Abstract
Land surface temperature (LST) is an important parameter in various fields including hydrology, climatology, and geophysics. Its derivation by thermal infrared remote sensing has long tradition but despite substantial progress there remain limited data availability and challenges like emissivity estimation, atmospheric correction, and cloud contamination. The annual temperature cycle (ATC) is a promising approach to ease some of them. The basic idea to fit a model to the ATC and derive annual cycle parameters (ACP) has been proposed before but so far not been tested on larger scale. In this study, a new global climatology of annual LST based on daily 1 km MODIS/Terra observations was processed and evaluated. The derived global parameters were robust and free of missing data due to clouds. They allow estimating LST patterns under largely cloud-free conditions at different scales for every day of year and further deliver a measure for its accuracy respectively variability. The parameters generally showed low redundancy and mostly reflected real surface conditions. Important influencing factors included climate, land cover, vegetation phenology, anthropogenic effects, and geology which enable numerous potential applications. The datasets will be available at the CliSAP Integrated Climate Data Center pending additional processing.
Highlights
Land surface temperature (LST) is the temperature of the Earth’s surface or “skin” [1]
In this study a new global dataset of annual land surface temperature based on MODIS LST on 1 km resolution was presented and potential applications were highlighted
The annual cycle parameters (ACP): yearly amplitude of surface temperature (YAST), mean annual surface temperature (MAST), θ, root mean squared error (RMSE), and number of clear sky acquisitions (NCSA) for daytime and nighttime were calculated from all acquisitions from
Summary
Land surface temperature (LST) is the temperature of the Earth’s surface or “skin” [1]. Among the most frequently used approaches, are classification based methods [19] and normalized difference vegetation index (NDVI) based methods [20,21] While the former assume constant emissivity within a particular land cover class, the latter assume that the vegetation fraction is the dominant influence on the emissivity. Promising methods to enhance the spatiotemporal resolution and availability of LST data include downscaling ( referred as disaggregation or fusion) [24,25,26,27,28] or estimation of sub-cloud temperature [29]. A Levenberg-Marquardt minimization scheme was utilized to fit a DTC model to the time series of cloud-screened brightness temperatures and describe the thermal behavior of the land surface by five determined model parameters per day, which were found to be related to relevant physical properties of the land surface, such as NDVI and thermal inertia.
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