| 应用田间行走式红外光谱进行土壤碳含量估测研究 |
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| 引用本文： | 沈掌泉,叶领宾,单英杰.应用田间行走式红外光谱进行土壤碳含量估测研究[J].土壤学报,2014,51(4):1011-1020. |
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| 作者姓名： | 沈掌泉 叶领宾 单英杰 |
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| 作者单位： | 浙江大学农业遥感与信息技术应用研究所,浙江大学农业遥感与信息技术应用研究所,浙江省土肥站 |
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| 基金项目： | 国家科技支撑计划项目（2012BAH29B04） |
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| 摘 要： | 对应用田间行走式设备获取的土壤红外光谱数据，通过特征变换和特征选择相结合，以提高所建立土壤碳校正模型的预测精度。首先应用独立成分分析（ICA）、主成分分析(PCA)和小波分析(WA)对土壤红外光谱数据进行特征变换，然后分别应用无信息变量消除法(UVE)、连续投影算法(SPA)、无信息变量消除结合连续投影算法(UVE-SPA)、基于遗传算法和偏最小二乘法的变量选择法(GA-PLS)来进行特征选择，基于所选择的特征建立了土壤碳校正模型。结果表明，通过ICA进行特征变换，然后进行特征选择，可以建立比直接对光谱数据进行波长选择精度更好的预测模型；而WA或PCA与特征选择方法结合，只能获得与对光谱数据直接进行波长选择相近的效果。因此，针对田间条件下通过行走式设备获得的光谱数据由于受复杂的环境条件下干扰多的情况，可以将ICA与特征选择方法结合起来对光谱数据进行特征变换和选择，以建立更可靠的土壤碳含量预测模型。
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| 关 键 词： | 特征变换 特征选择 土壤碳 田间行走式测定 近红外光谱 偏最小二乘回归法 |
| 收稿时间： | 2013/9/22 0:00:00 |
| 修稿时间： | 2014/2/14 0:00:00 |
Estimation of soil carbon using a field ambulatory infrared spectroscopy device |
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| Shen Zhangquan,Ye Lingbin and Shan Yingjie.Estimation of soil carbon using a field ambulatory infrared spectroscopy device[J].Acta Pedologica Sinica,2014,51(4):1011-1020. |
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| Authors: | Shen Zhangquan Ye Lingbin and Shan Yingjie |
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| Institution: | Institute of Agricultural Remote Sensing and Information Technology Application,Zhejiang University,Institute of Agricultural Remote Sensing and Information Technology Application,Zhejiang University,Zhejiang Soil and Fertilizer Station |
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| Abstract: | To improve accuracy of the prediction of soil carbon using a soil carbon calibration model, feature transformation and feature selection was done to soil infrared reflected spectral (NIRS) data obtained with a field ambulatory infrared spectroscopy device in situ. Firstly, feature transformation was done of the soil NIRS data through independent component analysis (ICA), principle component analysis (PCA) or wavelet analysis (WA), and then feature selection was through uninformative variable elimination (UVE), successive projection algorithm (SPA), uninformative variable elimination in combination with successive projection algorithm (UVE-SPA), and genetic algorithm with partial least squares regression (GA-PLS), separately. And in the end, a soil carbon calibration model was established. Results show that after the processing, a prediction model, better than subjecting the NIRS data to direct wave band selection in accuracy, can be built up, while the combination of the feature selection method with PCA or WA could only achieve some similar effects to those of subjecting NIRS data to direct wave band selection. Therefore, it is feasible to establish a more reliable soil carbon prediction model through feature transformation and selection with the feature selection method coupled with ICA of the NIRS data acquired with a field ambulatory device under complicated environmental condition. |
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| Keywords: | feature transformation feature selection soil carbon Field ambulatory measurement near-infrared spectra partial least square regression |
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