional yields (from official statistics) and farm specific yields. → may differ for different crops and growing conditions RS technology for crop insurance Dr. Joachim Herbold Slide 24
  25. 25. Need for further research 3. Susceptibility of crops in different vegetation periods to adverse climatic conditions  Hail: good knowledge  Frost: for some specialty crops in Mediterranean climate: good knowledge; for others not.  Drought, excessive rain and flooding: research required RS technology for crop insurance Dr. Joachim Herbold Slide 25
  26. 26. Summary Slide 26
  27. 27. Summary 1. Crop insurance currently in its infancy. 2. Potential to further develop and enhance crop insurance and disaster response by state authorities. 3. Major constraints:  heterogeneous structure in crop production  limited temporal availability of optical methods because of cloud cover. 4. Advances in the field of radar data may supplement optical data. RS technology for crop insurance Dr. Joachim Herbold Slide 27
  28. 28. Thank you. For further information please see Article: Herbold, J.: RS technology for crop insurance. Geospatial World, September 2013. Can be downloaded from: http://www.drjoachimherbold.de/ Further information: http://www.munichre.com/en/reinsurance/business/non-life/systemagro/services.aspx Slide 28

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