基于PCA的土壤Cd含量高光谱反演模型对比研究

doi:10.3964/j.issn.1000-0593(2021)05-1625-06

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Abstract The soil heavy metal pollution poses a great threat to the human health, thus, it is quite important make out the contamination in the soil. There are a series of advantages in the hyperspectral remote sensing technology, such as the high spectral resolution, rapid response, non-destructive, etc., making it a well- suited in retrieving the soil’s components. In this study, the impacts of the information redundancy in the spectral and spectral transformation on the inversion of Cd content in the soil are investigated. Further, based on the hyperspectral data before and after spectral transformation, the performance comparations of hyperspectral models are carried out in this paper, as well. By so doing, the Cd contents and the corresponding lab spectrum (350～2 500 nm) of 56 soil samples are measured by the ICP-MS and ASD Fieldspec4. Then, the reciprocal and logarithm changes are performed to weaken the impacts of the light variation and soil surface roughness on the experimental results. Due to the fact that there is much redundant information in the obtained data, the Principal Component Analysis (PCA) is carried out to reduce the dimensionality of the spectral bands in the data. After this processing, only 12 principal components are selected as the input variables of the model. Regarding the hyperspectral models, the Partial Least-Squares Regression (PLSR), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) are chosen to establish the relationship between the Cd content and PCA components. Finally, for evaluating the prediction capabilities of the regression models, three precision evaluation indexes are preferred to assess the accuracy of regression models in this study, they are the correlation coefficient (R²), Root Mean Squared Error (RMSE) and Residual Predictive Deviation (RPD). Analysis results show that the cumulative contribution rate of 12 principal components of the original data after processed by the PCA can be up to 99.99%. Using principal components as the inputs, all four hyperspectral models show excellent performances in predicting the Cd content in the soil. The PCA-RF, in particular, has the most accurate prediction capability regardless of whether the spectral transformation is performed or not (whose R² before and after spectral transformation are 0.856 and 0.855, respectively, while the RPD under both conditions are 3.39). In conclusion, the PCA is used to reduce hyperspectral data’s dimensionality, this processing can effectively reduce the redundancy of hyperspectral data and guarantee the predictive capability of hyperspectral models. Also, the principal component selected by the PCA method could be excellent input variables of the hyperspectral models. Further, the hyperspectral model based on the PCA-RF shows the most excellent performance for rapid detecting the Cd element in the soil within the study area and similar regions, which could be a new supplement for the inversion of heavy metals in the soil.

Key words：Cd content; Hyperspectral; PCA; Inversion model comparison

Received: 2020-05-26 Accepted: 2020-08-31

ZTFLH:

TP79

Corresponding Authors: XU Zhen E-mail: xuzhen@radi.ac.cn

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)05-1625-06 OR http://www.gpxygpfx.com/EN/Y2021/V41/I05/1625