scholarly journals Use of Land Surface Temperature Estimation with Geographical Information Tools for Validation of Numerical Microclimate Studies at Urban Scale

2021 ◽  
Vol 312 ◽  
pp. 06004
Author(s):  
Mileyka Bustamante ◽  
Dafni Mora ◽  
Miguel Chen Austin
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Geographical Information ◽  
Dynamic Simulations ◽  
Temperature Estimation ◽  
Historic Center ◽  
Information Tools ◽  
Landsat 7

For this study, different approaches found in the literature to obtain the Land Surface Temperature (LST) were evaluated through Geographic Information tools, to validate the temperature results obtained from dynamic simulations at urban scale with the Envi-met software. Here, the case study is an urban section of the Historic center of Panama City named Casco Antiguo. From the dynamic simulation results, the surface temperature was analyzed for March at 15:00. Concerning the results obtained for March, the Online Global Land Surface Temperature Estimation tool provided the best characteristics when validating the data obtained with Envi-met. It was found that the Landsat 7 images, applying the emissivity method ASTER and NDVI, provided data more stable and closer to the ones we wanted to validate.

scholarly journals SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 401-405
Author(s):  
M. K. Firozjaei ◽  
M. Makki ◽  
J. Lentschke ◽  
M. Kiavarz ◽  
S. K. Alavipanah
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Incident Angle ◽  
Surface Characteristics ◽  
Slope Aspect ◽  
Biophysical Parameters ◽  
The Impact

Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.

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