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Remote Sensing Technology and Application  2022, Vol. 37 Issue (3): 608-619    DOI: 10.11873/j.issn.1004-0323.2022.3.0608
    
Influence Factors Analysis on Accuracies of Winter Wheat Distribution from Low and Medium Resolution Composited Remote Sensing Images
Shuang Zhu1,2(),JinShui Zhang2,3()
1.Beijing Polytechnic College,Beijing 100042,China
2.Beijing Engineering Research Center for Global Land Remote Sensing Products,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China
3.Institute of Remote Sensing Science and Engineering,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China
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Abstract  

The quality and quantity of sample dataset from medium resolution remote sensing images is the key factor to contribute to the efficiency of low and medium resolution identification model. For winter wheat in this paper, we constructed a support vector regression model coupled with low and medium resolution images, to decomposed of mixed pixels, and exact winter wheat extent. Then analyzed the influences of sample quantity and quality of medium resolution remote sensing images respectively. The results states that only 10% quantity of samples are enough to achieve stable accuracy. Under this quantity, regional accuracy and pixel accuracy could reach higher than 98% and 92% respectively in typical winter wheat area. In terms of sample quality, the accuracy of result improved accompanying with the sample quality increment. We found that high accuracy could achieved when the sample quality is better than 60%. While in the area where medium resolution sample did not exist in area with medium samples, regional accuracy and pixel accuracy also increased accompanying with the sample amount and quality increment. In this area, 20% quantity of medium resolution sample was needed enough to achieve 97% of regional accuracy and 92% of pixel accuracy respectively. The above demonstrate the successful generalization of winter wheat identification by medium resolution sample to non-medium resolution area.

Key words:  Support Vector Regression(SVR)      Pixel unmixing      Sample quantity/quality      TM      MODIS     
Received:  08 March 2021      Published:  25 August 2022
ZTFLH:  S127  
Corresponding Authors:  JinShui Zhang     E-mail:  zhushuang@ mail.bnu.edu.cn;zhangjs@bnu.edu.cn
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Shuang Zhu
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Cite this article: 

Shuang Zhu,JinShui Zhang. Influence Factors Analysis on Accuracies of Winter Wheat Distribution from Low and Medium Resolution Composited Remote Sensing Images. Remote Sensing Technology and Application, 2022, 37(3): 608-619.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.3.0608     OR     http://www.rsta.ac.cn/EN/Y2022/V37/I3/608

Fig.1  Technological flow chart
Fig.2  Study area
Fig.3  Sketch map of MODIS image
Fig.4  Wheat fractional map generated from MODIS mixed pixel unmixing with various amount of sample data
Fig.5  Wheat accuracy based on SVR with various amount of sample data
Fig.6  Regional and pixel accuracy within representative wheat areas
Fig.7  Pixel accuracy with non-wheat area
Fig.8  The scatter dot diagram calculated between TM and MODIS with 10% sample
Fig.9  Error histograms of five experiments with 10% sample
Fig.10  Accumulated error histograms of five experiments with 10% sample
Fig.11  MODIS based wheat results with different error fraction
Fig.12  PA and RA with different error fraction mixture
Fig.13  MODIS based wheat extent within TM sample area
Fig.14  The change of SVR based wheat accuracy accompanying with sample increment
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