The all-weather, day-and-night imaging capability of synthetic aperture radar (SAR) has become essential for flood monitoring. We have developed and implemented a fully automatic flood detection algorithm using SAR data from Advanced Land Observing Satellite-2 (ALOS-2). The algorithm rapidly processes ALOS-2 and ancillary data (flood simulation, hazard map, and other geographical information) and provides damage information less than an hour after the input data are entered. The algorithm has been operational since 2022 for disaster response users and is still being improved for better accuracy and robustness. During the two years of operation, our algorithm has responded to 15 flood events, demonstrated superior performance, and provided information used in disaster response activities.

This paper summarizes an operational calibration result of the Advanced Land Observing Satellites-2 (ALOS-2, nicknamed "DAICHI-2"). ALOS-2, the successor to the Phased Array-type L-band Synthetic Aperture Radar (PALSAR) onboarded ALOS "DAICHI" from 2006 to 2011, was launched in 2014 and has been operating smoothly beyond its design mission life of 7 years; the PALSAR-2 onboard ALOS-2 has shown its maximum contribution in practical applications by the basic observations of global land areas with time-series high resolution, and in emergency observations during natural disasters. The observation capability of PALSAR-2 has been improved over PALSAR e.g., a finer spatial resolution of 1 x 3m by the Spotlight observing mode, a wider swath width of 490 km by the ScanSAR mode, and a right-and-left looking function. To take full advantage of these capabilities, sensor calibration is extremely important. This paper reports the latest operational calibration result of PALSAR-2 during more than 9 years of its mission operation period.

Ionospheric total electron content (TEC) can significantly affect the range geometric accuracy of spaceborne L-band synthetic aperture radar (SAR) data. This study aims to estimate the ionospheric TEC from single spaceborne L-band SAR data based on the concept of the split-spectrum method. We estimated the SAR-based TEC from the range misregistration between lower- and higher-range spectral split-band amplitude images acquired using the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2). A comparison of the SAR-based TEC with the TEC observed by the Global Navigation Satellite System showed a linear relationship with a slope of 0.76 after correcting for offsets in the estimated value. This slope seemingly represents the ionospheric TEC ratio below the ALOS-2 orbit. Correcting the ionospheric range delay in PALSAR-2 data with the SAR-based TEC estimates improved the slant range accuracy from 2.67 m to 0.77 m.

The south coast of Java is located in an active subduction zone where the Indo-Australian plate is beneath the Eurasian plate with a velocity of 7,0 cm/year. The phenomena lead to the area front of disaster, especially earthquakes and tsunami. Some areas along the coast of JAVA are being pressed (subsidence) or lifted due to the tectonic activity of the plate. Also, the activity can result in the creation of areas that have the potential to experience ground movement. However, information about it with scientific evidence is still inadequate. Therefore it is necessary to observe the effects of tectonic activity in southern Java on land surface changes along the coastline and estimate the potential epicenter of earthquakes due to this activity. In this study, we observed land deformation along the southern coast of Java to determine which areas experienced significant deformation. Based on the result, we can estimate the future earthquake's potential epicenter and the land movement area. Also, we analyzed the duration required since the last observation of earthquake events or land movement in the area. The land deformation observation uses satellite radar SAR data of ALOS-2 PALSAR-2 provided by JAXA. The type of SAR data is a stripmap with a 3 x 8-meter resolution. The time acquisition varied for each area observed from 2017 to 2020. The covered area of studies is spread from West to East (Pandagelang, Lebak, Sukabumi, Cianjur, Garut, Pangandaran, Banjar, Cilacap, Kebumen, Purworejo, and Bantul).

Japan Aerospace Exploration Agency (JAXA) Earth Observation Research Center (EORC) has produced a high-resolution land-use and land-cover classification map for Japan indicating the average cover in 2018–2020. The classification accuracy was improved by using a combination of Sentinel-2 and ALOS-2/PALSAR-2 data. We developed a convolutional neural network-based algorithm specialized for pattern recognition in multispectral and time-series feature spaces, and achieved an overall accuracy of 88.85% in 12 classifications. This is the most accurate high-resolution land-cover map ever produced by JAXA. This highly accurate product is being used for the calculation of SDGs global indicators such as 15.4.2 “Mountain Green Cover Index”.

Ionospheric path delay (IPD) correction methods for creating an ortho-rectification product of L-band SAR were examined using the Phased Array type L-band Synthetic Aperture Rader-2 (PALSAR-2). Slant range correction values for the IPD were estimated using two methods. First one is coregistration between a radar geometry simulated image created using digital surface model (DSM) and a SAR amplitude image. Second one is using a total electron content (TEC) map. The geometric accuracy of the ortho products created using each correction was evaluated. The TEC map correction improved the geometric accuracy, while the coregistration one decreased. The individual slant-range shifts calculated by the co-registration showed a relationship with forest canopy height. The reason why the error increased in the co-registration correction is inferred that a false slant range shift is created due to the height difference between the scattering center point of the L-band SAR and the DSM in the forest, which results in adding an error to the slant-range correction value.

This study conducted simultaneous experiments using a flood experiment field that can reproduce the conditions of flooded buildings, along with satellite monitoring using the L-band synthetic aperture radar (SAR) aboard the Advanced Land Observing Satellite-2 (ALOS-2). Through the experiments, we investigated the relationship between the multi-look number of interferometric processing, flooded building detection using interferometric coherence, and floodwater depth. For a better interpretation of the experiment results, we also performed a theoretical coherence simulation. Our results revealed that 1) coherence statistically depends on the multi-look number, 2) coherence-based change detection method can classify a flooded building with 6 cm or less water depth, and 3) there is no clear correlation between coherence degradation and floodwater depth. These findings will be useful for robust flood-area estimation of actual urban flooding events.

The relationship between hurricane sea surface wind speed and L-band$\sigma^{0}$is investigated to enable wind estimation under extreme wind condition based on four the Phased Array type L-band Synthetic Aperture Rader-2 (PALSAR-2) hurricane images and wind speed simultaneously measured by National Oceanic and Atmospheric Administration (NOAA)'s airborne Stepped Frequency Microwave Radiometer (SFMR). It was confirmed that HV$\sigma^{0}$has a little dependence on the incidence angle and tends to increase without saturation at least up to 55 m/s and an L-band HV geophysical model function (GMF) is constructed using match-ups between HV$\sigma^{0}$and SFMR wind speed. A comparison between the PALSAR-2-derived and SFMR-measured wind speeds reveals a −0.2m/s bias and 4.1m/s root mean square error (RMSE). The calculated wind speed structure shows the asymmetry wind speed radii which is almost consistent with the best track data.

The all-weather, day-and-night imaging capability of synthetic aperture radar (SAR) has become essential for flood disaster monitoring. This study developed a SAR-based flood monitoring method that automatically estimates flood extent, floodwater depth, and their changes. The flood extent was estimated from a Bayesian probabilistic model using ALOS-2 SAR data (amplitude and coherence) and hydrodynamic simulation data provided by Today's Earth (TE). The floodwater depth was calculated using the SAR-derived flood extent and high-resolution digital elevation model (DEM). We examined our methods using ALOS-2 data for the 2015 flood in Japan.

A series of multi-mode ALOS-2 Synthetic Aperture Radar (SAR) images were used to examine surface change/deformation associated with the eruption of the Kilauea volcano in Hawaii on May 3, 2018. The first interferogram using ScanSAR InSAR images reveals large surface displacements in the Leilani Estates, the East Rift Zone, and the Kilauea Caldera. Stripmap polarimetric color-composite images show a spatial distribution of lava flow over Leilani Estates. Spotlight images captured the collapse of the Kilauea Caldera and the 3D displacement by offset tracking. ScanSAR time-series InSAR from September 2018 to December 2019 indicates a post-eruptive surface uplift in the East Rift Zone and the Kilauea Caldera.

In this study, we developed an automatic flood detection method using ALOS-2 and ancillary data. Our method can be automatically processed and provide easy-to-understand flood information rapidly for users working on-site disaster response. A Bayesian probabilistic model was used for integrating ancillary data (quasi-realtime flood simulation) and multiple SAR-derived images (amplitude and coherence). We also used a land-use/land-cover map to improve the flood classification result. We examined our methods using ALOS-2 data in the 2015 flood in Japan and obtained an accurate flood map in good agreement with reference data.

This study investigated a method of flood detection in built-up areas by L-band synthetic aperture radar (SAR) images, using interferometric phase statistics instead of the widely used coherence. Simulation and actual SAR data analysis showed that phase statistics are more accurate than coherence is, especially when there are many point-like scatterers. Accuracy was further improved by using an interferometric phase filter. The flood detection accuracies obtained by the phase statistics were κ = 0.535 (0.304 by coherence) in the 2015 flood in Joso and κ = 0.679 (0.609 by coherence) in the 2018 flood in Mabi. These results showed that coherence should be used with great care with point targets and that the phase-only statistics can be more robust in such cases. The proposed method is expected to apply to other types of disasters where coherence has been used previously.

We use L-band Synthetic Aperture Radar data to reveal crustal deformation associated with the eruption of Sierra Negra volcano, Galápagos Island, on June 26, 2018. Intensity analysis revealed the area of lava flowing out from the northside of the mountain. Interferograms resulted in large surface displacement in the crater as well as the northwestern slope of the summit. Using the offset fields from ascending and descending orbit data, we calculated 3D deformation, showing about 8 m subsidence in the crater. The amount of the deformation was greater than that in the previous eruptions, which implies the volume of magma accumulation and flowing out can be larger than that in the past events.

The Japan Aerospace Exploration Agency (JAXA) performed disaster monitoring for the 2011 Great East Japan Earthquake. The Advanced Land Observing Satellite (ALOS) acquired 450 scenes of data for the disaster. Time series flood extent change of the tsunami was obtained from the optical sensor aboard ALOS. Interferometric SAR (InSAR) analysis of the synthetic aperture radar (SAR) aboard the satellite revealed the whole picture of crustal deformation associated with the earthquake. The data and analysis results were provided to the relevant governmental ministries, agencies, and local governments.

Rapid and all-weather detection of flood areas is needed for monitoring and mitigating flood disasters. In this paper, we investigated flood area detection in built-up area using PALSAR-2 data acquired during the 2015 heavy rainfall disaster in the Kanto and Tohoku areas, Japan. We performed an interferometric processing using pre- and co-event PALSAR-2 data and compared proposed phase-only statistics with traditional coherence-based flood detection. The result indicated that the flood detection accuracy by thresholding of phase standard deviation (PSD) image is more accurate than that of coherence and the accuracy was a 0.443 kappa coefficient in flood/non-flood discrimination. This result demonstrated the feasibility of using PALSAR-2 data for rapid flood monitoring in built-up area without being affected by strong backscattering from the buildings.

The determination of the accurate composition ratio of scattering mechanisms (volume scattering, double-bounce scattering, and surface scattering) within a radar backscatter is essential to validate current polarimetric decomposition techniques. Multiincidence angle and multipolarimetric synthetic aperture radar (MIMP SAR) observations at the X- and L-bands were applied to rice paddies at late vegetative stage in Niigata City in Japan in 2014 and 2016, respectively. In this paper, multifrequency MIMP SAR analysis is introduced based on the observation results. The approach, combined with theoretical characterization of the data by a discrete scatterer model, showed that rice panicles affect the backscatter from rice paddies. Contrary to expectation, an effect of transmissivity by using different bands is not obvious. The similar level of copolarization (HH and VV) backscatter at X- and L-bands could be explained by the effective size of rice panicles. They are the most characteristic scatters in rice paddy field with respect to multiple frequency polarimetric sensing. In addition, HV shows a distinct sensitivity to the mean orientation angle and the size of panicles regardless of the wavelength. The mean orientation angle affects the polarimetric randomness under azimuthal symmetry, whereas the size of panicles directly affects the attenuation of the volume scattering from the grains. The multifrequency MIMP SAR analysis also indicated the importance of considering the backscatter and attenuation in the interpretation of the backscattering cross section from vegetated fields.

Ice shelves have a key role to assess mass balance in Antarctic Ice Sheet and its contribution to global sea-level rise. On July 12 2017, a large iceberg separated from Larsen-C Ice Shelf in Antarctic Peninsula, which lost greater than 12% of total ice mass. We use ALOS-2/PALSAR-2 ScanSAR images to capture the overall picture of this event and to understand what happens to Larsen-C. ALOS-2 images reveal that the iceberg has been gradually moved toward sea, sometimes affected by sea ice motion. The ALOS and ALOS-2 images show that the ice front has been advancing by ~ 8 km in the last 10 years. The interferometric coherence analysis reveals that there are some cracks on the ice shelf despite not seen in the intensity images. The distribution of the cracks derived from the coherence can be useful to assess the instability of Larsen-C.

SAR remote sensing has been expected as an efficient rice crop monitoring tool due to its flexible operability with wide swath. Though many researchers have intensively analysed the backscatter, there still exist non-negligible mysteries to extract physical parameters. To solve the problems, it is essential to conduct theoretical characterization of experimentally obtained radar backscatter from rice paddies. In this paper, the rice paddies were observed by airborne SAR's at X- and L-bands by systematically varying the observation parameters such as incidence angle and polarization. The obtained full polarimetric backscattering cross section in terms of incidence angles and wavelengths based on many sample points in Niigata city is discussed.

The Advanced Land Observing Satellite-2 (ALOS-2) was successfully launched on 24th May, 2014, carrying the state-of-the-art L-band synthetic aperture radar, PALSAR-2. The satellite was developed and is stably operated by Japan Aerospace Exploration Agency (JAXA). As of January 2018, the satellite bus system and PALSAR-2 system are working very well. The PALSAR-2 data were acquired based on a systematic observation strategy to achieve consistent data acquisitions in time and space. JAXA also respond to the emergency observation requests for disasters. The observation data are distributed to both scientific and commercial users and utilized for disaster monitoring, environmental monitoring, and many other fields of applications.

Rapid and all-weather detection of flood areas is needed for monitoring and mitigating flood disasters. In this paper, we investigated flood area detection using ALOS-2 PALSAR-2 data acquired during the 2015 heavy rainfall disaster in the Kanto and Tohoku areas, Japan. We detected flood areas by thresho ldlng amplitude images from PALSAR-2 and then evaluated their accuracies against inundation maps based on aerial photographs. The highest accuracy was a 0.583 kappa coefficient in flood/non-flood discrimination. Our comparative study results indicate that lower off-nadir angles (around 30 degrees or less) are effective in the detection of flood areas. We also found polarimetric analysis and speckle filtering to be effective in improving accuracy. The results of this study demonstrate the feasibility of using PALSAR-2 data for rapid flood monitoring and as a reference for future flood disasters.

Insufficient validation for polarimetric decomposition techniques has led to its less perceived popularity for more than 20 years. The true composition ratio of scattering mechanisms within a radar backscatter should be essential to make polarimetric synthetic aperture radar (SAR) operational applications. To achieve this, a novel comprehensive approach to accurately identify the contribution of each scattering mechanism by a multi-incidence angle and multipolarimetric (MIMP) SAR observation combined with a theoretical model simulation is newly applied to L-band SAR data. Rice paddies in Niigata, Japan having a simple vegetation structure without topography were observed by Polarimetric and Interferometric Airborne SAR L-band 2, by gradually varying the flight path in terms of incidence angle. In addition to the MIMP SAR observation, a dominant scattering mechanism is reliably isolated through the theoretical characterization of the data by a discrete scatterer model. Avoiding unnecessary Bragg scattering effect caused by the methodically distributed rice paddies, the volume scattering from grains is identified as a dominant scattering mechanism over incidence angles. In addition, HH and VV are strongly affected by the double-bounce scattering between stalks and the ground surfaces at only small incidence angles, whereas the contribution of the double-bounce scattering for HV is not obvious. The results at the L-band will be compared with another MIMP SAR data at the X-band obtained for the same rice paddies in 2014 so that multifrequency MIMP SAR data analysis shall be conducted for our next step.

In this paper, we demonstrated the possibility of performing land use and land cover (LULC) classification over a wide area by an L-band polarimetric synthetic aperture radar (SAR). In previous studies, there has been scant LULC classification by polarimetric SAR data over a wide area. We used satellite-based SAR data with an area of ca. 320 000 km2obtained by the Phased Array type L-band SAR (PALSAR)-2 phase array. We performed the LULC classification using full polarimetry (FP), compact polarimetry (CP), and dual polarimetry (DP) data by PALSAR-2 and compared their classification accuracy. Our results show FP to be the most accurate. The CP and the DP have the advantages of large-scale coverage and compact data volume but is slightly less accurate than the FP. To further improve accuracy of the classification process, texture analysis, observation date information, and feature elimination are effective. We determined the classification accuracy for seven classes to be 73.4% (the kappa coefficient is 0.668). We found the rice paddy, forest, grass, and urban areas to be sufficiently accurate (84.5%) for practical application. We compared the obtained classification map with an existing LULC map to grasp the LULC changes induced by a recent disaster and successfully detected the damage areas of the disaster. These results indicate the possibility of large-scale LULC monitoring by an L-band polarimetric SAR, which can acquire images rapidly without being affected by weather.

This paper reports the evaluation results of detection capability of crustal movement by an airborne SAR (Pi-SAR-L2) repeat-pass interferometry. Pi-SAR-L2 belongs to by JAXA (Japan Aerospace Exploration Agency). The object is to estimate the detection accuracy of crustal movement by Pi-SAR-L2.

In 2016, the Advanced Land Observing Satellite-2 (ALOS-2, “DAICHI-2”) observed various disaster affected areas. Japan Aerospace Exploration Agency (JAXA) operated the emergency observation more than hundred times in the year. The Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard ALOS-2 contributed for detecting the disaster affected area, ground deformation and flood affected area. Especially for the ground deformation and damaged area detection caused by the devastating earthquakes in 2016, e.g., Kumamoto earthquakes in Japan and Kaikoura earthquake in New Zealand, researchers provided variable analytical results from ALOS-2 observation data. In this paper, some examples of the emergency observation results are presented.

Land cover is an important earth observation parameter for understanding the Earth's ecosystem. Compared with optical sensors, Synthetic Aperture Radar (SAR) enables more rapid land cover monitoring with its all-weather, day-and-night imaging capability. We mosaicked 390 scenes of PALSAR-2 fully polarimetric data acquired in Japan and computed several polarimetric decompositions. Then, we applied supervised land-cover classification. The accuracy of seven categories classification in the Kanto region, Japan, was 75.0%.

The Advanced Land Observing Satellite-2 (ALOS-2) 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. At After launch, the initial checkout and the calibration and validation phase had been completed, and the PALSAR-2 standard products were released via web site at the end of November 2015. Until now, ALOS-2 has had contributed to a lot of emergency observations for disasters such as earthquakes flood, land slide which were impacted by typhoons, and volcano eruptions, not only in Japan but also in the world. Furthermore, based on the Basic Observation Scenario (BOS) of ALOS-2, base map data are collected and archived for the interferometry SAR processing in Japan area as well as global world. This document describes the results of ALOS-2 operation in routine operation phase.

One of the main missions of the Advanced Land Observing Satellite-2 (ALOS-2, “DAICHI-2” ) is the disaster monitoring. Japan Aerospace Exploration Agency (JAXA) has operated the emergency observation more than hundred times in 2015. Not only the most important event in 2015, the Mw 7.8 Gorkha earthquake on April 25, the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard ALOS-2 observed various floods, volcano eruptions and earthquakes. In this paper, we present some emergency observation results which were impossible to be performed by the previous ALOS. That is, automatically burst aligned ScanSAR to ScanSAR interferometry and, left / right looking for increasing acquisition opportunity.

ALOS-Next/Tandem-L is a proposal for a highly innovative L-band SAR satellite mission for the global observation of dynamic processes on the Earth's surface with hitherto unparalleled quality and resolution. It is based on a collaboration between DLR and JAXA which started with a pre-phase A study in 2013 and is currently undergoing a phase A study. Thanks to the novel imaging techniques and the vast recording capacity with up to 8 Tbytes/day, it will provide vital information for solving pressing scientific questions in the biosphere, geosphere, cryosphere, and hydrosphere. By this, the new L-band SAR mission will make an essential contribution for a better understanding of the Earth system and its dynamics. ALOS-Next/Tandem-L will, moreover, open new opportunities for risk analysis, disaster management and environmental monitoring by employing especially designed acquisition modes and techniques in combination with a reconfigurable tandem satellite configuration and an L-band SAR instrument with advanced digital beamforming techniques.

In the field of Synthetic Aperture Radar (SAR) interferometry, we generally expect that there is only unique dominant scatterer in one pixel. To make a SAR interferogram, we have to observe a place twice. The dominant scatterer in every pixel must be identical in those observations. However, in an actual situation, there are multiple scatterers in one pixel physically, and the dominant one may be changed in the two observations. Slight difference of incident angle may cause the change of the dominant scatterer and the propagation path. The change can generate singular points (SPs) in the inter-ferogram which prevent us from accurate phase unwrapping. Here, we present experimental results in an anechoic chamber which show the mechanism of the singular point generation.

Deorientation processing has been incorporated into model-based decomposition to cure the overestimation of volume scattering contribution, by rotating the coherency matrix to minimize the cross-polarization term. First, the derivation of the rotation angle is clarified for avoiding the ambiguity. Moreover, even with the implementation of deorientation processing, oriented built-up areas with large orientation angles are still misjudged as volume scattering dominant. Further to the investigation of the deorientation effect, we focus on oriented built-up patches. A parameter, named dominant polarization orientation angle (DPOA), is introduced to label each patch. The behavior of the deorientation on coherency matrix and model-based decomposition over purely oriented built-up areas with respect toDPOAis disclosed. Experimental studies from the Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) polarimetric SAR data set demonstrate that model-based decompositions with deorientation work well for oriented built-up areas when |DPOA| ≤ 22.5°. However, for large |DPOA| (e.g., |DPOA| >; 22.5°), even with the deorientation processing, for the conventional decompositions which assume that only the volume scattering contributes to the cross-polarization term, the decomposed volume scattering power may also be dominant even for purely oriented built-up areas. Thereby, misinterpretation still occurs, motivating further advancements.