Wetland Mapping of Northern Provinces of Iran Using Sentinel-1 and Sentinel-2 in Google Earth Engine
Mohammadali Hemati, Mahdi Hasanlou, Masaud Mahdianpari, Fariba Mohammadimanesh
Abstract
Wetlands are significant global contributors to the environment and climate, and there are increasing efforts for wetland conservation globally. Recent advances in cloud computing platforms and accessibility of free medium resolution data lead to affordable solutions for large scale wetland mapping with remote sensing tools. Three Northern provinces of Iran include several complex wetland regions. A classification scheme consists of four wetland classes and five upland classes were chosen to describe wetland types for this region. A combination of Sentinel-2 surface reflectance summer composite and Sentinel-l synthetic aperture radar (SAR) datasets were used to train the machine learning model. Simple non-iterative clustering (SNIC) and Random Forest classification were implemented in Google Earth Engine (GEE) to produce an object-based wetland inventory map with an overall accuracy of 94.10%.