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Abstract

Sediment distribution within intertidal flats varies widely, ranging from mud-dominant to sand-dominant, with extensive seasonal changes. However, retrieving grain size information from remotely sensed data is difficult because the optical reflectance of intertidal sediment is not a function of a single parameter but varies according to water content, grain size, topography, surface water, benthic algae, and halophytes. Among these, grain size and water content are two important parameters. The fact that intertidal sediments are always affected by tide necessitates the development of a water-independent grain size retrieval model. Mud and sand sediment are known to be well distinguished under dry conditions in principal component analysis (PCA) space but hardly distinguished under saturated conditions. Here, we introduce a new grain size retrieval model by removing the water-content dependency from optical reflectance via a two-step PCA transform. To define the relationship between grain size, water content, and optical reflectance, we prepared two different standard samples with different grain sizes by wet sieving. By exploiting simplified reflectance features of the standard samples, we established a two-step PCA transform model. This grain-size retrieval model was applied to LANDSAT-8 images for sediment classification within the Ganghwa tidal flats, South Korea. The results demonstrate that discriminating between sand-dominant and mud-dominant areas on the basis of the model is feasible. Seasonal changes of sediment distribution within the tidal flats are well observed from the results

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