Hailong Zhang

Downward shortwave radiation (DSR) is critical for understanding global radiation budget and climate dynamics. Traditional estimation of DSR commonly relied on horizontal surface assumptions, and thus neglecting critical topographic factors such as slope gradient, aspect orientation, and terrain-induced shadowing. This study proposed a novel hybrid method specifically designed for DSR estimation across the Tibetan Plateau (TP). The proposed method integrates multi-source datasets comprising paired daily MODIS atmospheric products, hourly ERA5 reanalysis data, and Digital Elevation Model (DEM). Direct solar irradiance on tilted surfaces was estimated from horizontal-surface direct radiation, accounting for solar elevation angle on the tilted surface and terrain shadowing effects. Diffuse solar irradiance was corrected by incorporating sky view factor and terrain-reflected radiation contributions. Compared to in-situ measurements, the method demonstrated superior performance, with RMSE values of 169.47 W/m² (hourly, exclude nighttime) and 41.56 W/m² (daily), exceeding the performance of benchmark datasets (ERA5-Land: 52.14 W/m²; GLASS: 46.38 W/m²; MCD18A1: 43.39 W/m²; Himawari-8: 55.36 W/m²). The proposed method reveals a stronger robustness over rugged surfaces compared to the state-of-the-art satellite-retrieved DSR products.

The quantitative characterization of the thermal conditions in the Tibetan Plateau has long been a focal point of global research. Downward shortwave radiation, as a crucial component, plays an important role in numerous land surface processes while also serving as a significant indicator of the plateau’s thermal state. In order to gain a more comprehensive understanding of the Earth’s radiation budget in the Tibetan Plateau region, this study undertook an evaluation of six radiation products (ISCCP-FH, CERES-SYN, GLASS DSR, Himawari-8, MCD18A1, and ERA5). Two sets of ground measurements (downward shortwave radiation values from 10 CMA sites and 6 sites provided by the National Tibetan Plateau Data Center) in 2015 and 2016 were used as validation data to verify the accuracy of the remote sensing products. The results show that in the Tibetan Plateau region, CERESC products show the highest accuracy among the six data products with a bias (relative bias) of −7.57 W/m² (3.46%), RMSE (relative RMSE) of 32.77 W/m² (14.99%), and coefficient of determination of 0.80. Among all products, only the ERA5 products overestimated the value of downward shortwave radiation in the Tibetan Plateau region with a bias (relative bias) of 15.62 W/m² (7.14%). By employing a spatial resolution upscaling approach, we assessed the influence of varying spatial resolutions on the validation accuracy, with the results indicating minimal impact. Through an analysis of the impact of cloud factors and aerosol factors on the validation accuracy, it is deduced that ERA5, Himawari-8, and MCD18A1 products are significantly influenced by cloud factors, whereas the CERES-SYN product is notably affected by aerosol factors.

Clouds are a critical factor in regulating the climate system, and estimating cloudy-sky Surface Downward Longwave Radiation (SDLR) from satellite data is significant for global climate change research. The models based on cloud water path (CWP) are less affected by cloud parameter uncertainties and have superior accuracy in SDLR satellite estimation when compared to those empirical and parameterized models relying mainly on cloud fraction or cloud-base temperature. However, existing CWP-based models tend to overestimate the low SDLR values and underestimate the larger SDLR. This study found that this phenomenon was caused by the fact that the models do not account for the varying relationships between cloud radiative effects and key parameters under different Liquid Water Path (LWP) and Precipitable Water Vapor (PWV) ranges. Based upon this observation, this study utilized Fengyun-4A (FY-4A) cloud parameters and ERA5 data as data sources to develop a new CWP-based model where the model coefficients depend on the cloud phase and cloud water path range. The accuracy of the new model’s estimated SDLR is 20.8 W/m² for cloudy pixels, with accuracies of 19.4 W/m² and 23.5 W/m² for overcast and partly cloudy conditions, respectively. In contrast, the accuracy of the old CWP-based model was 22.4, 21.2, and 24.8 W/m², respectively. The underestimation and overestimation present in the old CWP-based model are effectively corrected by the new model. The new model exhibited higher accuracy under various station locations, cloud cover scenarios, and cloud phase conditions compared to the old one. Comparatively, the new model showcased its most remarkable improvements in situations involving overcast conditions, water clouds with low PWV and low LWP values, ice clouds with large PWV, and conditions with PWV ≥ 5 cm. Over a temporal scale, the new model effectively captured the seasonal variations in SDLR.

Vegetation plays a fundamental role within terrestrial ecosystems, serving as a cornerstone of their functionality. Presently, these crucial ecosystems face a myriad of threats, including deforestation, overgrazing, wildfires, and the impact of climate change. The implementation of remote sensing for monitoring the status and dynamics of vegetation ecosystems has emerged as an indispensable tool for advancing ecological research and effective resource management. This study takes a comprehensive approach by integrating ecosystem monitoring indicators and aligning them with the objectives of SDG15. We conducted a thorough analysis by leveraging global 500 m resolution products for vegetation Leaf Area Index (LAI) and land cover classification spanning the period from 2016 to 2020. This encompassed the calculation of annual average LAI, identification of anomalies, and evaluation of change rates, thereby enabling a comprehensive assessment of the global status and transformations occurring within major vegetation ecosystems. In 2020, a discernible rise in the annual Average LAI of major vegetation ecosystems on a global scale became evident when compared to data from 2016. Notably, the ecosystems demonstrating a slight increase in area constituted the largest proportion (34.23%), while those exhibiting a significant decrease were the least prevalent (6.09%). Within various regions, such as Eastern Europe, Central Africa, and South Asia, substantial increases in both forest ecosystem area and annual Average LAI were observed. Furthermore, Eastern Europe and Central America recorded significant expansions in both grassland ecosystem area and annual average LAI. Similarly, regions experiencing notable growth in both cropland ecosystem areas and annual average LAI encompassed Southern Africa, Northern Europe, and Eastern Africa.

The radiation budget in polar regions plays an important role in global climate change study. This study investigates the performance of downward longwave radiation (DLR) of three satellite radiation products in polar regions, including GEWEX-SRB, ISCCP-FD, and CERES-SYN. The RMSEs are 35.8, 40.5, and 26.9 W/m² at all polar sites for GEWEX-SRB, ISCCP-FD, and CERES-SYN. The results in the Arctic are much better than those in the Antarctic, RMSEs of the three products are 34.7 W/m², 36.0 W/m², and 26.2 W/m² in the Arctic and are 38.8 W/m² and 54.8 W/m², and 28.6 W/m² in the Antarctic. Both GEWEX-SRB and CERES-SYN underestimate DLRs at most sites, while ISCCP-FD overestimates DLRs at most sites. CERES-SYN and GEWEX-SRB DLR products can capture most of the DLR seasonal variation in both the Antarctic and Arctic. Though CERES-SYN has the best results that RMSE within 30 W/m² in most polar sites, the accuracy of satellite products in polar regions still cannot meet the requirement of climate research. The improvement of satellite DLR products in polar regions mainly depends on the quality of improving input atmospheric parameters, the accuracy of improving cloud detection over the snow and ice surface and cloud parameters, and better consideration of spatial resolution and heterogeneity.

Evapotranspiration (ET) is an important part of surface–atmosphere interactions, connecting the transfer of matter and energy. Land surface heterogeneity is a natural attribute of the Earth’s surface and is an inevitable problem in calculating ET with coarse resolution remote sensing data, which results in significant error in the ET estimation. This study aims to explore the effect and applicability of the evaporative fraction and area fraction (EFAF) method for correcting 1 km coarse resolution ET. In this study we use the input parameter upscaling (IPUS) algorithm to estimate energy fluxes and the EFAF method to correct ET estimates. Five ground stations in the midstream and downstream regions of the Heihe River Basin (HRB) were used to validate the latent heat flux (LE) calculated by the IPUS algorithm and EFAF method. The evaluation results show that the performance of the EFAF method is superior to that of the IPUS algorithm, with the coefficient of determination (R²) increasing, the root mean square error (RMSE) decreasing, and the mean bias error (MBE) decreasing by 17 W/m² on average. In general, the EFAF method is suitable for correcting the deviation in LE estimated based on Sentinel data caused by land surface heterogeneity and can be applied to obtain accurate estimates of ET.