The RADARSAT Constellation Mission Core Applications: First Results

Dabboor, Mohammed; Olthof, Ian; Mahdianpari, Masoud; Mohammadimanesh, Fariba; Shokr, Mohammed; Brisco, Brian; Homayouni, Saeid

doi:10.3390/rs14020301

Cited by 12 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RCM was designed as the next generation of the Canadian RADARSAT program, following the highly successful RADARSAT-1 and RADARSAT-2 missions. This constellation configuration comprised three identical C-band SAR satellites launched in the summer of 2019 into closely coordinated orbits, providing a four-day repeat cycle [26,27]. With RCM datasets, the potential of CP emerged as a viable mode to address the needs of users seeking wide swath coverage while preserving polarization richness [28].…”

Section: B Rcm Cp-sar Datamentioning

confidence: 99%

“…The RCM CP-SAR architecture transmits a circularly polarized signal and coherently records both the horizontal and vertical components (H + V) of the backscattered signal, i.e., the circular-transmit and linear-receive (CL) configuration [27]. This configuration is achieved by simultaneously driving the H and V feeds with a 90-degree phase difference [28].…”

Section: B Rcm Cp-sar Datamentioning

confidence: 99%

See 1 more Smart Citation

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

Jafarzadeh,

Verma,

Mahdianpari

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

With the rapid advancements in SAR systems aiming for operational capabilities, crop characterization using Compact-Polarimetric (CP) 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 (CP S), differential compact-polarimetric signature (DCP S), 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, P d , and Pv provides 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 Circular Polarization Synthetic Aperture Radar (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.

show abstract

Section: B Rcm Cp-sar Datamentioning

confidence: 99%

Section: B Rcm Cp-sar Datamentioning

confidence: 99%

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

Jafarzadeh,

Verma,

Mahdianpari

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The RADARSAT Constellation Mission (RCM) is a Canadian SAR mission launched in 2019 as a successor to the RADARSAT-1&2 satellites. The RCM not only ensures C-band SAR data continuity for RADARSAT-2 users but also introduces new applications made possible through the constellation approach [5,6]. A unique feature of RCM is its Compact Polarimetric (CP) SAR configuration, offering operational CP SAR imagery in all imaging modes.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Soil Moisture Retrieval: Utilizing Compact Polarimetric Features with Advanced Machine Learning Techniques

Dabboor,

Atteia,

Alnashwan

2023

Land

Self Cite

View full text Add to dashboard Cite

Soil moisture plays a crucial role in various environmental processes and is essential for agricultural management, hydrological modeling, and climate studies. Synthetic Aperture Radar (SAR) remote sensing presents significant potential for estimating soil moisture due to its ability to operate in all weather conditions and provide day-and-night imaging capabilities. Among the SAR configurations, the Compact Polarimetric (CP) mode has gained increasing interest as it relaxes system constraints, improves coverage, and enhances target information compared to conventional dual polarimetric SAR systems. This paper introduces a novel approach for soil moisture retrieval utilizing machine learning algorithms and CP SAR features. The CP SAR features are derived from a series of RADARSAT Constellation Mission (RCM) CP SAR imagery acquired over Canadian experimental sites equipped with Real-Time In Situ Soil Monitoring for Agriculture (RISMA) stations. This study employs a diverse dataset of compact polarimetric SAR features and corresponding ground truth soil moisture measurements for training and validation purposes. The results of our study achieved a Root Mean Square Error (RMSE) of 6.88% with a coefficient of determination R2 equal to 0.60, which corresponds to a correlation R between true and predicted soil moisture values of 0.75, using optimized Ensemble Learning Regression (ELR) with a decision-tree-based model. These results improved, yielding an RMSE of 5.67% and an R2 equal to 0.73 (R = 0.85), using an optimized Gaussian Process Regression (GPR) model.

show abstract