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

IRSTEA, University of Montpellier, TETIS, 34090 Montpellier, France
National Center for Remote Sensing, National Council for Scientific Research (CNRS), Riad al Soloh, Beirut 1107 2260, Lebanon
CIHEAM-IAMM, UMR-System, 34090 Montpellier, France
Faculty of Agriculture, Lebanese University, Beirut 99, Lebanon
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2089;
Received: 3 May 2018 / Revised: 21 June 2018 / Accepted: 26 June 2018 / Published: 29 June 2018
(This article belongs to the Special Issue Advanced Sensor Technologies for Crop Phenotyping Application)
Global wheat production reached 754.8 million tons in 2017, according to the FAO database. While wheat is considered as a staple food for many populations across the globe, mapping wheat could be an effective tool to achieve the SDG2 sustainable development goal—End Hunger and Secure Food Security. In Lebanon, this crop is supported financially, and sometimes technically, by the Lebanese government. However, there is a lack of statistical databases, at both national and regional scales, as well as critical information much needed in the subsidy and compensation system. In this context, this study proposes an innovative approach, named Simple and Effective Wheat Mapping Approach (SEWMA), to map the winter wheat areas grown in the Bekaa plain, the primary wheat production area in Lebanon, in the years of 2016 and 2017. The proposed methodology is a tree-like approach relying on the Normalized Difference Vegetation Index (NDVI) values of four-month period that coincides with several phenological stages of wheat (i.e., tillering, stem extension, heading, flowering and ripening). The usage of the freely available Sentinel-2 imageries, with a high spatial (10 m) and temporal (5 days) resolutions, was necessary, particularly due to the small sized and overlapped plots encountered in the study area. Concerning the wheat areas, results show that there was a decrease from 11,063 ± 1309 ha in 2016 to 7605 ± 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. View Full-Text
Keywords: wheat; crop classification; Sentinel-2; NDVI; tree-like approach; Lebanon wheat; crop classification; Sentinel-2; NDVI; tree-like approach; Lebanon
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MDPI and ACS Style

Nasrallah, A.; Baghdadi, N.; Mhawej, M.; Faour, G.; Darwish, T.; Belhouchette, H.; Darwich, S. A Novel Approach for Mapping Wheat Areas Using High Resolution Sentinel-2 Images. Sensors 2018, 18, 2089.

AMA Style

Nasrallah A, Baghdadi N, Mhawej M, Faour G, Darwish T, Belhouchette H, Darwich S. A Novel Approach for Mapping Wheat Areas Using High Resolution Sentinel-2 Images. Sensors. 2018; 18(7):2089.

Chicago/Turabian Style

Nasrallah, Ali, Nicolas Baghdadi, Mario Mhawej, Ghaleb Faour, Talal Darwish, Hatem Belhouchette, and Salem Darwich. 2018. "A Novel Approach for Mapping Wheat Areas Using High Resolution Sentinel-2 Images" Sensors 18, no. 7: 2089.

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