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Satellite Yield Mapping in Kenya and Nepal

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Jake Campolo
WEBINAR
Using Satellite Imagery for Early Warning of Productivity Constraints
Organized by the Food Security Portal (FSP)
OCT 31, 2019 - 11:00 AM TO 12:30 PM EDT

Published in: Government & Nonprofit
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Satellite Yield Mapping in Kenya and Nepal

  1. 1. Satellite yield mapping in Kenya and Nepal Understanding variability and informing crop management Jake Campolo - Lobell Lab - Stanford University
  2. 2. Maize yield mapping in Kenya and Tanzania ● Smallholder farms (< 5 ha) produce more than 50% of food calories ● Survey-based measurements are time and cost intensive ● Satellite-based measurements offer a scalable, flexible alternative while capturing fine-scale heterogeneity Jin et al., 2019
  3. 3. Crop classification
  4. 4. Overcoming remote sensing challenges... Challenges: ● Cloudy images ● Haze / atmospheric interference ● Missing observations
  5. 5. … via time series statistical fitting 0 1 2 3 4 Seasonal maximum of red-edge CI
  6. 6. Yield Estimation Model: Yield = 𝛽𝛽0 + 𝛽𝛽1 VI + 𝛽𝛽2 W ● VI = Seasonal max of the vegetation index ● W = relevant weather variables at site (temperature, precipitation, solar radiation, etc) Input data from (1) field + satellite data (2) crop simulation output Yield validation with cropcut data, 2016
  7. 7. Application of Yield Maps: Kenya Explore drivers of heterogeneity E.g. Combine with soil nutrient maps to assess the influence of soil properties on yields Jin et al., 2019
  8. 8. Application of Yield Maps: Nepal Non-linear effects of soil properties on yield How do these interact with fertilizer effectiveness? Provide precision soil- based recommendations for management ΔYield(kg/ha)

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