A global seamless 1 km resolution daily land surface temperature dataset (2003 – 2020)

Abstract

Abstract. Land surface temperature (LST) is one of the most important and widely used parameter for studying land surface processes. Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (e.g., MOD11A1 and MYD11A1) can provide this information with high spatiotemporal resolution with global coverage. However, the broad applications of these data are hampered because of missing values caused by factors such as cloud contamination. In this study, we used a spatiotemporal gap-filling framework to generate a seamless global 1 km daily (mid-daytime and mid-nighttime) MODIS-like LST dataset from 2003 to 2020 based on standard MODIS LST products. The method includes two steps, 1) data pre-processing and 2) spatiotemporal fitting. In the data pre-processing, we filtered pixels with low data quality and filled gaps using the observed LST at another three time points of the same day. In the spatiotemporal fitting, first, we fitted the long-term trend (overall mean) of observations in each pixel (ordered by day of year). Then we spatiotemporally interpolated residuals between observations and overall mean values for each day. Finally, we estimated missing values of LST by adding the overall mean and interpolated residuals. The results show that the missing values in the original MODIS LST were effectively and efficiently filled, and there is no obvious block effect caused by large areas of missing values, especially near the boundary of tiles, which might exist in other seamless LST datasets. The cross-validation with different missing rates at the global scale indicates that the gap-filled LST data have high accuracies with the average root mean squared error (RMSE) of 1.88 °C and 1.33 °C, respectively for mid-daytime (1:30 pm) and mid-nighttime (1:30 am). The seamless global daily (mid-daytime and mid-nighttime) LST dataset at a 1 km spatial resolution is of great use in global studies of urban systems, climate research and modeling, and terrestrial ecosystems studies. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.5078492 (Zhang et al., 2021a).