Long-term remote observations of land surface temperature of the North-Western region of Russia

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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Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites

Remote Sensing of Environment ◽

10.1016/j.rse.2009.03.009 ◽

2009 ◽

Vol 113 (7) ◽

pp. 1556-1565 ◽

Cited By ~ 142

Author(s):

Kaicun Wang ◽

Shunlin Liang

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Longwave Radiation ◽

Modis Land Surface Temperature

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Detection of bad data images in long-term MODIS land surface temperature image time series using statistical outlier detection methods

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.13.048504 ◽

2019 ◽

Vol 13 (04) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Ritesh Mujawdiya ◽

Rajat S. Chatterjee ◽

Dheeraj Kumar ◽

Narendra Singh

Keyword(s):

Time Series ◽

Surface Temperature ◽

Outlier Detection ◽

Land Surface Temperature ◽

Land Surface ◽

Detection Methods ◽

Modis Land Surface Temperature ◽

Bad Data

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Validating MODIS land surface temperature products using long-term nighttime ground measurements

Remote Sensing of Environment ◽

10.1016/j.rse.2007.05.024 ◽

2008 ◽

Vol 112 (3) ◽

pp. 623-635 ◽

Cited By ~ 181

Author(s):

Wenhui Wang ◽

Shunlin Liang ◽

Tilden Meyers

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Modis Land Surface Temperature

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Changes in Vegetation Condition and Surface Fluxes during NAME 2004

Journal of Climate ◽

10.1175/jcli4088.1 ◽

2007 ◽

Vol 20 (9) ◽

pp. 1810-1820 ◽

Cited By ~ 51

Author(s):

Christopher J. Watts ◽

Russell L. Scott ◽

Jaime Garatuza-Payan ◽

Julio C. Rodriguez ◽

John H. Prueger ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

North American ◽

Land Surface ◽

Vegetation Index ◽

Surface Fluxes ◽

North American Monsoon ◽

Forest Site ◽

The North ◽

Dry Spell

Abstract The vegetation in the core region of the North American monsoon (NAM) system changes dramatically after the onset of the summer rains so that large changes may be expected in the surface fluxes of radiation, heat, and moisture. Most of this region lies in the rugged terrain of western Mexico and very few measurements of these fluxes have been made in the past. Surface energy balance measurements were made at seven sites in Sonora, Mexico, and Arizona during the intensive observation period (IOP) of the North American Monsoon Experiment (NAME) in summer 2004 to better understand how land surface vegetation change alters energy flux partitioning. Satellite data were used to obtain time series for vegetation indices and land surface temperature for these sites. The results were analyzed to contrast conditions before the onset of the monsoon with those afterward. As expected, precipitation during the 2004 monsoon was highly variable from site to site, but it fell in greater quantities at the more southern sites. Likewise, large changes in the vegetation index were observed, especially for the subtropical sites in Sonora. However, the changes in the broadband albedo were very small, which was rather surprising. The surface net radiation was consistent with the previous observations, being largest for surfaces that are transpiring and cool, and smallest for surfaces that are dry and hot. The largest evaporation rates were observed for the subtropical forest and riparian vegetation sites. The evaporative fraction for the forest site was highly correlated with its vegetation index, except during the dry spell in August. This period was clearly detected in the land surface temperature data, which rose steadily in this period to a maximum at its end.

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Land Surface Temperature and Its Impact on Land Use and Land Cover: Long-Term Rainfall Analysis Using CHIRPS PENTAD Data in Telangana's Mahabubnagar District

Environment and Ecology Research ◽

10.13189/eer.2021.090607 ◽

2021 ◽

Vol 9 (6) ◽

pp. 399-418

Author(s):

Aloshree Choudhury ◽

Vazeer Mahammood ◽

K. H. V. Durga Rao

Keyword(s):

Land Use ◽

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Rainfall Analysis

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Effect of Land Cover Change and Elevation on Decadal Trend of Land Surface Temperature: A Linear Model with Sum Contrast Analysis

10.21203/rs.3.rs-603120/v1 ◽

2021 ◽

Author(s):

Sahidan Abdulmana ◽

Apiradee Lim ◽

Sangdao Wongsai ◽

Noppachai Wongsai

Keyword(s):

Linear Regression ◽

Land Cover ◽

Surface Temperature ◽

Linear Model ◽

Land Surface Temperature ◽

Land Surface ◽

Control Group ◽

Mountain Range ◽

The North ◽

Decadal Trend

Abstract Land surface temperature (LST) is a significant factor in surface energy balance and global climatology studies. Land cover (LC) and elevation are two factors that affect the change of LST, and their effects depend on different geography. This study aims to demonstrate an alternative approach to examine the change of LST during 20 years (2001 to 2020) on Taiwan Island and to investigate the effect of LC change and elevation on a decadal trend of LST using a linear model that adjusting for each determinate factor. MODIS LST and LC data, as well as GMTED2010 elevation product, were downloaded available website. The natural cubic spline function was used to model annual seasonal patterns in LST. Linear regression model was used to estimate decadal change of long-term LST time series. Weighted sum contrasts linear regression was used to assess the effect of LC transformation and elevation on the decadal LST change by comparing adjusting mean of all factors. The adopted analysis method was an appropriate approach to assess categorical factors than those based on treatment contrasts, requiring specifying a control group to compare means and confidence intervals. Results showed that there was an increase in LST for most of the island. The average daytime and nighttime LST trends were 0.12 and 0.31°C/decade, respectively. However, areas in the southern part of the north-south direction mountain range show a statistically significant increase in LST in both daytime and nighttime. The major landslides caused this noticeable change of surface temperature due to the catastrophic damage of typhoon Morakot in 2009. The results also revealed that the different pattern of LC change has a significant effect on daytime LST, but not on nighttime LST trends. The elevation above 600 m had affected both daytime and nighttime LSTs.

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