[1]李凯,陈芸芝,汪小钦,等.采用地表反射率模型的Landsat时序数据集重构[J].华侨大学学报(自然科学版),2020,41(3):381-387.[doi:10.11830/ISSN.1000-5013.201904004]
　LI Kai,CHEN Yunzhi,WANG Xiaoqin,et al.Reconstruction of Landsat Time-Series Dataset Using Surface Reflectance Model[J].Journal of Huaqiao University(Natural Science),2020,41(3):381-387.[doi:10.11830/ISSN.1000-5013.201904004]
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采用地表反射率模型的Landsat时序数据集重构()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第41卷
期数:
2020年第3期
页码:
381-387
栏目:
出版日期:
2020-05-20

文章信息/Info

Title:
Reconstruction of Landsat Time-Series Dataset Using Surface Reflectance Model
文章编号:
1000-5013(2020)03-0381-07
作者:
李凯1; 2;  陈芸芝1; 2;  汪小钦1; 2;  陈雪娇1; 2
1. 福州大学 空间数据挖掘与信息共享教育部重点实验室, 福建 福州 350116;2. 福州大学 卫星空间信息技术综合应用国家地方联合工程研究中心, 福建 福州 350116
Author(s):
LI Kai1; 2;  CHEN Yunzhi1; 2;  WANG Xiaoqin1; 2;  CHEN Xuejiao1; 2
1. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350116, China; 2. National Eng. Research Center of Satellite Spatial Information Technology, Fuzhou University, Fuzhou 350116, China
关键词:
地表反射率模型;  Landsat卫星;  时间序列;  变化监测
Keywords:
surface reflectance model;  Landsat satellite;  time-series;  change monitoring
分类号:
TP79
DOI:
10.11830/ISSN.1000-5013.201904004
文献标志码:
A
摘要:
选择福州市区东部面积近300 km2的区域作为研究区,基于地表反射率模型,对2013-2016年研究区内所有的有效像元进行拟合,重构时序数据集,并通过原始影像与实测光谱对拟合结果进行评价.研究结果表明:拟合结果与原始影像在蓝、绿、红、近红外波段的相关系数均高于0.9,可见光波段均方根误差在0.01左右,近红外波段略高于0.02;拟合结果能清晰地表达不同类型植被的物候差异,与原始影像、实测归一化植被指数(NDVI)均保持较高的一致性;基于重构的数据集,可以保证选择相同时相的数据进行年度植被变化监测,进一步提高植被动态变化监测的准确性和实效性.
Abstract:
This paper selected a study area with an area of about 300 km2 in eastern Fuzhou, based on the surface reflectance model, fitting all the effective pixels in the study area from 2013 to 2016, reconstruction time-series dataset, and the fitting results were evaluated by original image and measured optical spectrum.The research results showed that the correlation coefficients between the fitting results and the original image in the blue, green, red and near infrared bands are higher than 0.9, the root mean square error of visible band is about 0.01, near infrared band is slightly higher than 0.02. The fitting results can clearly express the phenological differences of different types of vegetation, and maintain a high consistency with the original image and the measured normalized difference vegetation index(NDVI). Based on the reconstructed data set, annual vegetation change monitoring can be guaranteed by choosing the same timephase data, the accuracy and effectiveness of vegetation dynamic change monitoring can be further improved.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2019-04-04
通信作者: 陈芸芝(1982-),女,副研究员,博士,主要从事资源环境遥感应用的研究.E-mail:chenyunzhi@fzu.edu.cn.
基金项目: 国家自然科学基金资助项目(41401488); 国家重点研发计划项目(2017YFB0504203); 中央引导地方科技发展专项(2017L3012)
更新日期/Last Update: 2020-05-20