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黄河三角洲土壤有机质含量的高光谱反演

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【目的】土壤有机质( SOM)具有改良土壤结构、促进团粒结构形成、增加土壤疏松性、改善土壤通气性和透水性以及促进植物生长发育的作用。传统测定土壤有机质的方法,虽然精度高,但是实时性差。本文通过对土壤高光谱数据进行变换和分析,筛选出与土壤有机质含量相关性高的敏感波长,构建能够实时、快速反演黄河三角洲土壤有机质含量的数学统计模型。【方法】60个土壤样品采于黄河三角州。利用ASD Fie1dsPec3光谱仪,在室内环境下对黄河三角洲不同有机质含量的风干土壤样本进行了光谱测量,利用化学方法测定了土壤的有机质含量。在对土壤样品高光谱反射率进行去包络线处理的基础上,与土壤有机质含量进行相关分析,筛选敏感波长;运用主成分回归分析、多元线性回归分析、二次多项式逐步回归分析和支持向量机回归分析方法,分别建立了有机质含量的反演模型。【结果】确定了估测土壤有机质含量的敏感波长,建立了能够快速反演黄河三角洲土壤有机质含量的数学统计模型。从土壤光谱反射率曲线可以看出在1400 nm、1900 nm和2200 nm等波段附近有十分明显的水分吸收谷。经对比相关性可以看出,去包络线的数据处理方法明显提高了光谱反射率与土壤有机质之间的相关性。1278 nm、1307 nm、1314 nm、1322 nm、1328 nm、1334 nm、1343 nm 7个相关性较高的波长作为估测土壤有机质含量的敏感波长。基于主成分回归分析、多元线性回归分析、二次多项式逐步回归分析和支持向量机回归分析方法,分别构建了反演有机质含量的模型。其中,二次多项式逐步回归模型校正集的决定系数达到了0.865,验证集的决定系数最大,达到了0.837,为黄河三角洲土壤有机质含量的最佳反演模型。【结论】去包络线的数据处理方法可提高光谱反射率与土壤有机质之间的相关性,确定的1278 nm、1307 nm、1314 nm、1322 nm、1328 nm、1334 nm、1343 nm 7个波长是估测黄河三角洲土壤有机质含量的敏感波长。由于二次多项式逐步回归模型校证集的决定系数最高、均方根误差最小,其拟合效果最好。因此二次多项式逐步回归模型对反演黄河三角洲土壤有机质含量是最佳的。
Hyperspectral inversion models for soil organic matter content in the yelloW River Delta
Objectives]Soi1 organic matter( SOM)can imProve soi1 structure,Promote formation of granu1ar structure,increase soi1 1oose,imProve soi1 air Permeabi1ity and water Permeabi1ity,and Promote P1ant growth and deve1oPment. A1though the traditiona1 method is acurate in determination of SOM,it cannot Provide rea1-time measurement . The sensitive wave1engths high corre1ated with the SOM content are screened to bui1d a quick statistic mode1 to estimate the SOM content in Ye11ow River de1ta.[Methods]Sixty soi1 samP1es were co11ected in the Ye11ow River De1ta. Mathematica1 statistics was used to bui1d the inversion mode1 by studying the re1ationshiP between the sPectra1 ref1ectance and the SOM content. The SOM contents of soi1 samP1es were ana1yzed by a chemica1 method,and their hyPersPectra1 ref1ectences were measured in an indoor dark room environment by ASD Fie1dsPec3 sPectrometer. Sensitive wave1engths were screened according to their correction with the SOM content, after the hand1ing method of continuum-remova1. The PrinciPa1 comPonent regression ana1ysis,mu1tiP1e 1inear regression ana1ysis,quadratic Po1ynomia1 stePwise regression ana1ysis and the suPPort vector machine( SVM ) regression ana1ysis methods were used to estab1ish mode1s for Predicting the organic content.[Results]From the soi1 sPectra1 ref1ectance curve,around 1400 nm,1900 nm and 2200 nm bands have obvious moisture absorPtion va11ey. It is obvious1y imProved the corre1ation between the SOM content and sPectra1 ref1ectance after Processing method of continuum-remova1 by comParing corre1ation. The seven higher corre1ated wave1engths,1278 nm,1307 nm,1314 nm,1322 nm,1328 nm,1334 nm and 1343 nm,are considered as the sensitive wave1engths to estimate the SOM content. The quadratic Po1ynomia1 stePwise regression mode1 is Prevent to be the best inversion mode1 for SOM content Prediction in the Ye11ow River de1ta with the determination coefficient of 0. 865 and the the 1argest determination coefficient of the va1idation set,which reaches 0. 837.[Conclusions]The Processing method of continuum-remova1 cou1d imProve the corre1ation between the SOM and sPectra1 ref1ectance. The near infrared sPectra1 of 1278 nm,1307 nm,1314 nm,1314 nm,1328 nm,1334 nm and 1343 nm are the sensitive wave1engths for estimating the SOM content. The quadratic Po1ynomia1 stePwise regression mode1 has the best fitting effect,is aPProPriate for estimating the SOM content in Ye11ow River De1ta.

soi1 organic matterenve1oPe curvehyPer-sPectrumYe11ow River De1ta

韩兆迎、朱西存、刘庆、房贤一、王卓远

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山东农业大学资源与环境学院,山东泰安271018

土肥资源高效利用国家工程实验室,山东泰安271018

山东省黄河三角洲生态环境重点实验室 滨州学院,山东滨州256603

有机质 包络线 高光谱 黄河三角洲

国家自然科学基金山东省自然科学基金山东省黄河三角洲生态环境重点实验室开放基金山东农业大学青年科技创新基金山东农业大学博士后基金

41271369ZR2012DM0072010KFJJ012373189841

2014

植物营养与肥料学报
中国植物营养与肥料学会

植物营养与肥料学报

CSTPCDCSCD北大核心
影响因子:2.331
ISSN:1008-505X
年,卷(期):2014.(6)
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