The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the multiscale geographically weighted regression (MGWR) model for 2005, 2010, 2015, and 2018. Compared to results obtained using the ordinary least square (OLS) model, the MGWR model has a lower corrected Akaike information criterion value and significantly improves the model’s coefficient of determination (OLS: 0.087–0.666, MGWR: 0.616–0.894). The normalized difference vegetation index (NDVI) and nighttime light (NTL) are the most critical drivers of daytime and nighttime SUHIs, respectively. In terms of model bandwidth, population and Δfine particulate matter are typically global variables, while ΔNDVI, intercept (i.e., spatial context), and NTL are local variables. The nighttime coefficient of ΔNDVI is significantly negative in the more economically developed southern coastal region, while it is significantly positive in northwestern China. Our study not only improves the understanding of the complex drivers of SUHIs from a multiscale perspective but also provides a basis for urban heat island mitigation by more precisely identifying the heterogeneity of drivers.

Urban heat islands (UHIs) are an important issue in urban sustainability, and the standardized calculation of surface urban heat island (SUHI) intensity has been a common concern of researchers in the past. In this study, we used the administrative borders (AB) method and an optimized simplified urban-extent (OSUE) algorithm to calculate the surface urban heat island intensity from 2001 to 2017 for 36 major cities in mainland China by using Moderate Resolution Imaging Spectroradiometer (MODIS) images. The spatiotemporal differences between these two methods were analyzed from the perspectives of the regional and national patterns and the daily, monthly, and annual trends. Regardless of the spatial or temporal scale, the calculation results of these two methods showed extremely similar patterns, especially for the daytime. However, when the calculated SUHI intensities were investigated through a regression analysis with multiple driving factors, we found that, although natural conditions were the main drivers for both methods, the anthropogenic factors obtained from statistical data (population and gross domestic product) were more correlated with the SUHI intensity from the AB method. This trend was probably caused by the spatial extent of the statistical data, which aligned more closely with the rural extent in the AB method. This study not only explores the standardization of the calculation of urban heat intensity but also provides insights into the relationship between urban development and the SUHI.

The Hindu Kush Himalayan (HKH) region is one of the most ecologically vulnerable regions in the world. Several studies have been conducted on the dynamic changes of grassland in the HKH region, but few have considered grassland net ecosystem productivity (NEP). In this study, we quantitatively analyzed the temporal and spatial changes of NEP magnitude and the influence of climate factors on the HKH region from 2001 to 2018. The NEP magnitude was obtained by calculating the difference between the net primary production (NPP) estimated by the Carnegie–Ames Stanford Approach (CASA) model and the heterotrophic respiration (Rh) estimated by the geostatistical model. The results showed that the grassland ecosystem in the HKH region exhibited weak net carbon uptake with NEP values of 42.03 gC∙m⁻²∙yr⁻¹, and the total net carbon sequestration was 0.077 Pg C. The distribution of NEP gradually increased from west to east, and in the Qinghai–Tibet Plateau, it gradually increased from northwest to southeast. The grassland carbon sources and sinks differed at different altitudes. The grassland was a carbon sink at 3000–5000 m, while grasslands below 3000 m and above 5000 m were carbon sources. Grassland NEP exhibited the strongest correlation with precipitation, and it had a lagging effect on precipitation. The correlation between NEP and the precipitation of the previous year was stronger than that of the current year. NEP was negatively correlated with temperature but not with solar radiation. The study of the temporal and spatial dynamics of NEP in the HKH region can provide a theoretical basis to help herders balance grazing and forage.

Daily evapotranspiration (ET) integration is essential to various applications of agricultural water planning and management, ecohydrology, and energy balance studies. The constant reference evaporative fraction (EFr) temporal upscaling method has been proven to be efficient in extrapolating instantaneous ET to a daily timescale. Unlike upscaling methods, the direct calculation (DC) method developed in our previous study directly estimates daily ET without calculating instantaneous ET. The present study aimed to compare daily estimations of ET using the EFr and DC methods based on field and MODIS data at a site from the ChinaFLUX network. The estimation results were validated by eddy covariance (EC) ET both with and without the correction of energy imbalance. Based on field data, the results show that (i) the DC method performed with higher accuracy when compared to uncorrected EC measurements, while daily ET from both methods was overestimated; (ii) the DC method still performed better after the EC ET was corrected by the Residual Energy scheme, and the overestimations were significantly decreased; (iii) both methods performed best when compared with corrected ET by the Bowen Ratio scheme. The results from satellite data reveal that (i) the constant EFr method overestimated daily ET by a mean-bias-error (MBE) of 5.6 W/m², and a root-mean-square error (RMSE) of 18.6 W/m²; and (ii) the DC method underestimated daily ET by a smaller MBE of −4.8 W/m² and an RMSE of 22.5 W/m². Therefore, the DC method has similar or better performance than the widely used constant EFr upscaling method and can estimate daily ET directly and effectively.

Under the joint influence of climate change and human production and living activities on land and sea, the morphology and use function of the coastline have undergone notable changes, triggering more significant environmental and ecological effects. In this paper, we quantified the evolution characteristics of the coastline over the past 30 years and measured the possible environmental pollution and ecological degradation by means of spatiotemporal coupling analysis in terms of spatial form and land-use function. The Chinese Bohai Rim region was taken as an example, and the Google Earth Engine was applied to achieve a long time series of interannually continuous land use function classification of the coastal zone. The study shows that: (1) from 1987–2020, the coastline of Bohai Rim showed an overall trend of seaward expansion, with the length of the coastline increasing by 15.6%, most significantly from 2003 to 2011; (2) the proportion of construction function coastline increased from 14.66% to 42.8%, while the proportion of cropland coastline decreased from 52.01% to 18.16% during 2000–2020, with the natural ecological coastline decreasing in the early stage and recovering in the later stage, thus becoming more stable overall; (3) 82.73% of coastal districts and counties showed a linear correlation between changes in coastline function and water quality level, and 77.8% showed a consistent degree of change of coastline function and loss of ecological land, indicating that changes in the coastline have triggered significant problems in terms of environmental pollution and ecological degradation. With the exception of localized areas such as the Yellow River Delta, where coastline change is somewhat more significantly influenced by environmental climate change, most other coastline changes are primarily due to human land-based development; therefore, coastal policy constraints bring obvious mitigation.

Accurate estimation of terrestrial water storage anomalies (TWSA) is crucial for the sustainable management of water resources and human living. In this study, long-term TWSA estimates are reconstructed by integration of multiple meteorological products and the water balance (WB) method at 0.5° × 0.5° resolution, generating a total of 12 combinations of different meteorological data. This scheme is applied to 10 river basins (RBs) within China and validated against GRACE observations and GLDAS simulations from 2003 to 2020. Results indicate that similar seasonal characteristics can be observed between different precipitation and evapotranspiration products with the average correlation coefficient and Nash–Sutcliffe efficiency coefficient metrics larger than 0.96 and 0.90, respectively. Three GRACE solutions indicate similar seasonal variations and long-term trends of TWSA over 10 RBs, with the correlation above 0.90. Similar performance can also be observed concerning the root mean square error and mean absolute error metrics. Nevertheless, WB-based TWSA estimates represent larger discrepancies compared to GRACE observations and GLDAS simulations. Specifically, the variation amplitude and long-term trend of WB-based results are much larger than that of the GRACE observations, which is mainly caused by the inaccuracy of remote sensing products and the neglect of anthropogenic activities. Comparable TWSA estimates independently computed from the WB method can only be achieved in 4 out of 10 RBs. This study can provide insightful suggestions for an enhanced understanding of TWSA estimates and improving the performance of the water balance method.