ESSI2.10

Japan Aerospace Exploration Agency (JAXA) launched its first L-band SAR mission - Japanese Earth Resources Satellite (JERS-1) in 1992. Though the design life of JERS-1 was 2 years, the satellite had obtained observational data for more than 6 years and ended the mission in 1998. Following to JERS-1, Advanced Land Observing Satellite (ALOS) was launched in 2006. ALOS was equipped with three sensors: the Phased Array type L-band SAR (PALSAR), the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM), and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2). ALOS's observation data has been used in various areas including disaster mitigation through observing regions damaged by earthquakes, tsunami, or typhoons, as well as carrying out forest monitoring, natural environment maintenance, agriculture, and compiling a 1/25,000 topographical map. When the Great East Japan Earthquake hit Japan in 2011, ALOS took some 400 images over disaster-stricken areas to provide information to all parties concerned.

Technologies acquired from the ALOS are succeeded to the second Advanced Land Observing Satellite “ALOS-2.”, which was successfully launched on 24th May 2014. The mission sensor of ALOS-2 is the Phased Array type L-band Synthetic Aperture Radar-2 called PALSAR-2 which is the state-of-the-art L-band SAR system. Until now after the successful completion of initial checkout after launching, ALOS-2 has been contributed to a lot of emergency observations for natural disasters, not only in Japan but also in the world. Furthermore, based on the Basic Observation Scenario (BOS) of ALOS-2, 10m global map data and other mode data are routinely collected and archived. This paper describes the results of ALOS-2 operation in nominal operation phase and outline of future ALOS series missions, especially ALOS-4 launched JFY2022.

How to cite: Sobue, S., Tadono, T., Miura, S., Noda, A., Motooka, T., and Ohki, M.: Japan's L-SAR missions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-534, https://doi.org/10.5194/egusphere-egu21-534, 2021.

One of the limitations of presently available Synthetic Aperture Radar (SAR) surface soil moisture (SSM) products is their moderated temporal resolution (e.g., 3-4 days) that is non optimal for several applications, as most user requirements point to a temporal resolution of 1-2 days or less. A possible path to tackle this issue is to coordinate multi-mission SAR acquisitions with a view to the future Copernicus Sentinel-1 (C&D and Next Generation) and L-band Radar Observation System for Europe (ROSE-L).

In this respect, the recent agreement between the Japanese (JAXA) and European (ESA) Space Agencies on the use of SAR Satellites in Earth Science and Applications provides a framework to develop and validate multi-frequency and multi-platform SAR SSM products. In 2019 and 2020, to support insights on the interoperability between C- and L-band SAR observations for SSM retrieval, Sentinel-1 and ALOS-2 systematic acquisitions over the TERENO (Terrestrial Environmental Observatories) Selhausen (Germany) and Apulian Tavoliere (Italy) cal/val sites were gathered. Both sites are well documented and equipped with hydrologic networks.

The objective of this study is to investigate the integration of multi-frequency SAR measurements for a consistent and harmonized SSM retrieval throughout the error characterization of a combined C- and L-band SSM product. To this scope, time series of Sentinel-1 IW and ALOS-2 FBD data acquired over the two sites will be analysed. The short time change detection (STCD) algorithm, developed, implemented and recently assessed on Sentinel-1 data [e.g., Balenzano et al., 2020; Mattia et al., 2020], will be tailored to the ALOS-2 data. Then, the time series of SAR SSM maps from each SAR system will be derived separately and aggregated in an interleaved SSM product. Furthermore, it will be compared against in situ SSM data systematically acquired by the ground stations deployed at both sites. The study will assess the interleaved SSM product and evaluate the homogeneous quality of C- and L-band SAR SSM maps.

References

Balenzano. A., et al., “Sentinel-1 soil moisture at 1km resolution: a validation study”, submitted to Remote Sensing of Environment (2020).

Mattia, F., A. Balenzano, G. Satalino, F. Lovergine, A. Loew, et al., “ESA SEOM Land project on Exploitation of Sentinel-1 for Surface Soil Moisture Retrieval at High Resolution,” final report, contract number 4000118762/16/I-NB, 2020.

How to cite: Balenzano, A., Satalino, G., Lovergine, F., Palmisano, D., Mattia, F., Rinaldi, M., and Montzka, C.: Combining Sentinel-1 and ALOS-2 observations for soil moisture retrieval, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8660, https://doi.org/10.5194/egusphere-egu21-8660, 2021.

Emission and backscattering at different frequencies have varied responses to soil physical processes (e.g., moisture redistribution, freeze-thaw) and vegetation growing/senescencing. Combing the use of active and passive microwave multi-frequency signals may provide complementary information, which can be used to better retrieve soil moisture, and vegetation biomass and water content for ecological applications. To this purpose, a Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP) was adopted in this study to simulate both emission (T_B) and backscatter (σ⁰), in which the CLAP is backboned by the TorVergata model for modelling vegetation scattering, and an air-to-soil transition model (ATS) (accounting for surface dielectric roughness) integrated with the Advanced Integral Equation Model (AIEM) for modelling soil surface scattering. The accuracy of CLAP was assessed by both ground-based and spaceborne measurements, and the former was from the deployed microwave radiometer/scatterometer observatory at Maqu site on an alpine meadow over the Tibetan plateau. Specifically, for the passive case, simulated T_B (emissivity multiplied by effective temperature) were compared to the ground-based ELBARA-III L-band observations, as well as C-band Advanced Microwave Scanning Radiometer 2 (AMSR2) and L-band Soil Moisture Active Passive (SMAP) observations. For the active case, simulated σ⁰ were compared to the ground-based scatterometer C- and L-bands observations, and C-band Sentinel and L-band Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2) observations. This study is expected to contribute to improving the soil moisture retrieval accuracy for dedicated microwave sensor configurations.

How to cite: Zhao, H., Zeng, Y., Su, B., and Hofste, J.: Modelling of Microwave Multi-Frequency Emission and Backscatter by a Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6659, https://doi.org/10.5194/egusphere-egu21-6659, 2021.

The monitoring of inundation phenomena through synthetic aperture radar (SAR) data on vegetated areas can be improved through an integrated analysis of different spectral bands. The combination of data with different penetration depths beneath the vegetated canopy can help determine the response of flooded areas with distinct types of vegetation cover to the microwave signal. This is useful especially in cases, which actually constitute the majority, where ground data are scarce or not available.

The present study concerns the application of multi-temporal, multi-frequency, and multi-polarization SAR images, specifically data from the Sentinel-1 and PALSAR 2 SAR sensors, operating in C band, VV polarization, and L band, HH and HV polarizations, respectively, in synergy with globally-available land cover data, for improving flood mapping in densely vegetated areas, such as the Zambezi-Shire basin, Mozambique [1], characterized by wetlands, open and closed forest, cropland, grassland (herbaceous and shrubs), and a few urban areas.

We show how the combination of various data processing techniques and the simultaneous availability of data with different frequencies and polarizations can help to monitor floodwater evolution over various land cover classes. They also enable detection of different scattering mechanisms, such as double bounce interaction of vegetation stems and trunks with underlying floodwater, giving precious information about the distribution of flooded areas among the different ground cover types present on the site.

This kind of studies are expected to assume increasing importance as the availability of multi-frequency data from SAR satellite constellations will increase in the future, thanks to initiatives such as the EU Copernicus program L-band satellite mission ROSE-L [2], and their tight integration with Sentinel-1 as well as with other national constellations such as ALOS 2, or SAOCOM.

References

[1] Refice, A.; Zingaro, M.; D’Addabbo, A.; Chini, M. Integrating C- and L-Band SAR Imagery for Detailed Flood Monitoring of Remote Vegetated Areas. Water 2020, 12, 2745, doi:10.3390/w12102745.

[2] Pierdicca, N.; Davidson, M.; Chini, M.; Dierking, W.; Djavidnia, S.; Haarpaintner, J.; Hajduch, G.; Laurin, G.V.; Lavalle, M.; López-Martínez, C.; et al. The Copernicus L-band SAR mission ROSE-L (Radar Observing System for Europe). In Active and Passive Microwave Remote Sensing for Environmental Monitoring III; SPIE: Washington, DC, USA, 2019; Volume 11154, p. 13.

How to cite: Refice, A., D'Addabbo, A., Chini, M., and Zingaro, M.: Flood monitoring in remote areas: integration of multi-frequency SAR data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4452, https://doi.org/10.5194/egusphere-egu21-4452, 2021.

Approximately 90% of the world total paddies area and annual output of the rice production are concentrated in monsoon Asia, which has no more land/water resources for further expansion of cultivation. Most rice grows under lowland conditions where currently facing to the fresh water scarcity due to sea-water intrusion accelerated by sea-level rise and land-subsidence, and decelerating freshwater supply by upstream-dam construction. Since the rice production also requires large amount of water (3,000-5,000 L kg^-1 rice), water-saving irrigation practice (e.g., Alternate Wetting and Drying, a.k.a., AWD) is desirable to be implemented in this region to save the water-demand sustainably, and irrigation status need to be evaluated for the decision making on sustainable food security. In addition to the significance of AWD’s role as an adaptation to drought risks, AWD also has a potential to act as an important mitigation-measure by reducing methane emission from paddy soils. This function is very important since rice cropping is responsible for approximately 11% of global anthropogenic CH₄ emissions, and rice has the highest greenhouse gas intensity among the main food crops.

In order to implement AWD in Asian rice paddies as a mitigation-measure based on a carbon pricing scheme, it is important to evaluate the spatial distribution of AWD paddy fields in the target region. For the detection of AWD-fields versus continuously-flooding fields, it is essential to develop method using EO data to detect soil inundation under rice plants at various growth stages.  In this study, ALOS-2/PALSAR-2 and Sentinel-1 data were used to combine the penetration capacity of L-band SAR data with C-band data capacity to monitor rice growth status with their high temporal resolution.

The study was conducted in triple rice cropping systems in the Vietnamese Mekong delta (5 sites: Thot Not in Can Tho city; Chau Thanh, Cho Moi, Thoai Son and Tri Ton in An Giang Province, where AWD field campaign was conducted from 2012 to 2017. EO data consisted of ALOS-2/PALSAR-2 every 14 days in 2017/2018 in An Giang province at high resolution observation mode (3-6m resolution) and ScanSAR observation mode (25-100m resolution) every 42 days over the Mekong delta.

As the result of the classification using the dual-polarization ALOS-2/PALSAR-2 data, soil inundation status could be detected during various rice growth stages. To evaluate rice productivity and GHG emissions from rice fields, we developed a simulation system based on the DeNitrification-Decomposition (DNDC) model which can assimilate PALSAR-2 inundation map and ground observed GHG -flux and rice growth status data on a pixel basis. For spatial extension, rice map, together with rice calendar (sowing date, rice growth status), required as inputs by DNDC are provided by the GeoRice project, based on the use of Sentinel-1 6-day time series. This paper presents the performance of multi-sensor data fusion to realize sustainable agricultural management by mitigating the GHGs emission while maintaining or improving regional fresh water use efficiency for stable food production under climate change pressure.

How to cite: Arai, H., Le Toan, T., Takeuchi, W., Oyoshi, K., Phan, H., Nguyen, L. D., Fumoto, T., and Inubushi, K.: Detecting  rice inundation status for water saving and methane emission mitigation measures using Sentinel-1 & ALOS-2/PALSAR-2 Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7831, https://doi.org/10.5194/egusphere-egu21-7831, 2021.

The split-spectrum method (SSM) can largely isolate and correct for the ionospheric contribution in the L-band interferometric synthetic aperture radar (InSAR). The standard SSM is performed on the assumption of only the first-order ionospheric dispersive effect, which is proportional to the total electron content (TEC). It is also known that during extreme atmospheric events, either originated from the ionosphere or in the troposphere, other dispersive effects do exist and potentially provide new insights into the dynamics of the atmosphere, but there have been few detection reports of such signals by InSAR. We apply L-band InSAR into heavy rain cases and examine the applicability and limitation of the standard SSM. Since no events such as earthquakes to cause surface deformation took place, the non-dispersive component is apparently attributable to the large amount of water vapor associated with heavy rain, whereas there are spotty anomalies in the dispersive component that are closely correlated with the heavy rain area. The ionosonde and Global Navigation Satellite System (GNSS) rate of total electron content index (ROTI) map both show little anomalies during the heavy rain, which suggests few ionospheric disturbances. Therefore, we interpret that the spotty anomalies in the dispersive component of the standard SSM during heavy rain are originated not in the ionosphere but the troposphere. While we can consider two physical mechanisms, one is runaway electron avalanche and the other is the scattering due to rain, comparison with the observations from the ground-based lightning detection network and rain gauge data, we conclude that the rain scattering interpretation is spatiotemporally favorable. We further propose a formulation to examine if another dispersive phase than the first-order TEC effect is present and apply it to the heavy rain cases as well as two extreme ionospheric sporadic-E events. Our formulation successfully isolates the presence of another dispersive phase during heavy rain that is in positive correlation with the local rain rate. Furthermore, our formulation is also able to detect the occurrence of higher-order ionospheric effects during Sporadic-E cases.

How to cite: Setiawan, N. and Furuya, M.: Tropospheric Dispersive Phase Anomalies during Heavy Rain Detected by L-band InSAR and Their Interpretation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13944, https://doi.org/10.5194/egusphere-egu21-13944, 2021.

This research is related to the JAXA 6th Research Announcement for the Advanced Land
Observing Satellite-2 (ALOS-2) project "Improved Sea Ice Parameter Estimation with L-Band SAR (ISIPELS)".
In the study ALOS-2/PALSAR-2 dual-polarized Horizontal-transmit-Horizontal-receive/
Horizontal-transmit-Vertical-receive (HH/HV) ScanSAR mode L-band  Synthetic Aperture Radar (SAR) imagery
over an Arctic study area were evaluated for their suitability for operational sea ice monitoring.
The SAR data consisting of about 140 HH/HV ScanSAR ALOS-2/PALSAR-2 images were acquired during the winter 2017.
These L-band SAR data were studied for estimation of different sea ice parameters:
sea ice concentration, sea ice thickness, sea ice type, sea ice drift. Also some comparisons with nearly
coincident C-band data over the same study area have been made. The results indicate that L-band
SAR data from ALOS-2/PALSAR-2 are very useful for estimating the studied sea ice parameters and equally good
or better than using the conventional operational dual-polarized C-band SAR satellite data.

How to cite: Karvonen, J.: ALOS-2/PALSAR-2 dual-polarized L-band data for sea ice parameter estimation and sea ice classification, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3920, https://doi.org/10.5194/egusphere-egu21-3920, 2021.

Synthetic aperture radar (SAR) has become an essential component in ocean remote sensing due it’s high sensitivity to sea surface dynamics and its high spatial resolution. The ALOS-2 SAR data are underutilized for ocean surface wind and current retrieval. Although the primary goals of the ALOS-2 mission are focused on land applications, the extension of the satellite scenes over the coastal areas offers an opportunity for ocean applications. The underutilization of ALOS-2 data is mainly due to the fact that at low radar frequencies, e.g. L-band, the sensitivity of the radar scattering coefficient to wind speed and the sensitivity of the Doppler frequency shift to sea surface velocity is lower than at higher frequencies, e.g. C- and X-band. This is also due to the fact that most of ALOS-2 images are acquired in HH or HV polarization while the VV polarization is often preferred in ocean applications due the higher signal to noise ratio.

The wind speed is retrieved from Sentinel-1 and ALOS-2 using the existing empirical C- and L-band geophysical model functions. For Sentinel-1, the Doppler frequency shift provided in the OCN product is used. For ALOS-2, the Doppler frequency shift is estimated from the single look complex data using the pulse-pair processing method. The estimated Doppler shift converted to the surface radial velocity and the velocity is calibrated using land as a reference. The estimated L-band Doppler shift and surface velocity is compared to the C-band Doppler shift provided in the Sentinel-1 OCN product. Due the difference in the local time of ascending node (about 6 hours at the equator) of the two satellites, a direct pixel-by-pixel comparison is not possible, i.e. the wind and surface current can not be assumed to be constant during such a large time difference. Thus, the retrieved wind from each sensor is compared separately to model data and in-situ observations.

In this paper, the quality of the wind speed retrieved from the L-band SAR (ALOS-2) in coastal areas is assessed and compared to the C-band SAR (Sentinel-1). In addition, the feasibility of the surface current retrieval from the L-band Doppler frequency shift is investigated and also compared to Sentinel-1. Examples will be shown and discussed. This opens an opportunity for synergy between L-band and C-band SAR missions to increase the spatial and temporal coverage, which is one of the main limitations of SAR application in ocean remote sensing.

How to cite: Elyouncha, A. and Eriksson, L. E. B.: Assessment of the sea surface wind and current retrieval from ALOS-2 and Sentinel-1 SAR data over coastal areas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7490, https://doi.org/10.5194/egusphere-egu21-7490, 2021.

In support of ESA's Mission Advisory Group for ROSE-L (Radar Observing System for Europe at L-band), a project team consisting of members of operational ice services and the International Ice Charting Working Group,  the International Ice Patrol, and groups from universities and research institutes is investigating the benefits of using data from L-band SAR in addition to C-band SAR imagery for separating different sea ice classes and detecting icebergs. The tasks are: (1) a critical assessment of the current state-of-the-art in sea ice monitoring and iceberg detection, (2) matching C- and L-band SAR images acquired with temporal gaps of several hours, (3) tests and assessments of the practical use of L-band images in the operational mapping services, and (4) comparison of classification accuracies that can be achieved at C-band, L-band, and a combination of both, based on the results of automated segmentation and classification algorithms. Based on the suggestions of operational ice centers, data have been collected since April 2019 over six test sites for the Northern Hemisphere: Fram Strait, Belgica Bank, northern and southern parts of Greenland, Baffin Bay and Labrador Sea. The SAR images are acquired by Sentinel-1 at Extra Wide and Interferomeric Wide Swath modes, by RADARSAT-2 at ScanSAR mode, and by ALOS-2 PALSAR-2 at Wide Beam and Fine Beam modes. The PALSAR-2 data are provided through the 2019 to 2022 mutual cooperation project between ESA and JAXA on using SAR data in earth sciences and applications. The presentation - with contributions from project partners - will focus on the conclusions from the literature review, assessments of operational ice services regarding the gain they find in using L-band SAR images supplementary to routinely analyzed C-band imagery, and preliminary results of automated classification. 

How to cite: Dierking, W. and Davidson, M.: Use of L- and C-Band SAR Satellites for Sea Ice and Iceberg Monitoring (LC-ICE), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3916, https://doi.org/10.5194/egusphere-egu21-3916, 2021.

Millions of lakes and ponds occupy large areas of the Arctic discontinuous and continuous permafrost zones. During most of the year, the surfaces of these lakes remain covered by a thick layer of ice. Synthetic Aperture Radar (SAR) data have shown to be useful for studying the ice on Arctic lakes, especially for monitoring lake ice phenology and the grounding state of the ice (ice frozen to the lakebed versus floating lake ice). Significant backscatter is often observed from the floating ice regime in C-band due to scattering on a rough ice-water interface.

Recent research has revealed features of anomalously low backscatter in Sentinel-1 C-band SAR imagery on some of the West Siberian lakes that likely belong to the floating ice regime. These anomalies are characterized by prominent shapes and sizes and seem to expand throughout late winter and/or spring. It is currently assumed that some of these features are related to strong emissions of natural gas (methane from hydrocarbon reservoirs), making it important to assess their origin in detail and understand the associated mechanisms. However, in-situ data are still missing.

Here, we assess the potential of the combined use of C-band Sentinel-1 (freely available) and L-band ALOS PALSAR-2 data  (available through JAXA PI agreement #3068002) to study the backscatter anomalies. We highlight the differences between observed backscatter from the two sensors with respect to different surface types (ground-fast lake ice, floating lake ice and anomalies) and investigate backscatter differences between frozen and melting conditions. Further, polarimetric classification is performed on L-band PALSAR-2 imagery, which reveals differences in scattering mechanisms between anomalies and floating lake ice.

How to cite: Pointner, G. and Bartsch, A.: The potential of using Sentinel-1 and ALOS PALSAR-2 data for characterizing West Siberian lake ice backscatter anomalies, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11931, https://doi.org/10.5194/egusphere-egu21-11931, 2021.