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.
| Original language | English |
|---|---|
| Article number | 74 |
| Journal | Scientific data |
| Volume | 8 |
| Issue number | 1 |
| DOIs | |
| State | Published - 4 Mar 2021 |
Bibliographical note
Publisher Copyright:© 2021, The Author(s).
Funding
The authors wish to thank the anonymous reviewers for their valuable comments, which considerably contributed to the quality of the paper, and to Prof. Steve Brenner for valuable discussions and advice. The authors thank Israel Ministries of Agriculture and Rural Development (grant number 203-1184-19) and of Science & technology (grant number 18041).
| Funders | Funder number |
|---|---|
| Science & technology | 18041 |
| Ministry of Agriculture and Rural Development | 203-1184-19 |