Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model

文献类型： 外文期刊

第一作者： Wei, Lifei

作者： Wei, Lifei;Yuan, Ziran;Wang, Zhengxiang;Zhao, Liya;Zhang, Yangxi;Lu, Xianyou;Wei, Lifei;Wang, Zhengxiang;Zhao, Liya;Wei, Lifei;Yuan, Ziran;Cao, Liqin

作者机构：

关键词： hyperspectral remote sensing; soil organic matter; AdaBoost algorithm; pearson correlation analysis

期刊名称：SENSORS （ 影响因子：3.576； 五年影响因子：3.735 ）

ISSN：

年卷期： 2020 年 20 卷 10 期

页码：

收录情况： SCI

摘要： Soil organic matter (SOM) refers to all carbon-containing organic matter in soil and is one of the most important indicators of soil fertility. The hyperspectral inversion analysis of SOM traditionally relies on laboratory chemical testing methods, which have the disadvantages of being inefficient and time-consuming. In this study, 69 soil samples were collected from the Honghu farmland area and a mining area in northwest China. After pretreatment, 10 spectral indicators were obtained. Ridge regression, kernel ridge regression, Bayesian ridge regression, and AdaBoost algorithms were then used to construct the SOM hyperspectral inversion model based on the characteristic bands, and the accuracy of the models was compared. The results showed that the AdaBoost algorithm based on a grid search had the best accuracy in the different regions. For the mining area in northwest China, Rp2 = 0.91, RMSEp = 0.22, and MAEp = 0.2. For the Honghu farmland area, Rp2 = 0.86, RMSEp = 0.72, and MAEp = 0.56. The detection of SOM content using hyperspectral technology has the characteristics of a high detection precision and high speed, which will be of great significance for the rapid development of precision agriculture.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序