基于同类地物地表温度日变化相关性的MODIS LST重建算法

doi:10.3969/j.issn.0253-4967.2022.05.010

地震地质 ›› 2022, Vol. 44 ›› Issue (5): 1240-1256.DOI: 10.3969/j.issn.0253-4967.2022.05.010

基于同类地物地表温度日变化相关性的MODIS LST重建算法

崔建勇¹⁾(), 张曼玉¹^,²⁾, 宋冬梅¹^,³⁾^,^*(), 罗升⁴⁾, 单新建⁵⁾, 王斌¹⁾

1)中国石油大学(华东), 海洋与空间信息学院, 青岛 266580
2)中国石油大学(华东)研究生院, 青岛 266580
3)海洋矿物资源实验室青岛海洋科学技术国家实验室, 青岛 266071
4)四川省水利水电勘测设计研究院有限公司, 成都 610072
5)中国地震局地质研究所, 北京 100029

收稿日期:2021-09-22 修回日期:2021-12-30 出版日期:2022-10-20 发布日期:2022-11-28
通讯作者: 宋冬梅
作者简介:
崔建勇, 男, 1976年生, 2006年于北京师范大学获地图学与地理信息系统专业博士学位, 讲师, 现主要研究方向为地震热红外异常信息提取, E-mail: cui_jianyong@upc.edu.cn。
基金资助:
国家重点研发计划项目(2019YFC1509202); 国家自然科学基金(41772350); 国家自然科学基金(61371189); 国家自然科学基金(41701513)

MODIS LST RECONSTRUCTION ALGORITHM BASED ON DIURNAL CORRELATION OF SURFACE TEMPERATURE OF SIMILAR LAND FEATURES

CUI Jian-yong¹⁾(), ZHANG Man-yu¹^,²⁾, SONG Dong-mei¹^,³⁾(), LUO Sheng⁴⁾, SHAN Xin-jian⁵⁾, WANG Bin¹⁾

1) College of Oceanography and Spatial Information, China University of Petroleum, Qingdao 266580, China
2) Graduate School, China University of Petroleum, Qingdao 266580, China
3) The Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China
4) Sichuan Water Conservancy & Hydropower Survey Design & Research Institute Co., Ltd., Chengdu 610072, China
5) Institute of Geology, China Earthquake Administration, Beijing 100029, China

Received:2021-09-22 Revised:2021-12-30 Online:2022-10-20 Published:2022-11-28
Contact: SONG Dong-mei

摘要/Abstract

摘要：

地表温度(LST)是研究地表与大气之间物质和能量交换、地表过程变化以及地热探测与地震热异常前兆等方面不可或缺的重要参数, 然而云覆盖现象导致MODIS LST产品存在大量空值, 限制了LST的广泛应用。文中提出了一种基于同类地物不同时刻LST日变化相关性的MODIS LST重建算法。以新疆和田为研究区, 使用2003-2015年MODIS 8d合成地表温度产品为实验数据, 根据一天中同类地物不同时刻LST之间的相关性, 以地表覆盖类型产品为依据, 分别创建各类地物上午、下午和晚上与凌晨的LST回归模型, 将三者的LST拟合回归至凌晨时刻的LST, 然后取上午、下午和晚上拟合结果的最优组合以实现对凌晨LST的两步重建。实验结果表明, 该方法的最小误差为0.57K, 误差均在1.2K以下, 平均误差为0.92K。经验证, 将该方法应用于其余3个时刻的地表温度重建工作中仍可得到较好的补值效果。与现有的LST补值方法进行对比可知, 本方法以少量辅助数据实现了较高的补值精度和补值率, 可为基于温度的地表过程研究和地震热异常检测等研究提供坚实的数据基础。

关键词: LST, MODIS, 非线性回归, 数据重建, 地表覆盖类型

Abstract:

Land surface temperature(LST) is an indispensable parameter for studying the exchange of matter and energy between the earth surface and the atmosphere, the changes of surface processes, geothermal detection and precursors of seismic thermal anomalies, etc. However, cloud cover phenomenon results in a large number of null values in MODIS LST products, which limits the wide application of LST. In this study, a MODIS LST reconstruction algorithm based on LST diurnal variation correlation of similar land features at different time is proposed. Taking Hotan, Xinjiang as the research area, and using MODIS 8-day LST products from 2003 to 2015 as experimental data, based on the correlation of LST between similar features at different times of a day, with the help of the surface cover type, the regression models of morning, afternoon, evening and early morning surface temperature were established respectively. The LST fitting of the three was regressed to the LST of the early morning, and then the optimal combination of the fitting results of morning, afternoon and evening was taken to achieve the two-step reconstruction of the early morning LST. The experimental results show that the minimum error of this method is 0.57K, the errors are all below 1.2K, and the average error is 0.92K. It is proved that this method has also a good complement effect when applied to the reconstruction of surface temperature of the remaining three time periods. Compared with the existing LST complement methods, the proposed method achieves higher accuracy and rate of complement with a small amount of auxiliary data, which can provide a solid data basis for the research of surface process based on temperature and seismic thermal anomaly detection. The main conclusions of this study are verified as follows:
(1)By establishing regression models for MOD11A2 morning and evening and MYD11A2 afternoon and MYD11A2 early morning surface temperature of different land cover types, it can be seen that second-order polynomial fitting effect is better and the model has better stability.
(2)For the study area, the accuracy of using night data to complement the early morning LST is the highest, but the spatio-temporal continuity of reconstructed LST cannot be completely guaranteed. If morning and afternoon LST data are added, the complement rate can be further improved.
(3)The reconstruction data of land surface temperature based on this method has better regional consistency and no step change. Compared with the LST compensation method proposed in recent five years, it is found that this method uses less auxiliary data and has relatively high accuracy of compensation. The errors after compensation are all below 1.2K, with an average error of 0.92K and a minimum error of 0.57K, which can meet the requirements of surface temperature compensation.
(4)At the same time, the missing data of MODISLST data in the other three time periods of a day are reconstructed respectively in this paper, with high reconstruction accuracy. Based on this method, the accuracy of data reconstruction in the early morning and evening is higher than that in the morning and afternoon. Therefore, in the future anomaly extraction work, the evening or early morning data can be given priority as the main data source, so as to achieve accurate anomaly extraction.
(5)In order to verify the universality of this method in different regions, two sub-regions with different land cover types and locations in the study area were randomly selected to carry out regional usability evaluation, and the results showed that the accuracy of reconstruction in both regions was high, and missing value reconstruction could be achieved.
In conclusion, the proposed method in this paper provides a new idea and method for MODIS land surface temperature reconstruction. Compared with the existing LST complementary method, the proposed method achieves higher complementary accuracy and complementary rate with a small amount of auxiliary data, which can provide a solid data foundation for the study of surface processes based on temperature. In addition, the complete surface temperature data after reconstruction can provide data support for geothermal anomaly detection and extraction, and lay a theoretical foundation for the interpretation of seismic thermal anomaly mechanism.

Key words: LST, MODIS, different time, data reconstruction, land cover type

崔建勇, 张曼玉, 宋冬梅, 罗升, 单新建, 王斌. 基于同类地物地表温度日变化相关性的MODIS LST重建算法[J]. 地震地质, 2022, 44(5): 1240-1256.

CUI Jian-yong, ZHANG Man-yu, SONG Dong-mei, LUO Sheng, SHAN Xin-jian, WANG Bin. MODIS LST RECONSTRUCTION ALGORITHM BASED ON DIURNAL CORRELATION OF SURFACE TEMPERATURE OF SIMILAR LAND FEATURES[J]. SEISMOLOGY AND GEOLOGY, 2022, 44(5): 1240-1256.

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产品数据名称	MOD11A2	MYD11A2
空间分辨率	1km	1km
时间分辨率	8d	8d
温度单位	K	K
所属卫星	Terra	Aqua
传感器过境时间	上午10:30、晚上10:30	下午1:30、凌晨1:30

产品数据名称	MOD11A2	MYD11A2
空间分辨率	1km	1km
时间分辨率	8d	8d
温度单位	K	K
所属卫星	Terra	Aqua
传感器过境时间	上午10:30、晚上10:30	下午1:30、凌晨1:30

地物类型	数据类型	R²
		1阶	2阶	3阶	4阶	5阶
水体	MOD_DAY	0.423	0.423	0.436	0.445	0.453
	MOD_NIGHT	0.39	0.940	0.940	0.940	0.940
稀树草原	MOD_DAY	0.880	0.890	0.891	0.894	0.903
	MOD_NIGHT	0.981	0.982	0.982	0.982	0.982
裸地	MOD_DAY	0.573	0.590	0.590	0.614	0.628
	MOD_NIGHT	0.964	0.965	0.965	0.965	0.965

地物类型	数据类型	R²
		1阶	2阶	3阶	4阶	5阶
水体	MOD_DAY	0.423	0.423	0.436	0.445	0.453
	MOD_NIGHT	0.39	0.940	0.940	0.940	0.940
稀树草原	MOD_DAY	0.880	0.890	0.891	0.894	0.903
	MOD_NIGHT	0.981	0.982	0.982	0.982	0.982
裸地	MOD_DAY	0.573	0.590	0.590	0.614	0.628
	MOD_NIGHT	0.964	0.965	0.965	0.965	0.965

期数	补值误差/K					补值率/%
	方案1	方案2	方案3	方案4	方案5	方案1	方案2	方案3	方案4	方案5
2004-43	0.89	0.99	3.00	3.06	2.85	99	100	99	99	99
2007-41	1.03	1.15	2.65	2.74	2.52	100	100	99	99	99
2007-43	1.15	1.3	2.36	2.48	2.42	100	100	100	99	99
2009-38	1.28	1.4	2.59	2.7	2.53	100	100	100	100	100
2013-39	1.26	1.34	2.62	2.64	2.74	100	100	100	100	100
2013-40	0.99	1.18	2.58	2.6	3.1	100	100	99	99	97
2014-40	0.79	0.93	2.6	2.7	2.48	100	100	97	94	95
2015-25	1.34	1.58	2.54	2.4	2.85	100	100	100	100	100
2015-43	0.79	0.94	2.85	2.94	2.87	100	100	100	99	99
平均	1.06	1.20	2.64	2.70	2.71	99.89	100.00	99.33	98.78	98.67

基于同类地物地表温度日变化相关性的MODIS LST重建算法

MODIS LST RECONSTRUCTION ALGORITHM BASED ON DIURNAL CORRELATION OF SURFACE TEMPERATURE OF SIMILAR LAND FEATURES

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摘要/Abstract

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图/表 16

参考文献 30

相关文章 2

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Metrics

本文评价

期数			2004-43	2007-41	2007-43	2010-38	2013-39	2013-40	2014-40	2015-25	2015-43	平均
补值误差 /K	区域1	上午	0.86	1.14	1.38	1.07	1.44	1.51	1.34	1.68	1.14	1.28
		下午	0.92	1.11	1.38	1.22	1.58	1.53	1.29	1.87	1.15	1.33
		晚上	0.95	0.99	0.85	0.85	0.89	1.05	0.58	1.30	0.79	0.92
		凌晨	1.05	1.06	0.90	0.73	1.07	1.17	0.57	0.92	0.80	0.92
	区域2	上午	1.32	1.29	1.62	1.28	1.57	1.25	1.23	1.95	1.06	1.39
		下午	1.27	1.33	1.75	1.37	1.45	1.48	1.21	1.83	1.07	1.41
		晚上	0.73	0.81	1.12	1.07	0.92	0.89	0.75	1.37	0.70	0.93
		凌晨	0.89	1.03	1.15	1.28	0.96	0.99	0.79	1.34	0.79	1.02

期数	本文重建方法			对比方法
	补值误差/K	补值贡献率/%		空间域加权平均补值误差/K
		第1步	第2步
2004-43	1.05	0.99	0.01	1.06
2007-41	1.06	0.99	0.01	1.18
2007-43	0.90	0.99	0.01	1.10
2010-38	0.73	1.00	0	1.05
2013-39	1.07	1.00	0	0.89
2013-40	1.17	0.99	0.01	1.08
2014-40	0.57	1.00	0	1.33
2015-25	0.92	1.00	0	1.60
2015-43	0.80	1.00	0	0.91
平均	0.92			1.13

来源	方法	所用数据类型的数量	平均误差/K
齐会娟, 2017	谐波分析、 S-G滤波	3	2.00
黄晶晶, 2019		1	2.34
Kang et al., 2018	多时相重建	4	4.55
Zeng et al., 2018		4	4.71
吴迪, 2018	回归分析模型	4	1.25
Zhao et al., 2020		3	1.41
Gutierrez et al., 2021		3	1.42
李楠等, 2018	多源数据融合	1	1.13
赵冰, 2020		6	1.42
王爱辉等, 2021	相似像元重建	3	2.17
Tan et al., 2021		5	3.66
张德军等, 2021	RSDAST模型	1	1.15
本文所提出的方法	非线性回归模型	2	0.92

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