Surface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR), its visible component, are key parameters needed for many land process models and terrestrial applications. Most existing DSR and PAR products were developed for climate studies and therefore have coarse spatial resolutions, which cannot satisfy the requirements of many applications. This paper introduces a new global high-resolution product of DSR (MCD18A1) and PAR (MCD18A2) over land surfaces using the MODIS data. The current version is Collection 6.0 at the spatial resolution of 5 km and two temporal resolutions (instantaneous and three-hour). A look-up table (LUT) based retrieval approach was chosen as the main operational algorithm so as to generate the products from the MODIS top-of-atmosphere (TOA) reflectance and other ancillary data sets. The new MCD18 products are archived and distributed via NASA’s Land Processes Distributed Active Archive Center (LP DAAC). The products have been validated based on one year of ground radiation measurements at 33 Baseline Surface Radiation Network (BSRN) and 25 AmeriFlux stations. The instantaneous DSR has a bias of −15.4 W/m² and root mean square error (RMSE) of 101.0 W/m², while the instantaneous PAR has a bias of −0.6 W/m² and RMSE of 45.7 W/m². RMSE of daily DSR is 32.3 W/m², and that of the daily PAR is 13.1 W/m². The accuracy of the new MODIS daily DSR data is higher than the GLASS product and lower than the CERES product, while the latter incorporates additional geostationary data with better capturing DSR diurnal variability. MCD18 products are currently under reprocessing and the new version (Collection 6.1) will provide improved spatial resolution (1 km) and accuracy.

Land surface temperature (LST) plays a dominant role in the surface energy budget (SEB) and hydrological cycling. Thermal infrared (TIR) remote sensing is the primary method of estimating LST globally. However, cloud cover leaves numerous data gaps in satellite LST products, which seriously restricts their applications. Efforts have been made to produce gap-free LST products from polar-orbiting satellites (e.g., Terra and Aqua); however, satellite data from limited overpasses are not suitable for characterizing the diurnal temperature cycle (DTC), which is directly related to heat waves, plant water stress, and soil moisture. Considering the high temporal variability in LST and the importance of the DTC, we refined the SEB-based cloudy-sky LST recovery method by improving its feasibility and efficiency and produced a global hourly, 5 km, all-sky land surface temperature (GHA-LST) dataset from 2011 to 2021. The GHA-LST product was generated using TIR LST products from geostationary and polar-orbiting satellite data from the Copernicus Global Land Service (CGLS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). Based on ground measurements at the 201 global sites from the Surface Radiation Budget (SURFRAD), Baseline Surface Radiation Network (BSRN), Fluxnet, AmeriFlux, Heihe River basin (HRB), and Tibetan Plateau (TP) networks, the overall root-mean-square error (RMSE) of the hourly GHA-LST product was 3.31 K, with a bias of −0.57 K and R² of 0.95. Thus, this product was more accurate than the clear-sky CGLS and MODIS MYD21C1 LST samples. The RMSE value of the daily mean LST was 1.76 K. Validation results at individual sites indicate that the GHA-LST dataset has relatively larger RMSEs for high-elevation regions, which can be attributed to high surface heterogeneity and input data uncertainty. Temporal and spatial analyses suggested that GHA-LST has satisfactory spatiotemporal continuity and reasonable variation and matches the reference data well at hourly and daily scales. Furthermore, the regional comparison of GHA-LST with other gap-free hourly datasets (ERA5 and Global Land Data Assimilation System, GLDAS) demonstrated that GHA-LST can provide more spatial texture information. The monthly anomaly analysis suggests that GHA-LST couples well with global surface air temperature datasets and other LST datasets at daily mean and minimum temperature scales, whereas the maximum temperature and diurnal temperature range of LST and air temperature (AT) have different anomalous magnitudes. The GHA-LST dataset is the first global gap-free LST dataset at an hourly, 5 km scale with high accuracy, and it can be used to estimate global evapotranspiration, monitor extreme weather, and advance meteorological forecasting models. GHA-LST is freely available at https://doi.org/10.5281/zenodo.7487284 (Jia et al., 2022b) and http://glass.umd.edu/allsky_LST/GHA-LST (last access: 10 February 2023; Jia et al., 2022c).

Downward shortwave radiation (DSR) is an essential parameter in the terrestrial radiation budget and a necessary input for models of land-surface processes. Although several radiation products using satellite observations have been released, coarse spatial resolution and low accuracy limited their application. It is important to develop robust and accurate retrieval methods with higher spatial resolution. Machine learning methods may be powerful candidates for estimating the DSR from remotely sensed data because of their ability to perform adaptive, nonlinear data fitting. In this study, the gradient boosting regression tree (GBRT) was employed to retrieve DSR measurements with the ground observation data in China collected from the China Meteorological Administration (CMA) Meteorological Information Center and the satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) at a spatial resolution of 5 km. The validation results of the DSR estimates based on the GBRT method in China at a daily time scale for clear sky conditions show an R² value of 0.82 and a root mean square error (RMSE) value of 27.71 W·m⁻² (38.38%). These values are 0.64 and 42.97 W·m⁻² (34.57%), respectively, for cloudy sky conditions. The monthly DSR estimates were also evaluated using ground measurements. The monthly DSR estimates have an overall R² value of 0.92 and an RMSE of 15.40 W·m⁻² (12.93%). Comparison of the DSR estimates with the reanalyzed and retrieved DSR measurements from satellite observations showed that the estimated DSR is reasonably accurate but has a higher spatial resolution. Moreover, the proposed GBRT method has good scalability and is easy to apply to other parameter inversion problems by changing the parameters and training data.

Earth surface net radiation (R_n) characterizes the surface radiation budget and plays a critical role in ecological, biogeochemical, and hydrological processes. The R_n products from remote sensing and reanalysis have not been validated comprehensively. In this study, four R_n products (Clouds and the Earth's Radiant Energy System [CERES], ERA‐Interim, Modern‐Era Retrospective analysis for Research and Applications version 2, and Japanese 55‐year Reanalysis) were validated using global ground measurements on monthly (255 sites) and annual (172 sites) timescales. These products have similar accuracies, with average root‐mean‐square error (RMSE) ranges of 5.35 W m⁻² (monthly) and 2.30 W m⁻² (annually). However, varying accuracies and systemic biases exist across different climatic zones. The annual land R_n intercomparison illustrates that large uncertainty exists over polar regions and deserts. A significantly negative annual anomaly in the CERES product for the 2001–2008 period is identified when examining annual R_n anomalies over the global land surface. Detailed uncertainty analysis indicates that the global CERES R_n anomaly is mainly due to different versions of input data such as aerosol optical thickness and atmospheric profiles (in 2006 and 2008) and cloud properties (in 2002). This work demonstrates that temporal analysis provides powerful quality control for global time series satellite products when the validation using ground measurements fails to capture potential issues.