Improving the Accuracy of Landsat 8 Land Surface Temperature in Arid Regions by MODIS Water Vapor Imagery

Land surface temperature (LST) is a significant environmental factor in many studies. LST estimation methods require various parameters, such as emissivity, temperature, atmospheric transmittance and water vapor. Uncertainty in these parameters can cause error in LST estimation. The present study sh...

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Bibliographic Details
Published in:Atmosphere 2023-10, Vol.14 (10), p.1589
Main Authors: Arabi Aliabad, Fahime, Zare, Mohammad, Ghafarian Malamiri, Hamidreza, Ghaderpour, Ebrahim
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Arid regions
Arid zones
Atmospheric transmittance
Atmospheric water
Atmospheric water vapor
cross-validation
Earth resources technology satellites
Earth temperature
Emissivity
Environmental factors
Environmental monitoring
Error analysis
Imagery
Infrared radiation
Land surface temperature
Landsat
Mathematical analysis
Measurement
Methods
MODIS
Parameters
Remote sensing
Root-mean-square errors
Satellites
Sensors
Sentinel-2
Spectroradiometers
split-window algorithm
Surface temperature
Temperature
Temperature requirements
Vegetation
Water vapor
Water vapor variations
Water vapour
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Summary:Land surface temperature (LST) is a significant environmental factor in many studies. LST estimation methods require various parameters, such as emissivity, temperature, atmospheric transmittance and water vapor. Uncertainty in these parameters can cause error in LST estimation. The present study shows how the moderate resolution imaging spectroradiometer (MODIS) water vapor imagery can improve the accuracy of Landsat 8 LST in different land covers of arid regions of Yazd province in Iran. For this purpose, water vapor variation is analyzed for different land covers within different seasons. Validation is performed using T-based and cross-validation methods. The image of atmospheric water vapor is estimated using the MODIS sensor, and its changes are investigated in different land covers. The bare lands and sparse vegetation show the highest and lowest accuracy levels for T-based validation, respectively. The root mean square error (RMSE) is also calculated as 0.57 °C and 1.41 °C for the improved and general split-window (SW) algorithms, respectively. The cross-validation results show that the use of the MODIS water vapor imagery in the SW algorithm leads to a reduction of about 2.2% in the area where the RMSE group is above 5 °C.
ISSN:2073-4433
2073-4433
DOI:10.3390/atmos14101589