This study develops an integrated framework for all-sky surface longwave downward radiation (SLDR) estimate for the MERSI-II imager onboard the Fengyun-3D (FY-3D) satellite. The framework comprises a hybrid method for the clear-sky SLDR estimate and a cloud base temperature (CBT)-based single-layer cloud model (SLCM) for the cloudy-sky SLDR estimate. In-situ validation indicates that the hybrid method yields a bias/RMSE of -0.78/21.70 W/m2, whereas the SLCM achieves a bias/RMSE of 5.79/23.61 W/m2. The bias/RMSE of the all-sky SLDR is 3.37/22.93 W/m2. The estimated all-sky instantaneous SLDR was combined with ERA5 temporal information to derive daily SLDR using a bias-corrected sinusoidal integration method, yielding a bias of 0.04 W/m2and an RMSE of 16.77 W/m2. These results demonstrate the robustness of the proposed framework and its substantial potential in generating both instantaneous and daily SLDR products at 1 km spatial resolution.

Land surface temperature (LST) is a direct result of earth-atmosphere interactions and has been widely used in earth system science and climate change. Thus, it is recognized as one of the essential climate variables (ECVs). Thermal infrared (TIR) remote sensing is the most effective way to obtain high-quality LST at a large scale. However, TIR cannot penetrate the Cloud and obtain the cloudy sky LST, which seriously hinders its applications. To address this challenge, we proposed a paradigm for estimating the seamless LST at regional and global scales. Validation results showed the root mean square error (RMSE) of the produced seamless LST achieved approximately 3K. The produced seamless LST at China landmass, East Asia, and global have been freely released to the public (https://elite.bnu.edu.cn).

Polar regions play a crucial role in global climate change. Surface longwave downward radiation (SLDR) is a primary energy source for the polar surface and plays an essential role in studies of polar hydrology, temperature, and climate. Therefore, accurately estimating the SLDR over polar regions is highly important. However, the accuracies of existing polar SLDR datasets and SLDR inversion methods are insufficient to meet the requirements of relevant research. In this study, we developed a data-driven model for high spatial resolution clear-sky SLDRs estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery in polar regions. The model comprises two layers: the first layer incorporates three machine learning models, namely, eXtreme gradient boosting (XGBoost), convolutional neural network (CNN), and transformer, while the second layer consists of a stacking meta-model. The ground measurements collected from 51 sites were used to train and validate the developed model. The bias, RMSE, and R2 of the model training are zero, 14.15 W/m2, and 0.95, respectively, whereas the values for the validation are 0.49, 15.35 W/m2, and 0.9, respectively. We also compared the accuracies of the ERA5 and CERES-SYN SLDR data with the SLDR estimated by the developed model. The results indicate that the developed model is superior to the ERA5 and CERES-SYN SLDR models when evaluated at the validation sites. In addition, we analyzed the performance of the developed model under different elevations and seasons, demonstrating its robustness in different situations.

Surface longwave upward radiation (SLUR) is a key parameter in studying hydrological and climate models. This study develops a framework for estimating the all-sky SLUR from the Advanced Geostationary Radiation Imager (AGRI) onboard the Chinese geostationary weather satellite FengYun-4A. The framework is composed of a hybrid method for estimating clear-sky SLUR and a machine learning (ML) method for estimating cloudy-sky SLUR. According to the in-situ validation, the R2/bias/RMSE of the developed hybrid method is 0.95/0.59/18.41 W/m2, which is clearly superior to the AGRI official SLUR and ERA5 SLUR with a R2/bias/RMSE of 0.95/-7.79/19.04 W/m2and 0.92/-4.94/23.2 W/m2, respectively. The developed hybrid method performs better than the classical land surface temperature-broadband emissivity (LST-BBE) method. The R2, bias/RMSE of the developed cloudy sky SLUR estimate LightGBM model is 0.85/ 0.56/21.16 W/m2, which is also better than the accuracy of the LST-BBE method and comparable to the accuracy of the ERA5 SLUR. The R2, bias and RMES of the all-sky SLUR are 0.93, 0.57 and 19.58 W/m2, respectively. The developed framework is employed to determine hourly all-sky SLUR from AGRI data. This study provides a promising solution to obtain hourly all-sky SLUR from geostationary satellites.

As one of the four components of surface radiation balance, surface longwave downward radiation (SLDR) greatly affects the accurate characterization of hydrological, ecological, and biochemical processes. Since cloud-base temperature (CBT)-based single-layer cloud models (SLCMs) have advantages in both their strong physical mechanisms and abilities to produce high spatial resolution SLDR products, this study validated a CBT-based SLCM developed at the global scale usingin situobservations collected by the baseline surface radiation network (BSRN) in conjunction with Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products and Modern-Era Retrospective Analysis For Research And Applications, Version 2 (MERRA-2) reanalysis data. Overall, the CBT-based SLCM achieved a relatively high SLDR estimation accuracy for the Terra and Aqua satellites, with biases better than −1.2 W/m2and root-mean-squared error (RMSE) values better than 29.9 W/m2. However, its random RMSE was slightly worse than those of two Clouds and the Earth’s Radiant Energy System (CERES) Single Scanner Footprint (SSF) SLDR products due to the larger spatial variability that exists at the Earth’s surface when it is quantified at a high spatial resolution (1 km). Additionally, the CBT-based SLCM outperformed two existing cloud-top temperature (CTT)-based SLCMs. In the future, we will continue to improve the performance of the CBT-based SLCM and will update the Global LAnd Surface Satellite (GLASS) SLDR product under cloudy sky conditions.

The calculation of the SDLR is split into a clear-sky contribution calculated from bulk formula, and a cloud contribution depending on cloud fraction and cloud effective temperature (CET). This study used cloud base temperature (CBT) and cloud top temperature (CTT) as CET to estimate cloud contribution to SDLR, respectively. The retrieved SDLR are validated by the TIPEX-III surface budget network data. The validation results showed that accuracy of SDLR estimate was significantly improved when calculating cloud contribution using CBT compared with using CTT in Tibetan Plateau, especially when the surface elevation values for some sites are more than 4000 m, SDLR had higher accuracy with RMSE and BIAS of 27.46 W·m-2and -5.27 W·m-2, respectively. However, when estimating cloud contribution with CBT, some SDLR inversion results had relatively lower accuracy. Reasons may be as follows: (1) when the surface elevation values for some sites are relatively lower, the CALIPSO product probably cannot detect the real cloud base; (2) and also, the multi-layer cloud may have not been considered.