PALSAR-2 polarimetric performance and the simulation study using the PI-SAR-L2

Shimada, Masanobu; Motooka, Takeshi; Kankaku, Yukihiro

doi:10.1109/igarss.2013.6723280

Cited by 10 publications

(9 citation statements)

References 2 publications

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“…For Radarsat‐2, the scene specific NESZ values are provided with the meta data. For ALOS‐2, the NESZ is the sensor specific value provided by JAXA [ Shimada et al ., ] and does not vary with incidence angle, channel, or scene.…”

Section: Data Collectionmentioning

confidence: 99%

Combined observations of Arctic sea ice with near‐coincident colocated X‐band, C‐band, and L‐band SAR satellite remote sensing and helicopter‐borne measurements

et al. 2017

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In this study, we compare colocated near‐coincident X‐, C‐, and L‐band fully polarimetry SAR satellite images with helicopter‐borne ice thickness measurements acquired during the Norwegian Young sea ICE 2015 (N‐ICE2015) expedition in the region of the Arctic Ocean north of Svalbard in April 2015. The air‐borne surveys provide near‐coincident snow plus ice thickness, surface roughness data, and photographs. This unique data set allows us to investigate how the different frequencies can complement one another for sea ice studies, but also to raise awareness of limitations. X‐band and L‐band satellite scenes were shown to be a useful complement to the standard SAR frequency for sea ice monitoring (C‐band) for lead ice and newly formed sea ice identification. This may be in part be due to the frequency but also the high spatial resolution of these sensors. We found a relatively low correlation between snow plus ice thickness and surface roughness. Therefore, in our dataset ice thickness cannot directly be observed by SAR which has important implications for operational ice charting based on automatic segmentation.

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Section: Data Collectionmentioning

confidence: 99%

Combined observations of Arctic sea ice with near‐coincident colocated X‐band, C‐band, and L‐band SAR satellite remote sensing and helicopter‐borne measurements

et al. 2017

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“…In the preprocessing stage, the satellite scene is radiometrically calibrated using the included metadata calibration information provided by JAXA (Shimada and others, 2015) through SNAP (ESA Sentinel Application Platform v2.0.2). Thereafter, a segmentation algorithm, based on feature extraction and segmentation using the ‘extended polarimetric feature space’ (EPFS) method described in Doulgeris and Eltoft (2010) and Doulgeris (2013) is applied.…”

Section: Methodsmentioning

confidence: 99%

Can we extend local sea-ice measurements to satellite scale? An example from the N-ICE2015 expedition

et al. 2017

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ABSTRACT. Knowledge of Arctic sea-ice conditions is of great interest for Arctic residents, as well as for commercial usage, and to study the effects of climate change. Information gained from analysis of satellite data contributes to this understanding. In the course of using in situ data in combination with remotely sensed data, the question of how representative local scale measurements are of a wider region may arise. We compare in situ total sea-ice thickness measurements from the Norwegian young sea ICE expedition in the area north of Svalbard with airborne-derived total sea-ice thickness from electromagnetic soundings. A segmented and classified synthetic aperture radar (SAR) quad-pol ALOS-2 Palsar-2 satellite scene was grouped into three simplified ice classes. The area fractions of the three classes are: 11.2% 'thin', 74.4% 'level', and 14.4% 'deformed'. The area fractions of the simplified classes from ground-and helicopter-based measurements are comparable with those achieved from the SAR data. Thus, this study shows that there is potential for a stepwise upscaling from in situ, to airborne, to satellite data, which allow us to assess whether in situ data collected are representative of a wider region as observed by satellites.

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“…The Amazonian rainforest near the geomagnetic equator, at free Faraday rotation [15], has been used as the ideal site for the assessment and calibration of L-band ALOS-PALSAR and PALSAR2 [3,5,6,22]. CRs deployed at free Faraday rotation provide accurate estimation of antenna cross-talks in contrast to the biased measurements obtained with CRs deployed at significant Faraday rotation, as shown in [6].…”

Section: Calibration Sitesmentioning

confidence: 99%

Calibration and Validation of Polarimetric ALOS2-PALSAR2

Touzi¹,

Shimada

Motohka

2022

Remote Sensing

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PALSAR2 polarimetric distortion matrix is measured using corner reflectors deployed in the Amazonian forest. The Amazonian forest near the geomagnetic equator provides ideal sites for the assessment of L-band PALSAR2 antenna parameters, at free Faraday rotation. Corner reflectors (CRs) deployed at free Faraday rotation provide accurate estimation of antenna cross-talks in contrast to the biased measurements obtained with CRs deployed at significant Faraday rotation. The extended Freeman–Van Zyl calibration method introduced and validated for ALOS-PALSAR calibration is used for the assessment of PALSAR-2 calibration parameters. Six datasets collected over the Amazonian rainforests (with CRs) are used to assess PALSAR-2 distortion matrix for five beams (FP6-3 to FP6-7) with incidence angle varying from 25° to 40°. It is shown that the PALSAR2 antenna is highly isolated with very low cross-talks (lower than −40 dB). Finally, the impact of a significant Faraday rotation on antenna cross-talk measurements using CR is discussed.

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PALSAR-2 polarimetric performance and the simulation study using the PI-SAR-L2

Cited by 10 publications

References 2 publications

Combined observations of Arctic sea ice with near‐coincident colocated X‐band, C‐band, and L‐band SAR satellite remote sensing and helicopter‐borne measurements

Combined observations of Arctic sea ice with near‐coincident colocated X‐band, C‐band, and L‐band SAR satellite remote sensing and helicopter‐borne measurements

Can we extend local sea-ice measurements to satellite scale? An example from the N-ICE2015 expedition

Calibration and Validation of Polarimetric ALOS2-PALSAR2

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