Process-buffered carbon storage in dynamic intertidal flats

This dataset contains the satellite imagery, in situ field measurements in Ohiwa Harbour, processed datasets, and analysis codes used to classify intertidal surface facies and estimate sediment properties using machine learning approaches. The dataset includes original satellite imagery downloaded from the European Space Agency (ESA), together with field-measured sediment data, including facies classes, mud content, loss on ignition (LOI), dry bulk density, and carbon stock. The accompanying processing scripts document the workflow used to prepare the satellite-derived predictors, link them with in situ measurements, and apply different machine learning models for facies classification and sediment property prediction. The processed outputs include machine-learning-derived maps and datasets for mud content, facies, LOI, dry bulk density, and estimated carbon stock. These materials are provided to support reproducibility and further application of satellite-based sediment and carbon mapping in intertidal environments.