Evaluation of the long-term high-resolution infrared radiation sounder land surface temperature during 1980-2009 in Jiangsu Province, China

Abstract Land surface temperature (LST) is a critical state variable for surface energy exchanges as it is one of the controls on emitted radiation at Earth’s surface. LST also exerts an important control on turbulent fluxes through the temperature gradient between LST and air temperature. Although observations of surface energy balance components are widely accessible from in situ stations in most developed regions, these ground-based observations are not available in many underdeveloped regions. Satellite remote sensing measurements provide wider spatial coverage to derive LST over land and are used in this study to form a high-resolution, long-term LST data product. As selected by the Global Energy and Water Exchanges project (GEWEX) Data and Assessments Panel (GDAP) for development of internally consistent datasets, the High Resolution Infrared Radiation Sounder (HIRS) data are used for the primary satellite observations because of the data record length. The final HIRS-consistent, hourly, global, 0.5° resolution LST dataset for clear and cloudy conditions from 1979 to 2009 is developed through merging the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) LST estimates with the HIRS retrievals using a Bayesian postprocessing procedure. The Baseline Surface Radiation Network (BSRN) observations are used to validate the HIRS retrievals, the CFSR LST estimates, and the final merged LST dataset. An intercomparison between the original retrievals and CFSR LST datasets, before and after merging, is also presented with an analysis of the datasets, including an error assessment of the final LST dataset.

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Spatially Continuous and High-Resolution Land Surface Temperature Product Generation: A Review of Reconstruction and Spatiotemporal Fusion Techniques

IEEE Geoscience and Remote Sensing Magazine ◽

10.1109/mgrs.2021.3050782 ◽

2021 ◽

pp. 0-0

Author(s):

Penghai Wu ◽

Zhixiang Yin ◽

Chao Zeng ◽

Si-Bo Duan ◽

Frank-M. Gottsche ◽

...

Keyword(s):

High Resolution ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Product Generation

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Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran

Land Use Policy ◽

10.1016/j.landusepol.2017.11.023 ◽

2018 ◽

Vol 71 ◽

pp. 459-469 ◽

Cited By ~ 22

Author(s):

Amin Tayyebi ◽

Hossein Shafizadeh-Moghadam ◽

Amir H. Tayyebi

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Temporal Patterns ◽

Rapid Urbanization ◽

Spatio Temporal

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Global long-term land surface temperature for NOAA AVHRR: extension from 1981-2000 to 1981-2020

10.5194/egusphere-egu21-14373 ◽

2021 ◽

Author(s):

Jin Ma ◽

Ji Zhou

Keyword(s):

Climate Change ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Global Scale ◽

Mean Bias Error ◽

Bias Error ◽

Important Indicator ◽

Noaa Avhrr

As an important indicator of land-atmosphere energy interaction, land surface temperature (LST) plays an important role in the research of climate change, hydrology, and various land surface processes. Compared with traditional ground-based observation, satellite remote sensing provides the possibility to retrieve LST more efficiently over a global scale. Since the lack of global LST before, Ma et al., (2020) released a global 0.05 &#215;0.05&#160; long-term (1981-2000) LST based on NOAA-7/9/11/14 AVHRR. The dataset includes three layers: (1) instantaneous LST, a product generated based on an ensemble of several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST at 14:30 solar time; and (3) monthly averages of ODC LST. To meet the requirement of the long-term application, e.g. climate change, the period of the LST is extended from 1981-2000 to 1981-2020 in this study. The LST from 2001 to 2020 are retrieved from NOAA-16/18/19 AVHRR with the same algorithm for NOAA-7/8/11/14 AVHRR. The train and test results based on the simulation data from SeeBor and TIGR atmospheric profiles show that the accuracy of the RF-SWA method for the three sensors is consistent with the previous four sensors, i.e. the mean bias error and standard deviation less than 0.10 K and 1.10 K, respectively, under the assumption that the maximum emissivity and water vapor content uncertainties are 0.04 and 1.0 g/cm2, respectively. The preliminary validation against in-situ LST also shows a similar accuracy, indicating that the accuracy of LST from 1981 to 2020 are consistent with each other. In the generation code, the new LST has been improved in terms of land surface emissivity estimation, identification of cloud pixel, and the ODC method in order to generate a more reliable LST dataset. Up to now, the new version LST product (1981-2020) is under generating and will be released soon in support of the scientific research community.

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Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval

Remote Sensing ◽

10.3390/rs12050791 ◽

2020 ◽

Vol 12 (5) ◽

pp. 791 ◽

Cited By ~ 6

Author(s):

Jingjing Yang ◽

Si-Bo Duan ◽

Xiaoyu Zhang ◽

Penghai Wu ◽

Cheng Huang ◽

...

Keyword(s):

Time Series ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Single Channel ◽

Landsat 8 ◽

Atmospheric Profiles ◽

Atmospheric Profile

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm.

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