This paper aims to develop a supervised classification integrating synthetic aperture radar (SAR) Sentinel-1 (S1) and optical Sentinel-2 (S2) data for land use/land cover (LULC) mapping in a heterogeneous Mediterranean forest area. The time-series of each SAR and optical bands, three optical indices (normalized difference vegetation index, NDVI; normalized burn ratio, NBR; normalized difference red-edge index, NDRE), and two SAR indices (radar vegetation index, RVI; radar forest degradation index, RFDI), constituted the dataset. The coherence information from SAR interferometry (InSAR) analysis and three optical biophysical variables (leaf area index, LAI; fraction of green vegetation cover, fCOVER; fraction of absorbed photosynthetically active radiation, fAPAR) of the single final month of the time-series were added to exploit their correlation with the canopy structure and improve the classification. The random forests (RF) algorithm was used to train and classify the final dataset, and an exhaustive grid search analysis was applied to set the optimal hyperparameters. The overall accuracy reached an F-scoreM of 90.33% and the integration of SAR improved it by 2.53% compared to that obtained using only optical data. The whole process was performed using freely available data and open-source software and libraries (SNAP, Google Earth Engine, Scikit-Learn) executed in Python-script language.

Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region

De Luca G.;Di Fazio S.;Modica G.
2022-01-01

Abstract

This paper aims to develop a supervised classification integrating synthetic aperture radar (SAR) Sentinel-1 (S1) and optical Sentinel-2 (S2) data for land use/land cover (LULC) mapping in a heterogeneous Mediterranean forest area. The time-series of each SAR and optical bands, three optical indices (normalized difference vegetation index, NDVI; normalized burn ratio, NBR; normalized difference red-edge index, NDRE), and two SAR indices (radar vegetation index, RVI; radar forest degradation index, RFDI), constituted the dataset. The coherence information from SAR interferometry (InSAR) analysis and three optical biophysical variables (leaf area index, LAI; fraction of green vegetation cover, fCOVER; fraction of absorbed photosynthetically active radiation, fAPAR) of the single final month of the time-series were added to exploit their correlation with the canopy structure and improve the classification. The random forests (RF) algorithm was used to train and classify the final dataset, and an exhaustive grid search analysis was applied to set the optimal hyperparameters. The overall accuracy reached an F-scoreM of 90.33% and the integration of SAR improved it by 2.53% compared to that obtained using only optical data. The whole process was performed using freely available data and open-source software and libraries (SNAP, Google Earth Engine, Scikit-Learn) executed in Python-script language.
2022
biophysical indicators
Google Earth Engine (GEE)
interferometric coherence
SAR and multispectral time-series analysis
SAR interferometry (inSAR)
Synthetic aperture radar (SAR)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/119420
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