In this paper, we comprehensively assessed the newly released long-term high-spatial resolution Global LAnd Surface Satellite (GLASS) surface longwave (LW) radiation product using site measurements of LW fluxes. In total, three years of ground-measured LW fluxes (surface longwave upward radiation (LWUP), surface longwave downward radiation (LWDN) and surface longwave net radiation (LWNR)) collected from 141 sites in six independent networks (AmeriFlux, AsiaFlux, BSRN, CEOP, HiWATER-MUSOEXE and TIPEX-III) are used to evaluate the GLASS LW radiation product. These sites cover various land cover types, surface elevations, and climatic types. According to the evaluation results, the biases are -4.33 W/m2, -3.77 W/m2 and 0.70 W/m2 and the RMSEs are 18.15 W/m2, 26.94 W/m2, and 26.70 W/m2 for clear-sky LWUP, LWDN and LWNR, respectively. The GLASS LW radiation product performs well in climate change-sensitive areas such as poleward areas, semi-arid areas and the “third pole”, namely, the Tibetan Plateau. The accuracy of the GLASS LW product is higher or comparable to that of available LW products and studies but has a high spatial resolution of 1 km and a time span of 19 years. In conclusion, the overall accuracy of the clear-sky GLASS LW radiation product can satisfy the requirements of the hydrological, meteorological, and agricultural research communities on a global scale. We will continue to improve the retrieval algorithms and update the products accordingly

Surface longwave radiation is sensitive to climate change on Earth. This study first comprehensively evaluates the accuracies of surface longwave upward radiation (SLUR) and surface longwave downward radiation (SLDR) among the mainstream surface longwave (LW) radiation products (GLASS, CERES SYN and ERA5); then, the global annual mean values of surface LW radiation as well as its temporal variations from 2003 to 2020 are quantified. The ERA5 SLUR and SLDR show the best accuracies by direct validation, with biases/Stds/RMSEs of −1.05/18.34/18.37 W/m² and −9.41/24.15/25.92 W/m², respectively. The GLASS SLUR has the best accuracy under clear-sky conditions with a bias/Std/RMSE of −6.73/14.21/15.72 W/m². The accuracy of the GLASS SLDR is comparable to CERES SYN. The merit of the GLASS LW radiation is that it can provide rich spatial details due to its high spatial resolution. The global annual mean SLUR is 399.77/398.92/398.19 W/m², and that of the SLDR is 342.64/347.98/340.47 W/m² for GLASS, CERES SYN and ERA5, respectively. The interannual variation trends for the three products produce substantially growing long-term trends for the global mean SLUR and SDLR over the globe and land, while there are almost no trends over the ocean. The long-term trends of the seasonal mean SLUR and SDLR in the Northern and Southern Hemispheres are asymmetrical. Our comprehensive evaluation and trend analysis of the mainstream surface LW radiation products can aid in understanding the global energy balance and climate change.

Surface longwave net radiation (SLNR) is the key component of the surface radiation budget. SLNR is estimated by using the conventional method, in which surface longwave downward radiation (SLDR) and surface longwave upward radiation (SLUR) are estimated separately, and then SLNR is calculated by subtracting SLUR from SLDR. However, uncertainties in SLDR and SLUR may accumulate and propagate into the calculated SLNR, affecting the SLNR estimate accuracy. This paper proposes a direct algorithm that links clear-sky SLNR with the top-of-atmosphere (TOA) radiances in MODIS channels 29, 31 and 32 and column water vapour (CWV). The multivariate regression (MVR) and extreme gradient boosting (×GBoost) were employed to establish the relationships sequentially with collocated ground measurements and satellite data for high-latitude, mid-latitude, and low-latitude areas. According to the training and validation results, the GBoost model performs slightly better than the MVR model, which indicates that weak nonlinearity exists between clear-sky SLNR and its predictors. The absolute biases of the XGBoost models are approximately 1.0 Wm⁻², and the RMSEs of the XGBoost model are 17.77, 27.33, and 19.60 Wm⁻² in high-latitude, mid-latitude, and low-latitude areas. These results indicate that nonlinear relationships exist between clear-sky SLNR and predictors, but this nonlinearity is not strong. The superiority of the direct algorithm over the conventional method was also demonstrated. In the future, we will collect more data to improve the performance of the proposed direct algorithm especially in high-latitude and mid-latitude areas.

In this paper, time extension methods, originally designed for clear-sky land surface conditions, are used to estimate high-spatial resolution surface daily longwave (LW) radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product. The performance of four time methods were first tested by using ground based flux measurements that were collected from 141 global sites. Combined with the accuracy of daily LW radiation estimated from the instantaneous GLASS LW radiation, the linear sine interpolation method performs better than the other methods and was employed to estimate the daily LW radiation as follows: The bias/Root Mean Square Error (RMSE) of the linear sine interpolation method were −6.30/15.10 W/m² for the daily longwave upward radiation (LWUP), −1.65/27.63 W/m² for the daily longwave downward radiation (LWDN), and 4.69/26.42 W/m² for the daily net longwave radiation (LWNR). We found that the lengths of the diurnal cycle of LW radiation are longer than the durations between sunrise and sunset and we proposed increasing the day length by 1.5 h. The accuracies of daily LW radiation were improved after adjusting the day length. The bias/RMSE were −4.15/13.74 W/m² for the daily LWUP, −1.3/27.52 W/m² for the daily LWDN, and 2.85/25.91 W/m² for the daily LWNR. We are producing long-term surface daily LW radiation values from the GLASS LW radiation product.

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 using in situ observations 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/m ² and root-mean-squared error (RMSE) values better than 29.9 W/m ² . 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.

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 R ² /bias/RMSE of the developed hybrid method is 0.95/0.59/18.41 W/m ² , which is clearly superior to the AGRI official SLUR and ERA5 SLUR with a R ² /bias/RMSE of 0.95/-7.79/19.04 W/m ² and 0.92/-4.94/23.2 W/m ² , respectively. The developed hybrid method performs better than the classical land surface temperature-broadband emissivity (LST-BBE) method. The R ² , bias/RMSE of the developed cloudy sky SLUR estimate LightGBM model is 0.85/ 0.56/21.16 W/m ² , which is also better than the accuracy of the LST-BBE method and comparable to the accuracy of the ERA5 SLUR. The R ² , bias and RMES of the all-sky SLUR are 0.93, 0.57 and 19.58 W/m ² , 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.