Publications

Yoo, C; Im, J; Park, S; Quackenbush, LJ (2018). Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 137, 149-162.

Abstract
Urban air temperature is considered a significant variable for a variety of urban issues, and analyzing the spatial patterns of air temperature is important for urban planning and management. However, insufficient weather stations limit accurate spatial representation of temperature within a heterogeneous city. This study used a random forest machine learning approach to estimate daily maximum and minimum air temperatures (T-max and T-min) for two megacities with different climate characteristics: Los Angeles, USA, and Seoul, South Korea. This study used eight time-series land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS), with seven auxiliary variables: elevation, solar radiation, normalized difference vegetation index, latitude, longitude, aspect, and the percentage of impervious area. We found different relationships between the eight time-series LSTs with T-max/T-min for the two cities, and designed eight schemes with different input LST variables. The schemes were evaluated using the coefficient of determination (R-2) and Root Mean Square Error (RMSE) from 10-fold cross-validation. The best schemes produced R-2 of 0.850 and 0.777 and RMSE of 1.7 degrees C and 1.2 degrees C for T-max and T-min in Los Angeles, and R-2 of 0.728 and 0.767 and RMSE of 1.1 degrees C and 1.2 degrees C for T-max and T-min in Seoul, respectively. LSTs obtained the day before were crucial for estimating daily urban air temperature. Estimated air temperature patterns showed that T-max was highly dependent on the geographic factors (e.g., sea breeze, mountains) of the two cities, while T-min showed marginally distinct temperature differences between built-up and vegetated areas in the two cities. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

DOI:
10.1016/j.isprsjprs.2018.01.018

ISSN:
0924-2716