Snowfall data are vital in calculating the surface mass balance of the Antarctic Ice Sheet (AIS), where in-situ and satellite measurements are sparse at synoptic timescales. CloudSat data are used to construct Antarctic snowfall data at synoptic timescales to compensate for the sparseness of synoptic snowfall data on the AIS and to better understand its surface mass balance. Synoptic CloudSat snowfall data are evaluated by comparison with daily snow accumulation measurements from ten automatic weather stations (AWSs) and the fifth generation of the European Centre for Medium-Range Weather Forecasts climate reanalysis (ERA5) snowfall. Synoptic snowfall data were constructed based on the CloudSat measurements within a radius of 1.41°. The results show that reconstructed CloudSat snowfall at daily and two-day resolutions cover about 28% and 29% of the area of the AIS, respectively. Daily CloudSat snowfall and AWS snow accumulation have similar trends at all stations. While influenced by stronger winds, >73.3% of extreme snow accumulation events correspond to snowfall at eight stations. Even if the CloudSat snowfall data have not been assimilated into the ERA5 dataset, the synoptic CloudSat snowfall data are almost identical to the daily ERA5 snowfall with only small biases (average root mean square error and mean absolute error < 3.9 mm/day). Agreement among the three datasets suggests that the CloudSat data can provide reliable synoptic snowfall data in most areas of the AIS. The ERA5 dataset captures a large number of extreme snowfall events at all AWSs, with capture rates varying from 56% to 88%. There are still high uncertainties in ERA5. Nevertheless, the result suggests that ERA5 can be used to represent actual snowfall events on the AIS at synoptic timescale.

The spatial heterogeneity of land surface temperature (LST) within cities is profoundly influenced by the built environment. Although significant progress has been made in the study of the urban thermal environment, there is still a lack of research on how the pattern and structural layout of the built environment affects the thermal environment. In this study, we take the Fifth Ring Road of Beijing as an example, invert the urban LST on the basis of multisource spatial data, characterize the built environment, and use k-means cluster analysis to investigate the main influencing factors of the LST in different functional areas and building patterns within the city, as well as the spatial relationship between the built environment and the urban LST. The results show the following: (1) The urban heat island (UHI) effect occurs to varying degrees over a large part of the study area, and these UHI areas are mainly concentrated in the southwestern part of the city, forming a large contiguous area between the second and fifth ring roads. (2) Class 1 is dominated by transport blocks, Class 3 is dominated by commercial blocks, and Class 5 is dominated by green space blocks, with a clustering index of 0.38. (3) The high-density, high-height class (HH-Class 2) has a greater number of blocks distributed in a ring shape around the periphery of the second ring road. The high-density, low-height class (HL-Class 2) has a relatively small number of blocks but a relatively large area, and the largest blocks are located in the western part of the study area. (4) In the HH and HL building patterns, extreme heat scenarios often occur; from the perspective of functional areas, the probability of extreme heat in the transport block is much higher than that of other functional areas, and except for the HH scenario, the green space functional area plays a very important role in reducing the temperature. This study explores the characteristics of the built environment that influence the urban LST from the perspective of different urban functional zones in cities to provide decision support for quantitative territorial spatial planning, optimization, and management.

Evaluating cooling effects within local climate zones (LCZs) is vital for urban planning, especially in countering urban heat islands and enhancing thermal comfort. While prior research has primarily focused on the influence of land cover type LCZs on land surface temperature in large cities, the role of LCZ characteristics across diverse urban environments in smaller regions remains insufficiently explored. This study investigates the cooling effects of land cover type LCZs within the urban built-up areas at the county level using ECOSTRESS land surface temperature data. We utilized a random forest regression model to assess the impact of LCZ factors—namely, landscape shape index, percentage of landscape, and nearest neighbor distance among six built type LCZs and three land cover type LCZs—on cooling effects. The key findings are as follows: (1) Densely constructed LCZs demonstrated higher land surface temperature due to increased heat retention, with LCZ2 showing the highest mean land surface temperature (37.64 °C), whereas land cover type LCZs, particularly LCZG, recorded significantly lower mean land surface temperature (31.86 °C). (2) Land cover type LCZs were crucial in reducing land surface temperature, with LCZG presenting the most significant cooling effects, ranging from 0.31 to 79.43. LCZA and LCZD displayed comparable cooling effects, with average values of 6.54 and 6.46, respectively. (3) The landscape shape index of the land cover type LCZs themselves contributed most significantly to cooling effects. The percentage of landscape and landscape shape index of LCZ3 and LCZ6 notably influenced cooling effects in LCZA and LCZD, while LCZ5 predominantly affected LCZG. (4) The interactions between key factors and cooling effects were found to be complex and nonlinear. U-shaped effects were observed (e.g., central percentage of landscape affecting LCZA and LCZD, and percentage of landscape in LCZ6 affecting LCZG), alongside inverted U-shaped effects (e.g., landscape shape index in LCZ6 affecting LCZG) and combined effects (e.g., landscape shape index of LCZ3 affecting LCZA). The results emphasize the significance of maintaining contiguous and well-structured land cover type LCZs to optimize cooling effects.

Integrating machine learning with spectral data provides an effective and cost-efficient scheme for estimating cadmium (Cd) contents in soils compared with labor-intensive laboratory analyses. However, conventional machine learning–based spectral estimation methods often yield unsatisfactory performance because they rely on an unrealistic assumption that the functional relationships across geographically distinct subregions are homogeneous, thereby reducing the accuracy and stability of soil Cd estimation. To address this issue, a geospatial information-guided XGBoost (GIGS) model was developed, in which a spatial weighting module was incorporated into the XGBoost framework to account for spatial heterogeneity in functional relationships among sampling locations. The spatial heterogeneity of each soil spectral sample was quantified and assigned a corresponding spatial weight, which was subsequently incorporated during model calibration. The optimal principal component spectra (PCS) derived from spectral data were used as model inputs, with measured soil Cd content as the dependent variable, thereby establishing a robust spectral estimation model for soil Cd. Results indicated that the GIGS model exhibited satisfactory performance in the spectral estimation of soil Cd content, with R², RMSE, and RPIQ values of 0.78, 0.04, and 2.02, respectively. Compared to commonly used spectral estimation models (e.g., XGBoost, random forest, support vector regression), the GIGS model achieved a maximum performance improvement of approximately 27.87% and a minimum improvement of approximately 16.42% (with reference to the R² value). Five PCS of soil spectral data were extracted as predictors for the model. PCS-1 was found to be closely associated with iron oxides, while PCS-2 primarily reflects the spectral characteristics of clay minerals. The spectral bands at 600 nm and 815 nm contribute most strongly to PCS-3, which is linked to soil organic matter and indirectly reflects the soil Cd status. PCS-4 and PCS-5 represent mixed spectral information derived from materials associated with soil Cd. An integrated framework combining the GIGS model and PCS data developed in this study provides an accurate and reliable tool for spectral estimation and mapping of soil Cd, thereby supporting cost-effective soil management and environmental sustainability worldwide.

The performance of recent reanalysis products (i.e., ERA-Interim, NCEP2, MERRA, CFSR, and JRA-55) was evaluated based on in situ observations from nine automatic weather stations and one stake network to investigate the monthly and seasonal variability of the surface mass balance in Antarctica. Synoptic precipitation simulations were also evaluated by an investigation of high precipitation events. The seasonal variations showed large fluctuations and were inconsistent at each station, probably owing to the large interannual variability of snow accumulation based on the short temporal coverage of the data. The ERA-Interim and JRA-55 datasets revealed better simulated precision, with the other three models presenting similar simulations at monthly and seasonal timescales. The JRA-55 dataset captured a greater number of synoptic high precipitation events at four of the nine stations. Such events at the other five stations were mainly captured by ERA and CFSR. The NCEP2 dataset was more weakly correlated with each station on all timescales. These results indicate that significant monthly or seasonal correlations between in situ observations and the models had little effect on the capability of the reanalyses to capture high precipitation events. The precision of the five reanalysis datasets widely fluctuated in specific regions or at specific stations at different timescales. Great caution is needed when using a single reanalysis dataset to assess the surface mass balance over all of Antarctica.