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[基于土壤异质性背景的玉米残茬覆盖遥感反演]

[Remote sensing retrieval of maize residue cover on soil heterogeneous background].

作者信息

Huang Jin-Yu, Liu Zhong, Wan Wei, Liu Zhi-Yu, Wang Jia-Ying, Wang Si

机构信息

College of Land Science and Technology, China Agricultural University/Ministry of Agriculture Key Laboratory of North China Arable Land Conservation, Beijing 100193, China.

Institute of Remote Sensing Application, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China.

出版信息

Ying Yong Sheng Tai Xue Bao. 2020 Feb;31(2):474-482. doi: 10.13287/j.1001-9332.202002.012.

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 R was 0.84 and RMSE was 13.3%, which was better than the non-zonal model (R was 0.75 and RMSE was 16.5%) and thus effectively improved the inversion accuracy.

摘要

玉米秸秆覆盖是一种保护性耕作方法,目前已在中国东北平原推广。区域作物残茬覆盖(CRC)的遥感估算能够快速获取大面积秸秆覆盖信息,这对秸秆覆盖工作的监测和推广具有重要作用。本研究从哨兵 - 2A 影像中提取归一化差异耕作指数(NDTI)、简单耕作指数(STI)、归一化差异残茬指数(NDRI)和归一化差异指数 7(NDI7),并用于建立吉林省梨树县 CRC 与光谱指数的线性回归模型。结果表明,研究区土壤具有较强的空间异质性,这会对光谱指数回归模型产生显著影响。利用土壤质地分类(分区)建立回归模型可提高反演精度。土壤空间异质性会增加模型的估计误差。四个光谱指数与 CRC 具有较强的相关性,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,R 为 0.84,RMSE 为 13.3%,优于非分区模型(R 为 0.75,RMSE 为 16.5%),从而有效提高了反演精度。

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