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Remote Sensing Technology and Application  2021, Vol. 36 Issue (2): 332-341    DOI: 10.11873/j.issn.1004-0323.2021.2.0332
A Comparative Study on Wheat Identification and Growth Monitoring based on Multi-source Remote Sensing Data
Jie Yin1,2(),Leilei Zhou1,2,Liwei Li2,Yaqiong Zhang3,Wenjiang Huang2,Helin Zhang4,Yan Wang2,5,Shijun Zheng2,5,Haisheng Fan6,Chan Ji6,Junjie Chen1,Dailiang Peng2()
1.School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454003,China
2.Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
3.Center for Satellite Application on Ecology and Environment,Ministry of Ecology and Environment,Beijing 100094,China
4.Beijing Engineering Research Center for Global Land Remote Sensing Products,Institute of Remote Sensing Science and Engineering,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China
5.University of Chinese Academy of Sciences,Beijing 100049,China
6.Zhuhai Orbita Aerospace Science & Technology Co. LTD,Zhuhai 519000,China
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Wheat is one of the main crops in China, which is of great significance to the economic development of China.With the continuous development of remote sensing technology, remote sensing technology has become an important means to extract wheat and growth monitoring. The identification of wheat is the premise of its planting area management, and the growth research is an important indicator of its growth evaluation and yield control. In this paper, the multi-source remote sensing data such as the hyperspectral zhuhai No.1 OHS-2A satellite, the multi-spectral Sentinel-2A satellite and MODIS were used to extract wheat by using Support Vector Machine(SVM) in Xiong'an as the research area. The accuracy of wheat was evaluated and analyzed by using the confusion matrix based on the field measurement data. Comparing the two important growth stages of wheat: the return green period and the heading period, wheat growth was divided into three grades (good growth, similar growth, worse growth) for growth monitoring and comparing. The results showed that under the same environmental conditions, the Overall accuracy of OHS-2A was 82.08%, and the Kappa coefficient was 0.76;The Overall accuracy of Sentinel-2A was 85.57% ,and the Kappa coefficient was 0.81, By contrast, Sentinel-2A is the best at identification wheat. In the process of growth monitoring, the Sentinel-2A satellite is more effective than MODIS in monitoring and analyzing the growth of Xiong'an wheat by comparing the growth conditions and the relative amplitude of the change of wheat growth.This study analyzed the status of wheat identification and growth monitoring in Xiong'an from remote sensing data of different resolutions, which is conducive to wheat planting management and the formulation of agricultural policies, which is of great significance for promoting the economic development of green Xiong’an and the city.

Key words:  OHS-2A      Growth monitoring      Wheat identification      Xiong'an     
Received:  24 July 2020      Published:  24 May 2021
ZTFLH:  S127  
Corresponding Authors:  Dailiang Peng     E-mail:;
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Jie Yin
Leilei Zhou
Liwei Li
Yaqiong Zhang
Wenjiang Huang
Helin Zhang
Yan Wang
Shijun Zheng
Haisheng Fan
Chan Ji
Junjie Chen
Dailiang Peng

Cite this article: 

Jie Yin,Leilei Zhou,Liwei Li,Yaqiong Zhang,Wenjiang Huang,Helin Zhang,Yan Wang,Shijun Zheng,Haisheng Fan,Chan Ji,Junjie Chen,Dailiang Peng. A Comparative Study on Wheat Identification and Growth Monitoring based on Multi-source Remote Sensing Data. Remote Sensing Technology and Application, 2021, 36(2): 332-341.

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Table 1  Introduction of band information used in Sentinel-2A image
MOD13Q12502 3301216
Table 2  Comparison of multi-remote sensing data
Fig.1  Study area classification point and verification point distribution
Table 3  Accuracy verification of identification results
Fig.2  Recognition results in Xiong'an
Fig.3  The relative range of Sentinel-2A and MODIS growth change rate in Xiong'an County


Table 4  Percentage of wheat area in the growth grade of the green return period and heading period in Xiong’an County
Fig.4  Comparison of wheat growth monitoring in Xiong’an
Fig.5  The wheat growth histogram of Sentinel-2A and MODIS
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