Modeling relationship between land surface temperature anomaly and environmental factors using GEE and Giovanni

Land surface temperature (LST) and vegetation cover changes are two indicators of landscapes in a region. The relationship between LST anomalies, elevation, vegetation, and urban growth is significant to conservation. This study addresses this issue using night-time satellite imagery, kernel methods (points aggregation), and the trend analysis for a long-term period (2001–2017) in Iran. Variables for two seasons (summer and winter) in urban and natural land uses were derived using the Google Earth Engine (GEE) and NASA's Giovanni. Point data derived from raster maps were quantified using statistical kernel and trend analysis. As result, it was observed that LST rise in various elevations, seasons, and land uses. The LST was analyzed through kernels (point aggregation in scatter graphs), which shifted to the right. The LST anomaly in the daytime had the highest maximum value (>4 °C) and lowest minimum value (