Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data
Authors: Jia Aolin , Liang Shunlin , Wang Dongdong , Ma Lei , Wang Zhihao , Xu Shuo 
Published: February 2023

Abstract. Land surface temperature (LST) plays a dominant role in
the surface energy budget (SEB) and hydrological cycling. Thermal infrared
(TIR) remote sensing is the primary method of estimating LST globally.
However, cloud cover leaves numerous data gaps in satellite LST products,
which seriously restricts their applications. Efforts have been made to
produce gap-free LST products from polar-orbiting satellites (e.g., Terra
and Aqua); however, satellite data from limited overpasses are not suitable
for characterizing the diurnal temperature cycle (DTC), which is directly
related to heat waves, plant water stress, and soil moisture. Considering
the high temporal variability in LST and the importance of the DTC, we
refined the SEB-based cloudy-sky LST recovery method by improving its
feasibility and efficiency and produced a global hourly, 5 km, all-sky land
surface temperature (GHA-LST) dataset from 2011 to 2021. The GHA-LST product
was generated using TIR LST products from geostationary and polar-orbiting
satellite data from the Copernicus Global Land Service (CGLS) and the Moderate
Resolution Imaging Spectroradiometer (MODIS). Based on ground
measurements at the 201 global sites from the Surface Radiation Budget
(SURFRAD), Baseline Surface Radiation Network (BSRN), Fluxnet, AmeriFlux,
Heihe River basin (HRB), and Tibetan Plateau (TP) networks, the overall root-mean-square error (RMSE) of the hourly GHA-LST product was 3.31 K, with a
bias of −0.57 K and R2 of 0.95. Thus, this product was more accurate
than the clear-sky CGLS and MODIS MYD21C1 LST samples. The RMSE value of the
daily mean LST was 1.76 K. Validation results at individual sites indicate
that the GHA-LST dataset has relatively larger RMSEs for high-elevation
regions, which can be attributed to high surface heterogeneity and input
data uncertainty. Temporal and spatial analyses suggested that GHA-LST has
satisfactory spatiotemporal continuity and reasonable variation and matches
the reference data well at hourly and daily scales. Furthermore, the
regional comparison of GHA-LST with other gap-free hourly datasets (ERA5 and
Global Land Data Assimilation System, GLDAS) demonstrated that GHA-LST can
provide more spatial texture information. The monthly anomaly analysis
suggests that GHA-LST couples well with global surface air temperature
datasets and other LST datasets at daily mean and minimum temperature
scales, whereas the maximum temperature and diurnal temperature range of LST
and air temperature (AT) have different anomalous magnitudes. The GHA-LST
dataset is the first global gap-free LST dataset at an hourly, 5 km scale
with high accuracy, and it can be used to estimate global
evapotranspiration, monitor extreme weather, and advance meteorological
forecasting models. GHA-LST is freely available at
https://doi.org/10.5281/zenodo.7487284 (Jia et al., 2022b) and
http://glass.umd.edu/allsky_LST/GHA-LST (last access: 10 February 2023; Jia et al., 2022c).