Improving Crop Yield Forecasting in Uruguay using Landsat Imagery

EarthzineAgriculture, Earth Observation, Original

Image of crops. Image Credit: DEVELOP
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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