Spatiotemporal Patterns of Land Surface Temperature Change in the Tibetan Plateau Based on MODIS/Terra Daily Product From 2000 to 2018

Abstract

Under the background of climate change, the Tibetan Plateau presents high spatial and temporal variability in land surface temperature (LST). To understand the spatiotemporal patterns of LST change, this study conducted a spatiotemporal analysis using the Mann-Kendall trend analysis method with time series of mean annual surface temperature (MAST) extracted from the MODIS/Terra daily LST product from 2000 to 2018. The analysis indicated that both daytime and nighttime MASTs show an obvious warming trend with the average rates of 0.028 K/year and 0.069 K/year, and the nighttime variation has larger spatial coverage. Areas ranging from 4500 to 5000 m exhibited the strongest warming effect. The Geodetector method was applied to detect the impacts from seven factors, including elevation, land cover type, latitude, normalized difference vegetation index (NDVI), precipitation, air temperature, and solar radiation on the spatial distribution of LST. The controlling effects of these factors were generally stronger in the nighttime than those in the daytime, and elevation was the most important factor with the contribution scores of 27.12% and 62.98% in the daytime and nighttime, respectively. In addition, the analysis revealed that the temporal changes of LST were mainly caused by surface properties (vegetation, snow cover, and water surface area) changes, radiant flux changes induced by cloud amount changes, and climate warming. In general, this study provides important insights into the spatiotemporal dynamics of LST in the TP since 2000 and helps to reveal the impact of climate change on ecoenvironmental conservation.

Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine

“…In recent years, there have been studies using MODIS data to analyze the temporal and spatial patterns of regional surface temperature, and good results have been achieved [64]. However, local meteorological conditions, such as clouds, have a great impact on satellitederived LST, resulting in many missing values, which makes it difficult to obtain continuous LST changes in space and time.…”

Impact of land-use dynamics on land surface temperature in Mumbai city, India: A geospatial approach

“…Similarly, a monthly analysis of temperature data for the years 2000, 2010, 2015) has also been carried out to assess the temperature dynamics. It is observed that after 2000, there is a slight increase in the monthly maximum and average temperature over the years and no significant changes in monthly minimum temperature.…”

“…Therefore, the land-use dynamics especially the increase of the built-up area makes a notable change in LST (Akter et al, 2021;Gazi et al, 2021), as it is strongly influenced by emissivity properties of different LULC classes (Cheng and Liang, 2018). The LST study has great potential for insolation, heat budget, and overall climate change studies (Halder et al, 2021;Salwan et al, 2021;Yang et al, 2021). This climate change has strong contributions to human life and biophysical parameters (Zhang et al, 2021).…”

The Key Technologies of Road Elevation Detection Based on Sensor Fusion

“…The vehicle’s GPS (Global Positioning System) can not only locate and detect the longitude and latitude coordinates of the vehicle, but can also detect the real-time elevation of the road surface [ 19 ]. However, conventional GPS can only detect statically or obtain the elevation data of the surface through calculation after the detection, which is relatively inefficient.…”