Abstract
Satellite land surface temperature (LST) is vital for climatological and environmental studies. However, LST datasets are not continuous in time and space mainly due to cloud cover. Here we combine LST with Climate Forecast System Version 2 (CFSv2) modeled temperatures to derive a continuous gap filled global LST dataset at a spatial resolution of 1 km. Temporal Fourier analysis is used to derive the seasonality (climatology) on a pixel-by-pixel basis, for LST and CFSv2 temperatures. Gaps are filled by adding the CFSv2 temperature anomaly to climatological LST. The accuracy is evaluated in nine regions across the globe using cloud-free LST (mean values: R2 = 0.93, Root Mean Square Error (RMSE) = 2.7 °C, Mean Absolute Error (MAE) = 2.1 °C). The provided dataset contains day, night, and daily mean LST for the Eastern Mediterranean. We provide a Google Earth Engine code and a web app that generates gap filled LST in any part of the world, alongside a pixel-based evaluation of the data in terms of MAE, RMSE and Pearson’s r.
Highlights
Background & SummaryLand Surface Temperature (LST) is a key variable in surface energy and water balances, as well as in climatological and environmental studies such as agriculture[1,2,3], epidemiology[4,5,6], and ecology[7,8,9].Land and air surface temperatures can be derived from in-situ measurements, satellite observations (LST) and numerical weather prediction (NWP) models
More complex methods include the use of data from meteorological stations and a “multiplier function” that depends on satellite-based normalized difference vegetation index (NDVI)[22], singular spectrum analysis[23], or a combined polar-orbiting thermal infrared and passive microwave (PMW) data[24]
(a) In the first set of files, we provide LSTcont - a continuous gap-filled land surface temperature (LST) dataset at 1 km spatial resolution, as described in this paper
Summary
Land Surface Temperature (LST) is a key variable in surface energy and water balances, as well as in climatological and environmental studies such as agriculture[1,2,3], epidemiology[4,5,6], and ecology[7,8,9]. More complex methods (those including various datasets and advanced statistical methods) include the use of data from meteorological stations and a “multiplier function” that depends on satellite-based normalized difference vegetation index (NDVI)[22], singular spectrum analysis[23], or a combined polar-orbiting thermal infrared and passive microwave (PMW) data[24]. While these methods are more promising in terms of spatial transferability, their complexity limits their use mostly to the remote sensing research community. A full dataset is provided for the Eastern Mediterranean that include day, night, and daily mean gapfilled LST for 2002–2020
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