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紫色土和黄壤含水率的室内光谱反演 被引量:7

Estimating Soil Moisture Content of Purple Soil and Yellow Soil Using Laboratory Spectral Conversion Models
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摘要 [目的]选择西南地区代表性土类紫色土和地带性黄壤,分析其光谱信息,构建土壤水分反演模型,估测土壤含水率,为西南地区土壤水分快速监测提供方法依据。[方法]通过室内配置紫色土和黄壤不同土壤含水率水平,运用地物光谱仪测量其光谱反射率,比较不同含水率条件下两类土壤的高光谱特征;采用多种数学变换和相关分析法提取特征波段,运用多元逐步回归(SMLR)和BP神经网络(BPNN)分别构建土壤含水率的高光谱估测模型。[结果](1)随土壤含水率的增加,紫色土和黄壤的光谱反射率均逐渐降低;在相同含水率条件下,紫色土的光谱反射率低于黄壤。(2)土壤含水率对可见光波段(380~760 nm)反射率的影响显著低于红外波段(760~2 500 nm);均在1 400,1 900,2 200 nm附近存在明显水分吸收谷。(3)经数学变换的紫色土和黄壤光谱反射率均与土壤含水率存在极强的相关性。(4)基于BPNN建立的土壤水分反演模型整体优于SMLR模型。[结论] BPNN模型为西南地区紫色土和黄壤土壤含水率光谱反演的最优模型,能够快速准确估测紫色土和黄壤土壤水分状况。 [Objective]Representative purple soil and zonal yellow soil in Southwest China were selected to analyze their spectral information and to estimate soil moisture content in order to provide a method basis for rapid soil moisture monitoring in Southwest China.[Methods]Different soil moisture content levels were established in two soil types in the laboratory,and spectral reflectance was measured by using a ground surface spectrometer.The hyperspectral characteristics were compared and analyzed,and the characteristic bands were extracted by various mathematical transformations and correlation analysis.Hyperspectral estimation models of soil moisture were then constructed by stepwise multiple linear regression(SMLR)and BP neural network(BPNN).[Results]①The spectral reflectance of both purple soil and yellow soil decreased as soil moisture content increased,and the spectral reflectance of purple soil was lower than that of yellow soil under the same soil moisture content.②The effect of soil moisture content on the reflectance of infrared wavelengths(760—2500 nm)was stronger than the reflectance of visible wavelengths(380—760 nm),and there were obvious water absorption valleys near 1400,1900 and 2200 nm.③There was a strong correlation between spectral reflectance and soil moisture content of purple soil and yellow soil after mathematical transformation.④The soil moisture prediction model based on BPNN was superior to SMLR.[Conclusion]The BPNN model was the best model for estimating soil moisture content of purple soil and yellow soil in Southwest China.The BPNN model can quickly and accurately obtain soil water status of purple soil and yellow soil.
作者 韩陈 唐强 韦杰 Han Chen;Tang Qiang;Wei Jie(College of Geography and Tourism Science,Chongqing Normal University,Chongqing 401331,China;School of Geographical Sciences,Southwest University,Chongqing 400715,China;Chongqing Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area,Chongqing 401331,China;Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station,Chongqing 400715,China)
出处 《水土保持通报》 CSCD 北大核心 2021年第5期174-180,190,共8页 Bulletin of Soil and Water Conservation
基金 重庆市杰出青年基金项目“三峡库区坡耕地埂坎优势流及失稳判据研究”(cstc2019jcyjjqX0025) 重庆英才青年拔尖人才项目(CQYC201905009) 四川省科技计划资助(2020 YFQ0002 2020YJ0202) 重庆市教委科技项目(KJZD-K201800502)。
关键词 土壤含水率 高光谱 BP神经网络 多元逐步回归 紫色土 黄壤 soil moisture content hyperspectral BP neural network stepwise multiple linear regression purple soil yellow soil
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