scholarly journals Editorial for the Special Issue “Remote Sensing Monitoring of Land Surface Temperature”

2021 ◽  
Vol 13 (9) ◽  
pp. 1765
Author(s):  
Juan M. Sánchez ◽  
César Coll ◽  
Raquel Niclòs
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
State Of The Art ◽  
Thermal Infrared ◽  
The State ◽  
Special Issue ◽  
Tir Domain ◽  
Remote Sensing Monitoring

The combination of the state-of-the-art in the thermal infrared (TIR) domain [...]

scholarly journals Application of Infrared Remote Sensing and Magnetotelluric Technology in Geothermal Resource Exploration: A Case Study of the Wuerhe Area, Xinjiang

2021 ◽  
Vol 13 (24) ◽  
pp. 4989
Author(s):  
Hong Chen ◽  
Xingbing Xie ◽  
Enqin Liu ◽  
Lei Zhou ◽  
Liangjun Yan
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Infrared ◽  
Geothermal Resource ◽  
Magnetotelluric Data ◽  
Thermal Infrared Remote Sensing ◽  
Infrared Remote Sensing

As a new green energy source, geothermal resource’s exploration, development, and utilization are an important direction in geophysical exploration at present. In this study, the actual land surface temperature was inferred based on the thermal infrared band of Landsat8 remote-sensing images, and the information about the surface anomalies and their spatial distribution was obtained through a multifactor analysis. In addition, three magnetotelluric sounding profiles were deployed in the study area, and the geo-electric sections in the study area were obtained through inversion of the measured data. Then, based on the inverse geo-electric information and the land surface temperature anomaly information, we analyzed and verified the geothermal resource genesis of the thermal anomaly area and inferred the favorable geothermal resource area in the study area. The results show that these two methods can be used to compare and analyze the possible distribution of the geothermal resources in the study area in two dimensions: the spatial distribution on the surface and the vertical distribution in the subsurface. Moreover, the results of the geothermal anomalies inferred from the thermal infrared remote sensing and the geo-electric results inferred from the magnetotelluric data are in good agreement. This study demonstrates that the integrated application of thermal infrared remote sensing and magnetotelluric technology is a promising tool for geothermal exploration.

A General Framework of Kernel-driven Modeling in the Thermal Infrared Band for Land Surface Temperature Normalization

2020 ◽  
Author(s):  
Biao Cao ◽  
Qinhuo Liu ◽  
Yongming Du ◽  
Hua Li ◽  
Zunjian Bian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
General Framework ◽  
Thermal Infrared ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Modeling Framework ◽  
New Models

<p>Land surface temperature (LST) is the direct driving force of turbulent heat fluxes at the surface and atmosphere interface and is widely used in the fields of evapotranspiration estimation (Su et al., 2002) and energy budget (Liang et al., 2019). Remote sensing products offer the only possibility for measuring LST with completely spatially averaged values. The thermal radiation directionality (TRD) effect has been widely concerned in the area of thermal infrared (TIR) remote sensing over 50 years which can lead to the directional brightness temperature (DBT) difference between different viewing directions up to 10 K (Cao et al., 2019). Many models have been proposed to simulate the DBT patterns over different underlying surfaces aimed to achieve the TRD effect correction for the satellite LST products. In practice, it is advised to handle only TRD models having a limited number of input parameters for operational normalization of LST products. The use of TIR kernel-driven models appears a good tradeoff between physical accuracy and operationality. It remains that the existing 4 TIR kernel-driven models (Ross-Li, LSF-Li, Vinnikov, RL) underestimate the hotspot effect, especially for continuous canopies. In this study, a new general framework of TIR kernel-driven modeling is proposed to overcome such issue. It is a linear combination of three kernels (including a base shape kernel, a hotspot kernel and an isotropic kernel) with the ability to simulate the bowl, dome and bell shapes in the solar principal plane. 4 specific models (Vinnikov-RL, LSF-RL, Vinnikov-Chen, LSF-Chen) within the new framework were further developed to assess their fitting abilities for both continuous and discrete vegetation canopies. To evaluate 4 existing models and 4 new models comprehensively, it was prepared 102 groups of 4SAIL/DART generated multi-angle datasets considering 6 different canopy architectures and 17 component temperatures. Results show that the 4 new models behave slightly better than the 4 existing models over discrete canopies (R2 increases from 0.791~0.989 to 0.976~0.996) whereas they significantly improved the fitting accuracy over continuous canopies (R2 increases from 0.661~0.970 to 0.940~0.997). The innovative new general framework with three kernels and four parameters improve the fitting ability significantly since the addition of one more degree of freedom. This new kernel-driven modeling framework is a potential tool to achieve angular correction of LST products.</p>

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