地理科学进展 ›› 2008, Vol. 27 ›› Issue (5): 27-34.doi: 10.11820/dlkxjz.2008.05.004

• 环境变化 • 上一篇    下一篇

高光谱遥感土壤有机质信息提取研究

周萍1, 王润生 阎柏琨1,2, 杨苏明1, 王青华2   

  1. 1. 中国地质大学( 北京) 地球科学与资源学院, 北京100083;
    2. 国土资源部航空物探遥感中心, 北京100083
  • 收稿日期:2008-02-01 修回日期:2008-07-01 出版日期:2008-09-25 发布日期:2008-09-25
  • 作者简介:周萍( 1964- )| 女, 副教授.主要从事遥感地学的教学与科研工作.

Extr action of Soil Organic Matter Information by Hyper spectr al Remote Sensing

ZHOU Ping1, WANG Runsheng1,2, YAN Bokun1, YANG Suming2, WANG Qinghua2   

  1. 1. School of Earth Science and Resources,China Univercity of Geosciences,Beijing 100083, China;
    2. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources,Beijing 100083, China
  • Received:2008-02-01 Revised:2008-07-01 Online:2008-09-25 Published:2008-09-25

摘要:

土壤反射光谱特征分析是反演土壤信息参量的基础资料。本文阐述了使用航空成像光谱仪OMIS- Ⅰ数据并 结合ASD FieldSpec FR(350~2500nm)便携式光谱仪获取野外光谱数据, 对山东省烟台市招远东良乡原状农用土有 机质含量进行反演, 从而实现有机质填图。通过对土壤原反射率对数一阶微分变换并确定其与SOM的相关性, 最 终建立相应的多元线性回归方程。分析认为土壤有机质的测定选用762nm、874nm 及1667nm 波段在本次研究中效 果最佳。该模型也可作为土壤有机质估测和评价的参考。

关键词: 多元线性回归分析, 高光谱遥感, 土壤有机质, 烟台

Abstract:

This paper reported research conducted to document the ability of the OMIS- 1 (400- 2500nm) reflectance sensor and the spectrophotometer ASD FieldSpec FR to predict soil organic matter (SOM) of surface soil, and correspondingly to achieve SOM mapping. Data acquired from field survey and hyperspectral airborne sensor were precessed to determine the surface soil characteristic of an agricultural area located in Dongliang village, Zhaoyuan of Shandong province. The method adopted was based on correlation and a forward stepwise multiple linear regression analysis linking SOM content. The results showed that SOM could be well evaluated by the logarithm 1st differential reflectivity using linear regression model. This model would be available for evaluating and predicting soil organic matter by spectral reflectance and would provide guidance for the management of SOMand the prediction of fertility in precision agriculture.

Key words: forward stepwise multiple linear regression analysis, hyperspectral remote sensing, soil organic matter, Yantai