Evaluation of estimating daily maximum and minimum air temperature with MODIS data in east Africa

Real time and spatially distributed Ta (air temperature) data are desired for many applications. Ts (land surface temperature) derived from remote sensors has been used to estimate Ta in previous studies. Exploring MODIS Aqua Ts and station measured daily maximum and minimum Ta over east Africa, we found that Ts did not agree well with Ta during the day (MAE (Mean Absolute Error) = 6.9 ± 5.0 °C) but had better agreement during the night (MAE = 1.9 ± 1.7 °C). A stepwise linear regression method was applied to construct possible models to predict Ta based on MODIS data. Our results showed that, only considering elevation, high spatial resolution Ta could be obtained by simple linear models, with MAE = 1.9 °C, agreement index = 0.79 for daily maximum Ta, and MAE = 1.9 °C, agreement index = 0.92 for daily minimum Ta. MODIS Ts data could provide temporal variation information and slightly improve the accuracy of model predictions (by 0.2 °C of MAE). However, considering (i) major absences (about 2/3 of days) of Ts data due to cloud cover and (ii) small Ta variations in time (σ = 2.1 °C) over east Africa, models without Ts might be more practical in particular applications such as tsetse fly distribution models. Other variables including solar zenith angle, low level precipitable water content, and vegetation index (NDVI and EVI) were insignificant in the daily maximum and minimum Ta estimation models after elevation and Ts had already been considered as predictors.

► Only with elevation as predictor, the air temperature could be estimated well.
► Land surface temperature only improved the prediction accuracies by 0.2 °C of MAE.
► Precipitable water, solar zenith angle, and vegetation index were insignificant.