First Advisor

Jiunn-Der Duh

Date of Publication

Fall 11-21-2014

Document Type


Degree Name

Master of Arts (M.A.) in Geography






Artificial satellites in agriculture, Crops -- Remote sensing, Field crops -- Argentina -- Santiago del Estero -- Phenology -- Case studies, Field crops -- Kansas -- Phenology -- Case studies, Soybean



Physical Description

1 online resource (xi, 159 pages)


Subtropical deforestation in Latin America is thought to be driven by demand for agricultural land, particularly to grow soybeans. However, existing remote sensing methods that can differentiate crop types to verify this hypothesis require high spatial or spectral resolution data, or extensive ground truth information to develop training sites, none of which are freely available for much of the world. I developed a new method of crop classification based on the phenological signatures of crops extracted from multi-temporal MODIS vegetation indices. I tested and refined this method using the USDA Cropland Data Layer from Kansas, USA as a reference. I then applied the method to classify crop types for a study site in Pellegrini, Santiago Del Estero, Argentina. The results show that this method is unable to effectively separate summer crops in Pellegrini, but can differentiate summer crops and non-summer crops. Unmet assumptions about agricultural practices are primarily responsible for the ineffective summer crop classification, underlining the need for researchers to have a complete understanding of ground conditions when designing a remote sensing analysis.


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Persistent Identifier (31 kB)
Python source code for pyhytemporal tools

Included in

Agriculture Commons