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A Novel Approach for Mapping Wheat Areas Using High Resolution Sentinel-2 Images
Ali Nasrallah1,2,3, Nicolas Baghdadi4, Mario Mhawej5
1IRSTEA, University of Montpellier, TETIS, 34090 Montpellier, France. ali.nasrallah@agroparistech.fr.
Sensors (Basel, Switzerland)
|July 4, 2018
Summary
A new Simple and Effective Wheat Mapping Approach (SEWMA) accurately maps wheat fields in Lebanon using Sentinel-2 imagery. This method provides early harvest predictions crucial for food security and agricultural support.
Area of Science:
- Agricultural Remote Sensing
- Crop Monitoring and Mapping
- Geospatial Analysis for Food Security
Background:
- Global wheat production is vital for food security, with mapping efforts supporting Sustainable Development Goal 2 (End Hunger).
- Lebanon faces challenges in wheat production due to a lack of comprehensive national and regional agricultural databases.
- Accurate wheat area statistics are essential for the Lebanese government's subsidy and compensation systems.
Purpose of the Study:
- To introduce and validate the Simple and Effective Wheat Mapping Approach (SEWMA) for mapping winter wheat in Lebanon's Bekaa plain.
- To assess the feasibility of early-season wheat classification for agricultural decision support.
- To differentiate wheat from other winter cereals using remote sensing data.
Main Methods:
- Utilized Sentinel-2 imageries with high spatial (10m) and temporal (5-day) resolution.
- Employed a tree-like approach based on Normalized Difference Vegetation Index (NDVI) values during key wheat phenological stages.
- Validated the SEWMA approach using ground truth data from 2016 and 2017.
Main Results:
- Wheat area in the Bekaa plain decreased from 11,063 ± 1309 ha in 2016 to 7605 ± 1184 ha in 2017.
- SEWMA achieved high overall accuracy: 87.0% for 2017 data using 2016 ground truth, and 82.6% for 2016 data using 2017 ground truth.
- The approach enabled early classification up to six weeks before harvest and distinguished wheat from barley and triticale.
Conclusions:
- SEWMA provides a simple, effective, and budget-friendly method for mapping wheat areas with high accuracy.
- The approach delivers crucial early-season classification information vital for governmental decision-making in agriculture.
- This methodology supports food production, trade, management, and financial support systems in Lebanon.
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