Estimating daily maximum air temperature from MODIS in British Columbia, Canada

Air temperature (Ta) is an important climatological variable for forest research and management. Due to the low density and uneven distribution of weather stations, traditional ground-based observations cannot accurately capture the spatial distribution of Ta, especially in mountainous areas with complex terrain and high local variability. In this paper, the daily maximum Ta in British Columbia, Canada was estimated by satellite remote sensing. Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the summer period (June to August) from 2003 to 2012 were collected to estimate Ta. Nine environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), latitude, longitude, distance to ocean, altitude, albedo, and solar radiation) were selected as predictors. Analysis of the relationship between observed Ta and spatially averaged remotely sensed LST indicated that 7 × 7 pixel size was the optimal window size for statistical models estimating Ta from MODIS data. Two statistical methods (linear regression and random forest) were used to estimate maximum Ta, and their performances were validated with station-by-station cross-validation. Results indicated that the random forest model achieved better accuracy (mean absolute error, MAE = 2.02°C, R2 = 0.74) than the linear regression model (MAE = 2.41°C, R2 = 0.64). Based on the random forest model at 7 × 7 pixel size, daily maximum Ta at a resolution of 1 km in British Columbia in the summer of 2003–2012 was derived, and the spatial distribution of summer Ta in this area was discussed. The satisfactory results suggest that this modelling approach is appropriate for estimating air temperature in mountainous regions with complex terrain.

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