Hyperspectral response and quantitative estimation on soil aggregate characters

文献类型： 外文期刊

第一作者： Qiao, Xingxing

作者： Qiao, Xingxing;Wang, Chao;Feng, Meichen;Zhang, Meijun;Song, Xiaoyan;Xiao, Lujie;Li, Guangxin;Yang, Wude;Jin, Xiuliang;Anwar, Sumera

作者机构：

关键词： Soil aggregate; Hyperspectral technology; Significant wavebands; Partial least squares; Estimation

期刊名称：CATENA （ 影响因子：5.198； 五年影响因子：5.594 ）

ISSN： 0341-8162

年卷期： 2021 年 202 卷

页码：

收录情况： SCI

摘要： Soil aggregate stability can be used to describe soil structure quality. In contrast, the conventional determination for soil aggregate stability, relying on dry-sieving and wet-sieving methods, is too resource consuming, especially for mass soil samples covering a large-scale field. Exploring the application of hyperspectral technology will provide a rapid, efficient and alternative method for the estimation of soil aggregate characteristics. In this study, we collected 199 soil samples (0-15 cm soil depth) from the Loess Plateau regions in the south areas of Shanxi province in China. Afterwards, we analyzed the relationship between spectra in region of 350-2500 nm and seven soil aggregate characters. The quantitative models for each soil aggregate character were built and the sensitive wavebands were determined by using the method of partial least squares. The results showed there was a negative relationship between soil spectra and soil aggregate stability, and the extreme correlation coefficient could reach 0.60 and 0.62, respectively, indicating that soil spectra could produce a significant response on soil aggregate characteristic. The significant waveband regions for soil aggregate characters were 350-870, 1250-1360, 1885-2020, and 2215-2450 nm. The important wavelengths were around 427, 599, 1326, 1890, 1926, 2208, 2311, and 2336 nm, which were also confirmed by the consistent correlation between soil spectra and each aggregate character. In addition, the reliable models (R-2 = 0.612-0.714; RPD = 1.584-1.796) were produced for most of the soil aggregate indicators, while percentage of aggregate destruction (PAD) led to the worst predictive model (R-2 = 0.599; RPD = 1.261). Aggregate fractal dimension (D) character was proved as the perfect indicator for spectral monitoring of soil aggregate stability, mainly due to the two-fold interpretation functions to soil structure distribution and soil adhesion with organic matter and soil moisture. It indicated that hyperspectral technology provides an alternate method for soil aggregate characters, especially for a large area.

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