Abstract
Land Surface Temperature (LST) is an important measurement in studies related to the Earth surface’s processes. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) instrument onboard the Terra spacecraft is the currently available Thermal Infrared (TIR) imaging sensor with the highest spatial resolution. This study involves the comparison of LSTs inverted from the sensor using the Split Window Algorithm (SWA), the Single Channel Algorithm (SCA) and the Planck function. This study has used the National Oceanic and Atmospheric Administration’s (NOAA) data to model and compare the results from the three algorithms. The data from the sensor have been processed by the Python programming language in a free and open source software package (QGIS) to enable users to make use of the algorithms. The study revealed that the three algorithms are suitable for LST inversion, whereby the Planck function showed the highest level of accuracy, the SWA had moderate level of accuracy and the SCA had the least accuracy. The algorithms produced results with Root Mean Square Errors (RMSE) of 2.29 K, 3.77 K and 2.88 K for the Planck function, the SCA and SWA respectively.
Highlights
Land Surface Temperature (LST) is the temperature of the surface of the Earth
The VNIR imagery used in this study were resampled to a spatial resolution of 90 m and thereafter projected to the Universal Transfer Mercator (UTM) for them to match to the spatial resolution of the ASTER instrument’s Thermal Infrared (TIR) bands
The bias between the LSTs estimated from the ASTER instruments was calculated by subtracting the LST obtained from the Surface Radiation (SURFRAD) station from the LST inverted from the where W13 and W14 represent the atmospheric water vapor content while τ13 and τ14 represent the band Sens
Summary
Land Surface Temperature (LST) is the temperature of the surface of the Earth. LST is among the most important datasets collected by satellites from space. LST is used in many applications such as evapotranspiration, hydrology, climate change, geothermal energy related studies, Earth heat budget studies and many others [1,2,3]. LST varies rapidly with time and location [4], and, as a result, in order be able to acquire accurate LST measurements over time, there arises a need to estimate LST in a relatively higher spatial resolution. Due to the high variation of temperature over land, satellite derived LST provides researchers with a unique opportunity to acquire LST of the entire globe with a relatively high spatial resolution in average values rather than values in a point form [5].
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