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国土资源遥感  2019, Vol. 31 Issue (2): 59-65    DOI: 10.6046/gtzyyg.2019.02.09
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于Savitzky-Golay滤波算法的FY-2F地表温度产品时间序列重建
吴迪1,陈健1(),石满1,覃帮勇2,李盛阳2
1.南京信息工程大学遥感与测绘工程学院,南京 210044
2.中国科学院空间应用工程与技术中心太空应用重点实验室,北京 100094
Reconstruction of land surface temperature time-series datasets of FY-2F based on Savitzky-Golay filter
Di WU1,Jian CHEN1(),Man SHI1,Bangyong QIN2,Shengyang LI2
1.School of Remote Sensing and Surveying Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China
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摘要 

卫星遥感技术可获取大面积、空间连续的地表温度(land surface temperature, LST),为全球变化、生态环境和农业生产等领域提供了宝贵的数据源,但受到云、气溶胶、观测角度和太阳光照角度等影响,遥感反演的LST在时间和空间上均存在不同程度的缺失,限制了LST遥感产品的应用。以长江三角洲地区为研究区,以风云2号F星(FY-2F) LST日均值产品为数据源,利用LST时间序列特征,基于Savitzky-Golay(S-G)滤波算法进行了LST长时间序列的重建研究。结果表明,研究区重建前FY-2F LST产品的平均时相缺失率为19.43%,经滤波后缺失率降低为1.69%,并能够保证LST空间一致性。通过模拟验证,S-G滤波重建LST的拟合精度为0.95,平均绝对误差为1.35 K,具有较高的精度,可以用于进一步热环境时空分布规律的研究。

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吴迪
陈健
石满
覃帮勇
李盛阳
关键词 地表温度Savitzky-Golay(S-G)滤波长时间序列风云2号F星(FY-2F)重建    
Abstract

Regional and spatial continuous land surface temperature (LST) can be retrieved from satellite remote sensing data, and has an important significance in such fields as global change, ecology, environment, and agricultural production. However, the LST retrieved by remote sensing usually has missing data in time and space due to the influence of clouds, aerosols, satellite viewing angle and solar illumination angle, which limits the application of LST products. In this paper, the authors reconstructed FY-2F daily LST data of 2013 in the Yangtze River delta region using Savitzky-Golay (S-G) filter based on the characteristics of long time-series LST. The results show that S-G filter can fill the missing values effectively and ensure the spatial distribution consistency of the LST after reconstruction. The average time-series loss rate of the original FY-2F LST product is 19.43%, and then decreases to 1.69% after S-G filtering. In order to verify the reconstruction accuracy of S-G filter, the authors randomly selected some regions that are not deficient, and then made comparison with the results after S-G filtering. It is proved that S-G filter reconstructing method has obtained high accuracy, with the mean absolute error 1.35 K and the fitting accuracy 0.95. Higher quality and long time-series FY-2F LST which is reconstructed based on S-G filter offers a good foundation to the study of temporal and spatial distribution of further thermal environment.

Key wordsland surface temperature    Savitzky-Golay(S-G) filter    time-series    FY-2F    reconstruction
收稿日期: 2018-01-31      出版日期: 2019-05-23
ZTFLH:  TP79  
基金资助:中国科学院太空应用重点实验室开放基金项目“长时间序列地表热环境监测与变化分析”(LSU-2016-06-02);国家自然科学基金项目“城市街道峡谷气温时空分布与变化机制模拟研究”共同资助(41571418)
通讯作者: 陈健     E-mail: chjnjnu@163.com
作者简介: 吴 迪(1993-),女,硕士,主要从事定量遥感研究。Email: wudinuist@163.com。
引用本文:   
吴迪,陈健,石满,覃帮勇,李盛阳. 基于Savitzky-Golay滤波算法的FY-2F地表温度产品时间序列重建[J]. 国土资源遥感, 2019, 31(2): 59-65.
Di WU,Jian CHEN,Man SHI,Bangyong QIN,Shengyang LI. Reconstruction of land surface temperature time-series datasets of FY-2F based on Savitzky-Golay filter. Remote Sensing for Land & Resources, 2019, 31(2): 59-65.
链接本文:  
http://www.gtzyyg.com/CN/10.6046/gtzyyg.2019.02.09      或      http://www.gtzyyg.com/CN/Y2019/V31/I2/59
缺值率/% 逐时产品 日产品
数量/幅 百分比/% 数量/幅 百分比/%
<10 856 11.80 200 57.80
[10,30) 1 117 15.40 50 14.45
[30,50) 1 066 14.90 42 12.14
[50,70) 1 194 16.46 28 8.09
[70,90) 1 441 19.86 20 5.78
≥90 1 580 21.78 6 1.73
总计 7 254 100 346 100
Tab.1  FY-2F LST产品空间缺值率统计
Fig.1  研究区气象站点分布示意图
Fig.2  不同连续缺值重建效果
Fig.3  LST时相缺失率空间分布
Fig.4  FY-2F LST重建示例
Fig.5  南京站点LST重建效果
Fig.6  实测值与重建前、后LST散点图
Fig.7  重建前、后LST散点图
类别 2014年 2015年
R2 MAE/K R2 MAE/K
实测值与重建前LST 0.65 4.52 0.69 4.31
实测值与重建后LST 0.63 4.53 0.68 4.32
人工模拟缺值点 0.95 1.37 0.96 1.58
Tab.2  2014—2015年LST重建精度评价
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