| 基于地类分层的土壤有机质光谱反演校正样本集的构建 |
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| 引用本文： | 刘艳芳,卢延年,郭 龙,肖丰涛,陈奕云.基于地类分层的土壤有机质光谱反演校正样本集的构建[J].土壤学报,2016,53(2):332-341. |
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| 作者姓名： | 刘艳芳 卢延年 郭 龙 肖丰涛 陈奕云 |
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| 作者单位： | 1. 武汉大学资源与环境科学学院，武汉 430079; 地球空间信息技术协同创新中心，武汉大学，武汉 430079;2. 武汉大学资源与环境科学学院,武汉,430079;3. 武汉大学资源与环境科学学院，武汉 430079; 地球空间信息技术协同创新中心，武汉大学，武汉 430079; 武汉大学苏州研究院，江苏苏州 215123 |
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| 基金项目： | 公益性行业科研专项项目（201412023）、卫星测绘技术与应用国家测绘地理信息局重点实验室经费项目（KLAMTA-201401）资助 |
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| 摘 要： | 以江汉平原滨湖地区不同土地利用类型的土壤样本为例,比较了基于目标土壤理化性质的浓度梯度法、扩展的基于多种理化性质的综合法(P-KS)、基于光谱信息的KS法、最邻近样本去除法(reduce nearest neighbor samples,RNNS)法和基于浓度分层并结合光谱信息的C-KS、C-RNNS法,基于地类分层再结合上述方法,构建具有不同层次土壤信息代表性的校正集,采用偏最小二乘回归法,建立土壤有机质可见光/近红外光谱反演模型。结果表明,具有单一代表性的浓度梯度法、KS法、RNNS法难以建立适用模型;具有光谱与理化性质二元代表性的C-KS方法模型预测精度得到了明显的提升,相对分析误差(ratio of performance to standard deviation,RPD)为1.66;考虑土地利用类型后,浓度梯度法、RNNS法与C-KS法模型预测精度有明显的提升,RPD分别达到了1.84、1.51、1.75,模型具有良好的适用性。说明具有多层次土壤信息代表性的校正集构建方法对提高土壤有机质可见光/近红外光谱反演模型的适用性具有较好作用。
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| 关 键 词： | 理化性质代表性 光谱代表性 地类分层 偏最小二乘回归 |
| 收稿时间： | 2015/3/30 0:00:00 |
| 修稿时间： | 2015/8/11 0:00:00 |
Construction of Calibration Set Based on the Land Use Types in Visible and Near-InfRared (VIS-NIR) Model for Soil Organic Matter Estimation |
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| LIU Yanfang,LU Yannian,GUO Long,XIAO Fengtao and CHEN Yiyun.Construction of Calibration Set Based on the Land Use Types in Visible and Near-InfRared (VIS-NIR) Model for Soil Organic Matter Estimation[J].Acta Pedologica Sinica,2016,53(2):332-341. |
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| Authors: | LIU Yanfang LU Yannian GUO Long XIAO Fengtao and CHEN Yiyun |
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| Abstract: | Soil organic matter (SOM) is not only an important indicator of soil fertility but also an important source and sink in the global carbon cycle. Therefore, it is essential to acquire the information of SOM for soil management. The visible and near-infrared (VIS-NIR) reflectance spectroscopy technique, known as a novel, rapid, accurate, environment-friendly and efficient approach when compared with conventional laboratory analyses, is a promising one to acquisition of soil properties. Construction of a calibration set is key to use of VIS-NIR quantitative analysis in building up a quality prediction model. Conventionally, selection of samples for the calibration set is based on soil physical and chemical properties or soil spectral information, like the concentration gradient method(C) and Kennard - Stone (KS) method, which are able to select samples that may be representative of physical and chemical properties or spectra, but not of geographical space and multivariate information. Impacts of the shortages on prediction accuracy of the model have rarely been reported. The aim of this paper is to explore how sample selection methods affect accuracy of the VIS-NIR reversion model in estimation of SOM, using soil samples collected from lands under different types of land use in the riparian areas of the Jianghan Plain. A total of 270 soil samples were collected, air dried and ground to pass a 2 mm sieve, for analysis of VIS-NIR spectra using a FieldSpec3 spectrometer. The spectral curves were preprocessed with log10, Savitzky-Golay (SG), multiplicative scatter correction (MSC) and mean center (MC).
A total of four categories of ten sample selection methods based on multivariate soil information were proposed for constructing calibration sets. The first category, including the concentration gradient method and the method adopted several properties (P-KS), depends on soil physical and chemical properties; the second category, including the KS method and the Reduce on Neighbor Samples (RNNS) method, is based on spectral information; the third category, including the C-KS and C-RNNS methods, combines soil physic-chemical properties with spectral information; and the forth category uses land use type hierarchy in combination with all the aforementioned methods. The P-KS method takes into comprehensive account parameters, like SOM, Fe, N, P and bulk density (BD), that may be quite high in weight of impacts on soil spectra and uses KS algorithm to select soil samples representative of a variety of physical-chemical properties for construction of the calibration set. The C-KS and C-RNNS methods divide SOM concentration into six levels, from each of which two-thirds of the samples were selected using the KS and RNNS methods to form the calibration sets. The methods based on land use type hierarchy divide the entire sample set into three categories, namely dry land, paddy field and the others. For each category, soil samples representative of SOM distribution or soil spectra were selected in combination with the concentration gradient method, KS, RNNS and C-KS, separately to form calibration sets, which were then merged into a calibration set representative of land use type.
On such a basis, a partial least squares regressions (PLSR) model was established, showing that in the first and second categories, the models with calibration sets formed with the C, KS and RNNS methods, representative of SOM distribution or soil spectra singularly, were not so good in prediction accuracy; and those with the P-KS method were much better, with determination coefficient for prediction (Rp2)being 0.55, root mean squared error of prediction (RMSEp) being 7.54 and ratio of performance to standard deviation (RPD) being 1.47. The models with calibration sets formed with the C-KS method, representative of both physical and chemical properties and spectra, were good in accuracy with Rp2 being 0.64, RMSEp being 7.13 and RPD being 1.66. The inclusion of land use type in forming calibration sets, greatly improved the models using the C, RNNS and C-KS methods in prediction accuracy, bring Rp2 up to 0.70, 0.59 and 0.68, RMSEp to 6.34, 6.47 and 6.58, and RPD to 1.84, 1.84 and 1.51, respectively. It is therefore, quite obvious that the use of calibration sets formed with soil samples representative of multi-layers of soil information can improve the models in prediction accuracy. The L-C method has turned out to be the best method for sample selection in construction of calibration sets for VIR-NIR models for prediction of soil organic matter contents in the riparian areas of the Jianghan Plain. |
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| Keywords: | Representativeness of physical and chemical properties Representativeness of spectrum Land use type Partial Least Squares Regression(PLSR) |
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