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Estimation of maize residue cover on the basis of SAR and optical remote sensing image

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs, College of Land Science and Technology, China Agricultural University ,Beijing 100193, China

  • Email：Liuzhiyu_cau@163.com
  • Introduction：刘之榆，1994年生，男，硕士研究生，研究方向为土地利用与信息技术及农业遥感。E-mail：Liuzhiyu_cau@163.com
LIU Zhiyu,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs, College of Land Science and Technology, China Agricultural University ,Beijing 100193, China

  • Email：lzh@cau.edu.cn
  • Introduction：刘忠，1970年生，男，副教授，研究方向为土地利用与信息技术及农业遥感。E-mail：lzh@cau.edu.cn
LIU Zhong*,  
  • Affiliation：

    Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs, College of Land Science and Technology, China Agricultural University ,Beijing 100193, China

WAN Wei,  
  • Affiliation：

    Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs, College of Land Science and Technology, China Agricultural University ,Beijing 100193, China

HUANG Jinyu,  
  • Affiliation：

    Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs, College of Land Science and Technology, China Agricultural University ,Beijing 100193, China

WANG Jiaying,  
  • Affiliation：

    Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs, College of Land Science and Technology, China Agricultural University ,Beijing 100193, China

ZHENG Mandi

résumé

Crop residue is the remaining stems, leaves, and fruit pods in the field after crop harvest. Crop residue plays an important role in the farmland ecosystem. Remote sensing technology has advantages in time and space, and it has become the main method to estimate Crop Residue Cover (CRC). Using remote sensing technology to estimate CRC can obtain information about ground CRC quickly in a large scale, which is of great significance to the promotion of conservation tillage. On the basis of a Sentinel-1 SAR image and a Sentinel-2 optical image, radar index and optical remote sensing index were constructed, respectively. The autumn and spring field sample data in 2018 and 2019 in Lishu County, Jilin Province were combined. The correlation of the remote sensing index and the maize residue cover was explored, and the method of soil texture zoning modeling was adopted to reduce the influence of surface background factors on the estimation of CRC. To further improve the estimation accuracy of maize residue cover, the radar index and optical remote sensing index were combined. Moreover, the optimal subset regression and soil texture zoning were used to establish the maize residue cover estimation model, and the estimation mapping of maize residue cover in the study area was then completed. Results show that: soil texture zoning modeling can effectively solve the problem of soil heterogeneity, thus improving the accuracy of inversion. The performance of each remote sensing index in autumn high coverage period in 2018 is better than that in spring low coverage period in 2019. The STI and NDTI index have strong stability and the best performance in optical remote sensing index. R2 is 0.701 and 0.697, respectively; whereas in the radar index, the correlation between γVH0 based on cosine correction method and CRC measured is the highest, and R2 is 0.564. The combination of radar index and optical remote sensing index can effectively improve the accuracy of CRC estimation. The regression model based on the combined index has the best performance with the method of optimal subset regression and soil texture zoning. The R2 of the model is 0.799, and the RMSE is 13.67%, which show high accuracy. Therefore, the proposed method improves the accuracy of CRC estimation.

mots-clés

microwave remote sensing;straw;coverage;maize;SAR;optimal subset regression

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