With the rapid acceleration of global urbanization, the impact of land use/cover change (LUCC) on the environment and ecosystems has become increasingly prominent, particularly in terms of air quality, which has emerged as a significant issue demanding attention. Focusing on the coastal city of Lianyungang, the spatiotemporal dynamics of land use/cover changes were explored by utilizing land use dynamic degree and land use transfer matrix methods. By integrating a comprehensive historical dataset, multiple linear regression analysis was used to analyze the driving mechanism of land use conversion and to explore the effect of LUCC on the variations in PM₁₀ levels. The results showed an overall decreasing trend in PM₁₀ levels over the 24-year period from 2000 to 2023, with distinct seasonal fluctuations, showing higher concentrations in winter and lower concentrations in summer. The impact of land use on PM₁₀ variations can be categorized into three stages: initial (2000–2006), transitional (2007–2013), and deepening development (2014–2022). Notably, during the third stage, with the involvement of policy interventions and industrial upgrading, a strong negative correlation of −0.97 was identified between urban land expansion and the decrease in PM₁₀ levels. The correlation between LUCC and PM₁₀ levels was insignificant over shorter periods, but the analysis of data from 2000 to 2022 revealed a significant positive correlation of 0.77, emphasizing the importance of adopting a long-term perspective to accurately assess the impact of LUCC on air quality. This research provides valuable insights into the implications of LUCC on air quality during urbanization and establishes a scientific foundation for developing air pollution management strategies in Lianyungang and similar regions.

A synthetic aperture radar (SAR) provides vital global observations of ocean waves. However, the quasi-linear inversion algorithm routinely used for Sentinel-1 Level-2 Ocean Swell Wave (OSW) products suffers from inherent nonlinear imaging limitations. These include severe distortions and the inability to resolve wind-sea components under a strong azimuth cut-off effect. To address these challenges, this paper proposes a novel prior-knowledge-integrated machine learning framework to reconstruct complete and accurate directional wave spectra from Sentinel-1A SAR wave mode data. First, an extreme gradient boosting model is trained to accurately estimate wind-sea heights, which are then used to construct a theoretical JONSWAP prior spectrum. Subsequently, a U-Net architecture seamlessly integrates this physical prior knowledge with the official OSW swell spectra baseline. Independent validation demonstrates that the proposed framework significantly increases the spectral similarity against ERA5 reanalysis compared to the standard OSW. Furthermore, the derived parameters of total significant wave height, mean wave period, and mean wave direction exhibit remarkable improvements, with root mean square errors of 0.4026 m, 0.4342 s and 20.42°, respectively. The enhancement of SAR inferred two-dimensional wave spectra is also examined and discussed by three typical case studies. It is indicated that integrating physical wave knowledge with machine learning robustly mitigates the non-linear limitations of SAR imaging, providing highly reliable directional wave spectra for global ocean monitoring and forecasting.

The suspended matter concentration (TSM) and Secchi disk depth (SDD) are key parameters describing seawater quality, directly reflecting the turbidity of seawater and the degree of light absorption and scattering by seawater. In this study, based on GOCI data, three different atmospheric correction (AC) algorithms were employed to retrieve the TSM and SDD of the Yellow Sea and Bohai Sea, and conducted with in-situ data. Leveraging the high observation frequency of GOCI, differences in the hourly variation characteristics of TSM and SDD under different AC algorithms were further analyzed. The results show that at the same pixel, the values of TSM and SDD retrieved using different atmospheric correction algorithms exhibit non-negligible differences. In some sea areas, there are obvious anomalies in the coupled response of TSM and SDD obtained using the same atmospheric correction algorithm. Most critically, the hourly trends of TSM and SDD obtained by different AC algorithms may diverge or even be completely opposite, especially in highly turbidities waters. This suggests that AC algorithmic selection is not just a preprocessing detail, but a factor that can radically alter the interpretation of short-term biogeochemical dynamics. The findings suggest an important methodological warning for high-frequency time-series research in ocean color remote sensing.