| Near surface air temperature estimation through parametrization of MODIS products |
| Paper ID : 1148-GEOSPATIAL (R7) |
| Authors: |
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Elahe Khesali *, Mohammad Reza Mobasheri Dept. of Photogrammetry and Remote Sensing,, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran |
| Abstract: |
| Near-surface air temperature is a key factor in many studies and its spatiotemporal patterns are highly dependent on the ground surface characteristics and vary over time and space. So Land Surface Temperature (LST) is an important parameter for air temperature estimation. In this study, it is tried to model the air temperature by deploying some of the parameters that affect it . The parameters that have been taken into account in this study include land surface temperature, Normalized Difference Vegetation Index (NDVI), Vapor Pressure (VP) and Lifted Index (LI) as a measure of atmospheric stability. To assess the impact of each of these parameters, different linear regression models, were tested. Support Vector Regression (SVR) and hybrid artificial neural network methods were also performed. To model and evaluate the time series data of Georgia State in The United States of America over 1 year have been used. The NDVI, Total Precipitable Water (TPW), LST and LI parameters are products of MODIS. VP is calculated by using a logarithmic model from the TPW. Finally, it was found out that the LST and VP have positive effects, LI has negative and NDVI had a slightly positive impact on the air temperature at 2 meters height. The achieved accuracy in the linear model when all parameters are involved was 2.29°C with a correlation coefficient of R=0.96. Next, the SVR model was examined and applied to the linear model taking all parameters into account. It was found that it does not end up to any significant increase in accuracy but certainly increases the computation time. The accuracy of this model was about 2.25°C with a correlation coefficient of 0.96. Finally, a hybrid artificial neural network was examined. It was found that it increases the accuracy but certainly increases the computation time. The achieved accuracy of this model was about 2.14 °C with a correlation coefficient of 0.96. |
| Keywords: |
| Air temperature; Atmospheric parameters; Linear regression; SVR; MODIS; Remote Sensing. |
| Status : Paper Accepted (Oral Presentation) |
