Special Issue "Remote Sensing for Land Surface Temperature (LST) Estimation, Generation, and Analysis"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: 31 March 2018
Dr. Zhaoliang Li
Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Website | E-Mail
Phone: +(86) 10 82 10 50 77
Interests: thermal infrared remote sensing; land surface temperature; land surface emissivity; evapotranspiration; scaling problem; hyperspectral analysis; radiative transfer modelling
Dr. Bo-Hui Tang
State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: retrieval and validation of land surface temperature/emissivity; land surface net radiation
As the direct driving force in the exchange of long-wave radiation and turbulent heat fluxes at the surface–atmosphere interface, land surface temperature (LST) is one of the most important parameters in the physical processes of surface energy and water balance at local to global scales. Knowledge of reliable estimates of LST is crucial because many applications such as evapotranspiration, climate change, hydrological cycle, vegetation monitoring, urban climate and environmental studies, etc., rely on it.
With the development of remote sensing from space, satellite data offer the only possibility for measuring LST over the entire globe with sufficiently high temporal resolution and with complete spatially averaged rather than ground point-based values. Consequently, many efforts have been carried out to estimate LST from satellite thermal infrared (TIR) data. Up to now, many methods have been developed for retrieving LST from polar-orbit and geostationary satellite TIR data, and several methods are used to generate global LST products with fine spatial resolution, such as MODIS and ASTER LST products. However, there is still no “best method” for retrieving LST from space. All of the methods either rely on statistical relationships or assumptions and constraints to solve the inherent, ill-posed retrieval problem. Currently, TIR remote sensing measurements have been greatly improved in terms of spectral, spatial, and temporal resolution. These improvements will soon produce a clearer picture of the land surface than ever before. This is a good opportunity and also a big challenge to solve the inherent, ill-posed problem of retrieving LST from satellite data.
On the other hand, TIR data lose efficiency when the land surface is fully or partly covered by clouds. The passive microwave can observe the Earth’s surface under all-weather conditions but with a coarser spatial resolution. Its measurements are proposed to retrieve LST over cloudy skies and an effective model of combining LSTs retrieved from TIR and passive microwave satellite data is attempted to generate an all-weather high spatial LST product. This Special Issue plans to demonstrate the state-of-the-art reflecting the retrieval of LST from space measurements and the growing interest in generation and analyses of this parameter.
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Tang, H.; Shao, K.; Li, Z.L.*; Wu, H.; Nerry, F.; Zhou, G. Estimation and validation of land surface temperature from Chinese second generation polar-orbiting FY-3A VIRR data. Remote Sens. 2015, 7, 3250–3273, doi:10.3390/rs70303250.
Tang, H.; Shao, K.; Li, Z.L.*; Wu, H.; Tang, R. An improved NDVI-based threshold method for estimating land surface emissivity using MODIS satellite data. Int. J. Remote Sens. 2015, 36, 4864–4878. doi:10.1080/01431161.2015.1040132.
Tang, B.H.*; Wang, J. A physics-based method to retrieve land surface temperature from MODIS daytime mid-infrared data. IEEE Trans. Geosci. Remote Sens. 2016, 54, 4672–4679. doi:10.1109/TGRS.2016.2548500.
Dr. Zhao-Liang Li
Dr. Bo-Hui Tang
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Land surface temperature
Land surface emissivity
Thermal infrared data
Passive microwave data
LST product generation
Temperature and emissivity separation