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Air temperatures in urban environments are usually obtained from sparse weather stations that provide limited information with regard to spatial patterns. Effective methods that predict air temperatures (Tair) in urban areas are based on statistical models which utilize remotely sensed and geographic data. This work aims to compute Tair predictions for diurnal and nocturnal time intervals using predictive models that do not exploit information on Land Surface Temperatures. The models are developed based on explanatory variables that describe the urban morphology, land cover and terrain, aggregated at 100 m × 100 m resolution, combined with in situ Tair measurements from urban meteorological stations. The case study is the urban and per-urban area of Heraklion, Greece, where a dense meteorological station network is available since 2016. Moran’s eigenvector filtering and an autoregressive moving average residual specification are implemented to account for spatial and temporal correlations. The statistical models display satisfactory predictive performance, with mean annual Mean Absolute Error (MAE) equal to 0.36 °C, 0.34 °C, 0.42 °C and 0.54 °C, for 11:00–12:00, 14:00–15:00, 22:00–23:00 and 02:00–03:00 (UTC + 2), respectively. The minimum (maximum) MAE for the estimated datasets is 0.22 °C (0.81 °C). The mean annual MAE for all Tair interpolations is 0.42 °C, the mean annual Root Mean Square Error (RMSE) is 0.49 °C and the mean annual bias

Surface albedo is one of the essential climate variables as it influences the radiation budget and the energy balance. Because it is used in a variety of scientific fields, from local to global scale, spatially and temporally disaggregated albedo data are required, which can be derived from satellites. Satellite observations have led to directional-hemispherical (black-sky) and bi-hemispherical (white-sky) albedo products, but time series of high spatial resolution true (blue-sky) albedo estimations at global level are not available. Here, we exploit the capabilities of Google Earth Engine (GEE) for big data analysis to derive global snow-free land surface albedo estimations and trends at a 500-m scale, using satellite observations from 2000 to 2015. Our study reveals negative albedo trends mainly in Mediterranean, India, south-western Africa and Eastern Australia, whereas positive trends mainly in Ukraine, South Russia and Eastern Kazakhstan, Eastern Asia, Brazil, Central and Eastern Africa and Central Australia. The bulk of these trends can be attributed to rainfall, changes in agricultural practices and snow cover duration. Our study also confirms that at local scale, albedo changes are consistent with land cover/use changes that are driven by anthropogenic activities.