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Mingsong Li
School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu, 611731, China

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Journal article
Published: 12 June 2019 in Agricultural and Forest Meteorology
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Ground measured component radiative temperatures are basic inputs for modelling energy and hydrological processes and for simulating land surface temperature (LST) as “viewed” by remote sensors. However, knowledge of factors affecting the component temperatures and about their potential for upscaling LST over sparsely vegetated surfaces with high heterogeneity is still lacking. Here, a MUlti-Scale Observation Experiment on land Surface temperature (MUSOES) was performed under HiWATER over an arid sparsely vegetated surface. Component temperatures were obtained with different instruments on multiple spatial scales; for LST upscaling, a three-dimensional scene model was employed for two forest stations (MFS and PFS) and a two-dimensional model for a shrub station (SUP). Results show that intrinsic characteristics contribute to the temperature variability between different components and even within a single component. Using a thermal infrared (TIR) imager at MFS, average temperature difference of 24.9 K between sunlit bare soil and tree canopy was found; different components exhibit different internal temperature differences at direction-level and pixel-level. Furthermore, illumination conditions, viewing directions, and instrument types significantly affected the measured component temperatures. The measurements of the TIR radiometer and the imager can deviate considerably (e.g. 14.9 K for sunlit bare soil at MFS). When the longwave radiometers were selected as target sensors, the component temperatures measured by the imager exhibit good potential for LST upscaling: the upscaled LST has MBD/RMSD values of 2.0 K/2.3 K at MFS and 2.0 K/2.5 K at PFS. The TIR radiometer’s measurements introduce large uncertainties into LST upscaling at MFS and PFS, but result in good accuracy at SUP, mainly due to its simpler land cover and surface structure. Findings from this study can benefit our understanding of factors affecting observations of component temperatures and the LST upscaling process and are, therefore, relevant for further studying the evaluation of satellite LST products.

ACS Style

Mingsong Li; Ji Zhou; Zhixing Peng; Shaomin Liu; Frank-Michael Göttsche; Xiaodong Zhang; Lisheng Song. Component radiative temperatures over sparsely vegetated surfaces and their potential for upscaling land surface temperature. Agricultural and Forest Meteorology 2019, 276-277, 107600 .

AMA Style

Mingsong Li, Ji Zhou, Zhixing Peng, Shaomin Liu, Frank-Michael Göttsche, Xiaodong Zhang, Lisheng Song. Component radiative temperatures over sparsely vegetated surfaces and their potential for upscaling land surface temperature. Agricultural and Forest Meteorology. 2019; 276-277 ():107600.

Chicago/Turabian Style

Mingsong Li; Ji Zhou; Zhixing Peng; Shaomin Liu; Frank-Michael Göttsche; Xiaodong Zhang; Lisheng Song. 2019. "Component radiative temperatures over sparsely vegetated surfaces and their potential for upscaling land surface temperature." Agricultural and Forest Meteorology 276-277, no. : 107600.

Journal article
Published: 25 November 2016 in Remote Sensing
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Most current statistical models for downscaling the remotely sensed land surface temperature (LST) are based on the assumption of the scale-invariant LST-descriptors relationship, which is being debated and requires an in-depth examination. Additionally, research on downscaling LST to high or very high resolutions (~10 m) is still rare. Here, a simple analytical model was developed to quantify the scale effect in downscaling the LST from a medium resolution (~100 m) to high resolutions. The model was verified in the Zhangye oasis and Beijing city. Examinations of the simulation datasets that were generated based on airborne and space station LSTs demonstrate that the developed model can predict the scale effect in LST downscaling; the scale effect exists in both of these two study areas. The model was further applied to 12 ASTER images in the Zhangye oasis during a complete crop growing season and one Landsat-8 TIRS image in Beijing city in the summer. The results demonstrate that the scale effect is intrinsically caused by the varying probability distribution of the LST and its descriptors at the native and target resolutions. The scale effect depends on the values of the descriptors, the phenology, and the ratio of the native resolution to the target resolution. Removing the scale effect would not necessarily improve the accuracy of the downscaled LST.

ACS Style

Ji Zhou; Shaomin Liu; Mingsong Li; Wenfeng Zhan; Ziwei Xu; Tongren Xu. Quantification of the Scale Effect in Downscaling Remotely Sensed Land Surface Temperature. Remote Sensing 2016, 8, 975 .

AMA Style

Ji Zhou, Shaomin Liu, Mingsong Li, Wenfeng Zhan, Ziwei Xu, Tongren Xu. Quantification of the Scale Effect in Downscaling Remotely Sensed Land Surface Temperature. Remote Sensing. 2016; 8 (12):975.

Chicago/Turabian Style

Ji Zhou; Shaomin Liu; Mingsong Li; Wenfeng Zhan; Ziwei Xu; Tongren Xu. 2016. "Quantification of the Scale Effect in Downscaling Remotely Sensed Land Surface Temperature." Remote Sensing 8, no. 12: 975.

Journal article
Published: 29 May 2015 in Remote Sensing
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Validation and performance evaluations are beneficial for developing methods that estimate the remotely sensed land surface temperature (LST). However, such evaluations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data are rare. By selecting the middle reach of the Heihe River basin (HRB), China, as the study area, the atmospheric correction (AC), mono-window (MW), single-channel (SC), and split-window (SW) methods were evaluated based on in situ measured LSTs. Results demonstrate that the influences of surface heterogeneity on the validation are significant in the study area. For the AC, MW, and SC methods, the LSTs estimated from channel 13 are more accurate than those from channel 14 in general cases. When the in situ measured atmospheric profiles are available, the AC method has the highest accuracy, with a root-mean squared error (RMSE) of about 1.4–1.5 K at the homogenous oasis sites. In actual application without sufficient in situ measured inputs, the MW method is highly accurate; the RMSE is around 1.5–1.6 K. The SC method systematically overestimates LSTs and it is sensitive to error in the water vapor content. The two SW methods are simple to use but their performances are limited by accuracies, revealed by the simulation dataset. Therefore, when the in situ atmospheric profiles are available, the AC method is recommended to generate reliable ASTER LSTs for modeling the eco-hydrological processes in the middle reach of the HRB. When sufficient in situ measured inputs are not available, the MW method can be used instead.

ACS Style

Ji Zhou; Mingsong Li; Shaomin Liu; Zhenzhen Jia; Yanfei Ma. Validation and Performance Evaluations of Methods for Estimating Land Surface Temperatures from ASTER Data in the Middle Reach of the Heihe River Basin, Northwest China. Remote Sensing 2015, 7, 7126 -7156.

AMA Style

Ji Zhou, Mingsong Li, Shaomin Liu, Zhenzhen Jia, Yanfei Ma. Validation and Performance Evaluations of Methods for Estimating Land Surface Temperatures from ASTER Data in the Middle Reach of the Heihe River Basin, Northwest China. Remote Sensing. 2015; 7 (6):7126-7156.

Chicago/Turabian Style

Ji Zhou; Mingsong Li; Shaomin Liu; Zhenzhen Jia; Yanfei Ma. 2015. "Validation and Performance Evaluations of Methods for Estimating Land Surface Temperatures from ASTER Data in the Middle Reach of the Heihe River Basin, Northwest China." Remote Sensing 7, no. 6: 7126-7156.