| 基于高光谱数据的天山北坡积雪孔隙率反演研究 |
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| 引用本文： | 习阿幸,刘志辉,徐倩,张波.基于高光谱数据的天山北坡积雪孔隙率反演研究[J].干旱区地理,2015,38(6):1253-1261. |
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| 作者姓名： | 习阿幸 刘志辉 徐倩 张波 |
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| 作者单位： | 1. 新疆大学资源与环境科学学院, 新疆乌鲁木齐 830046;
2. 新疆大学教育部绿洲生态重点实验室, 新疆乌鲁木齐 830046;
3. 新疆大学干旱生态环境研究所, 新疆乌鲁木齐 830046;
4. 干旱半干旱区可持续发展国际研究中心, 新疆乌鲁木齐 830046 |
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| 基金项目： | 国家自然科学基金面上项目:天山北坡典型流域积雪-冻土水热耦合中融水产流机制研究(41171023)资助;水利部公益性行业科研专项经费项目:内陆干旱区实施最严格水资源管理关键技术(201301103)资助 |
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| 摘 要： | 以新疆天山北坡中段典型流域季节性积雪为研究对象,基于高光谱遥感监测技术,分析了融雪期积雪孔隙率与光谱反射率的相关性。采用偏最小二乘法(PLS)对相关性较高的波段进行压缩,并提取贡献率最高的前四个主成分,以此用来确定神经网络的隐含节点数、输入层、输出层的初始权值,建立PLS-BP模型进行积雪孔隙率反演研究。结果表明:当隐含节点数为3,模型的线性确定相关系数(R2)较高为0.9159,RMSE为0.04,相对误差为0.23。与传统偏最小二乘回归(PLSR)、主成分回归(PCA)建模方法相比,精度较高,所建定量模型可用于高光谱遥感反演积雪孔隙率。
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| 关 键 词： | 高光谱遥感 融雪期 积雪孔隙率 PLS-BP |
| 收稿时间： | 2015-01-11 |
Retrieval method for estimating snow porosity in the northern slope of Tianshan Mountains based on hyperspectral data |
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| XI A-xing,LIU Zhi-hui,XU Qian,ZHANG Bo.Retrieval method for estimating snow porosity in the northern slope of Tianshan Mountains based on hyperspectral data[J].Arid Land Geography,2015,38(6):1253-1261. |
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| Authors: | XI A-xing LIU Zhi-hui XU Qian ZHANG Bo |
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| Institution: | 1. School of Resources and Environment Science, Xinjiang University, Urumqi 830046, Xinjiang, China;
2. Key Laboratory of Oasis Ecology, Ministry of Education, Xinjiang University, Urumqi 830046, Xinjiang, China;
3. Institute of Ecology and Environment, Xinjiang University, Urumqi 830046, Xinjiang, China;
4. International Center for Desert Affairs-Research on Sustainable Development in Arid and Semi-arid Lands, Urumqi 830046, Xinjiang, China |
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| Abstract: | As one of the most important surface water resources in arid areas,snowcover plays an irreplaceable role in water resources management,global climate change,snowmelt runoff simulation and forecasting. Snow porosity is one of the important indexes reflecting physical characterization of snowmelt and snowpack. Using the hyper spectral technology,this paper analyzed the impacts of snow porosity on spectral reflectance in a typical snowmelt watershed in Northern Slope of the Tianshan Mountain. Combined the BP neural network method and partial least-squares regression,we further proposed a new quantitative modeling approach of hyper spectral to specifically invert the snow porosity by remote sensing data. Results show as follows:(1)the snow porosity was obviously responded to spectral reflectance,and showed significant correlation in the near-infrared bands including 1165-1478 nm,1615-1921 nm,and 1930-2100 nm.(2)When the implied nodes was set 3,the inversion ability of snow porosity with hyper spectral data can be greatly improved by PLS-BP model,coefficient of determination R2 and the root mean square error(RMSE) of linear regression between the snow porosity measured and simulated of PLS-BP in the same period were 0.9159 and 0.04,respectively.(3)Compared with the traditional partial least-squares regression(PLSR) and principal components regression(PCR),PLS-BP model obtained higher inversion,for example,the coefficient of determination R2 and the root mean square error (RMSE) of PCA-BP were 0.6042 and 0.42,respectively,but for the traditional PLSR,they were 0.3536 and 0.63,respectively. The innovation of this study is as follows:(1)using principal component to obtain main information bands as input of PLS-BP model,which is conducive to reflect reflectivity of snow porosity and help improving the model precision;(2)the PLS-BP combined with hyperspectral remote sensing data could estimate the snow porosity better,and this method can be referenced for analysis of near infrared spectrum;(3)the monitoring and estimating model can not only predict snow indexes but also can provide remote assistance for snowmelt flow and flood forecasting. |
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| Keywords: | hyperspectral remote sensing melt period snow porosity PLS-BP |
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