干旱区研究

干旱区研究 ›› 2015, Vol. 32 ›› Issue (5): 890-896.doi: 10.13866/j.azr.2015.05.09

• 土壤及土壤保护 • 上一篇    下一篇

基于Landsat 8 OLI影像的三江源区表层土壤全氮空间格局反演

贾伟, 高小红, 杨扬, 张威, 杨灵玉, 田成明   

  1. 青海师范大学生命与地理科学学院,青藏高原环境与资源教育部重点实验室,青海省自然地理与环境过程重点实验室,青海 西宁 810008
  • 收稿日期:2013-09-13 修回日期:2013-10-14 出版日期:2015-09-15 发布日期:2015-10-14
  • 作者简介:贾伟(1988-),男,硕士研究生,研究方向为遥感应用与地理空间数据分析. E-mail: jiawei1212@126.com
  • 基金资助:
    国家自然科学基金项目(40861022);国家科技支撑计划课题(2012BAC08B04);青海省科技厅自然科学基金项目(2011-Z-903);青海师范大学创新基金项目(QS2012-08)

Inversion of Spatial Distribution Pattern of Topsoil Total Nitrogen Contents in Sanjiangyuan Regions Based on OLI Images

JIA Wei, GAO Xiao-hong, YANG Yang, ZHANG Wei, YANG Ling-yu, TIAN Cheng-ming   

  1. College of Life and Geographical Sciences, Key Laboratory of Ministry of Education on Environment and Resource in Qinghai-Tibetan Plateau, Key Laboratory of Physical Geography and Environmental Process in Qinghai Province, Qinghai Normal University, Xining 810008, Qinghai, China
  • Received:2013-09-13 Revised:2013-10-14 Published:2015-09-15 Online:2015-10-14

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摘要： 利用Landsat 8 OLI影像反演三江源区玉树、称多及玛多县的表层土壤全氮含量空间分布格局,选取光谱反射率(R)、光谱反射率的倒数(1/R)、光谱反射率倒数的对数〔lg(1/R)〕 3个光谱指标,与表层土壤(0～30 cm)全氮实测数据进行相关性分析,筛选相关性最高的光谱指标,以达到显著性相关水平波段的主成分分量建立回归模型。结果表明:OLI影像的B1～B4和B7的R、1/R、lg(1/R)均与实测全氮数据达到显著性相关水平,以lg(1/R)变换最为明显;利用这5个波段lg(1/R)的第一、第二主成分建立负二次多项式回归模型,其中建模样本的R2为0.621, RMSE为2.075,验证样本的R2为0.730,RMSE为1.493,RPD为1.849,反演模型精度较高,稳定性较好。利用OLI影像可较好的估算表层土壤全氮含量的空间分布格局。

关键词: 土壤, 全氮, Landsat 8 OLI影像, 多光谱反演, 三江源区

Abstract: In this paper,taking Yushu county, Chengduo county and Maduo county in Sanjiangyuan Regions as a case, the Landsat 8 OLI image was used to predict the spatial distribution pattern of topsoil total nitrogen contents. The spectrum reflectance ( R) and its two kinds of transformation forms, including the spectrum reflectance reciprocal (1/ R) and the logarithm of spectrum reflectance reciprocal [lg(1/ R)],selected to relate to soil total nitrogrn measured in laboratory. Firstly, correlation analysis between above three spectral index and the measured topsoil (0-30 cm) total nitrogen was conducted. Secondly, according correlation analysis results, the spectral index with the highest correlation was selected. In the end, the regression models were established using principal component with significant levels of correlated bands. The results show that the spectral reflectance and its two transformation forms from B1-B4, B7 were significantly correlated levels with the measured data, in which the lg(1/ R) was the most obvious. The negative quadratic polynomial model was set up through the first and second principal components of lg(1/ R) of these five bands, in which the R2 of calibration model R2 was 0.621, RMSE was 2.075, validation samples R2 was 0.730, RMSE was 1.493 and RPD was 1.849, suggesting the predicting model having a high precision, good stability. Therefore the OLI images could be used to estimate the spatial distribution pattern of topsoil total nitrogen better.

Key words: soil, total nitrogen, Landsat 8 OLI image, multispectral inversion, Sanjiangyuan area