Classification of coastal salt marsh based on Sentinel-1 time series backscattering characteristics: The case of the Yangtze River delta

Salt marshes are highly productive ecosystems in the mid-high latitude coastal zone, and the ecological service functions provided by different types of salt marsh vegetation are significantly different. The combination of human activities and natural factors such as reclamation, invasion of Spartina alterniflora, and sea level rise has led to rapid changes in the structure and spatial distribution of salt marshes in China's coastal zone. Existing optical methods are subject to tidal and cloud interference in the coastal zone. Obtaining large-scale and high-efficiency salt marsh vegetation information using hyperspectral or LiDAR data is difficult.This study took the Yangtze River estuary as the research area and proposed a coastal salt marsh vegetation classification method based on vegetation phenology and multi-temporal radar backscatter feature. Sentinel-1 radar data were used to analyze the annual time-series characteristics of radar backscattering in salt marshes, intertidal forest swamps, mudflats, and water bodies. Based on the phenological characteristics of salt marsh vegetation, the separability between the monthly backscattering characteristics of a typical salt marsh was calculated based on the separation threshold method. According to the optimal time-series radar classification characteristics, the random forest method was used to obtain the species, structure, and spatial distribution of salt marsh vegetation.Results showed the following. (1) The average value of VH polarization backscattering can distinguish water bodies, light beaches, intertidal forest swamps, and salt marshes well. (2) The mean backscattering values of VV polarization in April, VH polarization in November, and VV polarization in March were the optimal characteristics of Scirpus × mariqueter, Spartina alterniflora, and Phragmites australis. (3) Obtained by the optimal characteristics and random forest classification algorithm, the general classification accuracy of salt marsh vegetation was 85% with a Kappa coefficient of 0.80.Compared with optical remote sensing, radar images can effectively obtain the inter-annual and inter-monthly time series backscattering characteristics of salt marsh vegetation, and accurately obtain the spatial dynamics of coastal salt marsh vegetation. This study has shown the application potential of radar images in coastal zone research and provides important technical means and data support for coastal biodiversity conservation, wetland ecosystem function enhancement, and ecological environment management. © 2022, Science Press. All right reserved.