农业工程学报
農業工程學報
농업공정학보
2014年
1期
81-88
,共8页
魏昌龙%赵玉国%李德成%张甘霖%邬登巍%陈吉科
魏昌龍%趙玉國%李德成%張甘霖%鄔登巍%陳吉科
위창룡%조옥국%리덕성%장감림%오등외%진길과
土壤%光谱%模型%相似光谱%光谱角%偏最小二乘回归
土壤%光譜%模型%相似光譜%光譜角%偏最小二乘迴歸
토양%광보%모형%상사광보%광보각%편최소이승회귀
soils%spectroscopy%models%spectral similarity%spectral angle mapper%PLSR (partial least square regression)
土壤可见光-近红外波段光谱（350～2500 nm）包含了大量的土壤属性信息，相同类型的土壤具有相似的光谱曲线特征，但相似光谱曲线是否具有相似的属性含量？探讨此问题可为土壤光谱库的应用提供依据，从而最终服务于快速获取土壤信息技术体系的构建。该研究以安徽宣城为研究区，根据母质、地形特征和土地利用等信息，采集91个典型土壤剖面，共含400个土壤发生层样品，测定了有机质（soil organic matter，SOM）和阳离子交换量（cation exchange capacity，CEC）含量，同时采用VARIAN公司的Cary 5000分光光度计测定了土壤光谱，并将光谱数据变换为反射率（R）、反射率一阶导数（FDR）和吸收度（Log(1/R)）3种形式。该文采用光谱角（spectral angle mapper，SAM）、偏最小二乘回归（partial least square regression，PLSR）和SAM-PLSR（spectral angle mapper-partial least square regression，SAM-PLSR）3种方法预测土壤SOM和CEC。SAM方法是通过对测试集104个光谱曲线与参考集的296个光谱曲线进行相似性计算，并以此实现土壤SOM和CEC含量的预测。SAM-PLSR方法以SAM算法下的匹配结果作为建模样本建立PLSR模型和进行预测分析。结果表明，具有相似光谱曲线的土壤具有相似的SOM和CEC含量，SAM算法下相似光谱匹配可直接预测SOM（R2=0.78，RPD=2.17）和CEC（R2=0.82， RPD=2.41）。PLSR方法可很好地预测SOM（R2=0.87，RPD=2.77）和CEC（R2=0.87，RPD=2.59）；相较之下，SAM-PLSR方法不仅可以更加准确预测SOM（R2=0.89，RPD=3.00）和CEC（R2=0.91，RPD=3.06），而且大大减少了建模样本的数量。该研究使可见光-近红外光谱可更加高效地用于土壤属性分析，并为土壤光谱数据库的建设及应用提供技术参考。
土壤可見光-近紅外波段光譜（350～2500 nm）包含瞭大量的土壤屬性信息，相同類型的土壤具有相似的光譜麯線特徵，但相似光譜麯線是否具有相似的屬性含量？探討此問題可為土壤光譜庫的應用提供依據，從而最終服務于快速穫取土壤信息技術體繫的構建。該研究以安徽宣城為研究區，根據母質、地形特徵和土地利用等信息，採集91箇典型土壤剖麵，共含400箇土壤髮生層樣品，測定瞭有機質（soil organic matter，SOM）和暘離子交換量（cation exchange capacity，CEC）含量，同時採用VARIAN公司的Cary 5000分光光度計測定瞭土壤光譜，併將光譜數據變換為反射率（R）、反射率一階導數（FDR）和吸收度（Log(1/R)）3種形式。該文採用光譜角（spectral angle mapper，SAM）、偏最小二乘迴歸（partial least square regression，PLSR）和SAM-PLSR（spectral angle mapper-partial least square regression，SAM-PLSR）3種方法預測土壤SOM和CEC。SAM方法是通過對測試集104箇光譜麯線與參攷集的296箇光譜麯線進行相似性計算，併以此實現土壤SOM和CEC含量的預測。SAM-PLSR方法以SAM算法下的匹配結果作為建模樣本建立PLSR模型和進行預測分析。結果錶明，具有相似光譜麯線的土壤具有相似的SOM和CEC含量，SAM算法下相似光譜匹配可直接預測SOM（R2=0.78，RPD=2.17）和CEC（R2=0.82， RPD=2.41）。PLSR方法可很好地預測SOM（R2=0.87，RPD=2.77）和CEC（R2=0.87，RPD=2.59）；相較之下，SAM-PLSR方法不僅可以更加準確預測SOM（R2=0.89，RPD=3.00）和CEC（R2=0.91，RPD=3.06），而且大大減少瞭建模樣本的數量。該研究使可見光-近紅外光譜可更加高效地用于土壤屬性分析，併為土壤光譜數據庫的建設及應用提供技術參攷。
토양가견광-근홍외파단광보（350～2500 nm）포함료대량적토양속성신식，상동류형적토양구유상사적광보곡선특정，단상사광보곡선시부구유상사적속성함량？탐토차문제가위토양광보고적응용제공의거，종이최종복무우쾌속획취토양신식기술체계적구건。해연구이안휘선성위연구구，근거모질、지형특정화토지이용등신식，채집91개전형토양부면，공함400개토양발생층양품，측정료유궤질（soil organic matter，SOM）화양리자교환량（cation exchange capacity，CEC）함량，동시채용VARIAN공사적Cary 5000분광광도계측정료토양광보，병장광보수거변환위반사솔（R）、반사솔일계도수（FDR）화흡수도（Log(1/R)）3충형식。해문채용광보각（spectral angle mapper，SAM）、편최소이승회귀（partial least square regression，PLSR）화SAM-PLSR（spectral angle mapper-partial least square regression，SAM-PLSR）3충방법예측토양SOM화CEC。SAM방법시통과대측시집104개광보곡선여삼고집적296개광보곡선진행상사성계산，병이차실현토양SOM화CEC함량적예측。SAM-PLSR방법이SAM산법하적필배결과작위건모양본건립PLSR모형화진행예측분석。결과표명，구유상사광보곡선적토양구유상사적SOM화CEC함량，SAM산법하상사광보필배가직접예측SOM（R2=0.78，RPD=2.17）화CEC（R2=0.82， RPD=2.41）。PLSR방법가흔호지예측SOM（R2=0.87，RPD=2.77）화CEC（R2=0.87，RPD=2.59）；상교지하，SAM-PLSR방법불부가이경가준학예측SOM（R2=0.89，RPD=3.00）화CEC（R2=0.91，RPD=3.06），이차대대감소료건모양본적수량。해연구사가견광-근홍외광보가경가고효지용우토양속성분석，병위토양광보수거고적건설급응용제공기술삼고。
The potential of visible-near infrared (vis-NIR, 350~2500nm) laboratory spectroscopy for the estimation of soil properties has been previously demonstrated in the literature. Spectroscopy is rapid, inexpensive, and non-destructive. A single spectrum allows for the simultaneous characterization of various soil properties. The question that always arises when two samples are close in spectral space is whether they are close in terms of soil composition. This paper explores three different approaches to improving prediction accuracy. The first, called the SAM Approach, predicts soil properties via similar soil spectra using a spectral angle mapper (SAM). The second one, called the PLSR Approach, predicts soil properties using partial least-squares regression (PLSR). The third, called the SAM-PLSR Approach, first uses the SAM to choose similar soil spectra, which are then used as calibration samples for the PLSR. These tests were performed on a collection of 400 soil samples from 91 profiles from the Xuancheng region of the Anhui Province. Spectra data include reflectance (R), first derivatives of reflectance (FDR), and the logarithm of the inverse of the reflectance (Log(1/R)). The aims of the work were threefold: (1) to investigate the relationship between soil vis-NIR similarity and soil attribute similarity (soil organic matter (SOM) and cation exchange capacity (CEC)) using a spectral angle mapper (SAM);(2) to predict soil properties by PLSR with different calibration samples, which were independently validated;(3) to compare the accuracy of predictions from the SAM Approach, PLSR Approach, and SAM-PLSR Approach. This study showed that soil vis-NIR similarity reflected the similarity of SOM and CEC content, the SAM Approach can be directly used to predict the content of SOM (R2=0.78, RPD=2.17) and CEC (R2=0.82, RPD=2.41). The PLSR Approach obtained good prediction accuracy of SOM (R2=0.87, RPD=2.77) and CEC (R2=0.87, RPD=2.59). The SAM-PLSR Approach, which was calibrated with FDR, produced more accurate predictions for SOM (R2=0.89, RPD=3.0) and CEC (R2=0.91, RPD=3.06) than the other approaches, and this method can greatly reduce the number of calibration samples. This work demonstrated the potential of diffuse reflectance spectroscopy using the vis-NIR with SAM-PLSR Approach for more efficient soil analysis.
