A high spatiotemporal resolution land surface temperature research over Qinghai-Tibet Plateau for 2000–2020

As ‘the third pole of the world’, the land surface temperature (LST) of the Qinghai-Tibet Plateau (QTP) has a profound impact on the climate of central Asia and even the whole earth. Studying the impact of the LST over QTP depends on long time and high spatiotemporal resolution LST dataset. However, the unavailability of such dataset has hindered LST-related researches: one of the most important reasons is that traditional spatiotemporal fusion methods such as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) have heavy computation demands to process big data. To fill this gap, this paper outlines one new cloud spatiotemporal fusion method by combining Google Earth Engine and STARFM to develop for the first time a 3-hourly 30m LST dataset over the QTP from 2000 to 2020 through fusing Landsat and GLDAS-2.1 derived LST data. The outlined method first fuses the LSTs obtained from Landsat and GLDAS-2.1 data within one year to synthesize the LST of the entire QTP on one base time, and then the base time LST is spatiotemporally fused with GLDAS-2.1 LSTs on the base and prediction times to derive the 3-hourly 30m QTP's LSTs on prediction times. The outlined method provides a promising technical scheme for batch processing big data in combining traditional spatiotemporal fusion methods with cloud computing platforms. Derived LST dataset is validated by station observations at multiple time and spatial scales to have high accuracy, which provides a guarantee for analyzing water and heat exchange and climate change over the QTP. • A new cloud spatiotemporal fusion method is proposed with Google Earth Engine. • A QTP's 3-hourly 30m LST dataset for 2000–2020 is created for the first time. • The statistical and trend characteristics of QTP's LSTs in 2000–2020 are analyzed.