Abstract
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).
Highlights
Land surface temperature (LST), which is controlled by land–atmosphere interactions and energy fluxes, is an essential parameter for the physical processes of the surface energy balance and water cycle at regional and global scales (Li et al, 2013; Wan et al, 2014; Benali et al, 2012)
We describe the reconstruction model, which uses a combination of Moderate Resolution Imaging Spectroradiometer (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 further improved by establishing a regression analysis model
This paper presents a new long-term spatially and temporally continuous MODIS LST dataset in a monthly temporal and 5600 m spatial resolution for China from 2003 to 2017 that filters out invalid pixels and low-quality pixels and reconstructs them based on multisource data
Summary
Land surface temperature (LST), which is controlled by land–atmosphere interactions and energy fluxes, is an essential parameter for the physical processes of the surface energy balance and water cycle at regional and global scales (Li et al, 2013; Wan et al, 2014; Benali et al, 2012). LST datasets are required for high-temperature and drought research over various spatial scales and are important elements for improving global hydrological and climate prediction models. The LST directly influences glaciers and snow on the Qinghai–Tibet Plateau (Tibetan Plateau), which is known as the “world water tower”. These changes directly affect the living conditions of nearly 40 % of the world’s population (Xu et al, 2008). LST research at regional and global scales is crucial for further improving and refining global hydroclimatic and climate prediction models. LST is measured by meteorological stations which have the advantages of high reliability and long time series. The smoothed spatial pattern obtained after interpolation may suffer from low reliability because the ground station density is far from sufficient in most regions
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