ESA and JAXA has been developed in the context of observation, monitoring and study of the Earth’s surface and atmosphere from space, with a view to cooperate for the use of synthetic aperture radar (SAR) satellites in the fields of earth science and earth observation applications.

ESA and JAXA recognize and agree to have an agreement for SAR cooperation. Since ESA and JAXA have both developed new generation L-band SAR missions, ESA and JAXA recognize the value to share an important experience in operational use of L-band SAR and intend to increase the benefits of synergies in the use of C- and L- band spaceborne assets. To proceed this cooperation, ESA and JAXA agreed to share the existing available SAR data from Sentinel-1 in Copernicus program and from ALOS-2 in JAXA to validate the value of C-band and L-band data to mutual interest area.

At present, both agencies jointly work for Polar Area Monitoring, Forest and Wetland Mapping, Ocean Monitoring, Snow Water equivalent, Soil Moisture, Monitoring Agriculture and GHG, Urban Monitoring, Natural and Urban Forest Monitoring, Monitoring of Geohazards and Joint validation Algorithm development of SAR.

In this session, invited speakers are expected to report ongoing and planning SAR satellites missions including ALOS-2, ALOS-4, Sentinel-1 and ROSE-L. Invited speakers are also expected to report the joint science and application early results using Sentinel-1 and ALOS-2 with ground-based observation data.

During the yearlong MOSAIC expedition (2019-2020) a significant number of synthetic aperture radar (SAR) images were collected from different sensors and in different modes. Here, we investigate the change in polarimetric features over sea ice from the freeze up to the advanced melt season using fully polarimetric L-band images from the ALOS-2 PALSAR-2 and C-band images from the RADARSAT-2 satellite SAR sensors. The sea ice is separated into four different sea ice types: (1) lead, (2) young (YI), (3) level (second-year ice (SYI) and first year ice (FYI) and (4) deformed sea ice.

Data and Method
R/V Polarstern drifted with two different floes and here we focus on the first drift that took place between 1 October 2019 and 31 July 2020. Areas of all four different ice types are observed in the vicinity of R/V Polarstern. These areas are included whenever possible in the yearlong time series of sea ice types. Though to densify the time series images not containing the ship are also included.

The SAR images were analyzed for seasonal changes in backscatter intensity values, and the scattering mechanisms were employed to further investigate separability between the different ice types. In particular there was a focus on the separability between the high backscatter older sea ice, typically SYI, and YI and FYI. Both sets of L- and C-band images were radiometrically calibrated using the included meta-data information and a 9 × 9-pixel median filter is applied to the data to reduce the noise effect before extraction of the polarimetric features. The normalized radar backscatter information for the HH, HV and VV channels were extracted together with the polarization difference (PD, VV-HH on a linear scale), the co-polarized and cross-polarized ratios (VV/HH and HH/HV) and the circular correlation coefficient. The images were incidence angle corrected to 35◦ using the method and slope values outlined in Mahmud et al. (2018), and the different sea ice types were identified using manually drawn regions of interests (ROIs). Data from helicopter borne instruments as well as in-situ data was used to evaluate the different sea ice types. The results are also compared to images collected during the Norwegian Young Ice (N-ICE) campaign in January-June 2015 (Johansson et al, 2017, 2018).

Results and discussion
Analyzing the backscatter values several observations can be made: (i) as expected we observe that there is a larger difference in the co-polarization channels between smooth and deformed ice in L-band compared to C-band during the freezing season, though (ii) this separation is significantly reduced during the early melt season. Moreover, we observe (iii) larger differences between young ice and deformed ice backscatter values in the L-band data compared to the C-band data, and (iv) linear kinematic features (LKF) are easier to detect in the L-band images. Throughout the year the HV-backscatter values show larger differences between level and deformed sea ice in L-band than C-band. The L-band data variability is significantly smaller for the level sea ice compared to the deformed sea ice, and this variability was also smaller than that observed for the overlapping C-band data. Thus L-band data could be more suitable to reliable separate deformed from level sea ice areas, as well as investigating the LKFs.

Within the L-band images a noticeable shift towards higher backscatter values in early melt season compared to the freezing season for all polarimetric channels is observed. Though no such strong trend is found in the C-band data. The change in backscatter values is first noticeable in the C-band images and later followed by a change in the L-band images, probably caused by their different penetration depth and volume scattering sensitivities. This change also results in a smaller backscatter variability for all polarimetric channels.

PD show a seasonal dependency for the smooth and deformed sea ice within the L-band data. For the L-band data were the PD variability for all ice classes reasonably small for the freezing season, with a significant shift towards larger variability during the early melt season, though during this period the mean PD values remained similar. However, once the temperatures reached above 0°C both the variability and the mean values increased significantly. For the C-band data no such trend is observed. However, for C-band the absolute PD values show significantly higher mean values for the thinner sea ice areas regardless of if these areas are low or high backscatter, and these areas also have low standard deviations. Compared to the high backscatter areas offered by SYI there is a significant difference were the PD values have a high standard deviation but a low mean value. Using PD, we can therefore separate out the young ice types from the surrounding sea ice and the SYI types. PD is also suitable for separation between the level and deformed sea ice areas during the freeze-up, as the variability is much higher for the deformed sea ice areas than for the level ice areas. To confirm the roughness level from the different ice types the circular correlation coefficient (CCC) is calculated, compared to airborne laser scanner (ALS) data and show good separability between the deformed and level sea ice types. However, CCC is sensitive to the signal-to-noise levels and care must be taken when analyzing the results. PD on the other hand has a small incidence angle dependency and a low sensitivity to the signal-to-noise ratio (SNR).

We observe that fully polarimetric C- and L-band data are complementary to one another and that through their slightly different dependencies on season and sea ice types, a combination of the two frequencies can aid improved sea ice classification. The availability of a high spatial and temporal resolution dataset combined with in-situ information offered during the MOSAiC expedition ensures that seasonal changes can be fully explored.

References
Johansson A.M., C. Brekke, G. Spreen, J. King, 2018, X-, C-, and L-band SAR signatures of newly formed sea ice in Arctic leads during winter and spring, Remote Sensing of Environment, 204: 162-180

Johansson A.M, King J.A., Doulgeris A.P., Gerland S., Singha S., Spreen G., Busche T, 2017, Combined observations of Arctic sea ice with near-coincident co-located X-band, C-band and L-band SAR satellite remote sensing and helicopter-borne measurements, JGR-Oceans, 122: 669-691

M. S. Mahmud, T. Geldsetzer, S. E. L. Howell, J. J. Yackel, V. Nandan and R. K. Scharien, 2018, Incidence Angle Dependence of HH-Polarized C- and L-Band Wintertime Backscatter Over Arctic Sea Ice, IEEE Transactions on Geoscience and Remote Sensing, 56(11): 6686-6698

Strong winds induced by typhoons and hurricanes cause disasters and have a great impact on social activities, therefore there is an increasing demand for their monitoring and prediction. A Synthetic Aperture Radar (SAR) is only satellite sensor capable of measuring sea surface winds with high spatial resolution O (100 m). For wind speed detection by Japanese L-band SAR named the Phased Array type L-band Synthetic Aperture Rader-2 (PALSAR-2) and use for operational weather forecasting under typhoon conditions, Japan Aerospace Exploration Agency (JAXA)-Meteorological Research Institute (MRI) joint research has launched. The purpose of the research is to verify the effect of the data assimilation of the SAR-retrieved winds on typhoon forecasting. Typhoons/hurricanes observations were being carried out under the joint research by programming the PALSAR-2 observations based on the predicted course of typhoons and hurricanes. So far, simultaneous observations with the National Oceanic and Atmospheric Administration (NOAA)'s airborne Stepped Frequency Microwave Radiometer (SFMR) have been made for five cases of hurricanes. Based on these data, estimating the wind structure of the hurricane by PALSAR-2 was developed.
The 3 km average PALSAR-2 normalized radar cross section (σ0) and the incidence angle were collocated with the SFMR-measured ocean surface wind speed and rain rate. It was confirmed that the incidence angle dependence was small for the cross-polarized (HV) σ0, so we developed a model function for the strong winds for the HV polarization. In order to investigate the dependency of σ0 on wind speed and incidence angle, the match-ups were classified into “bins” of 2 m/s wind speed and 5° incidence angle. Any data of which deviation exceeded 2σ in each bin were excluded.
A relationship between the PALSAR-2 HV σ0 and ocean surface wind speeds measured by SFMR showed that σ⁰ increased with respect to the wind speed up to about 55 m/s. Based on the method proposed by Hwang et al. (2015), a geophysical model function (GMF) was constructed as a function of wind speed and incidence angle. The wind speed was then inversely estimated from the matchup data (HV σ⁰) and compared with the wind speed of SFMR. Bias and RMSE are -0.2 m/s and 4.1 m/s, respectively. It indicates that the wind speed can be detected up to about 50 m/s or more without depending on the incidence angle.
The derived GMF was applied to the PALSAR-2 HV image of Hurricane Laura to calculate the ocean surface wind speed, and the comparison was performed along the SFMR observation tracks. Although there are some biased differences, fluctuation trends including maximum wind speed of about 60 m/s and sudden changes in wind speed near the eyewall are captured. The derived wind speed structure of the hurricane was compared with the best track data. Omnidirectional surface wind profiles as a function of distance from the hurricane center for the four geographical quadrants (NW, SW, SE, and NE) were calculated from the PALSAR-2-derived wind speed and compared with wind speed radii at three wind speed levels (34 Knot, 50 Knot, 64 Knot) obtained from the best track data. Wind speed radius is smaller in NW and SW than in NE and SE, which indicates the same spatial asymmetry structure as the best track. In addition, the absolute value of the wind speed radius and the decreasing tendency with respect to the distance are approximately the same.
A comparison of hurricane sea surface winds calculated from L-band PALSAR-2 and C-band Sentinel-1 was performed. The Sentinel-1 wind product was obtained from the Earth Observation Data Access (EODA) (https://eoda.cls.fr/client/oceano/) operated by Collecte Localisation Satellites (CLS). Among the typhoons and hurricanes observed by PALSAR-2, there were two cases where wind speed calculation was also carried out by Sentinel-1.
The first case is Hurricane Douglas, observed by Sentinel-1 at 3:59 on July 25, 2020 (UTC), and by PALSAR-2 at 9:41 on July 26, approx. 1 day and 6 hours later. According to the best track data, the hurricane has weakened during this period, and the sustained maximum wind speed has dropped from about 49 m/s to about 40 m/s. On the other hand, the maximum wind speeds by SAR were 52.8 m/s and 42.9 m/s, respectively, which is almost consistent with the best track data in terms of the decreasing tendency and width.
Another example is Hurricane Laura, which was observed by PALSAR-2 at 17:49 on August 26, 2020, and approx. 6 hours later by Sentinel-1. According to the best track data, the hurricane has been strengthened from about 60 m/s to 69 m/s in the 6 hours. The maximum wind speed of SAR is 65 m/s and 76 m/s, which tend to be strengthened in the same way as the best track data although absolute values are large.
It was confirmed that the L-band HV σ⁰ has a relationship with wind speed up to about 55 m/s in the data used in the present study and the wind speed can be estimated. On the other hand, the stronger the wind, the lower the increasing rate of σ0 with respect to the wind speed, so the radiometric accuracy of the SAR product has a strong impact on the wind speed estimation especially under the extreme wind condition. The comparison with C-band's Sentinel-1 product showed that increasing observation opportunities could enable detailed detection of hurricane temporal evolution at high spatial resolution.
As a next step, it is expected that the effect of these data on typhoon forecasting will be verified through data assimilation experiments. In addition, it is necessary to improve the detection accuracy by accumulating data and clarify the characteristics due to the difference in bands such as the maximum detectable wind speed and the rain contamination.

The complementarity between C- and L-band Synthetic Aperture Radar (SAR) images for the separation of sea ice types and identification of ice structures such as ridges or ice floe edges was first systematically analyzed when the first airborne multi-frequency data over sea ice were acquired in the late 80s. Since then, differences in the scattering characteristics and advantages of each band in ice classification and ice parameter retrieval have been analyzed and presented in various studies. Also, different supervised and unsupervised classification methods were applied to combinations of C- and L-band multi-polarization images, which showed improvements in classification accuracy. However, in contrast to data from different C-band satellite missions, L-band images were not available for operational ice mapping on a regular basis, which means that the gain of adding L-band to operational interpretation of ice conditions has not been evaluated in detail yet. For the identification of icebergs in open water and in sea ice, comparisons of C- and L-band images were lacking.
In a recent project of the European Space Agency (ESA) supported by the Japan Aerospace Exploration Agency (JAXA), the synergies between C- and L-band SAR missions for the retrieval of ice conditions and iceberg occurrences have been explored. The project aims to better define the benefits of future SAR missions working together at C- and L-band e.g. as part of a multi-agency international constellation of radar missions in the post-2026 time frame. The project team involves researchers from different universities and research institutes; and analysts from operational ice services in Canada (CIS), Denmark (DMI), and Norway (Met.No.). Input is also provided by the International Ice Patrol (IIP) and by a task team of the International Ice Charting Working Group (IICWG). The work comprises a literature study (e.g. [1]), and comprehensive analyses of Sentinel-1 and PALSAR-2 images acquired over different test sites in the Arctic: Labrador Sea, Baffin Bay, Lincoln Sea, Fram Strait, Belgica Bank and the Cape Farewell region. All-in-all, more than 1000 PALSAR-2 images have been acquired in WBD and FBD mode of which many were interpreted and analysed as stand-alone. About 200 images have a sufficient spatial overlap with and a sufficiently short time difference in acquisition to Sentinel-1 images at EWS or IWS mode over Lincoln Sea, Fram Strait, and Belgica Bank. For the Labrador Sea and Baffin Bay, 59 PALSAR-2 WBD images could be compared to Radarsat-2 SCWA images. In our presentation, we give an overview of the results achieved during project work. It has to be noted that pros and cons determined for each band also depend on the respective image properties (spatial resolution, number of looks, noise level, local incidence angle). The experts from the operational centres found that L-band is superior for earlier detection of fractures and fast ice breakup, for easier FY/MY discrimination during the melt season, for recognizing more structures in the ice, and for better discrimination of ridges. The latter, however, requires Stripmap mode but is not possible at the coarser ScanSAR mode. Because of the very low backscattering level of young thin ice at L-band it can be better distinguished from thicker ice. The weaknesses of L-band are the low separability of thin ice types relative to one another and relative to open water. The latter affects determination of ice concentration. At L-band, multi-year ice floes appear less prominent relative to first-year ice under freezing conditions. L-band is less sensitive to wind / sea state compared to C- or X-band which is, e.g., important for the Cape Farewell and the Labrador Sea region. Specifically for icebergs located in open water, it was observed that the detection rate depends on sea state and is decreased in melting conditions. L-band seems to be better at detecting icebergs in rough seas than C-band. Icebergs inside sea ice are easier to identify in L-band HV-images than in Sentinel-1 HV-images. Another topic in the project was to test classification and detection algorithms which also requires to align PALSAR-2 and Sentinel-1 images corresponding to each other but acquired with a temporal gap. This has been mainly carried out by the university partners. For moderately dynamic ice conditions with distinct ice structures, the alignment of C- and L-band image pairs was possible for time separation of hours up to even a few days in a very stable ice cover, but not always over the full overlap area between the C- and L-band image. For some ice regimes (e.g. South Greenland, Labrador Sea) and during the melting season, an alignment is extremely difficult or not possible. Experiments with supervised classification in a decision tree reveal for which specific ice conditions the additional use of L-band is beneficial. Investigations of icebergs captured in fast ice show that they appear brighter relative to the background (the ice) in L-band than in C-band imagery, and that icebergs and sea ice are in general difficult to distinguish at C-band. At L-band internal reflections in the interior of an iceberg increase the backscattering and may cause ghost reflections next to icebergs. The conclusion is that L-band SAR imagery clearly provides an advantage for ice charting and iceberg detection. Requirements are regular acquisitions with a sufficient coverage of the sea ice regions monitored by the operational centres and timely availability of the images. Acquisitions in a tandem mode, i.e., with a C- and L-band SAR satellite pair collecting their data with the smallest possible time gap between them, would benefit in particular automated dual-frequency classification and detection.
The following members of the project team contributed to the investigations that will be reported: Melanie Lacelle, Tom Zagon, and Benjamin Deschamps from CIS, Keld Qvistgaard from DMI, Nick Hughes from Met.No., Mike Hicks from IIP, Leif Eriksson, Anders Hildeman from Chalmers University of Technology, Denis Demchev from Nansen Environmental and Remote Sensing Center, and Johannes Lohse, Laust Færch, and Anthony P. Doulgeris from the Arctic University of Norway / Tromsø.

[1] Dierking, W., Synergistic Use of L- and C-Band SAR Satellites for Sea Ice Monitoring, Proceedings 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, DOI: 10.1109/IGARSS47720.2021.9554359

In this paper, we present recent results of volcano monitoring using the Advanced Land Observing Satellite-2 (ALOS-2) and Sentinel-1 satellites. ALOS-2 carries an L-band synthetic aperture radar (SAR) named PALSAR-2 and enables quick disaster response with high resolution (1 by 3 meters by the Spotlight mode and 3 to 10 meters by the Stripmap mode). Sentinel-1A/B satellites carry C-band SAR and focus on periodic observations (every 6 or 12 days) with medium spatial resolution (20 meters by the Interferometric Wide mode).

Mount Nyiragongo located in the eastern area of the Democratic Republic of the Congo erupted in May 2021. During the eruption, we estimated quasi-vertical displacements and quasi-east-west displacements around the volcano by multi-angle interferometric SAR (InSAR) analysis using the data from ascending and descending orbits. The results from both ALOS-2 and Sentinel-1 data implied the occurrence of dyke intrusion by underground magma, but the two results had different characteristics. Sentinel-1 (C-band) showed more fringes corresponding to the shorter wavelength and higher sensitivity for small displacements. ALOS-2 (L-band) showed higher coherence in largely displaced and highly vegetated areas, as the longer wavelength has higher penetration capability.

Kilauea Volcano in Big Island, Hawaii began erupting in May 2018 and caused fissures in residential areas. We performed InSAR, multiple aperture interferometry (MAI), and polarimetric analysis using a series of ALOS-2 data. The displacements derived from InSAR and MAI of Stripmap and ScanSAR data revealed the presence of dyke intrusion in the East rift zone and subsidence in Kilauea Caldera. Using polarimetric images by HH and HV polarization data, we also created a map of lava flow in Leilani Estates. Moreover, a series of Spotlight mode data captured the detailed process of the collapse of Halemaumau Crater in Kilauea Caldera.

In conclusion, multi-frequency (L-/C-bands), multi-angle (ascending/descending), multi-mode, and multi-polarimetric SAR data provides us with various information on tectonic and surface changes to better understand volcanic activities.