Study of land surface temperature and spectral emissivity using multi-sensor satellite data

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

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Assessing the land surface temperature and spectral emissivity with microwave data

Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.1994.399193 ◽

2002 ◽

Author(s):

J.R. Givri ◽

S.H. Souffez

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Spectral Emissivity ◽

Microwave Data

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Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method

Remote Sensing of Environment ◽

10.1016/j.rse.2021.112566 ◽

2021 ◽

Vol 263 ◽

pp. 112566

Author(s):

Aolin Jia ◽

Han Ma ◽

Shunlin Liang ◽

Dongdong Wang

Keyword(s):

Energy Balance ◽

Surface Energy ◽

Surface Temperature ◽

Land Surface Temperature ◽

Satellite Data ◽

Land Surface ◽

Surface Energy Balance

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Plot-scale retrieval of land surface temperature and emissivity estimation

10.5194/egusphere-egu21-3962 ◽

2021 ◽

Author(s):

Gitanjali Thakur ◽

Stan Schymanski ◽

Kaniska Mallick ◽

Ivonne Trebs

Keyword(s):

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Satellite Data ◽

Land Surface ◽

Longwave Radiation ◽

Landscape Scale ◽

The Earth ◽

Downwelling Longwave Radiation

The surface energy balance (SEB) is defined as the balance between incoming energy from the sun and outgoing energy from the Earth&#8217;s surface. All components of the SEB depend on land surface temperature (LST). Therefore, LST is an important state variable that controls the energy and water exchange between the Earth&#8217;s surface and the atmosphere. LST can be estimated radiometrically, based on the infrared radiance emanating from the surface. At the landscape scale, LST is derived from thermal radiation measured using&#160; satellites.&#160; At the plot scale, eddy covariance flux towers commonly record downwelling and upwelling longwave radiation, which can be inverted to retrieve LST&#160; using the grey body equation : &#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; Rlup = &#949;&#963; Ts4 + (1 &#8722; &#949;) R ldw&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; (1) where Rlup is the upwelling longwave radiation, Rldw is the downwelling longwave radiation, &#949; is the surface emissivity, Ts&#160; is the surface temperature and &#963;&#160; is the Stefan-Boltzmann constant. The first term is the temperature-dependent part, while the second represents reflected longwave radiation. Since in the past downwelling longwave radiation was not measured routinely using flux towers, it is an established practice to only use upwelling longwave radiation for the retrieval of plot-scale LST, essentially neglecting the reflected part and shortening Eq. 1 to: &#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; Rlup = &#949;&#963; Ts4 &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160;&#160; (2) Despite&#160; widespread availability of downwelling longwave radiation measurements, it is still common to use the short equation (Eq. 2) for in-situ LST retrieval. This prompts the question if ignoring the downwelling longwave radiation introduces a bias in LST estimations from tower measurements. Another associated question is how to obtain the correct &#949; needed for in-situ LST retrievals using tower-based measurements. The current work addresses these two important science questions using observed fluxes at eddy covariance towers for different land cover types. Additionally, uncertainty in retrieved LST and emissivity due to uncertainty in input fluxes was quantified using SOBOL-based uncertainty analysis (SALib). Using landscape-scale emissivity obtained from satellite data (MODIS), we found that the LST&#160; obtained using the complete equation (Eq. 1) is 0.5 to 1.5 K lower than the short equation (Eq. 2). Also, plot-scale emissivity was estimated using observed sensible heat flux and surface-air temperature differences. Plot-scale emissivity obtained using the complete equation was generally between 0.8 to 0.98 while the short equation gave values between 0.9 to 0.98, for all land cover types. Despite additional input data for the complete equation, the uncertainty in plot-scale LST was not greater than if the short equation was used. Landscape-scale daytime LST obtained from satellite data (MODIS TERRA) were strongly correlated with our plot-scale estimates, but on average higher by 0.5 to 9 K, regardless of the equation used. However, for most sites, the correspondence between MODIS TERRA LST and retrieved plot-scale LST estimates increased significantly if plot-scale emissivity was used instead of the landscape-scale emissivity obtained from satellite data.

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Spatial-temporal variation of land surface temperature of Jubail Industrial City, Saudi Arabia due to seasonal effect by using Thermal Infrared Remote Sensor (TIRS) satellite data

Journal of African Earth Sciences ◽

10.1016/j.jafrearsci.2019.03.008 ◽

2019 ◽

Vol 155 ◽

pp. 54-63 ◽

Cited By ~ 3

Author(s):

P. Sheik Mujabar

Keyword(s):

Saudi Arabia ◽

Surface Temperature ◽

Temporal Variation ◽

Land Surface Temperature ◽

Satellite Data ◽

Land Surface ◽

Thermal Infrared ◽

Seasonal Effect ◽

Industrial City ◽

Remote Sensor

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Physically constrained spatiotemporal modeling: generating clear-sky constructions of land surface temperature from sparse, remotely sensed satellite data

Journal of Applied Statistics ◽

10.1080/02664763.2019.1681384 ◽

2019 ◽

Vol 47 (8) ◽

pp. 1439-1459 ◽

Cited By ~ 1

Author(s):

Gavin Q. Collins ◽

Matthew J. Heaton ◽

Leiqiu Hu

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Satellite Data ◽

Land Surface ◽

Remotely Sensed ◽

Spatiotemporal Modeling ◽

Clear Sky

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An improved physical method with linear spectral emissivity constraint to retrieve land surface temperature, emissivity and atmospheric profiles from satellite-based hyperspectral thermal infrared data

2012 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2012.6350991 ◽

2012 ◽

Cited By ~ 1

Author(s):

Ning Wang ◽

Hua Wu ◽

Lingling Ma ◽

Xinhong Wang ◽

Yonggang Qian ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Physical Method ◽

Thermal Infrared ◽

Spectral Emissivity ◽

Atmospheric Profiles ◽

Infrared Data

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Airborne mapping and in situ validation of European land surface temperature using the NASA-JPL HyTES sensor. Results from the 2019 European NET-Sense Campaign in support of the Copernicus High Priority Candidate satellite mission development.

10.5194/egusphere-egu2020-21846 ◽

2020 ◽

Author(s):

Martin Wooster ◽

James Johnson ◽

Tom Dowling ◽

Mark de Jong ◽

Mark Grosvenor ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Thermal Emission ◽

Spectral Emissivity ◽

Development Effort ◽

Satellite Mission ◽

Evapotranspiration Estimation ◽

The Uk

The NASA ESA Temperature Sensing Experiment (NET-Sense) is a NASA and ESA funded campaign in support of the Copernicus Land Surface Temperature Monitoring (LSTM) satellite mission.The LSTM mission would carry a calibrated, high spatial-temporal resolution thermal infrared imager whose data would be used to provide the land-surface temperature information required for such applications as evapotranspiration estimation at the European field-scale. The LSTM mission responds to priority requirements of the agricultural user community for improving sustainable agricultural productivity in a world of increasing water scarcity and variability.As part of the effort to LSTM mission development effort, the first non-US flights of NASA JPL&#8217;s state-of-the-art Hyperspectral Thermal Emission Spectrometer (HyTES) were conducted on a UK research aircraft in both the UK and Italy in June and July 2019. HyTES is an airborne thermal hyperspectral imager providing extremely high quality and radiometrically precise infrared radiances within 256 spectral channels across the spectral range 7.5 to 12 &#181;m, with the primary aim to map LST and surface spectral emissivity. Flights in Italy were accompanied by the HyPLANT and TASI instruments, operated by FZ-Juelich, Germany installed aboard a second aircraft from CzechGlobe (CZ).We provide an overview of the NET-Sense campaign, example results from HyTES and comparisons to in situ LST and surface spectral emissivity data collected co-incident with the aircraft overflights using tower-mounted radiometers and portable FTIR spectrometers adapted for the purpose. We explain the integration of NET-Sense into the broader science strategy for the LSTM mission, and highlight planned activities for the coming years, including NET-Sense 2020 European campaign plans.

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