| 引用本文: | 石晓光,杨倩,周超,纪文政,陶锋,李维邦,赵瑞雪,林楠.查干湖冰封期光谱特征及影响因素.湖泊科学,2023,35(4):1491-1500. DOI:10.18307/2023.0444 |
| Shi Xiaoguang,Yang Qian,Zhou Chao,Ji Wenzheng,Tao Feng,Li Weibang,Zhao Ruixue,Lin Nan.Spectral characteristics and influencing factors of lake ice in Lake Chagan during frozen season. J. Lake Sci.2023,35(4):1491-1500. DOI:10.18307/2023.0444 |
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| 查干湖冰封期光谱特征及影响因素 |
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石晓光1,2, 杨倩2, 周超3, 纪文政4,5, 陶锋1,2, 李维邦1, 赵瑞雪1,2, 林楠1
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1.吉林建筑大学测绘与勘查工程学院, 长春 130118;2.中国科学院东北地理与农业生态研究所, 长春 130102;3.国家海洋环境监测中心, 大连 116023;4.中国科学院西北生态环境资源研究所, 兰州 730000;5.中国科学院大学, 北京 100049
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| 摘要: |
| 湖冰光谱特征是湖冰遥感反演的物理基础,是研究湖冰光学特性和空间分布的理论依据。本文以查干湖为例,使用ASD Field Spec 4便携式地物光谱仪采集冰封期不同类型湖冰、积雪和水体光谱,利用Savitzky-Golay滤波法和包络线去除法分析白冰、灰冰、黑冰、雪冰、积雪和水体的反射光谱特征,探索气泡对湖冰反射光谱特征的影响。积雪和雪冰、白冰和灰冰、黑冰和水体的反射特征随着波长的变化特征基本一致,冰的反射率介于积雪和水体之间,其中白冰的反射率高于灰冰和黑冰,在包络线去除结果中,黑冰和水体在440 nm吸收谷处的吸收面积为5.184和10.878、吸收深度为0.052和0.106,雪、雪冰、白冰、灰冰在800和1030 nm吸收谷处的吸收面积和吸收深度的变化表现为雪<雪冰<灰冰<白冰。气泡是影响湖冰光谱特征的重要因素,气泡使白冰反射率减小和黑冰反射率增大,并且气泡使得白冰在800/1030 nm和黑冰在440 nm处的吸收面积和吸收深度减小,其中气泡大小和疏密程度的不同会导致湖冰反射率的影响程度存在差异。同时,本文选取时间同步的Landsat 8 OLI遥感影像,在完成辐射校正、大气校正和Fmask去云处理后,根据实测光谱特性差异选取敏感波段和最佳遥感指数实现湖冰遥感分类。结果显示,红波段和近红外波段是湖冰遥感分类的最佳波段,结合光谱特征、波段运算和阈值法能有效区分积雪、湖冰和水体。 |
| 关键词: 湖冰 光谱 反射特征 气泡 查干湖 |
| DOI：10.18307/2023.0444 |
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| 基金项目:吉林省教育厅科学研究规划项目(JJKH20210290KJ)、吉林省科技发展计划项目(20210203016SF)和长春市朝阳区科技项目(朝科技合(2021)01号)联合资助。 |
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| Spectral characteristics and influencing factors of lake ice in Lake Chagan during frozen season |
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Shi Xiaoguang1,2, Yang Qian2, Zhou Chao3, Ji Wenzheng4,5, Tao Feng1,2, Li Weibang1, Zhao Ruixue1,2, Lin Nan1
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1.School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, P. R. China;2.Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, P. R. China;3.National Marine Environment Monitoring Center, Dalian 116023, P. R. China;4.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, P. R. China;5.University of Chinese Academy of Sciences, Beijing 100049, P. R. China
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| Abstract: |
| Lake ice spectral characteristics are the physical basis of remote sensing inversion of lake ice and the theoretical basis for studying the optical properties and spatial distribution of lake ice. In this paper, taking Lake Chagan as an example, we used ASD FieldSpec 4 portable feature spectrometer to collect spectra of different types of lake ice, snow and water bodies during frozen season, and used Savitzky-Golay filtering method and envelope removal method to analyze the reflection spectral characteristics of white ice, gray ice, black ice, snow ice, snow and water bodies, and to explore the influence of air bubbles on the reflection spectral characteristics of lake ice. The reflectance characteristics of snow and snow ice, white and gray ice, black ice, and water bodies were basically the same with wavelength. The reflectance of ice was between that of snow and water bodies, where the reflectance of white ice was higher than that of gray and black ice. In the envelope removal results, the absorption areas of black ice and water bodies at the absorption valley of 440 nm were 5.184 and 10.878, and the absorption depths were 0.052 and 0.106. For snow, snow ice, white ice, and gray ice at 800 nm and 1030 nm absorption valley, the variation of absorption area and absorption depth showed as snow < snow ice < gray ice < white ice. The bubbles reduced the reflectance of white ice and increased the reflectance of black ice, and the bubbles reduced the absorption area and absorption depth of white ice at 800/1030 nm, and black ice at 440 nm, where the differences in the size and density of bubbles led to the differences in the reflectance of lake ice. Moreover, the time-synchronized Landsat 8 OLI remote sensing images were selected, and the sensitive bands and the best remote sensing indices were selected according to the differences of the measured spectral properties to realize the remote sensing classification of lake ice after completing the radiometric correction, atmospheric correction, and Fmask de-clouding process. The results showed that the red band and near-infrared band were the best bands for remote sensing classification of lake ice, and the combination of spectral characteristics, band math, and threshold method could effectively distinguish snow, lake ice, and water bodies. |
| Key words: Lake ice spectra reflectance characteristics bubbles Lake Chagan |
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