ORIGINAL RESEARCH article
Front. Remote Sens.
Sec. Image Analysis and Classification
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1535418
Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform
Provisionally accepted
- Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Bari, Italy
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Land use/land cover (LULC) mapping in fragmented landscapes, characterized by multiple and small land uses, is still a challenge. This study aims to evaluate the effectiveness of Synthetic Aperture Radar (SAR) and multispectral optical data in land cover mapping using Google Earth Engine (GEE), a cloud computing platform allowing big geospatial data analysis. The proposed approach combines multisource satellite imagery for accurate land cover classification in a fragmented municipal territory in Southern Italy over a five-month vegetative period. Within the GEE platform, a set of Sentinel-1, Sentinel-2, and Landsat 8 data was acquired and processed to generate a land cover map for the 2021 greenness period. A supervised pixel-based classification was performed, using a Random Forest (RF) machine learning algorithm, to classify the imagery and derived spectral indices into eight land cover classes. Classification accuracy was assessed using Overall Accuracy (OA), Producer's and User's accuracies (PA, UA), and F-score. McNemar's test was applied to assess the significance of difference between classification results. The optical integrated datasets in combination with SAR data and derivate indices (NDVI, GNDVI, NDBI, VHVV) produce the most accurate LULC map among those produced (OA: 89.64%), while SAR-only datasets performed the lowest accuracy (OA: 61.30%).The classification process offers several advantages, including widespread spectral information, SAR's ability to capture almost all-weather, day-and-night imagery, and the computation of vegetation indices in the near infrared spectrum interval, in a short revisit time.The proposed digital techniques for processing multi-temporal satellite data provide useful tools for understanding territorial and environmental dynamics, supporting decision-making in land use planning, agricultural expansion, and environmental management in fragmented landscapes.
Keywords: Google Earth Engine, Landsat 8, Sentinel-1, Sentinel-2, land cover classification, Spectral indices, RF classifier
Received: 27 Nov 2024; Accepted: 30 Apr 2025.
Copyright: © 2025 Romano, Ricci and Gentile. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Giovanni Romano, Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Bari, Italy
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