Surface air temperature (Ta) is an important physical quantity, usually measured at ground weather station networks. Measured Ta data is inadequate to characterize the complex spatial patterns of Ta field due to low density and unevenness of the networks. Remote sensing can provide satellite imagery with large scale spatial coverage and fine resolution. Estimating spatially continuous Ta by integrating ground measurements and satellite data is an active research area. A variety of methods have been proposed and applied in this area. However, the existing studies primarily focused on daily Ta and failed to quantify uncertainties in model parameter and estimated results. In this paper, a Bayesian Kriging regression (BKR) method is proposed to model and estimate monthly Ta using satellite-derived land surface temperature (LST) as the only input. The BKR is a spatial statistical model with the capacity to quantify uncertainties via Bayesian inference. The BKR method was applied to estimate monthly maximum air temperature (Tmax) and minimum air temperature (Tmin) over the conterminous United States in 2015. An exploratory analysis shows a strong relationship between LST and Ta at the monthly scale, indicating LST has the great potential to estimate monthly Ta. 10-fold cross-validation approach was adopted to compare the predictive performance of the BKR method with the linear regression method over the whole region and the urban areas of the contiguous United States. For the whole region, the results show that the BKR method achieves a competitively better performance with averaged RMSE values $1.23$ K for Tmax and $1.20$ K for Tmin, which are also lower than previous studies on estimation of monthly Ta. In the urban areas, the cross-validation demonstrates similar results with averaged RMSE values $1.21$ K for Tmax and $1.27$ K for Tmin. Posterior samples for model parameters and estimated Ta were obtained and used to analyze uncertainties in the model parameters and estimated Ta. The BKR method provides a promising way to estimate Ta with competitively predictive performance and to quantify model uncertainties at the same time.

Spatially continuous surface air temperature (SAT) is of great significance for various research areas in geospatial communities, and it can be reconstructed by the SAT estimation models that integrate accurate point measurements of SAT at ground sites with wall-to-wall datasets derived from remotely sensed observations of spaceborne instruments. As land surface temperature (LST) strongly correlates with SAT, estimation models are typically developed with LST as a primary input. Geostationary satellites are capable of observing the Earth’s surface across large-scale areas at very high frequencies. Compared to the substantial efforts to estimate SAT at daily or monthly scales using LST derived from MODIS, very limited studies have been performed to estimate SAT at high-temporal scales based on LST from geostationary satellites. Estimation models for hourly SAT based on the LST derived from FY-4A, the first geostationary satellite in China’s new-generation meteorological observation mission, were developed for the first time in this study. The models were fully cross-validated for a very large-scale region with diverse geographic settings using random forest, and specified differently to explore the influence of time and location variables on model performance. Overall predictive performance of the models is about 1.65–2.08 K for sample-based cross-validation, and 2.22–2.70 K for site-based cross-validation. Incorporating time or location variables into the hourly models significantly improves predictive performance, which is also confirmed by the analysis of predictive errors at temporal scales and across sites. The best-performing model with an average RMSE of 2.22 K was utilized for reconstructing maps of SAT for each hour. The hourly models developed in this study have general implications for future studies on large-scale estimating of hourly SAT based on geostationary LST datasets.

The estimation of spatially resolved near-surface air temperature (NSAT) has been extensively performed in previous studies using satellite-derived land surface temperature (LST) from MODIS. However, there remains a need for estimating daily NSAT based on LST data from other satellites, which has important implications for integrating multi-source LST in estimating NSAT and ensuring the continuity of satellite-derived estimates of NSAT over long-term periods. In this study, we conducted a comprehensive comparison of LST derived from Metop with MODIS LST in the modeling and mapping of daily NSAT. The results show that Metop LST achieves consistent predictive performance with MODIS LST in estimating daily NSAT, and models based on Metop LST or MODIS LST have overall predictive performance of about 1.2–1.4 K, 1.5–2.0 K, and 1.8–1.9 K in RMSE for estimating Tavg, Tmax, and Tmin, respectively. Compared to models based on nighttime LST, daytime LST can improve the predictive performance of Tmax by about 0.26–0.28 K, while performance for estimating Tavg or Tmin using different schemes of LST is comparable. Models based on Metop LST also exhibit high consistency with models utilizing MODIS LST in terms of the variability in predictive performance across months, with RMSE of 1.03–1.82 K, 1.3–2.49 K, and 1.26–2.66 K for Tavg, Tmin, and Tmax, respectively. This temporal variability in performance is not due to sampling imbalance across months, which is confirmed by comparing models trained using bootstrapped samples in balance, and our results imply that sampling representativeness, complicated by retrieval gaps in LST, is an important issue when analyzing the variability in predictive performance for estimating NSAT. To fully assess the predictive capability of Metop LST in estimating daily NSAT, more studies need to be performed using different methods across areas with a range of scales and geographical environments.

Spatially continuous surface air temperature (SAT) is critically important for a wide range of fields such as eco-environmental assessments and hydrology. Remotely sensed estimation models based on satellite-derived thermal infrared data provides a structurally different approach for reconstructing SAT compared to spatial interpolation of ground observations of SAT and numerical modelling, which are mainly limited by the coverage of stations and coarse spatial resolutions, respectively. However, the data products of remotely sensed estimates of SAT developed in previous studies are only available at daily or monthly resolutions, and are primarily restricted for local regions. In this study, we generated the first hourly dataset (GHRSAT) of all-sky remotely sensed SAT estimates for the global land areas except Antarctica between 2011 and 2023. The hourly estimates in GHRSAT were reconstructed from land surface temperature using the hybrid estimation models that integrate random forest (RK) models and kriging techniques. The hybrid models were developed for different regions on a monthly basis. We adopted ordinary kriging (OK) and fixed rank kriging (FRK) in the modelling of the site residuals from the RF models for regions with low-density and high-density stations, respectively. Our results show that the hybrid models for generating GHRSAT have the predictive performance between 1.48 °C to 2.28 °C in overall cross-validation RMSE. The mean RMSE for estimating hourly SAT can be significantly reduced by 0.18–0.41 °C by the hybrid models compared to the RF models. We analyzed the variability in the predictive errors of estimating hourly SAT across regions, months and sites. The variability is apparently decreased when using the hybrid models. We found that the RF models are less sensitive to the parameter tuning of the RF models, which greatly impacts the hybrid models. Improving the RF models by parameter tuning can drastically improve the hybrid models based on the RF models. Additionally, we found the performance difference between OK and FRK in developing the hybrid models for regions with large amounts of stations is slight with the mean RMSE of 0.05 °C. In summary, the scheme of the hybrid models can result in satisfactorily higher performance for estimating SAT, and has the general practicability of applying to regions at various scales. The GHRSAT dataset is publicly available at http://doi.org/10.11888/RemoteSen.tpdc.301540 (Zhang, 2024).