• 土地利用与环境变化 •

### GWR模型在土壤重金属高光谱预测中的应用

1. 1. 福建师范大学地理科学学院,福州 350007
2. 闽江学院地理科学系,福州 350108
• 收稿日期:2016-09-27 修回日期:2016-12-13 出版日期:2017-03-15 发布日期:2017-05-03
• 作者简介:

作者简介：江振蓝(1977-), 女, 福建政和人, 博士, 副教授, 主要从事生态环境遥感与信息技术应用研究。E-mail: jessie33cn@163.com

• 基金资助:
国家自然科学基金项目(41601601);福建省自然科学基金项目(2016J01194);科技部国际合作重大专项(247608)

### Application of GWR model in hyperspectral prediction of soil heavy metals

Zhenlan JIANG1,2(), Yusheng YANG1, Jinming SHA1()

1. 1. School of Geographical Science, Fujian Normal University, Fuzhou 350007, China
2. Geographical Sciences Department, Minjiang University, Fuzhou 350108, China
• Received:2016-09-27 Revised:2016-12-13 Online:2017-03-15 Published:2017-05-03
• Supported by:
National Natural Science Foundation of China, No.41601601;Natural Science Foundation of Fujian Province, No.2016J01194;Special Project of International Cooperation under Ministry of Science and Technology, No.247608

Abstract:

The inversion models applied in hyperspectral prediction of soil heavy metals include multiple linear regression, partial least squares regression, artificial neural network, and wavelet analysis. They are mostly based on the presumed homogeneous influence of heavy metal contents on spectral reflectance in different locations. This presumption, however, ignores the spatial heterogeneity of the correlation between heavy metal and spectral variables. In comparison, GWR model effectively reveals the spatial heterogeneity among different variables, which is well evidenced in the studies involving the spatial prediction of soil properties. But no publications can be found so far on the application of this model in hyperspectral prediction of soil heavy metals. In this paper, Cd, Cu, Pb, Cr, Zn and Ni were studied to establish GWR model for soil heavy metal prediction, with 132 soil samples taken from Fuzhou, a major city in southeastern China. Increasing soil pollution emerges in this area as a result of dense population and developed industrial and agricultural sectors. And the spatial distribution of soil heavy metals in the area features great heterogeneity because of complex and fragmented terrains. At first, metal concentrations of the samples were determined through inductively coupled plasma-mass spectrometry (ICP-MS) analysis, and reflectance was measured with an ASD (Analytical Spectral Devices) field spectrometer covering a spectral range of 350-2500 nm. Then a series of transformations were conducted to enhance the spectral features of heavy metals, such as derivative transformation, reciprocal transformation, absorbance transformation, and continuum removal. And then an analysis was made on the correlation between heavy metal contents and the transformed spectral data, and sensitive spectral bands were selected according to the highest correlation coefficient. With heavy metal contents as dependent variables, and sensitive spectral bands as independent variables, a stepwise regression analysis was conducted to select variables with low multi-collinearity, which were then used to establish prediction models. At last, the applicability and limitation of GWR model in the hyperspectral prediction of heavy metals was assessed by comparing the outcome of predictions based on GWR and OLS regression respectively. Some conclusions can be drawn as follows: (1) The applicability of GWR model is dependent on the spatial heterogeneity level of heavy metal influence on spectral variables: For Cr, Cu, Zn and Pb, whose influence on spectral variables features high-level spatial heterogeneity, GWR-based prediction performance was evidently better than that of OLS. It was shown in an obvious increase of adjusted R2 (by 2.69, 2.01, 1.87 and 1.53 times respectively) and an obvious decrease of AIC (by over 3 units) and RSS (by 74.67%, 69.91%, 52.78% and 13.16% respectively); For Cd and Ni, whose influence on spectral variables features low-level spatial heterogeneity, GWR-based prediction displayed an increase of adjusted R2 (by 0.015 and 0.007 respectively), a decrease of RSS (by 5.97% and 4.18% respectively) and a rise of AIC (by 2.737 and 2.762 respectively), with less significant improvement in prediction performance; (2) Heavy metal spectral properties are intensified by different spectral transformations, among which reciprocal transformation is most effective. And reciprocal transformation and its derivative patterns improve the performance of heavy metal prediction models; (3) With spatial non-stationarity as the prerequisite of application, GWR model is applicable in hyperspectral prediction of heavy metals that feature obvious spatial heterogeneity with soil spectral variables.