Evaluation of polarimetry and interferometry of sentinel-1A SAR data for land use and land cover of the Brazilian Amazon Region

Abstract

Synthetic aperture radar (SAR) data has been an alternative for monitoring ground targets, especially in areas with cloud cover. This study evaluates the potential of Sentinel-1A attributes for mapping land use and land cover (LULC) in a region of the Brazilian Amazon, using two different machine learning classifiers: Random Forest (RF) and Support Vector Machine (SVM). Different scenarios were used that combined backscattering, polarimetry, and interferometry to the classification process, which was divided into two phases to improve the results. The RF shows superiority over the SVM for almost all scenarios for the two phases of the mapping. The scenario with all data, presented the best results with both classifiers. The final maps with RF and SVM, obtained a global accuracy of 82.7% and 74.5%, respectively. This study demonstrated the potential of Sentinel-1 to map LULC classes in the Amazon region using a classification in two phases.

KEYWORDS:

• Dual-polarimetric
• interferometric coherence
• machine learning algorithms
• land cover mapping

Acknowledgments

The authors thanks the project “Monitoramento Ambiental por Satélite no Bioma Amazônia” by the fieldwork support.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001”.