Integration of Sentinel-1 and Sentinel-2 data for Earth surface classification using Machine Learning algorithms implemented on Google Earth Engine

TL;DR

This paper demonstrates how integrating Sentinel-1 SAR and Sentinel-2 optical data using Machine Learning on Google Earth Engine enhances Earth surface classification accuracy by leveraging complementary information from both data sources.

Contribution

It introduces a method for combining SAR and optical satellite data with ML algorithms on GEE, showcasing improved classification results and highlighting GEE's effectiveness for large-scale remote sensing tasks.

Findings

Radar and optical data provide complementary information.

Integration improves surface cover classification accuracy.

GEE is effective for handling large satellite datasets.

Abstract

In this study, Synthetic Aperture Radar (SAR) and optical data are both considered for Earth surface classification. Specifically, the integration of Sentinel-1 (S-1) and Sentinel-2 (S-2) data is carried out through supervised Machine Learning (ML) algorithms implemented on the Google Earth Engine (GEE) platform for the classification of a particular region of interest. Achieved results demonstrate how in this case radar and optical remote detection provide complementary information, benefiting surface cover classification and generally leading to increased mapping accuracy. In addition, this paper works in the direction of proving the emerging role of GEE as an effective cloud-based tool for handling large amounts of satellite data.