A support vector machine to identify irrigated crop types using time-series Landsat NDVI data

Baojuan Zheng, Soe Myint, Prasad S. Thenkabail, Rimjhim Aggarwal

Research output: Contribution to journalArticle

140 Scopus citations

Abstract

Site-specific information of crop types is required for many agro-environmental assessments. The study investigated the potential of support vector machines (SVMs) in discriminating various crop types in a complex cropping system in the Phoenix Active Management Area. We applied SVMs to Landsat time-series Normalized Difference Vegetation Index (NDVI) data using training datasets selected by two different approaches: Stratified random approach and intelligent selection approach using local knowledge. The SVM models effectively classified nine major crop types with overall accuracies of >86% for both training datasets. Our results showed that the intelligent selection approach was able to reduce the training set size and achieved higher overall classification accuracy than the stratified random approach. The intelligent selection approach is particularly useful when the availability of reference data is limited and unbalanced among different classes. The study demonstrated the potential of utilizing multi-temporal Landsat imagery to systematically monitor crop types and cropping patterns over time in arid and semi-arid regions.

Original languageEnglish (US)
Pages (from-to)103-112
Number of pages10
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume34
Issue number1
DOIs
StatePublished - Jan 1 2015

Keywords

  • Crop classification
  • Landsat
  • NDVI
  • SVM
  • Support vector machines

ASJC Scopus subject areas

  • Global and Planetary Change
  • Earth-Surface Processes
  • Computers in Earth Sciences
  • Management, Monitoring, Policy and Law

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