气候变化领域集成服务门户(CCPortal): Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information

CCPortal

DOI	10.3390/rs10071112
	Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information
	Kang, Jian; Tan, Junlei; Jin, Rui; Li, Xin; Zhang, Yang
通讯作者	Jin, R (通讯作者)
发表日期	2018
EISSN	2072-4292
卷号	10 期号:7
英文摘要	Land surface temperature (LST) products derived from the moderate resolution imaging spectroradiometer (MODIS) sensor are one of the most important data sources used to research land surface energy and water balance at regional and global scales. However, MODIS data are severely contaminated by cloud cover, which limits the applications of LST products. In this paper, based on the spatio-temporal autocorrelation of land surface variables, a reconstruction algorithm depending on the correlations between spatial pixels in multiple time phases from available MODIS LST data is developed to reconstruct clear-sky LST values for missing pixels. Considering the impacts of correlation and bias between predictors and reconstructed data on the modeling error, the known data in the reconstructed time phase are combined with the data temporally nearest to them as predictor variables to establish their temporal relationships with the reconstructed data. The reconstructed results are validated by a series of evaluation indices. The average correlation coefficient between the reconstructed results and ground-based observations is 0.87, showing high temporal change accuracy. The difference in Moran's I, representing spatial structure characteristics between the known and reconstructed data, is 0.03 on average, indicating a slight loss of spatial accuracy. The average reconstruction rate is approximately 87.0%. The modeling error, as part of the reconstruction error, is only 1.40 K on average and accounts for 5.0% of the total error. If the product and modeling errors are removed, the residual error represents approximately 3.5 K and 5.6 K of the annual mean difference between the cloudy and cloudless LST at night and during the day, respectively. In addition, different reconstruction cases are demonstrated using various predictor data, including many combinations of multi-temporal MODIS LST data, the microwave brightness temperature, and the combination of the normalized difference vegetation index and terrain data. Comparisons among cases show that the known MODIS LST data are more reliable as predictor variables and that the data combination advocated in this paper is optimal.
关键词	EMISSIVITY LST RADIATION VALIDATION
英文关键词	temporal correlation; time series; multi-temporal; reconstruction of MODIS land surface temperature
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000440332500135
来源期刊	REMOTE SENSING
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259117
推荐引用方式 GB/T 7714	Kang, Jian,Tan, Junlei,Jin, Rui,et al. Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information[J]. 中国科学院青藏高原研究所,2018,10(7).
APA	Kang, Jian,Tan, Junlei,Jin, Rui,Li, Xin,&Zhang, Yang.(2018).Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information.REMOTE SENSING,10(7).
MLA	Kang, Jian,et al."Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information".REMOTE SENSING 10.7(2018).