Relationship between land surface temperature and land cover types based on GWR model: A case of Beijing-Tianjin-Tangshan urban agglomeration, China.

Abstract

We used land cover data derived from Landsat thematic mapper (TM) and land surface temperature (LST) data from moderate-resolution imaging spectro-radiometer (MODIS) satellite images to study the variations in LST in July of different land cover types in Beijing-Tianjin-Tangshan urban agglomeration. Ordinary linear regressions (OLS) models and geographically weighted regressions (GWR) models were used to investigate the relationships between the proportions of land cover types and LST. The results showed that great variations in LST occurred among different land cover types. The average LST ranged from high to low in the order of developed land (40.92±3.49 ℃), cultivated land (39.74±3.74 ℃), wetland (35.42±4.33 ℃), and forested land (34.43±4.16 ℃). The proportions of land cover types were significantly related to LST, but with spatial non-stationarity. This might be due to inherent difference in land cover across locations, and the surrounding environments. GWR models had higher R2 values, compared to OLS, indicating better model performance. In addition, GWR models could reveal the spatial non-stationarity of the relations between LST and the proportions of different land cover types.