SHAP-DNN Approach Advances Remote Sensing Mapping of Forested Wetlands.

Potential of Sample Migration and Explainable Machine Learning Model for Monitoring Spatiotemporal Changes of Wetland Plant Communities.

Mapping mangrove functional traits from Sentinel-2 imagery based on hybrid models coupled with active learning strategies.

Mapping Phragmites australis Aboveground Biomass in the Momoge Wetland Ramsar Site Based on Sentinel-1/2 Images.

Annual Wetland Mapping in Metropolis by Temporal Sample Migration and Random Forest Classification with Time Series Landsat Data and Google Earth Engine.

Mapping Wetland Plant Communities Using Unmanned Aerial Vehicle Hyperspectral Imagery by Comparing Object/Pixel-Based Classifications Combining Multiple Machine-Learning Algorithms.