Rapid urbanization has heightened concerns over ecological degradation. This study analyzes spatiotemporal dynamics of land use in Jiangsu Province from 2000 to 2020 and integrates the intPLUS model to simulate and project land-use patterns for 2030–2050. Habitat quality was assessed with the InVEST model, and key driving factors were identified using the GeoDetector method. The results show that (1) from 2000 to 2020, cultivated land, forest, and grassland decreased markedly by 10.50%, 4.38%, and 35.55%, respectively, whereas built-up land and water bodies increased by 46.70% and 8.92%. (2) Projections for 2030–2050 indicate that land-use change will generally follow the 2000–2020 trajectory but with reduced land-use intensity, a slower expansion of built-up land, and relatively minor changes in ecological land. (3) Habitat quality declined overall during 2000–2020: areas of high habitat quality decreased by 1024.29 km², while low-quality areas increased by 6386.78 km². Spatially, habitat quality exhibited a pattern of “higher in the central region and lower in the south and north,” with relatively low values in southern and northern Jiangsu and higher values in central Jiangsu. By 2050, habitat quality is expected to improve gradually. (4) Nighttime light intensity and elevation exerted strong effects on habitat quality changes, with vegetation cover identified as the dominant driver. Among factor interactions, the interaction between nighttime light intensity and elevation showed the greatest explanatory power.

The Grand Canal Basin contains exceptionally rich historical and cultural heritage, and understanding the spatial distribution patterns and determinants of this heritage can provide a scientific basis for conservation and sustainable development. This study aims to reveal the spatiotemporal synergy and dislocation–coupling relationship between immovable and intangible cultural heritage in the Grand Canal Basin, and to provide a scientific basis for the emergency management of cultural heritage and the integrated protection of Grand Canal culture. The results indicate that both heritage categories generally exhibit “coaxial agglomeration with local differentiation” in the Grand Canal Basin; although their spatial distributions differ, they co-exist geographically and complement each other functionally, forming a dislocation–coupling system that links “historical space–modern space” and “material carriers–living practices.” These findings provide theoretical support for integrated conservation and the activation-oriented safeguarding and inheritance of cultural heritage in the Grand Canal Basin.

Zenith Tropospheric Delay (ZTD) and its associated atmospheric water vapor information constitute essential environmental variables for Earth observation (EO)-based atmospheric monitoring and environmental variable retrieval. High-quality ZTD products are therefore of great importance for the post-processing, refinement, and reconstruction of atmospheric environmental variables at regional scales. Among existing observation techniques, Global Navigation Satellite System (GNSS) measurements provide high-precision ZTD estimates and have become an important means for retrieving tropospheric delay and water vapor. However, the sparse and uneven spatial distribution of GNSS stations limits their direct applicability for continuous environmental monitoring. Reanalysis-based products, such as ERA5 provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), offer EO big data with excellent spatiotemporal continuity but suffer from pronounced systematic biases compared to precision GNSS retrievals, restricting their direct use in high-accuracy regional applications. To address these limitations, this study proposes a Residual Correction Kriging method for ZTD (RK ZTD) that integrates GNSS ZTD and ERA5 ZTD grids through a multi-source data fusion framework. High-precision GNSS ZTD is treated as reference data, and the differences between GNSS ZTD and ERA5 ZTD at modeling stations are defined as residuals to characterize the systematic bias in ERA5 ZTD grids. A Kriging interpolation algorithm is then employed to model the spatial distribution of these residuals and generate residual correction grids. By superimposing the interpolated residual grids onto the ERA5 ZTD grids, a refined and high-precision regional ZTD product is reconstructed. Experiments were conducted using observations collected in 2023 from 36 GNSS stations in the Netherlands, including 10 modeling stations and 26 independent validation stations, together with concurrent ERA5-derived ZTD grids. The results demonstrate that the proposed RK ZTD model provides spatially robust and high-precision ZTD products across the study region. The RK ZTD achieves a Root Mean Square Error (RMSE) of 5.70 mm, representing improvements of 58.4% and 35.4% compared with the original ERA5 ZTD (13.69 mm) and the GNSS-Kriging ZTD (8.82 mm), respectively. Moreover, the absolute bias is reduced to 0.41 mm, in contrast to 5.15 mm for the ERA5 ZTD, indicating that systematic biases are effectively mitigated. Spatial and seasonal analyses further confirm that the proposed method maintains stable performance across all seasons and significantly alleviates interpolation inaccuracies caused by sparse GNSS stations, even under extreme weather conditions such as Storm Ciarán, proving its value for advanced Earth environmental science applications.

Rapid urbanization and associated greenhouse gas emissions pose severe challenges to global climate goals. Accurately estimating urban carbon emissions at fine administrative scales is a critical prerequisite for spatially differentiated mitigation policies and achieving carbon neutrality. However, while current research has validated the feasibility of using nighttime light (NTL) remote sensing for carbon estimation, most studies predominantly focus on macro scales, paying limited attention to intra-urban spatial heterogeneity and the value of high-resolution imagery. Using Nanjing, China, as a case study, this study examines the optimal scale, model, and data source for estimating urban total carbon emissions. NTL features from NPP/VIIRS and Luojia1-01 imagery were extracted at the district and township levels. Spatial lag and spatial error models were compared, and geographically weighted regression was further applied at the township level. The results show that urban carbon emissions in Nanjing exhibit clear scale effects and spatial non-stationarity. At the township level, the total indicator (TCE-TNLI) better reflects emission expansion in peripheral areas, while the intensity indicator (CI-ANLI) shows better predictive performance and robustness. With high-resolution Luojia1-01 imagery, the intensity model further reduces the effects of pixel saturation and administrative scale differences, achieving better model performance. These findings establish a robust methodological framework for fine-scale urban carbon accounting, demonstrating that integrating high-resolution imagery with intensity-based models is crucial for supporting spatially differentiated low-carbon planning in high-density megacities.