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Air temperature (T_air or T_2m) is an important climatological variable for forest biosphere processes and climate change research. Due to the low density and the uneven distribution of weather stations, traditional ground-based observations cannot accurately capture the spatial distribution of T_air. In this study, T_air in Berlin is estimated during the day and night time over six land cover/land use (LC/LU) types by satellite remote sensing data over a large domain and a relatively long period (7 years). Aqua and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the period from 2007 to 2013 were collected to estimate T_air. Twelve environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), Julian day, latitude, longitude, Emissivity31, Emissivity32, altitude, albedo, wind speed, wind direction and air pressure) were selected as predictors. Moreover, a comparison between LST from MODIS Terra and Aqua with daytime and night time air temperatures (T_day, T_night) was done respectively and in addition, the spatial variability of LST and T_air relationship by applying a varying window size on the MODIS LST grid was examined. An analysis of the relationship between the observed T_air and the spatially averaged remotely sensed LST, indicated that 3 × 3 and 1 × 1 pixel size was the optimal window size for the statistical model estimating T_air from MODIS data during the day and night time, respectively. Three supervised learning methods (Adaptive Neuro Fuzzy Inference system (ANFIS), Artificial Neural Network (ANN) and Support vector machine (SVR)) were used to estimate T_air during the day and night time, and their performances were validated by cross-validation for each LC/LU. Moreover, tuning the hyper parameters of some models like SVR and ANN were investigated. For tuning the hyper parameters of SVR, Simulated Annealing (SA) was applied (SA-SVR model) and a multiple-layer feed-forward (MLF) neural networks with three layers and different nodes in hidden layers are used with Levenber-Marquardt back-propagation (LM-BP), in order to achieve higher accuracy in the estimation of T_air. Results indicated that the ANN model achieved better accuracy (RMSE= 2.16°C, MAE = 1.69°C, R² = 0.95) than SA_SVR model (RMSE= 2.50°C, MAE = 1.92°C, R² = 0.91) and ANFIS model (RMSE= 2.88°C, MAE= 2.2°C, R² = 0.89) over six LC/LU during the day and night time. The Q-Q diagram of SA-SVR, ANFIS and NN show that all three models slightly tended to underestimate and overestimate the extreme and low temperatures for all LC/LU classes during the day and night time. The weak performance in the extreme and low temperatures are a consequence of the small number of data in these temperatures. These satisfactory results indicate that this approach is proper for estimating air temperature and spatial window size is an important factor that should be considered in the estimation of air temperature.

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