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母质分组对利用可见-近红外光谱预测丘陵地区土壤有机质含量的影响

Effects of Subsetting by Parent Materials on Prediction of Soil Organic Matter Content in a Hilly Area Using Vis-NIR Spectroscopy.

作者信息

Xu Shengxiang, Shi Xuezheng, Wang Meiyan, Zhao Yongcun

机构信息

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China.

University of Chinese Academy of Sciences, Beijing, China.

出版信息

PLoS One. 2016 Mar 14;11(3):e0151536. doi: 10.1371/journal.pone.0151536. eCollection 2016.

Abstract

Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy (350-2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis-NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km2 and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0-20 cm) samples were collected for SOM analysis and scanned with a Vis-NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23-4.69 g kg-1; R2 (coefficient of determination) = 0.80-0.84; RPD (ratio of standard deviation to RMSE) = 2.19-2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88-3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55-3.49 g kg-1; R2 = 0.87-0.93; RPD = 2.67-3.12; RPIQ = 3.15-4.02). Among the four different parent materials, the largest R2 and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis-NIR spectroscopy.

摘要

评估和监测土壤有机质(SOM)质量对于理解SOM动态以及制定能够提高和维持农业土壤生产力的管理措施至关重要。近几十年来,可见和近红外(Vis-NIR)漫反射光谱法(350 - 2500 nm)作为一种有前景的SOM分析技术受到了越来越多的关注。虽然样本集的异质性是一个使从Vis-NIR光谱预测土壤性质变得复杂的关键因素,但需要构建一个代表当地土壤多样性的光谱库。研究区域面积为927平方公里,位于江苏省余江县,其特点是为一个具有不同土壤母质(如红砂岩、页岩、第四纪红粘土和河流冲积物)的丘陵地区。总共采集了232个表层土壤(0 - 20厘米)样本用于SOM分析,并在实验室中用Vis-NIR光谱仪进行扫描。通过偏最小二乘回归(PLSR)方法以及几种数据预处理技术(如带平滑滤波器的一阶和二阶导数)将反射率数据与表层SOM含量相关联。在不同的校准/验证集组合(根据母质分层的全局和局部校准)下测试了PLSR模型的性能。结果表明,基于全局校准的模型只能对SOM含量进行近似预测(均方根误差(RMSE)= 4.23 - 4.69克/千克;决定系数(R2)= 0.80 - 0.84;标准差与RMSE之比(RPD)= 2.19 - 2.44;性能与四分位间距之比(RPIQ)= 2.88 - 3.08)。在局部校准下,每种母质的单个PLSR模型改进了SOM预测(RMSE = 2.55 - 3.49克/千克;R2 = 0.87 - 0.93;RPD = 2.67 - 3.12;RPIQ = 3.15 - 4.02)。在四种不同的母质中,页岩土壤的R2最大且RMSE最小,其粘土(18.95%)、游离铁氧化物(15.93%)和pH(1.04%)的变异系数(CV)值最低。这证明了一种实用的子集划分策略对于利用Vis-NIR光谱持续改进SOM预测的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8196/4790961/780c09f8da3b/pone.0151536.g001.jpg

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