岩溶区石灰土全氮含量高光谱反演研究  

Hyperspectral inversion of total nitrogen content in calcareous soil in karst areas

作　　者：何文 李艳琼[3] 余玲 王金叶 倪隆康 李宁 HE Wen;LI Yanqiong;YU Ling;WANG Jinye;NI Longkang;LI Ning(College of Environmental Science and Engineering,Guilin University of Technology,Guilin,Guangxi 541006,China;Guangxi Key Laboratory of Plant Conservation and Restoration Ecology in Karst Terrain,Guangxi Institute of Botany,Guangxi Zhuang Autonomous Region and Chinese Academy of Sciences,Guilin,Guangxi 541006,China;Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems,South China Botanical Garden,Chinese Academy of Sciences,Guangzhou,Guangdong 510650,China;Guilin University of Aerospace Technology,Guilin,Guangxi 541004,China;Nanning College of Technology,Nanning,Guangxi 530100,China)

机构地区：[1]桂林理工大学环境科学与工程学院,广西桂林541006 [2]广西喀斯特植物保育与恢复生态学重点实验室,广西壮族自治区中国科学院广西植物研究所,广西桂林541006 [3]中国科学院华南植物园退化生态系统植被恢复与管理重点实验室,广东广州510650 [4]桂林航天工业学院,广西桂林541004 [5]南宁理工学院,广西南宁530100

出　　处：《中国岩溶》2024年第5期1047-1056,1064,共11页Carsologica Sinica

基　　金：广西自然科学基金项目(2024GXNSFAA010318);广西重点研发计划项目(AB22035060);国家自然科学基金(32060369);广西植物研究所基本业务费(桂植业23005);广西喀斯特植物保育与恢复生态学重点实验室资助(22-035-26)。

摘　　要：石灰土是岩溶地区主要的土壤类型之一,准确快速估测石灰土全氮(TN)含量是科学评价岩溶区土壤环境质量的重要保障。文章以广西岩溶区石灰土为研究对象,对土壤光谱数据进行5种数学变换,对比分析偏最小二乘回归(PLSR)、广义神经网络(GRNN)以及二者组合(PLSR_GRNN)三种模型对土壤TN含量的高光谱反演能力。结果表明:(1)石灰土TN对光谱600 nm、1 300 nm、1 600 nm、1900 nm以及2 300 nm附近波段反射率较为敏感;(2)对土壤原始光谱做微分变换、倒数对数变换以及包络线去除变换均在一定程度上能够提高光谱对石灰土TN含量的反演能力,并以微分变换效果最佳;(3)建立的PLSR_GRNN高光谱反演模型能够综合PLSR模型和GRNN模型的优点,反演精度较高,并以二阶微分变换(SDR)建立的反演模型效果最好,模型验证决定系数高达0.90,均方根误差仅为0.51,适合于岩溶区石灰土TN含量高光谱反演。基于高光谱模型能够对岩溶区石灰土TN含量进行快速、高精度反演,研究结果可为区域土壤修复和开发利用提供科学依据。Nitrogen is a component of many important compounds in plants,such as proteins,nucleic acids and enzymes,and hence is indispensable for the growth of plants.The nitrogen content in soil is one of the key indicators of soil fertility.Calcareous soil is one of the main soil types in karst areas.A rapid and accurate estimation of total nitrogen(TN)content in calcareous soil is an important guarantee for the scientific evaluation of soil environmental quality in karst areas.In recent years,the rapid development of hyperspectral remote sensing technology has brought new opportunities for a quick assessment of soil physical and chemical properties.However,it is still extremely challenging to rapidly assess soil nitrogen content in karst areas by hyperspectral remote sensing due to the weak optical signal of soil nitrogen and the interference of factors such as the complex ecological environment and the strong spatial heterogeneity of soil TN content in karst areas.Karst areas are extensively distributed in China,where calcareous soil is one of the main soil types,exerting a great influence on ecological protection and agricultural development.Therefore,it is of great theoretical and practical significance for us to develop hyperspectral inversion models suitable for the TN content of calcareous soil.Karst landforms are distributed across 70 counties/cities in Guangxi,with an area of 97,700 km^(2),accounting for 41% of the total area of Guangxi and 10.8%of the total area of karst landforms in China.Taking calcareous soil in the karst areas of Guangxi as the research object,this study performed five mathematical transformations on soil spectra to improve the detection ability of spectral signals while eliminating spectral noise.Meanwhile,given the coexistence of linear and nonlinear relationships between soil TN content and spectra,the hyperspectral inversion capability of three models,namely partial least squares regression(PLSR),generalized neural network(GRNN)and PLSR_GRNN(a combined model of PLSR and GRNN),for soil T

关 键 词：土壤全氮 石灰土 高光谱 偏最小二乘回归 广义回归神经网络 

分 类 号：S153.6[农业科学—土壤学] S127[农业科学—农业基础科学]