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Enhanced Crop Discrimination and Monitoring Using Compact-Polarimetric SAR Signature Analysis From RADARSAT Constellation Mission.


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Jafarzadeh, Hamid ORCID logoORCID: https://orcid.org/0000-0001-7785-1872; Verma, Abhinav ORCID logoORCID: https://orcid.org/0000-0002-8349-8697; Mahdianpari, Masoud ORCID logoORCID: https://orcid.org/0000-0002-7234-959X; Bhattacharya, Avik ORCID logoORCID: https://orcid.org/0000-0001-6720-6108 et Homayouni, Saeid ORCID logoORCID: https://orcid.org/0000-0002-0214-5356 (2024). Enhanced Crop Discrimination and Monitoring Using Compact-Polarimetric SAR Signature Analysis From RADARSAT Constellation Mission. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 17 . pp. 6308-6327. DOI: 10.1109/JSTARS.2024.3366883.

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With the rapid advancements in SAR systems aiming for operational capabilities, crop characterization using compact-polarimetric synthetic aperture radar (CP-SAR) data has gained considerable attention. This study thoroughly assesses the potential usefulness of C-band SAR data in CP mode using the RADARSAT Constellation Mission (RCM) for crop monitoring. The research unfolds across two separate phases: 1) Extensive crop scattering characterization and 2) Crop classification. In the first part, we introduce three descriptors: compact-polarimetric SAR signature (CPS), differential CPS (DCPS), and the geodesic distance (GD) between signatures, to characterize the scattering pattern of four crop types: soybean, hay, corn, and cereal. We, then, derive the μ parameter and employ it in the μ−χ decomposition method. Time-series investigation of the proposed descriptors and the three power components: Ps , Pd , and Pv provide valuable insights into the scattering responses exhibited by crops, facilitating a robust assessment and tracking of their growing cycle, thus, enabling the potential for improving crop discrimination. In the second part, we employ the μ−χ and m−χ decompositions and wave descriptors to extract a stack of CP features for crop mapping. Combining diverse feature types and leveraging single- and multi-date RCM images, classification experiments yield an optimal classification map with an overall accuracy of 89.71%, particularly when utilizing features extracted from multi-date datasets. This study illustrates a substantial effort in crop classification, underscoring the potential of the RCM CP-SAR mission. Furthermore, our findings emphasize the potential of CP-SAR data from the RCM mission in contributing to precision agriculture and sustainable crop management practices.

Type de document: Article
Mots-clés libres: crops; receiving antennas; monitoring; synthetic aperture radar; transmitting antennas; remote sensing; polarization
Centre: Centre Eau Terre Environnement
Date de dépôt: 20 mars 2024 18:30
Dernière modification: 20 mars 2024 18:30
URI: https://espace.inrs.ca/id/eprint/15506

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