Abstract. Land surface temperature (LST) is a key variable for high
temperature and drought monitoring and climate and ecological environment
research. Due to the sparse distribution of ground observation stations,
thermal infrared remote sensing technology has become an important means of
quickly obtaining ground temperature over large areas. However, there are
many missing and low-quality values in satellite-based LST data because
clouds cover more than 60 % of the global surface every day. This article
presents a unique LST dataset with a monthly temporal resolution for China from
2003 to 2017 that makes full use of the advantages of MODIS data and
meteorological station data to overcome the defects of cloud influence via a
reconstruction model. We specifically describe the reconstruction model,
which uses a combination of MODIS daily data, monthly data and
meteorological station data to reconstruct the LST in areas with cloud
coverage and for grid cells with elevated LST error, and the data
performance is then further improved by establishing a regression analysis
model. The validation indicates that the new LST dataset is highly
consistent with in situ observations. For the six natural subregions with
different climatic conditions in China, verification using ground
observation data shows that the root mean square error (RMSE) ranges from
1.24 to 1.58 ∘C, the mean absolute error (MAE)
varies from 1.23 to 1.37 ∘C and the Pearson
coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately
captures the spatiotemporal variations in LST at annual, seasonal and
monthly scales. From 2003 to 2017, the overall annual mean LST in China showed
a weak increase. Moreover, the positive trend was remarkably unevenly
distributed across China. The most significant warming occurred in the
central and western areas of the Inner Mongolia Plateau in the Northwest
Region, and the average annual temperature change is greater than 0.1 K (R>0.71, P<0.05), and a strong negative trend was
observed in some parts of the Northeast Region and South China Region.
Seasonally, there was significant warming in western China in winter, which
was most pronounced in December. The reconstructed dataset exhibits
significant improvements and can be used for the spatiotemporal evaluation
of LST in high-temperature and drought-monitoring studies. The data are
available through Zenodo at https://doi.org/10.5281/zenodo.3528024 (Zhao et al., 2019).
LC/8YSZ TBCs Thermal Cycling Life and Failure Mechanism under Extreme Temperature Gradients
