基于高光谱和多光谱融合的喀斯特地区石灰土有机碳含量反演  

Inversion of organic carbon content in calcareous soil in karst area based on hyperspectral and multispectral fusion

作　　者：谭咏诗 韦真茜 肖雁 黄玉林 黎宗鑫 杨舒婷 邹林 杨岚惠 邓羽松 TAN Yongshi;WEI Zhenxi;XIAO Yan;HUANG Yulin;LI Zongxin;YANG Shuting;ZOU Lin;YANG Lanhui;DENG Yusong(Guangxi Key Laboratory of Forest Ecology and Conservation,School ot Forestry,Guangxi University,Nanning 530004,China)

机构地区：[1]广西大学林学院,广西森林生态与保育重点实验室,南宁530004

出　　处：《应用生态学报》2025年第1期197-207,共11页Chinese Journal of Applied Ecology

基　　金：国家重点研发计划项目(2023YFD1902801);广西重点研发计划项目(桂科AB22080103)资助。

摘　　要：有机碳作为土壤的重要组成部分之一,评估其质量和稳定性具有重要意义。在喀斯特区,研究土壤有机碳含量分布特征可以识别潜在的土壤侵蚀风险区域,为优化土地利用、制定有效的水土保持措施提供科学依据。本研究以桂南喀斯特石灰土为研究对象,采集不同土地利用类型下土壤样品,利用5款智能手机拍摄获取土壤颜色图像,提取土壤颜色参数,使用光谱仪获取土壤光谱信息;并结合机器学习方法和线性算法,即人工神经网络(BPNN)、支持向量机(SVM)、随机森林(RF),以及线性算法偏最小二乘回归(PLSR),建立有机碳含量反演预测模型,使用决定系数、均方根误差、相对分析误差作为模型精度评价指标,筛选并明确适用于该地区土壤有机碳含量的智能手机及对应的预测模型和光谱仪方法下的预测模型。结果表明:5款智能手机基于4种建模方法的建模结果呈现出不同效果,表现为:Redmi Note11T pro+>IQOO Neo7 SE>华为nova 5Z>realme X7 pro>iPhone X;采用5款智能手机采集的多光谱数据与光谱仪采集的高光谱数据综合建模效果表现一致,其中,SVM精度评估系数最好,建模效果最佳,其次是BPNN、RF和PLSR,相较于PLSR,机器学习算法展现出更好的预测效果;结合模型估算散点图,当土壤有机碳含量低于10 g·kg^(-1)时,模型预测结果较为分散,当土壤有机碳含量高于10 g·kg^(-1)时,模型预测结果更为集中。本研究为深入了解喀斯特地区土壤有机碳含量的空间特征提供了理论支持和实践基础,可为解决该地区水土流失问题和改善农业生产环境提供依据。Organic carbon, as one of the important components of soil, is of great significance in assessing soil quality and stability. In karst areas, understanding the distribution characteristics of soil organic carbon content can identify potential soil erosion risk areas and provide a scientific basis for optimizing land use and formulating effective soil and water conservation measures. We collected limestone soil samples under different land use types in Guinan karst, captured soil color images by using five smartphones to extract soil color parameters, and used a spectrometer to obtain soil spectral information. We combined machine learning methods and linear algorithms i.e., artificial neural network(BPNN), support vector machine(SVM), and random forest(RF), as well as the linear algorithm partial least squares regression(PLSR), to establish an inverse prediction model for organic carbon content. We used the coefficient of determination, root mean square error, and relative analytical error as the model accuracy evaluation indices, to screen and specify the smartphones and the corresponding prediction models applicable to soil organic carbon content in the region and the prediction models under the spectrometer method. The results showed that the modeling results of five smartphones based on the four modeling methods presented different effects: Redmi Note11T pro+>IQOO Neo7 SE>Huawei nova 5Z>realme X7 pro>iPhone X. The integrated modeling effects of the multispectral data collected by the five smartphones and the hyperspectral data collected by the spectrometer were consistent. The SVM accuracy assessment coefficient was the best and the modeling effect showed superiority, followed by BPNN, RF and PLSR, compared with PLSR, the machine learning algorithm showed a better prediction effect. Combined with the scatter plot of model estimation, the model predictions were more dispersed when soil organic carbon content was lower than 10 g·kg^(-1) and more concentrated when the soil organic carbon content was higher than

关 键 词：喀斯特 土壤有机碳 土壤颜色 光谱反演 机器学习 

分 类 号：S153.6[农业科学—土壤学]