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Sensors 2017, 17(6), 1210; doi:10.3390/s17061210

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

1
College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China
2
College of Public Administration, Nanjing Agricultural University, Nanjing 210095, China
*
Author to whom correspondence should be addressed.
Academic Editors: Mehrez Zribi and Nicolas Baghdadi
Received: 7 April 2017 / Revised: 21 May 2017 / Accepted: 21 May 2017 / Published: 25 May 2017
View Full-Text   |   Download PDF [8137 KB, uploaded 25 May 2017]   |  

Abstract

Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure–up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data. View Full-Text
Keywords: winter wheat classification; Sentinel-1A; multi-temporal; Landsat-8; urban agricultural region winter wheat classification; Sentinel-1A; multi-temporal; Landsat-8; urban agricultural region
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhou, T.; Pan, J.; Zhang, P.; Wei, S.; Han, T. Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region. Sensors 2017, 17, 1210.

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