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应用生态学报 ›› 2020, Vol. 31 ›› Issue (2): 474-482.doi: 10.13287/j.1001-9332.202002.012

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基于土壤异质背景的玉米秸秆覆盖度遥感反演

黄晋宇1,2, 刘忠1*, 万炜1, 刘之榆1, 王佳莹1, 王思2   

  1. 1中国农业大学土地科学与技术学院/农业部华北耕地保育重点实验室, 北京 100193;
    2四川省农业科学院遥感应用研究所, 成都 610066
  • 收稿日期:2019-06-25 出版日期:2020-02-15 发布日期:2020-02-15
  • 通讯作者: * E-mail: lzh@cau.edu.cn
  • 作者简介:黄晋宇, 男, 1994年生, 硕士。主要从事资源环境信息技术研究。E-mail: ph430070@163.com
  • 基金资助:
    本文由国家重点研发计划项目(2016YFD030080101)和四川省科技计划项目(2017GZ0160,2019YFS0049)资助

Remote sensing retrieval of maize residue cover on soil heterogeneous background

HUANG Jin-yu1,2, LIU Zhong1*, WAN Wei1, LIU Zhi-yu1, WANG Jia-ying1, WANG Si2   

  1. 1College of Land Science and Technology, China Agricultural University/Ministry of Agriculture Key Laboratory of North China Arable Land Conservation, Beijing 100193, China;
    2Institute of Remote Sensing Application, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China
  • Received:2019-06-25 Online:2020-02-15 Published:2020-02-15
  • Contact: * E-mail: lzh@cau.edu.cn
  • Supported by:
    This work was supported by the National Key R&D Program of China (2016YFD030080101) and the Sichuan Science and Technology Program (2017GZ0160, 2019YFS0049).

摘要: 玉米秸秆覆盖还田是东北平原当前大力推广的一种保护性耕作方式。区域作物秸秆覆盖度(CRC)的遥感估算可大范围快速获取耕地秸秆覆盖还田信息,对于政府监测和推广秸秆覆盖还田工作有重要作用。本研究以吉林省梨树县为研究区,基于Sentinel-2A卫星影像,选取归一化耕作指数(NDTI)、归一化秸秆指数(NDRI)、简单耕作指数(STI)和归一化差值指数(NDI7)4种光谱指数,建立光谱指数与玉米秸秆覆盖度的线性回归模型,进行秸秆覆盖度反演。结果表明: 研究区土壤背景空间异质性较强,对光谱指数回归模型影响显著,采用土壤质地分类(分区)分别建立回归模型的方法可提高反演精度。土壤背景空间异质性会增大模型估算误差;4种光谱指数与玉米秸秆覆盖度均有较强相关性,其中,NDTI和STI模型表现更好;基于NDTI和STI的分区线性回归模型验证R2为0.84、RMSE为13.3%,优于不分区的模型(R2为0.75,RMSE为16.5%),有效提升了反演精度。

Abstract: Maize stalk mulching is a conservation tillage method that has been currently promoted in northeastern China Plain. Remote sensing estimation of regional crop residue cover (CRC) can quickly obtain the information of straw mulching in a large area, which plays an important role in monitoring and popularizing the work of straw mulching. In this study, the normalized difference til-lage index (NDTI), simple tillage index (STI), normalized difference residue index (NDRI), and normalized difference index 7 (NDI7) were extracted from Sentinel-2A image and used to establish a linear regression model for CRC and spectral indices in Lishu County of Jilin Province. The results showed that soils had strong spatial heterogeneity in the study area, which would lead to a significant impact on the spectral index regression model. Using soil texture classification (zoning) to establish regression model could improve the inversion accuracy. Soil spatial heterogeneity would increase the estimation error of the model. The four spectral indices had a strong correlation with CRC, among which the NDTI and STI models performed better. The zonal linear regression model based on NDTI and STI verified that R2 was 0.84 and RMSE was 13.3%, which was better than the non-zonal model (R2 was 0.75 and RMSE was 16.5%) and thus effectively improved the inversion accuracy.