基于被动微波与时空联合算法的云下像元LST重建

doi:10.11873/j.issn.1004-0323.2016.4.0764

遥感技术与应用

2016, Vol. 31

Issue (4): 764-772 DOI: 10.11873/j.issn.1004-0323.2016.4.0764

数据与图像处理

基于被动微波与时空联合算法的云下像元LST重建

臧琳^1,2,宋冬梅¹,单新建³,崔建勇¹,邵红梅⁴,沈晨⁴,时洪涛^1,2,宋先月⁵

(1.中国石油大学(华东)地球科学与技术学院,山东青岛 266580;
2.中国石油大学(华东)研究生院,山东青岛 266580;3.中国地震局地质研究所,北京 100029;
4.中国石油大学(华东)理学院,山东青岛 266580;5.上海市地震局,上海 200062)

Reconstruction of LST under the Cloud based on Passive Microwave Remote Sensing and Spatio-temporal Domain Algorithm

Zang Lin^1,2,Song Dongmei¹,Shan Xinjian³,Cui Jianyong¹,Shao Hongmei⁴,Shen Chen⁴,Shi Hongtao^1,2,Song Xianyue⁵

(1.School of Geosciences,China University of Petroleum,Qingdao 266580,China;
2.Graduate School,China University of Petroleum,Qingdao 266580,China;
3.Institute of Geology,China Earthquake Administration,Beijing 100029,China;
4.College of Science,China University of Petroleum,Qingdao 266580,China;
5.Shanghai Earthquake Administration,Shanghai 200062,China)

全文: PDF(9278 KB)

摘要：

地表温度作为衡量地球表面水热平衡的关键参数,具有两大时空分布特征:第一,空间分布一致性,即属性相近的像元地表温度与其地表亮温间的相关关系相对稳定;第二,时间序列周期性,且同一地区时间越接近地表温度值越相似。基于这两大特征将空间统计模型与时间序列滤波相结合,提出了用于云下像元地表温度重建的时空联合算法。以2008年MODIS地表温度产品为研究对象,采用Landsat TM数据和AMSR_E地表亮温数据重建中国9个省份的地表温度值,并与基于MODIS地表分类产品的多通道统计模型重建结果进行对比。实验结果表明,所提算法实用性强,能有效实现大面积复杂下垫面区域的地表温度重建;平均重建误差约为1.2 K,相较于基于下垫面分类的多通道统计模型下降了76%,算法精度明显提高。


关键词： 地表温度; MODIS; 被动微波; AMSR_E; 时空联合

Abstract:

As a key parameter to measure the water\|heat balance of earth surface,land surface temperature has two spatio\|temporal distribution characteristics:One is spatial distribution stability,that is,the correlation between the land surface temperature and the land surface bright temperature among those pixels whose properties are similar and relatively stable;the other is time series periodicity,and for one pixel,the time is closer,the temperatures are more similar.based on these characteristics,combined space statistical model with time series filtering,a spatio\|temporal domain algorithm was used for the reconstruction of land surface temperature,which was proposed.In the paper,the temperatures were reconstructed in 9 provinces (Xinjiang,Qinghai,Sichuan,Yunnan,Henan,Anhui,Hubei,Hunan,Jiangxi) of China with MODIS temperature products (MOD11A2),Landsat TM data and AMSR_E brightness temperature data (AMSR_EL2A) in 2008.Then,the inversion precisions in 9 provinces of the proposed algorithm were calculated based on arithmetic average method,and compared with the reconstruction results of multi\|channel statistical model based on the surface classification products from MODIS (MOD12).The results show that the proposed algorithm is practical,and that can be applied in any kind of LST images even there are lots of null values;and the average inversion error of this method for China with MOD11A2 products is about 1.2 K,decreased by 76% compared with multi\|channel statistical model,therefore the reconstruction accuracy is significantly improved.

Key words: Land surface temperature;MODIS;Passive microwave;AMSR_E;Spatio\ temporal domain

收稿日期: 2016-01-05 出版日期: 2016-10-14

TP 75

基金资助:

地震动力学国家重点实验室开放基金“卫星热红外地震异常信号提取算法研究”(LED2012B02),上海市科学技术委员会项目“上海地区地壳活动图像天地联合监测分析”(14231202600)。

通讯作者: 宋冬梅（1973-），女，吉林长春人，副教授，博士，主要从事景观生态、海洋遥感与热红外遥感等方面的研究。Email:songdongmei1973@126.com。

作者简介: 臧琳（1991-），女，山东济宁人，硕士研究生，主要从事热红外遥感研究。Email：18765920116@163.com。

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引用本文:

臧琳,宋冬梅,单新建,崔建勇,邵红梅,沈晨,时洪涛,宋先月. 基于被动微波与时空联合算法的云下像元LST重建[J]. 遥感技术与应用, 2016, 31(4): 764-772.

Zang Lin,Song Dongmei,Shan Xinjian,Cui Jianyong,Shao Hongmei,Shen Chen,Shi Hongtao,Song Xianyue. Reconstruction of LST under the Cloud based on Passive Microwave Remote Sensing and Spatio-temporal Domain Algorithm. Remote Sensing Technology and Application, 2016, 31(4): 764-772.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2016.4.0764 或 http://www.rsta.ac.cn/CN/Y2016/V31/I4/764

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