Increasing population size and economic dependence on the coastal zone, coupled with the growing need for residential, agricultural, industrial, commercial and green space infrastructure, are key drivers of land reclamation. Until now, there has been no comprehensive assessment of the global distribution of land use on reclaimed space at the coast. Here, we analyze Landsat satellite imagery from 2000 to 2020 to quantify the spatial extent, scale, and land use of urban coastal reclamation for 135 cities with populations in excess of 1 million. Findings indicate that 78% (106/135) of these major coastal cities have resorted to reclamation as a source of new ground, contributing a total 253,000 ha of additional land to the Earth's surface in the 21st century, equivalent to an area the size of Luxembourg. Reclamation is especially prominent in East Asia, the Middle East, and Southeast Asia, followed by Western Europe and West Africa. The most common land uses on reclaimed spaces are port extension (>70 cities), followed by residential/commercial (30 cities) and industrial (19 cities). While increased global trade and the rapid urbanization have driven these uses, we argue that a city's prestigious place‐making effort to gain global reputation is emerging as another major driver underlying recent reclamation projects to create tourist and green spaces Meanwhile, the study suggests that 70% of recent reclamation has occurred in areas identified as potentially exposed to extreme sea level rise (SLR) by 2100 and this presents a significant challenge to sustainable development at the coast.

Offshore wind farms are widely adopted by coastal countries to obtain clean and green energy; their environmental impact has gained an increasing amount of attention. Although offshore wind farm datasets are commercially available via energy industries, records of the exact spatial distribution of individual wind turbines and their construction trajectories are rather incomplete, especially at the global level. Here, we construct a global remote sensing-based offshore wind turbine (OWT) database derived from Sentinel-1 synthetic aperture radar (SAR) time-series images from 2015 to 2019. We developed a percentile-based yearly SAR image collection reduction and autoadaptive threshold algorithm in the Google Earth Engine platform to identify the spatiotemporal distribution of global OWTs. By 2019, 6,924 wind turbines were constructed in 14 coastal nations. An algorithm performance analysis and validation were performed, and the extraction accuracies exceeded 99% using an independent validation dataset. This dataset could further our understanding of the environmental impact of OWTs and support effective marine spatial planning for sustainable development.

The composition and distribution of wetland vegetation is critical for ecosystem diversity and sustainable development. However, tidal flat wetland environments are complex, and obtaining effective satellite imagery is challenging due to the high cloud coverage. Moreover, it is difficult to acquire phenological feature data and extract species-level wetland vegetation information by using only spectral data or individual images. To solve these limitations, statistical features, temporal features, and phenological features of multiple Landsat 8 time-series images obtained via the Google Earth Engine (GEE) platform were compared to extract species-level wetland vegetation information from Chongming Island, China. The results indicated that (1) a harmonic model obtained the phenological characteristics of wetland vegetation better than the raw vegetation index (VI) and the Savitzky–Golay (SG) smoothing method; (2) classification based on the combination of the three features provided the highest overall accuracy (85.54%), and the phenological features (represented by the amplitude and phase of the harmonic model) had the greatest impact on the classification; and (3) the classification result from the senescence period was more accurate than that from the green period, but the annual mapping result on all seasons was the most accurate. The method described in this study can be applied to overcome the impacts of the complex environment in tidal flat wetlands and to effectively classify wetland vegetation species using GEE. This study could be used as a reference for the analysis of the phenological features of other areas or vegetation types.

Loss of coastal wetland habitats has been directly linked to a decline in waterbird populations including migratory species, leading to calls to reverse this trend in part by restoring these habitats. However, distinct "sediment scarcity" has hindered coastal habitat restoration. Here, taking the Yangtze River Delta, China as an example, we put forward a feasible solution to solve the sediment shortage in habitat restoration so necessary to restore migratory waterbird numbers. Four biological indices including total wetland area, wetland vegetation area and waterbird species richness and abundance, were used to compare and assess the restorative efforts. Three solutions were adopted for the rehabilitation sites, including promoting sediment deposition and settlement through engineering intervention in Chongming Dongtan (CD) and Eastern Nanhui (EN), and using dredged sediments to nourish and create new habitats in Hengsha Eastern Shoal (HES). The mean wetland area increased 19.66 km²/yr in EN, 8.78 km²/yr in HES and 3.83 km²/yr in CD after rehabilitation. Along with the increase of wetlands and habitats, the abundance of waterbirds increased 1.3 times, 121 times and 1.5 times in EN, HES and CD, respectively. In contrast, in the site of Fengxian and Jinshan (FJ) where no any rehabilitation measure was taken after reclamation, the habitats were lost almost completely and the waterbird abundance dropped drastically. The comparison and assessment results demonstrate that proper coastal silting structures and ecological utilization of nearby dredged sediments are the feasible and effective solutions to retain sediments, restore coastal habitats and increase waterbird diversity and abundance.