MODIS_LST与AMSR-E_BT的相关性及地表温度反演

doi:10.3724/SP.J.1047.2016.00564

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (4): 564-574.doi: 10.3724/SP.J.1047.2016.00564

• • 上一篇

MODIS_LST与AMSR-E_BT的相关性及地表温度反演

时洪涛¹(), 宋冬梅^1,^2,*^*(), 单新建^2,³, 崔建勇¹, 臧琳¹, 沈晨⁴, 屈春燕^2,³, 任鹏⁵, 邵红梅⁴, 盛辉¹, 吴会胜¹, 宋先月⁶

1. 中国石油大学（华东）地球科学与技术学院,青岛 266580
2. 中国地震局地质研究所地震动力学国家重点实验室,北京 100029
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,青岛 266580
5. 中国石油大学（华东）信息与控制工程学院,青岛 266580
6. 上海市地震局,上海 200062

收稿日期:2015-03-05 修回日期:2015-05-27 出版日期:2016-04-20 发布日期:2016-04-19
作者简介:
作者简介：时洪涛（1990-）,男,硕士生,研究方向为地震热红外异常信息提取。E-mail: shihongtaosg@163.com
基金资助:
地震动力学国家重点实验室资助项目（LED2012B02）;上海地区地壳活动图像天地联合监测分析资助项目（14231202600）

The Correlation Analysis Between MODIS_LST and AMSR-E_BT and Study of LST Retrieval Method

SHI Hongtao¹(), SONG Dongmei^1,^2,^*(), SHAN Xinjian^2,³, CUI Jianyong¹, ZANG Lin¹, SHEN Chen⁴, QU Chunyan^2,³, REN Peng⁵, SHAO Hongmei⁴, SHENG Hui¹, WU Huisheng¹, SONG Xiaoyue⁶

1. School of Geoscience, China University of Petroleum, Qingdao 266580, China
2. State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China
3. Institute of Geology, China Earthquake Administration, Beijing 100029, China
4. School of Science, China University of Petroleum, Qingdao 266580, China
5. College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China
6. Earthquake Adminstration of Shanghai Municipality, Shanghai 20062, China

Received:2015-03-05 Revised:2015-05-27 Online:2016-04-20 Published:2016-04-19
Contact: SONG Dongmei

摘要/Abstract

摘要：

本文以2007年和2008年MODIS每日地表温度（LST）数据及AMSR-E地表亮温（BT）数据为研究对象,结合土地覆盖类型数据,统计分析MODIS_LST与AMSR-E_BT在不同土地覆盖类型、频率和极化方式条件下的相关性。结果表明,频率在18.7、23.8和36.5 GHz的AMSR-E-BT与MODIS_LST的相关性较大,且在垂直极化通道上的相关性较在水平极化上大;不同土地覆盖类型,与MODIS_LST相关性较大所对应的AMSR-E微波通道不同。同时,考虑混合像元问题对相关性的影响,对25种不同地物类型组合下MODIS_LST与AMSR-E-BT的相关性进行统计分析,发现混合像元中地物类型越多,则二者相关性越小。最后,采用多元线性回归分析法,根据不同土地覆盖类型建立反演回归模型,对部分研究区域MODIS-LST进行反演,误差平均在±3.15 K以内,与不考虑下垫面覆盖的模型比较,反演MODIS_LST精度平均提高了1.5 K。

关键词: MODIS地表温度, AMSR-E地表亮温, 相关性分析, 反演

Abstract:

By taking MODIS daily land surface temperature (LST) data and AMSR-E brightness temperature (BT) data from 2007 to 2008 as the input, combining with the land cover type data, the statistics and analysis of the relativity between MODIS_LST and AMSR-E_BT in different land cover types, channels and polarization ways are produced. Based on the International Geosphere-Biosphere Program (IGBP) vegetation classification scheme, land cover data is re-classified into seven types, including water, forest land, grass land, farmland, urban land, desert land and other land cover types. The statistical result shows that the correlation is apparent between MODIS_LST and AMSR-E_BT in 18.7 GHz, 23.8 GHz and 36.5 GHz channel, and it reveals a higher correlation in the vertical channel compared to the horizontal channel. Moreover, this paper finds out that the microwave channel of AMSR-E_BT, which has the highest relativity with MODIS-LST, is different with respect to different land cover types. In addition, by considering the impact of mixed pixel, this paper analyzes the correlation between MODIS_LST and AMSR-E_BT for 25 types of land cover type combinations. It is inferred that the correlation declines as the quantity of land cover types in the single mixed pixel increases. Finally, according to different land cover type combinations, the inversion model is established by adopting the multivariate linear regression method, and this model has been applied to inverse MODIS_LST in a part of the study area. Inversion results reveal that the error is limited in ±3.15 K in average, and the inversion accuracy is raised by 1.5 K successfully in comparison with the inversion model without considering land cover type combination. However, there are some problems with MODIS land surface temperature inversion by using AMSR-E brightness temperature, such as the space resolution variation between MODIS LST and AMSR-E_BT, changes of land cover type with changing seasons and the influence of relative humidity of land cover on the retrieval accuracy. Therefore, selecting land surface bright temperature with a high quality and space resolution, considering season variations and classification accuracy of land cover type factors are future research directions.

Key words: MODIS_LST, AMSR-E_BT, correlation analysis, retrieval

时洪涛, 宋冬梅, 单新建, 崔建勇, 臧琳, 沈晨, 屈春燕, 任鹏, 邵红梅, 盛辉, 吴会胜, 宋先月. MODIS_LST与AMSR-E_BT的相关性及地表温度反演[J]. 地球信息科学学报, 2016, 18(4): 564-574.DOI:10.3724/SP.J.1047.2016.00564

SHI Hongtao,SONG Dongmei,SHAN Xinjian,CUI Jianyong,ZANG Lin,SHEN Chen,QU Chunyan,REN Peng,SHAO Hongmei,SHENG Hui,WU Huisheng,SONG Xiaoyue. The Correlation Analysis Between MODIS_LST and AMSR-E_BT and Study of LST Retrieval Method[J]. Journal of Geo-information Science, 2016, 18(4): 564-574.DOI:10.3724/SP.J.1047.2016.00564

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参考文献 21

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AMSR-E微波亮温数据				MODIS数据
频率/GHZ		极化方式	空间分辨率/ km	数据类型	空间分辨率/km	数据集
6.9	V\H	5.4		地表温度（MOD 11A1）	1	LST_Night_1 Km
10.7	V\H	5.4
18.7	V\H	5.4
23.8	V\H	5.4		土地覆盖类型（MCD 12Q2）	0.5	Land_Cover_Type_1
36.5	V\H	5.4
89.0	V\H	5.4

分类DN值	IGBP	重分类后	分类DN值	IGBP	重分类后
0	水体	水体	8	多树的草原	草地
1	常绿针叶林	林地	9	稀树草原
2	常绿阔叶林		10	草原
3	落叶针叶林		11	永久湿地
4	落叶阔叶林		12	作物	农用地
5	混交林		14	作物和自然植被的镶嵌体	农用地
6	郁闭灌丛		16	裸地或低植被覆盖地	荒漠
7	开放灌丛		15	雪、冰	其他未分类区
13	城市和建成区	城镇	254、255	未分类区填充值	其他未分类区

单一地物类型	两种地物类型	3种地物类型	4种地物类型	5种地物类型	6种地物类型
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MODIS_LST与AMSR-E_BT的相关性及地表温度反演

The Correlation Analysis Between MODIS_LST and AMSR-E_BT and Study of LST Retrieval Method

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