Spatio-Temporal Distribution Characteristics of Global Annual Maximum Land Surface Temperature Derived from MODIS Thermal Infrared Data From 2003 to 2019

Abstract

Land surface temperature (LST) is an important parameter in the physical processes of energy and water balance at the local and global scales. Annual maximum composite of LST provides important information about ecosystem exposure patterns to extreme LST. It has the ability to characterize the changes associated with extreme climatic events and significant land-cover changes. In this study, the spatio-temporal distribution characteristics of global annual maximum LST extracted from the MODIS LST product (MYD11A1) during the period 2003-2019 were investigated. The results indicate that the spatial pattern of annual maximum LST is associated with the biophysical and biogeographic factors of Earth's ecosystems. The inter-annual variability of annual maximum LST during the period 2003-2019 is relatively small. The change trend of annual maximum LST during the period 2003-2019 in the globe is 0.1 °C/decade. The annual maximum LST data was applied to detect thermal anomalies, including drought, heat wave, and ice melting. Some significant thermal anomaly events were well identified using the annual maximum LST data with standardized anomaly index larger than 2.5.

Evaluating Land Surface Temperature Trends and Explanatory Variables in the Miami Metropolitan Area from 2002–2021

“…The LST is a variable that is related to other geographical, climatological, and hydrological elements [28]. As expected, the explanatory variables related to pavement and buildings have positive coefficients while the land cover Tree Canopy variables have negative coefficients in regression models predicting LST.…”

“…The Moderate Resolution Imaging Spectroradiometer (MODIS) is an Earth Observing System that serves as an important source for global studies of atmosphere, land, and ocean processes [26]. MODIS is aboard the Terra and Aqua satellites [27] and has 36 spectral bands providing global coverage every 1 to 2 days during the day and night [28]. The LST data were accessed using the Google Earth Engine (GEE) which is a popular cloud computing system for geospatial analytics providing users access to a wide web-based set of data [29].…”

Attribution of Urban Diurnal Thermal Environmental Change: Importance of Global–Local Effects

“…At present, MODIS and Landsat remote sensing images are widely used in global long-term LST research [25], [26]. Compared to Landsat, MODIS LST data has a low spatial resolution (1 km) but a high temporal resolution (daily), which is beneficial for reducing the interference of abnormal phenomena in long-term thermal environment research [27]- [29]. This study obtained the characteristics of thermal environment changes by deconstructing the MODIS LST dataset.…”