基于DEM修正的MODIS地表温度产品空间插值

doi:10.12082/dqxxkx.2018.180340

摘要/Abstract

摘要：

地表温度是资源环境、气候变化、陆地生态系统等科学研究的重要参数之一。MODIS LST（Land Surface Temperature, LST）产品是地表温度相关研究的重要数据源。而现有MODIS LST产品均存在云覆盖区域,因此云覆盖区域地表温度估计已成为热红外遥感的前沿性研究难题。为解决MODIS LST产品云遮挡区域地表温度信息缺失,以秦岭地区为研究区,选用2001-2017年的MOD11A2数据,在传统的反距离权重（IDW）、规则样条函数（SPLINE）、普通克里金（OK）、趋势面（TREND）空间插值方法中引入高程因子,通过反复试验形成基于DEM修正的MODIS LST空间插值方法。分析空间插值结果表明： ① 空间插值精度由高到低为：OK>SPLINE>IDW>TREND,基于DEM修正后精度分别提高了约0.38、0.31、0.32和0.78℃; ② 空间插值结果的精度呈现季节差异,夏季6、7、8月的精度较高,1月的精度最低;③ 插值精度与云区的范围存在一定的关系,当云覆盖区域<1.1 km²时,DEM+OK方法的插值误差<0.55 ℃,当云覆盖区域<3.1 km²,插值误差<1 ℃;DEM+SPLINE方法在云覆盖区域<2.7 km²时,插值误差<0.55 ℃,云覆盖区域<10.4 km²,插值误差<1℃;当云覆盖为1.1~2.7 km²时,DEM+SPLINE方法的插值精度高于DEM+OK方法。

关键词: MODIS LST, 空间插值, 地表温度, DEM, 秦岭地区

Abstract:

Land surface temperature is one of the important parameters of scientific research such as resource environment, climate change and terrestrial ecosystem. MODIS LST (Land Surface Temperature, LST) products are important data sources for land surface temperature related research. The land surface temperature information of MODIS LST products is lost in the cloud coverage area. Therefore, the land surface temperature estimation of cloud coverage areas has become a frontier research problem of thermal infrared remote sensing. In order to solve the problem of missing land surface temperature information in the cloud occlusion area of MODIS LST products. In this paper, the Qinling area is used as the research area and the experimental data of MOD11A2 from 2001 to 2017 is selected. In the traditional Inverse Distance Weighting (IDW), Regular Spline (SPLINE), Ordinary Kriging (OK) and Trend Surface (TREND) spatial interpolation method, the important influence factor of elevation is introduced. Through a large number of spatial interpolation experiments, the traditional spatial interpolation method is improved, and a MODIS LST spatial interpolation method based on DEM correction is formed. Analysis of spatial interpolation results indicates: （1） The spatial interpolation accuracy is from high to low: OK> SPLINE > IDW>TREND, and the accuracy of the OK, SPLINE, IDW, and TREND methods based on DEM correction is increased by about 0.38°C, 0.31°C, 0.32°C, and 0.78°C, respectively; (2) The accuracy of spatial interpolation results shows seasonal differences. The interpolation accuracy is higher in summer, July, and August, and the interpolation accuracy is the lowest in January. （3）The interpolation accuracy has a certain relationship with the cloud area. When the cloud coverage area is less than 1.1km², the interpolation error of the DEM+OK interpolation method is less than 0.55°C, and when the cloud coverage area is less than 3.1km², the spatial interpolation error is less than 1°C. When the cloud coverage area is less than 2.7 km², the interpolation error of the DEM+SPLINE method is less than 0.55°C, and the interpolation error of the DEM+SPLINE method is less than 1°C when the cloud coverage area is less than 10.4 km². When the cloud coverage is 1.1~2.7 km², the interpolation accuracy of DEM+SPLINE interpolation method is higher than of the DEM+OK interpolation method.

Key words: MODIS LST, spatial interpolation, land surface temperature, DEM, Qinling region

崔晓临, 程贇, 张露, 卫晓庆. 基于DEM修正的MODIS地表温度产品空间插值[J]. 地球信息科学学报, 2018, 20(12): 1768-1776.DOI:10.12082/dqxxkx.2018.180340

CUI Xiaolin,CHENG Yun,ZHANG Lu,WEI Xiaoqing. Spatial Interpolation of MODIS Land Surface Temperature Products Based on DEM Correction[J]. Journal of Geo-information Science, 2018, 20(12): 1768-1776.DOI:10.12082/dqxxkx.2018.180340

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方法	MEAN/℃	RMS/℃
IDW	0.0064	1.29
DEM+IDW	0.0182	0.91
SPLINE	0.0021	1.11
DEM+ SPLINE	0.0015	0.80
OK	0.0006	1.10
DEM+OK	0.0004	0.79
TREND	0.0007	3.22
DEM+TREND	0.0001	2.39

月份	DEM+IDW	DEM+SPLINE	DEM+OK	DEM+TREND
1	1.23	1.07	1.06	2.65
2	0.90	0.75	0.75	2.24
3	1.15	1.04	1.02	2.76
4	0.85	0.77	0.74	2.48
5	0.94	0.86	0.83	2.61
6	0.73	0.62	0.62	2.03
7	0.70	0.61	0.60	1.99
8	0.77	0.67	0.66	2.26
9	1.01	0.93	0.90	2.70
10	0.87	0.75	0.75	2.35
11	0.91	0.80	0.78	2.38
12	0.83	0.72	0.73	2.23

云覆盖像元（pixels）	DEM+OK	DEM+SPLINE
100	0.49	0.50
300	0.59	0.44
500	0.50	0.41
700	0.49	0.42
900	0.49	0.41
1100	0.54	0.40
1300	0.72	0.51
1900	0.72	0.47
2300	0.75	0.51
2700	0.82	0.55
3100	1.02	0.66
3900	1.02	0.73
4500	1.07	0.74
5700	1.19	0.92
6900	1.09	0.96
10 400	1.23	1.03