Estimating near-surface air temperature across Israel using a machine learning based hybrid approach

Rising global temperatures over the last decades have increased heat exposure among populations worldwide. An accurate estimate of the resulting impacts on human health demands temporally explicit and spatially resolved monitoring of near-surface air temperature (T_a). Neither ground-based nor satellite-borne observations can achieve this individually, but the combination of the two provides synergistic opportunities. In this study, we propose a two-stage machine learning-based hybrid model to estimate 1 × 1 km² gridded intra-daily T_a from surface skin temperature (T_s) across the complex terrain of Israel during 2004–2016. We first applied a random forest (RF) regression model to impute missing T_s from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra satellites, integrating T_s from the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellite and synoptic variables from European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. The imputed T_s are in turn fed into the Stage 2 RF-based model to estimate T_a at the satellite overpass hours of each day. We evaluated the model's performance applying out-of-sample fivefold cross validation. Both stages of the hybrid model perform very well with out-of-sample fivefold cross validated R² of 0.99 and 0.96, MAE of 0.42°C and 1.12°C, and RMSE of 0.65°C and 1.58°C (Stage 1: imputation of T_s, and Stage 2: estimation of T_a from T_s, respectively). The newly proposed model provides excellent computationally efficient estimation of near-surface air temperature at high resolution in both space and time, which helps further minimize exposure misclassification in epidemiological studies.