多尺度土壤质地与光谱空间非平稳性关系探究

doi:10.14088/j.cnki.issn0439-8114.2021.10.008

湖北农业科学 ›› 2021, Vol. 60 ›› Issue (10): 38-45.doi: 10.14088/j.cnki.issn0439-8114.2021.10.008

多尺度土壤质地与光谱空间非平稳性关系探究

郑嵛珍¹, 陈奕云¹, 陈敏², 吴子豪¹, 蒋江俊男¹

1. 武汉大学资源与环境科学学院/自然资源部数字制图与国土信息应用重点实验室/地理信息系统教育部重点实验室,武汉 430079;
2. 广州市城市规划勘测设计研究院,广州 510060

收稿日期:2021-03-09 出版日期:2021-05-25 发布日期:2021-05-28
通讯作者: 陈奕云（1983-）,男,福建泉州人,副教授,博士,主要从事地理信息科学与可持续发展研究,（电话）17786486713（电子信箱）chenyy@whu.edu.cn。
作者简介:郑嵛珍（1996-）,女,湖南祁阳人,在读硕士研究生,研究方向为土壤近地传感与数字土壤制图,（电话）15200905220（电子信箱）yuzhenzyz@163.com。
基金资助:
国家自然科学基金项目（41771440）; 中央高校基本科研业务费专项资金项目（2042020kf0201）; 自然资源部城市国土资源监测与仿真重点实验室开放基金项目（KF-2018-03-031）

Exploration of the spatial non-stationary relationship between soil texture and spectra from a multiscale perspective

ZHENG Yu-zhen¹, CHEN Yi-yun¹, CHEN Min², WU Zi-hao¹, JIANG Jiang-jun-nan¹

1. School of Resource and Environmental Science/Key Laboratory of Digital Mapping and Land Information Application,Ministry of Natural Resources/Key Laboratory of Geographic Information System,Ministry of Education,Wuhan University,Wuhan 430079,China;
2. Guangzhou Urban Planning & Design Survey Research Institute,Guangzhou 510060,China;

Received:2021-03-09 Online:2021-05-25 Published:2021-05-28

摘要/Abstract

摘要： 基于210个杞麓湖流域实测土壤质地样本数据,对比了偏最小二乘回归（PLSR）、地理加权回归（GWR）、多尺度地理加权回归（MGWR）3种不同方法反演土壤质地的效果,并探究差异化作用尺度下土壤质地与光谱关系的空间异质性。结果表明,MGWR的R²、MAE、RMSE指标优于GWR和PLSR,其拟合效果最好,原因在于MGWR考虑了系数间差异化的空间尺度,能更好拟合土壤质地（黏粒、粉粒和沙粒）与光谱之间的关系;MGWR回归结果揭示了不同光谱潜变量对黏粒、粉粒和沙粒含量的多尺度差异化的空间响应规律。其中,光谱潜变量LV1对黏粒、粉粒、沙粒响应程度最大,其MGWR标准化回归系数分别为0.373、0.426、0.422。

关键词: 土壤质地, 高光谱, 多尺度地理加权回归, 空间非平稳性关系

Abstract: A total of 210 samples collected from the Qilu lake basin were used. The performances of partial least squares regression （PLSR）, geographically weighted regression （GWR） and multi-scale geographically weighted regression （MGWR） in measuring soil texture were evaluated, and the spatial heterogeneity of the relationship between soil texture and spectra under different scales were explored. The results showed that the R², mean absolute error and root mean square error of MGWR were better than those of GWR and PLSR, which indicated that MGWR outperformed other models. This was because MGWR can better fit the relationship between soil texture （clay, silt and sand contents） and spectra by considering different scales of different variables coefficients. MGWR results revealed that the spatial scale of spatial response of different spectral latent variables to clay, silt and sand content was different. Specifically, the spectral latent variable LV1 had the largest response to soil texture. Its standardized coefficients of clay, silt and sand contents were 0.373, 0.426 and 0.422, respectively.

Key words: soil texture, hyperspectral data, multiscale geographically weighted regression, spatial non-stationary relationship

中图分类号:

S152.3

郑嵛珍, 陈奕云, 陈敏, 吴子豪, 蒋江俊男. 多尺度土壤质地与光谱空间非平稳性关系探究[J]. 湖北农业科学, 2021, 60(10): 38-45.

ZHENG Yu-zhen, CHEN Yi-yun, CHEN Min, WU Zi-hao, JIANG Jiang-jun-nan. Exploration of the spatial non-stationary relationship between soil texture and spectra from a multiscale perspective[J]. HUBEI AGRICULTURAL SCIENCES, 2021, 60(10): 38-45.

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多尺度土壤质地与光谱空间非平稳性关系探究

Exploration of the spatial non-stationary relationship between soil texture and spectra from a multiscale perspective

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