The dynamic coastal zones, marked by rich biodiversity and rapid transformations due to human activities, present a challenging environment for monitoring and management. Tidal flats, a key natural feature of these zones, are increasingly subjected to anthropogenic stress, rising sea levels, and various environmental pressures. This study addresses the critical need for a robust, large-scale remote sensing approach to monitor these changes over time, particularly under fluctuating tides and evolving coastal landscapes. Using the three decades of Landsat 5 and Landsat 8 data (1990-2020), we developed an innovative approach for the automatic acquisition of low-tide imagery. Our method, which incorporates knowledge-based of tidal flat extraction, achieved a classification accuracy of over 95%. This technique effectively mitigates the impact of clouds, fog, and waves on image analysis, enabling precise and rapid delineation of large-scale intertidal zones. As a result, we produced the most extensive dataset on tidal flat areas in China's coastal zone, updated at three-year intervals. The spatial analysis results showed the primary distribution of tidal flats in Liaoning, Shandong, Jiangsu, Zhejiang, and Guangxi, which collectively account for over 70% of China's tidal flat areas. We observed distinct patterns of tidal flat evolution, with rapid changes in regions like the Liaohe River Delta, Yellow River Delta, Yangtze River Delta, and the Jiangsu Coast. These changes are closely linked to increased reclamation activities and salt marsh vegetation expansion. In contrast, coastal areas like Tianjin and Zhejiang showed a swift expansion of intertidal zones initially, followed by a stabilization post-2010, constrained by limited development space. Our study's approach to rapid tidal flat extraction has shown promising applications in other global river deltas. The comprehensive tidal flat mapping and data generation presented here offer valuable insights and support for the monitoring, management, and sustainable development of coastal wetlands.@en

Salt marshes provide crucial ecological functions and services and are experiencing rapid losses and degradation under global climate changes and high-intensity human activities in coastal zone. However, mapping salt marsh distributions and compositions with high accuracy on the national or global scale remains challenging. Here, we used Sentinel-1 time-series data and knowledge-based automatic decision tree classifiers to produce a 10-m map of the salt marshes in China coastal zones. We established annual synthetic aperture radar (SAR) composite features to address seasonal variations and tidal dynamics in salt marshes and created five partitioned-region classifiers according to the climatic and environmental characteristics. In total, 1729 Sentinel-1 images in 2019 and 3418 in situ sites were included for the classification. The results showed that (1) the annual mean SAR composite is more stable and practical than the seasonal composite for salt marsh classification along the China coastal zones in terms of these features addressing variations in tidal fluctuation, climate zones, and salt marsh compositions; (2) SAR surface roughness can effectively separate salt marshes into sparse density and red phenotype salt marshes; (3) the total area of Chinese coastal salt marshes was 127,477.37 ha with an overall accuracy of 87.30%, it included 48.30% Spartina alterniflora, 29.19% Phragmites australis, 13.76% Suaeda spp. and 8.75% Scirpus spp., and more than 85% of salt marshes were distributed from Shandong Province to Zhejiang Province. This study showed the potential application of time-series Sentinel-1 SAR data, annual composite data and knowledge-based classifiers for large-scale coastal zones, and these data will be valuable for coastal ecological restoration and sustainability management.

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The variation of suspended sediment concentration (SSC) in coastal waters plays a key role in the marine physical and chemical processes. Hangzhou Bay is a macrotidal estuary in eastern China. Under the influence of trapped tides, the SSC of Hangzhou Bay responds significantly commensurate with tidal frequencies, which leads to considerable changes in the ocean physical and chemical regimes, such as the underwater light field and turbidity front. However, polar-orbiting imaging satellites are unable to monitor the rapid variation of the SSC due to insufficient temporal resolution. In this study, we used the Geostationary Ocean Colour Imager (GOCI) data and in situ data of Hangzhou Bay to develop an appropriate remote sensing model to quantify the SSC of Hangzhou Bay and analyzed the impact of tides on the SSC. The research revealed the following findings: (1) The exponential model based on B8/B6 is best suited to remotely estimating the SSC in Hangzhou Bay, and the Mean Relative Error (MRE) is only 15.21%, (2) the variation of SSC in Hangzhou Bay is related to tidal forcing, especially during the ebb tide and the middle of the rising tide, (3) The effect of tidal driven SSC changes is less than the effect of diurnal tidal level variation of the middle tide day and the spring tide day tidal type, and (4) The maximum variation of SSC under tidal forcing is in the coastal ocean near the south bank of Hangzhou Bay and Nanhui Spit, while the origin of the variation of the Zhapu deep trough and the northern estuary is not obvious, which is speculated to be mainly related to the terrain and runoff of Hangzhou Bay. Furthermore, this study established a new model to map SSC in turbid seas and contributes to our understanding of SSC variation in the Hangzhou Bay, driven by strong tidal dynamics.@en