基于CARS算法的不同类型土壤有机质高光谱预测

    Hyperspectral prediction on soil organic matter of different types using CARS algorithm

    • 摘要: 不同土壤类型的理化性质和光谱性质存在差异,以往研究多以高光谱反射率或光谱吸收特征建立模型,输入变量类型结构单一,往往导致土壤有机质(Soil Organic Matter,SOM)预测模型的精度不高。为提高SOM高光谱预测模型精度,该研究以黑龙江省海伦市为研究区,将不同类型土壤分别以竞争自适应重加权采样(Competitive Adaptive Reweighted Sampling,CARS)筛选的特征波段、数字高程模型(Digital Elevation Model,DEM)数据和光谱指数作为输入变量,结合随机森林(Random Forest,RF)算法建立SOM预测模型。结果表明:1)通过CARS算法筛选后,各土壤类型特征波段压缩至全波段数目的16%以下,在很大程度上降低土壤高光谱变量维度和计算复杂程度,从而提高了模型的预测能力,说明CARS算法在提取特征关键波段变量、优化模型结构方面起到重要作用;2)不同类型土壤的SOM预测精度存在差异,沼泽土的预测精度最高为0.768,性能与四分位间隔距离的比率(Ratio of Performance to InterQuartile distance,RPIQ)为3.568;黑土次之,草甸土的预测精度最低,仅0.674,RPIQ为1.848。3类土壤的RPIQ均达到1.8以上,模型具有较好的预测能力;3)局部回归预测精度最优,验证集的调整后决定系数为0.777,均方根误差(Root Mean Square Error,RMSE)为0.581%,模型验证RPIQ为2.689,模型稳定性高。该试验筛选的预测因子通过RF模型可实现SOM含量的快速预测,简化了传统复杂的程序,可为中尺度区域不同类型土壤的SOM预测提供依据,为输入量的选择提供参考。

       

      Abstract: Abstract: Soil organic matter (SOM) can improve the physical, chemical and biological properties of the soil through a variety of functions. An important role of SOM is performed on the soil function and quality, further to prevent the emission of greenhouse gas in global carbon circulation. Spectral characteristics of SOM depend mainly on types of soils, as well as different physical and chemical properties. Previous models constructed by the hyperspectral reflectance or spectral absorption characteristics often lead to the low accuracy in SOM prediction, due mainly to the input type structure was single. In order to improve the accuracy and speed of the prediction model, specific characteristic variables can be selected to reduce the high collinearity between spectral bands, where there is a large amount of hyperspectral data in the presence of redundancy and overlap. The spectral index is set to minimize the influence of independent wavelengths on iterative calculation. Furthermore, the topography significantly determines the surface microclimate, the movement of water on the surface and in the soil, as well as the process of material redistribution. In this study, taking the Hailun City, Heilongjiang Province as the research area, a SOM prediction random forest (RF) model was established for the different types of soil, in order to improve the accuracy of the SOM hyperspectral model. The characteristic bands were selected by a Competitive Adaptive Reweighted Sampling (CARS), while, the Digital Elevation Model (DEM) data and spectral index were data sources. The results showed that: 1) In CARS screening, the characteristic bands of each soil type were compressed to less than 16% of the total wavelength number, which greatly reduced the dimension of soil hyperspectral variables and computational complexity, thereby improving the prediction ability of the calibration model. The CARS was suitable for the extraction of characteristic key wavelength variables, further optimizing model structure. 2) Three types of input variables that extracted by the grouping experiment were then utilized for the prediction of different types of SOM. After grouping, the SOM prediction accuracy depended mainly on the type of soil. Specifically, the maximum prediction accuracy achieved in the Boggy soil of 0.768, where the Ratio of Performance to Interquartile distance (RPIQ) was 3.568. Black soil was the second most accurate. The prediction accuracy of meadow soil was the lowest, only 0.674, and RPIQ was 1.848. The RPIQ for the three types of soil was above 1.8, indicating the good prediction ability of the model. 3) Local regression was conducted to improve the prediction accuracy of SOM. The local regression prediction accuracy was the best. The adjusted coefficient of determination, RMSE and RPIQ of the validation set were 0.777, 0.581%, and 2.689, respectively, indicating the model was highly stable. The proposed prediction factors can be used to realize the rapid prediction of RF-SOM, where the traditional complex program can be simplified. The findings can provide a promising basis for the selection of input variables, thereby predicting the types of SOM in different regions.

       

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