Improving Crop Yield Forecasting in Uruguay using Landsat Imagery

By Uruguay Agriculture Team , posted on April 3rd, 2013 in Agriculture, Earth Observation

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Image of crops. Image Credit: DEVELOP

Image Credit: DEVELOP

Authors: Sunny Ng, Pietro Ceccato

Mentors/Advisers (affiliation): Pietro Ceccato (International Research Institute for Climate and Society, IRI)

Team Location: IRI in Palisades, New York

Abstract: Using field observations of crops in Uruguay, Landsat images were classified into 95 classes for the 2011 and 2012 planting seasons. The SIAM (Satellite Image Automatic Mapper) algorithm classified images based on spectral properties instead of through trained pixels in a supervised classification method. The pixel counts for different crops such as maize and soybean were analyzed in a matrix to measure the level of accuracy of SIAM and plotted on a time series to visualize how pixels evolve over the planting season. If the results show a clear trend, this will improve crop-yield predictions at early stages in the planting season, which will be beneficial to Uruguay’s Ministry of Agriculture.

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5 comments
JasonBrentJones
JasonBrentJones

What software did you use to implement the SIAM classification algorithm?

Kelsey Rooks
Kelsey Rooks

Great project! What future work do you think could be done with this partner or methodology? 

anjward7
anjward7

What is the community concern for early crop yield predictions?  Supposing SIAM was able to detect a poor yield, what could be done to protect Uruguay's Ministry of Agriculture?

 

amadson
amadson

Would it be more beneficial to create maize and soybean yield predictions from imagery with a higher spatial or spectral resolution than the data your team utilized?

sunnyng
sunnyng

 @amadson  Improving spatial resolution and spectral resolution would certainly help.  For example, using hyperspectral sensors may help us determine differences in phenology for different crop types.

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