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A Novel Approach for Mapping Wheat Areas Using High Resolution Sentinel-2 Images

Authors
  • nasrallah, ali
  • baghdadi, nicolas
  • mhawej, mario
  • faour, ghaleb
  • darwish, talal
  • belhouchette, hatem
  • darwich, salem
Publication Date
Jun 29, 2018
Identifiers
DOI: 10.3390/s18072089
OAI: oai:mdpi.com:/1424-8220/18/7/2089/
Source
MDPI
Keywords
Language
English
License
Green
External links

Abstract

1184 in 2017. When SEWMA was applied using 2016 ground truth data, the overall accuracy reached 87.0% on 2017 data, whereas, when implemented using 2017 ground truth data, the overall accuracy was 82.6% on 2016 data. The novelty resides in executing early classification output (up to six weeks before harvest) as well as distinguishing wheat from other winter cereal crops with similar NDVI yearly profiles (i.e., barley and triticale). SEWMA offers a simple, yet effective and budget-saving approach providing early-season classification information, very crucial to decision support systems and the Lebanese government concerning, but not limited to, food production, trade, management and agricultural financial support.

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