Remote sensing of soil organic matter of farmland with hyperspectral image

文献类型： 外文期刊

第一作者： Gu, Xiaohe

作者： Gu, Xiaohe;Wang, Lei;Yang, Guijun;Zhang, Liyan

作者机构：

关键词： soil organic matter;continuous wavelet transform;imaging spectral technology;stepwise linear regression

期刊名称：REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XIX

ISSN： 0277-786X

年卷期： 2017 年 10421 卷

页码：

收录情况： SCI

摘要： Monitoring soil organic matter (SOM) of cultivated land quantitively and mastering its spatial change are helpful for fertility adjustment and sustainable development of agriculture. The study aimed to analyze the response between SOM and reflectivity of hyperspectral image with different pixel size and develop the optimal model of estimating SOM with imaging spectral technology. The wavelet transform method was used to analyze the correlation between the hyperspectral reflectivity and SOM. Then the optimal pixel size and sensitive wavelet feature scale were screened to develop the inversion model of SOM. Result showed that wavelet transform of soil hyperspectrum was help to improve the correlation between the wavelet features and SOM. In the visible wavelength range, the susceptible wavelet features of SOM mainly concentrated 460 similar to 603 nm. As the wavelength increased, the wavelet scale corresponding correlation coefficient increased maximum and then gradually decreased. In the near infrared wavelength range, the susceptible wavelet features of SOM mainly concentrated 762 similar to 882 nm. As the wavelength increased, the wavelet scale gradually decreased. The study developed multivariate model of continuous wavelet transforms by the method of stepwise linear regression (SLR). The CWT-SLR models reached higher accuracies than those of univariate models. With the resampling scale increasing, the accuracies of CWT-SLR models gradually increased, while the determination coefficients (R-2) fluctuated from 0.52 to 0.59. The R-2 of 5*5 scale reached highest (0.5954), while the RMSE reached lowest (2.41 g/kg). It indicated that multivariate model based on continuous wavelet transform had better ability for estimating SOM than univariate model.

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