Downscaling Land Surface Temperature in Complex Regions by Using Multiple Scale Factors with Adaptive Thresholds.

Yingbao Yang,Yong Zhang,Xin Pan,Xiaolong Li,Chen Cao

doi:10.3390/s17040744

Yingbao Yang, Yong Zhang + Show 3 more

Open Access

https://doi.org/10.3390/s17040744

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Abstract

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was downscaled by a multiple linear regression model between LST and multiple scale factors in mixed areas with three or four surface types. The correlation coefficients (CCs) between LST and the scale factors were used to assess the importance of the scale factors within a moving window. CC thresholds determined which factors participated in the fitting of the regression equation. The proposed downscaling approach, which involves an adaptive selection of the scale factors, was evaluated using the LST derived from four Landsat 8 thermal imageries of Nanjing City in different seasons. Results of the visual and quantitative analyses show that the proposed approach achieves relatively satisfactory downscaling results on 11 August, with coefficient of determination and root-mean-square error of 0.87 and 1.13 °C, respectively. Relative to other approaches, our approach shows the similar accuracy and the availability in all seasons. The best (worst) availability occurred in the region of vegetation (water). Thus, the approach is an efficient and reliable LST downscaling method. Future tasks include reliable LST downscaling in challenging regions and the application of our model in middle and low spatial resolutions.

Highlights

As an important parameter for characterizing the balance of surface energy, land surface temperature (LST) serves a key function in biophysical–chemical processes [1] and has been widely used in common applications, such as soil moisture estimation [2,3], forest fire detection [4], and urban heat environment monitoring [5,6,7]
Landsat satellites are frequently used in LST retrieval because of their high spatial resolution and wide availability of the data to the public, the LST retrieved from Landsat data is usually mixed with pixel temperature
This paper presents a strategy for downscaling LST in an area with various land cover types by using four scale factors, which are adaptively selected according to the CCs between LST and the scale factors within every moving window

Summary

Introduction

As an important parameter for characterizing the balance of surface energy, land surface temperature (LST) serves a key function in biophysical–chemical processes [1] and has been widely used in common applications, such as soil moisture estimation [2,3], forest fire detection [4], and urban heat environment monitoring [5,6,7]. Thermal infrared remote sensing (TIRS) can detect surface temperature and describe the spatial differences and diversity in LST [8] dynamically and macroscopically. Landsat satellites are frequently used in LST retrieval because of their high spatial resolution and wide availability of the data to the public, the LST retrieved from Landsat data is usually mixed with pixel temperature. Urban surfaces are characterized by high heterogeneity [5]. In this case, the LST retrieved from satellite-borne sensors has an insufficient spatial resolution for some urban applications. Downscaling may be applied to enhance the spatial resolution of thermal images with relatively low resolution [9]

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