SAR Radiometric Calibration Based on Differential Geometry: From Theory to Experimentation on SAOCOM Imagery

Imperatore, Pasquale; Martino, Gerardo Di

doi:10.3390/rs15051286

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…Radiometric calibration based on differential geometry SAR radiometric calibration based on differential geometry: from theory to experimentation on SAOCOM imagery [98] The limited adoption of data combination techniques in Africa can be attributed to several factors. One primary reason is the scarcity of high-quality and consistent satellite data.…”

Section: Radiometric Calibration Of Sar Imagerymentioning

confidence: 99%

Use of Optical and Radar Imagery for Crop Type Classification in Africa: A Review

Choukri,

Laamrani,

Chehbouni

2024

Sensors

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Multi-source remote sensing-derived information on crops contributes significantly to agricultural monitoring, assessment, and management. In Africa, some challenges (i.e., small-scale farming practices associated with diverse crop types and agricultural system complexity, and cloud coverage during the growing season) can imped agricultural monitoring using multi-source remote sensing. The combination of optical remote sensing and synthetic aperture radar (SAR) data has emerged as an opportune strategy for improving the precision and reliability of crop type mapping and monitoring. This work aims to conduct an extensive review of the challenges of agricultural monitoring and mapping in Africa in great detail as well as the current research progress of agricultural monitoring based on optical and Radar satellites. In this context optical data may provide high spatial resolution and detailed spectral information, which allows for the differentiation of different crop types based on their spectral signatures. However, synthetic aperture radar (SAR) satellites can provide important contributions given the ability of this technology to penetrate cloud cover, particularly in African tropical regions, as opposed to optical data. This review explores various combination techniques employed to integrate optical and SAR data for crop type classification and their applicability and limitations in the context of African countries. Furthermore, challenges are discussed in this review as well as and the limitations associated with optical and SAR data combination, such as the data availability, sensor compatibility, and the need for accurate ground truth data for model training and validation. This study also highlights the potential of advanced modelling (i.e., machine learning algorithms, such as support vector machines, random forests, and convolutional neural networks) in improving the accuracy and automation of crop type classification using combined data. Finally, this review concludes with future research directions and recommendations for utilizing optical and SAR data combination techniques in crop type classification for African agricultural systems. Furthermore, it emphasizes the importance of developing robust and scalable classification models that can accommodate the diversity of crop types, farming practices, and environmental conditions prevalent in Africa. Through the utilization of combined remote sensing technologies, informed decisions can be made to support sustainable agricultural practices, strengthen nutritional security, and contribute to the socioeconomic development of the continent.

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Section: Radiometric Calibration Of Sar Imagerymentioning

confidence: 99%

Use of Optical and Radar Imagery for Crop Type Classification in Africa: A Review

Choukri,

Laamrani,

Chehbouni

2024

Sensors

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“…Unavoidable radiometric distortions occur in the SAR imaging process, and their proper rectification is necessary to reconstruct the physically meaningful backscattering coefficient (σ 0 ) from SAR images [40]. Therefore, radiometric calibration operation should be systematically performed to obtain the backscattering coefficient, also referred to as normalized radar crosssection (NRCS), including the compensation of different SAR imaging distortion effects, such as the antenna pattern distortions and the local SAR radiometric distortions induced by topography [41][42][43]. It is worth noting that the dependence of the backscattering coefficient on the different parameters (e.g., surface roughness, wavelength, local incidence angle) [44] can be exploited to set thresholds and/or as auxiliary processing data.…”

Section: General Pre-processing Operationsmentioning

confidence: 99%

Flood Detection with SAR: A Review of Techniques and Datasets

Amitrano,

Di Martino,

Di Simone

et al. 2024

Remote Sensing

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Floods are among the most severe and impacting natural disasters. Their occurrence rate and intensity have been significantly increasing worldwide in the last years due to climate change and urbanization, bringing unprecedented effects on human lives and activities. Hence, providing a prompt response to flooding events is of crucial relevance for humanitarian, social and economic reasons. Satellite remote sensing using synthetic aperture radar (SAR) offers a great deal of support in facing flood events and mitigating their effects on a global scale. As opposed to multi-spectral sensors, SAR offers important advantages, as it enables Earth’s surface imaging regardless of weather and sunlight illumination conditions. In the last decade, the increasing availability of SAR data, even at no cost, thanks to the efforts of international and national space agencies, has been deeply stimulating research activities in every Earth observation field, including flood mapping and monitoring, where advanced processing paradigms, e.g., fuzzy logic, machine learning, data fusion, have been applied, demonstrating their superiority with respect to traditional classification strategies. However, a fair assessment of the performance and reliability of flood mapping techniques is of key importance for an efficient disasters response and, hence, should be addressed carefully and on a quantitative basis trough synthetic quality metrics and high-quality reference data. To this end, the recent development of open SAR datasets specifically covering flood events with related ground-truth reference data can support thorough and objective validation as well as reproducibility of results. Notwithstanding, SAR-based flood monitoring still suffers from severe limitations, especially in vegetated and urban areas, where complex scattering mechanisms can impair an accurate extraction of water regions. All such aspects, including classification methodologies, SAR datasets, validation strategies, challenges and future perspectives for SAR-based flood mapping are described and discussed.

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“…On the other hand, radiometric distortions caused by terrain orthography need to be corrected using an analytical approach [53][54][55][56][57][58]. Terrain slopes cause significant variations (radiometric distortions) on radar backscatter values, depending on the angle and aspect of the surface and on the radar configuration, in terms of frequency, polarization, and ascending or descending path.…”

Section: Radiometric Terrain Correctionmentioning

confidence: 99%

Monitoring Braided River-Bed Dynamics at the Sub-Event Time Scale Using Time Series of Sentinel-1 SAR Imagery

et al. 2023

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Remote sensing plays a central role in the assessment of environmental phenomena and has increasingly become a powerful tool for monitoring shorelines, river morphology, flood-wave delineation and flood assessment. Optical-based monitoring and the characterization of river evolution at long time scales is a key tool in fluvial geomorphology. However, the evolution occurring during extreme events is crucial for the understanding of the river dynamics under severe flow conditions and requires the processing of data from active sensors to overcome cloud obstructions. This work proposes a cloud-based unsupervised algorithm for the intra-event monitoring of river dynamics during extreme flow conditions based on the time series of Sentinel-1 SAR data. The method allows the extraction of multi-temporal series of spatially explicit geometric parameters at high temporal and spatial resolutions, linking them to the hydrometric levels acquired by reference gauge stations. The intra-event reconstruction of inundation dynamics has led to (1) the estimation of the relationship between hydrometric level and wet area extension and (2) the assessment of bank erosion phenomena. In the first case, the behavior exhibits a change when the hydrometric level exceeds 1 m. In the second case, the erosion rate and cumulative lateral erosion were evaluated. The maximum erosion velocity was greater than 1 m/h, while the cumulative lateral erosion reached 130 m. Time series of SAR acquisitions, provided by Sentinel-1 satellites, were analyzed to quantify changes in the wet area of a reach of the Tagliamento river under different flow conditions. The algorithm, developed within the Python-API of GEE, can support many types of analyses of river dynamics, including morphological changes, floods monitoring, and bio-physical habitat dynamics. The results encourage future advancements and applications of the algorithm, specifically exploring SAR data from ICEYE and Capella Space constellations, which offer significantly higher spatial and temporal resolutions compared to Sentinel-1 data.

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SAR Radiometric Calibration Based on Differential Geometry: From Theory to Experimentation on SAOCOM Imagery

Cited by 5 publications

References 35 publications

Use of Optical and Radar Imagery for Crop Type Classification in Africa: A Review

Use of Optical and Radar Imagery for Crop Type Classification in Africa: A Review

Flood Detection with SAR: A Review of Techniques and Datasets

Monitoring Braided River-Bed Dynamics at the Sub-Event Time Scale Using Time Series of Sentinel-1 SAR Imagery

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