Merging framework for estimating daily surface air temperature by integrating observations from multiple polar-orbiting satellites

Abstract

Reconstructing spatially continuous surface air temperature (SAT) is of great significance to climate and environmental studies. Substantial efforts have been made to estimate daily SAT based on land surface temperature (LST) derived from polar-orbiting satellites. However, previous studies are nearly all limited to estimating daily SAT based on MODIS LST from NASA's Terra or Aqua by applying different statistical learning methods. Various satellites from earth observation missions, particularly the missions for meteorological satellites, are capable of acquiring thermal infrared observations, but its implications for SAT estimation are significantly ignored. In this study, for the first time, we proposed a merging framework for estimating daily mean SAT by integrating LST datasets from multiple polar-orbiting satellites, including Metop-B from EUMETSAT's Polar System (EPS), SNPP and JPSS-1 from NOAA's Joint Polar Satellites System (JPSS), and Terra and Aqua from NASA's EOS. This study is also the first to explore the estimating of daily SAT based on LST derived from the meteorological satellites in EPS and JPSS. The framework integrates 10 estimation models based on different LST from the five satellites and generates daily merged SAT by averaging the daily SAT estimates from the models. Here we show that the framework significantly improves the spatial coverage of daily SAT estimates for cloud-free areas by an overall increase of 39% with respect to the mean coverage of the LST datasets from the five satellites. Daily coverage of the merged SAT from the framework is nearly all above 75% with an average of 91%. Compared to the SAT estimated from MODIS LST, overall increases in the coverage of daily SAT are 37%–51%. Estimation models in the framework all achieved comparable and satisfactory predicative performances with an average RMSE of 1.7–1.9 K for sample-based cross-validation, and 1.9–2.2 K for site-based cross-validation.