Toward a better understanding of coastal salt marsh mapping: A case from China using dual-temporal images

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Coastal salt marshes suffering from anthropogenic coastal development and sea level rise have attracted much attention because of their capacity for carbon sequestration and global climate change mitigation. Accurate mapping of coastal salt marshes is always the first step for their protection, management, and restoration. The inherent complexities of vegetation, dynamics of tides, and anthropogenic disturbances pose challenges for remote sensing-based approaches. Existing studies have utilized phenology information and various black-box algorithms to reduce misclassifications. However, the approaches with dual-temporal images containing phenology information have suffered from inefficiency; the misclassifications have been objectively post-processed rather than considered in the method design, and the tacit knowledge of the trained black-box models has not been revealed. To address the above issues, we proposed a new approach with solid improvements in dual-temporal image construction, misclassification processing, and tacit knowledge analysis, including an efficient method to synthesize dual-temporal images based on the common divisor of the green-up season or senescence season resulting from latitudinal gradients in narrow coastal areas of China, a detailed classification scheme by interpretation of iteratively collected samples, and a key decision rule approximating the trained model. We applied the approach to Sentinel-1/2 images and DEM data, thus deriving a 10-m resolution coastal salt marsh map of China with an overall accuracy of 92.5%. A qualitative comparison showed that the map produced in this study was fitted well with actual salt marsh distributions, resulting in improved accuracy when compared to recently generated maps. The most important contribution is that the overall nature of the trained model observed from the training samples was approximated by a four-feature decision rule following the principle of explainable artificial intelligence, i.e., B8senescence/B4senescence < 2.06 & B4green/B8green < 0.78 & B12green/B11green < 0.72 & elevation < 2.13, and thus established a new potential classification mechanism for dual-temporal black-box algorithms, i.e., the water signal was covered up by flourishing vegetation, but the situation changed when vegetation withered. This study not only generates an accurate coastal salt marsh map at a national scale but also provides a classification mechanism for dual-temporal image-based coastal salt marsh identification and mapping.

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Remote Sensing of Environment