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.

卫星陆地表面温度（LST）对于气候和环境研究至关重要。但是，LST数据集在时间和空间上并不连续，这主要是由于云层覆盖。在这里，我们将LST与气候预测系统第2版（CFSv2）建模的温度结合起来，以1 km的空间分辨率得出连续的，充满空白的LST数据集。对于LST和CFSv2温度，使用时间傅立叶分析以逐像素为基础得出季节性（气候）。通过将CFSv2温度异常添加到气候LST中来填补空白。使用无云LST在全球9个地区评估了准确性（平均值：R ² = 0.93，均方根误差（RMSE）= 2.7°C，平均绝对误差（MAE）= 2.1°C）。提供的数据集包含东地中海的白天，夜晚和每日平均LST。我们提供了Google Earth Engine代码和一个网络应用程序，可在世界任何地方生成空缺的LST，并提供基于像素的数据评估，包括MAE，RMSE和Pearson's r。