首页|基于高光谱信息的宁夏土壤有机质含量估算

基于高光谱信息的宁夏土壤有机质含量估算

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
土壤有机质(SOM)是反映和判断土壤肥力的关键指标。为探讨高光谱信息对低含量SOM的估算能力，本研究以宁夏红寺堡和西大滩表层土壤(0～20cm)为对象，野外高光谱反射率与SOM含量为数据源，对原始光谱反射率进行4种数学变换:倒数一阶微分(RTFD)、对数一阶微分(LTFD)、对数倒数一阶微分(LRFD)和一阶微分(FD)，采用相关性分析(PCC)和逐步回归(SR)筛选SOM敏感波段，然后基于主成分回归(PCR)、偏最小二乘回归(PLSR)、支持向量机(SVM)和地理加权回归(GWR)建立SOM估算模型。结果表明:原始光谱反射率经数学变换后与SOM间的相关性显著增强，FD变换增幅相对较大;采用PCC和SR相结合筛选敏感波段，很大程度对数据作了降维处理，减小了模型的运算难度并提高了模型的估算能力;基于LTFD-SVM模型的估算效果最优，其建模决定系数(Rc2)和验证决定系数(Rp2)分别为0。7593和0。9321，验证集均方根误差(RMSE)和相对分析误差(RPD)分别为0。62和3。47。研究结果可为研究区及相似地区低含量SOM的定量监测提供参考依据。
Estimation of soil organic matter content in Ningxia based on hyperspectral information
Soil organic matter(SOM)is a key indicator for soil fertility.To explore the estimation ability of hyper-spectral information for soils with low SOM content,this study focused on the surface soil(0-20 cm)of Hongsibu and Xidatan in Ningxia,and used field hyperspectral reflectance and SOM content as data sources.Four mathemati-cal transformations were performed on the original spectral reflectance:reciprocal transformation first-order differen-tial(RTFD),logarithm transformation first-order differential(LTFD),logarithmic reciprocal first-order differential(LRFD),and first-order differential(FD).Correlation analysis(PCC)and stepwise regression(SR)were used to screen for the sensitive bands.A SOM estimation model was established based on principal component regression(PCR),partial least squares regression(PLSR),support vector machine(SVM),and geographic weighted regression(GWR).The correlation between the original spectral reflectance and SOM was significantly enhanced after mathematical transformation,with the largest increase in FD transformation.The combination of PCC and SR was used to screen sensitive bands,which greatly reduced the dimensionality of the data,reduced the computational difficulty of the model,and improved its estimation ability.Based on the LTFD-SVM model,the estimation perform-ance was optimal,with modeling determination coefficients(Rc2)and validation determination coefficients(Rp2)of 0.7593 and 0.9321,respectively.The root mean square error(RMSE)and relative analysis error(RPD)of the validation set were 0.62 and 3.47,respectively.The results can provide a reference basis for quantitative monitoring of low-content SOM in the study region and similar regions.

hyperspectrumsoil organic matterestimation modelgeographically weighted regressionsupport vector machineinverse distance weighting

丁启东、王怡婧、张俊华、陈睿华、贾科利、李小林

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宁夏大学生态环境学院,银川 750021

宁夏大学地理科学与规划学院,银川 750021

西北退化生态系统恢复与重建教育部重点实验室,银川 750021

西部土地退化与生态恢复国家重点实验室培育基地,银川 750021

西安煤航遥感信息有限公司,西安 710199

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高光谱 土壤有机质 估算模型 地理加权回归 支持向量机 反距离权重法

国家重点研发计划项目国家自然科学基金项目国家自然科学基金项目清华大学-宁夏银川水联网数字治水联合研究院联合开放基金宁夏科技创新领军人才

2021YFD19006024206700342061047SKLHSE-2022-IOW112022GKLRLX02

2024

10.13292/j.1000-4890.202408.040

生态学杂志
中国生态学学会

生态学杂志

CSTPCD北大核心
影响因子：1.439
ISSN：1000-4890
年,卷(期)：2024.43(8)