Generation of land surface temperature with high spatial and temporal resolution based on FSDAF method

doi:10.6046/gtzyyg.2018.01.08

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Abstract

The application of the high spatio-temporal resolution data possesses very extensive foreground. Consequently, based on a flexible spatio-temporal data fusion(FSDAF)method and using MODIS and ASTER data,the authors generate the land surface temperature(LST) with high spatial and temporal resolution. FSDAF is a method based on spectral unmixing and thin plate spline interpolation function. Compared with the existing spatio-temporal data fusion method, its advantages lie in less input data,suitableness for heterogeneous surface and capability of predicting the gradient of land cover types and so on. The fusion results were verified by using the ASTER temperature products(7 days) and the surface radiation infrared temperature data(4 days)of the automatic weather station(AWS) sites. The results show that the LST images generated by the data fusion method based on FSDAF have higher clarity, the correlation coefficient of the fusion images and the ASTER LST products is higher than 0.91(September 28) , the room mean square error (RMSE) is less than 2.44 k(September 19), the mean absolute error (MAE) is less than 1.84 k (September 19)and the correlation coefficient of the fusion images and the AWS LST data R2 is higher than 0.64(August 18).

Keywords spatial and temporal fusion data fusion remote sensing land surface temperature(LST) automatic weather station(AWS)

TP751.1

Issue Date: 08 February 2018

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	Min YANG
	Guijun YANG
	Xiaoning CHEN
	Yongfeng ZHANG
	Jingni YOU

Cite this article:

Min YANG,Guijun YANG,Xiaoning CHEN, et al. Generation of land surface temperature with high spatial and temporal resolution based on FSDAF method[J]. Remote Sensing for Land & Resources, 2018, 30(1): 54-62.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.01.08 OR https://www.gtzyyg.com/EN/Y2018/V30/I1/54

Fig.1 Location of study area and distribution of AWS sites

Tab.1 Remote sensing data used in study

Fig.2 Technique flowchart

Fig.3 Average standard deviation of 17 AWS stations in 4 days

Fig.4 Scatter plots for ground measured LST by AWS station and LST generated by FSDAF fusion

Fig.5-1 LST distribution in study area in different dates

Fig.5-2 LST distribution in study area in different dates

Fig.6 Difference image of true LST and predicted LST

Fig.7-1 Scatter plots of LST predicted by FSDAF method and ASTER LST product

Fig.7-2 Scatter plots of LST predicted by FSDAF method and ASTER LST product

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