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Many SAR images have been utilized for geologic disasters investigations with the continuous launch of new Synthetic Aperture Radar (SAR) satellites such as ALOS-2/PALSAR-2. However, to proactively respond to transient slope failures caused by heavy rainfall, rapid extraction of areas of surface change accompanying slope failures is required. This study proposes two methods for quantitatively extracting slope failure areas using L-band SAR observations with slope units (SUs) as the evaluation units. The first method is based on the threshold method, which automates the selection of thresholds for various disaster-affected conditions, such as land use and topography. The second method is a machine-learning-based density ratio estimation method, which uses multi-temporal periodic observation data and pre- and post-disaster data to detect outliers through feature selection optimization. In the observation direction with the shortest satellite observation period, the F1 score (The F1 score is the harmonic mean of the precision and recall) of the threshold method for accuracy evaluation is 61.91%, and the F1 score of the density ratio method is 65.87%. Both methods can reduce the problem of low extraction accuracy caused by the effect of speckle noise. When slope failure occurs, both methods can extract the area of surface change within hours of a disaster. The method proposed in this study displays good applicability in supporting emergency rescue and the prevention of secondary disasters. [ABSTRACT FROM AUTHOR]

Abstract Many SAR images have been utilized for geologic disasters investigations with the continuous launch of new Synthetic Aperture Radar (SAR) satellites such as ALOS-2/PALSAR-2. However, to proactively respond to transient slope failures caused by heavy rainfall, rapid extraction of areas of surface change accompanying slope failures is required. This study proposes two methods for quantitatively extracting slope failure areas using L-band SAR observations with slope units (SUs) as the evaluation units. The first method is based on the threshold method, which automates the selection of thresholds for various disaster-affected conditions, such as land use and topography. The second method is a machine-learning-based density ratio estimation method, which uses multi-temporal periodic observation data and pre- and post-disaster data to detect outliers through feature selection optimization. In the observation direction with the shortest satellite observation period, the F1 score (The F1 score is the harmonic mean of the precision and recall) of the threshold method for accuracy evaluation is 61.91%, and the F1 score of the density ratio method is 65.87%. Both methods can reduce the problem of low extraction accuracy caused by the effect of speckle noise. When slope failure occurs, both methods can extract the area of surface change within hours of a disaster. The method proposed in this study displays good applicability in supporting emergency rescue and the prevention of secondary disasters.

Lithium mining has become a controversial issue in the transition to green technologies due to the intervention in natural basins that impact the native flora and fauna in these environments. Large resources of this element are concentrated in Andean salt flats in South America, where extraction is much easier than in other geological configurations. The Pozuelos highland salt flat, located in northern Argentina (Salta's Province), was chosen for this study due to the presence of different evaporitic crusts and its proven economic potential in lithium-rich brines. A comprehensive analysis of a 5.5-year-long time series of its microwave backscatter with Synthetic Aperture Radar (SAR) images yielded significant insights into the dynamics of their crusts. During a field campaign conducted near the acquisition of three SAR images (Sentinel-1, ALOS-2/PALSAR-2, and SAOCOM-1), field measurements were collected for computational modeling of the SAR response. The temporal backscattering coefficients for the crusts in the salt flat are directly linked to rainfall events, where changes in surface roughness, soil moisture, and water table depth represent the most critical variables. Field parameters were employed to model the backscattering response of the salt flat using the Small Slope Approximation (SSA) model. Salt concentration of the subsurface brine and the water table depth over the slightly to moderately roughed crusts were quantitatively derived from Bayesian inference of the ALOS-2/PALSAR-2 and SAOCOM-1 SAR backscattering coefficient data. The results demonstrated the potential for subsurface estimation with L-band dual-polarization images, constrained to crusts compatible with the feasibility range of the layered model. [ABSTRACT FROM AUTHOR]

The polarimetric synthetic aperture radar (PolSAR) provides us with a two-by-two scattering matrix data set. The ensemble averaged coherency matrix in an imaging window derived using a scattering matrix has all non-zero elements in its three-by-three matrix. It is a full 3 × 3 matrix that bears nine real-valued and independent polarimetric parameters inside. In the proposed decomposition method, G5U, we preprocess observed coherency matrix [T] by using two consecutive unitary transformations to become an ideal form for five-component decomposition. The transformation reduces nine parameters to seven, which is the best fit for five-component scattering model expansion. We can retrieve five powers corresponding to surface scattering, double bounce scattering, volume scattering, oriented dipole scattering, and compound dipole scattering, directly. These powers can be calculated easily and used to display superb polarimetric RBG images as never before, and are further applicable for polarimetric calibration, classification, validation, etc. [ABSTRACT FROM AUTHOR]

When a large-scale flood disaster occurs, it is important to identify the flood areas in a short time in order to effectively support the affected areas afterwards. Synthetic Aperture Radar (SAR) is promising for flood detection. A number of change detection methods have been proposed to detect flooded areas with pre- and post-event SAR data. However, it remains difficult to detect flooded areas in built-up areas due to the complicated scattering of microwaves. To solve this issue, in this paper we propose the idea of analyzing the local changes in pre- and post-event SAR data as well as the larger-scale changes, which may improve accuracy for detecting floods in built-up areas. Therefore, we aimed at evaluating the effectiveness of multi-scale SAR analysis for flood detection in built-up areas using ALOS-2/PALSAR-2 data. First, several features were determined by calculating standard deviation images, difference images, and correlation coefficient images with several sizes of kernels. Then, segmentation on both small and large scales was applied to the correlation coefficient image and calculated explanatory variables with the features at each segment. Finally, machine learning models were tested for their flood detection performance in built-up areas by comparing a small-scale approach and multi-scale approach. Ten-fold cross-validation was used to validate the model, showing that highest accuracy was offered by the AdaBoost model, which improved the F1 Score from 0.89 in the small-scale analysis to 0.98 in the multi-scale analysis. The main contribution of this manuscript is that, from our results, it can be inferred that multi-scale analysis shows better performance in the quantitative detection of floods in built-up areas. [ABSTRACT FROM AUTHOR]

This study assesses ALOS-2/PALSAR-2 (ALOS2) polarimetric images for detecting forest volume losses due to selective logging in a region in the Brazilian Amazon. Two logging-intensive areas, APU 2016, and APU 2017, were studied. ALOS2 imagery attributes, including backscatter and phase data, were analyzed for differences between logged and unlogged regions using Wilcoxon’s nonparametric test at a 95% confidence level. The Radar Normalized Difference Vegetation Index proved effective in detecting selective logging-induced forest volume losses, with consistent results (p-values of 0.003 for APU 2016 and 0.037 for APU 2017). These findings provide insights for monitoring and mitigating ecological impacts of logging in complex forest ecosystems.

Synthetic aperture radar (SAR) backscatter based above-ground biomass (AGB) estimates are limited by the saturation of the backscatter-AGB curve. This work explores the potential of combining backscatter with polarimetric SAR interferometry (PolInSAR) estimated forest stand height for improved AGB estimation. The models combining L-band backscatter and TanDEM-X height are compared with established backscatter based models. The models are also temporally cross-validated, i.e., trained on one acquisition date and validated for other dates. It is observed that with the input of height, the combined models perform significantly better than backscatter based models, with an improvement in root mean square error (RMSE) between 19% and 46%. The model utilizing HV-polarized backscatter and TanDEM-X PolInSAR height provide the best case AGB inversion with an R2 = 0.78 and an RMSE of 27.1 Mg/ha or 22% of mean AGB. The results demonstrate the potential of the synergistic combination of L-band PolSAR (backscatter) and X-band PolInSAR (height) products for AGB mapping over a tropical forest range in India.

Abstract I have been conducting a study of postseismic deformation following the 2016 Kumamoto earthquake using ALOS-2/PALSAR-2 acquired till 2018. I apply ionospheric correction to interferograms of ALOS-2/PALSAR-2. L-band SAR gives us high coherence enough to reveal surface deformation even in vegetated or mountainous area for pairs of images acquired more than 2 years. Postseismic deformation following the Kumamoto earthquake exceeds 10 cm during 2 years at some spots in and around Kumamoto city and Aso caldera. Westward motion of ~ 6 cm/year was dominant on the southeast side of the Hinagu fault, while westward shift was detected on both sides of the Futagawa fault. The area of latter deformation seems to have correlation with distribution of pyroclastic flow deposits. Significant uplift was found around the eastern Futagawa fault and on the southwestern frank of Aso caldera, whose rate reaches 4 cm/year. There are sharp changes across several coseismic surface ruptures such as Futagawa, Hinagu, and Idenokuchi faults. Rapid subsidence between Futagawa and Idenokuchi faults also found. It is confirmed that local subsidence continued along the Suizenji fault, which newly appeared during the mainshock in Kumamoto City. Subsidence with westward shift of up to 4 cm/year was also found in Aso caldera. Time constant of postseismic decay ranges from 1 month to 600 days at selected points, but that postseismic deformation during the first epochs or two is dominant at point in the Kumamoto Plain. This result suggests multiple source of deformation. Westward motion around the Hinagu fault may be explained with right lateral afterslip on the shallow part of this fault. Subsidence along the Suizenji fault can be attributed to normal faulting on dipping westward. Deformation around the Hinagu and Idenokuchi faults cannot be explained with right lateral afterslip of Futagawa fault, which requires other sources. Deformation in northern part of Aso caldera might be the result of right lateral afterslip on a possible buried fault.

Abstract Coeruptive deformation helps to interpret physical processes associated with volcanic eruptions. Because phreatic eruptions cause small, localized coeruptive deformation, we sometimes fail to identify plausible deformation signals. Satellite synthetic aperture radar (SAR) data allow us to identify extensive deformation fields with high spatial resolutions. Herein, we report coeruptive crustal deformation associated with the 2018 Kusatsu-Shirane phreatic eruption detected by time series analyses of L-band satellite SAR (ALOS-2/PALSAR-2) data. Cumulative deformation maps derived from SAR time series analyses show that subsidence and eastward displacement dominate the southwestern side of an eruptive crater with a spatial extent of approximately 2 km in diameter. Although we were unable to identify any significant deformation signals before the 2018 eruption, posteruptive deformation on the southwestern side of the crater has been ongoing until the end of 2019. This prolonged deformation implies the progression of posteruptive physical processes within a confined hydrothermal system, such as volcanic fluid discharge, similar to the processes observed during the 2014 Ontake eruption. Although accumulated snow and dense vegetation hinder the detection of deformation signals on Kusatsu-Shirane volcano using conventional InSAR data, L-band SAR with various temporal baselines allowed us to successfully extract both coeruptive and posteruptive deformation signals. The extracted cumulative deformation is well explained by a combination of normal faulting with a left-lateral slip component along a southwest-dipping fault plane and an isotropic deflation. Based on the geological background in which the shallow hydrothermal system develops across Kusatsu-Shirane volcano, the inferred dislocation plane can be considered as a degassing pathway from the shallow hydrothermal system to the surface due to the phreatic eruption. We reconfirmed that SAR data are a robust tool for detecting coeruptive and posteruptive deformations, which are helpful for understanding shallow physical processes associated with phreatic eruptions at active volcanoes.

The polarimetric synthetic aperture radar (PolSAR) provides us with a two-by-two scattering matrix data set. The ensemble averaged coherency matrix in an imaging window derived using a scattering matrix has all non-zero elements in its three-by-three matrix. It is a full 3 × 3 matrix that bears nine real-valued and independent polarimetric parameters inside. In the proposed decomposition method, G5U, we preprocess observed coherency matrix [T] by using two consecutive unitary transformations to become an ideal form for five-component decomposition. The transformation reduces nine parameters to seven, which is the best fit for five-component scattering model expansion. We can retrieve five powers corresponding to surface scattering, double bounce scattering, volume scattering, oriented dipole scattering, and compound dipole scattering, directly. These powers can be calculated easily and used to display superb polarimetric RBG images as never before, and are further applicable for polarimetric calibration, classification, validation, etc.

When a large-scale flood disaster occurs, it is important to identify the flood areas in a short time in order to effectively support the affected areas afterwards. Synthetic Aperture Radar (SAR) is promising for flood detection. A number of change detection methods have been proposed to detect flooded areas with pre- and post-event SAR data. However, it remains difficult to detect flooded areas in built-up areas due to the complicated scattering of microwaves. To solve this issue, in this paper we propose the idea of analyzing the local changes in pre- and post-event SAR data as well as the larger-scale changes, which may improve accuracy for detecting floods in built-up areas. Therefore, we aimed at evaluating the effectiveness of multi-scale SAR analysis for flood detection in built-up areas using ALOS-2/PALSAR-2 data. First, several features were determined by calculating standard deviation images, difference images, and correlation coefficient images with several sizes of kernels. Then, segmentation on both small and large scales was applied to the correlation coefficient image and calculated explanatory variables with the features at each segment. Finally, machine learning models were tested for their flood detection performance in built-up areas by comparing a small-scale approach and multi-scale approach. Ten-fold cross-validation was used to validate the model, showing that highest accuracy was offered by the AdaBoost model, which improved the F1 Score from 0.89 in the small-scale analysis to 0.98 in the multi-scale analysis. The main contribution of this manuscript is that, from our results, it can be inferred that multi-scale analysis shows better performance in the quantitative detection of floods in built-up areas.

From June 29 to July 1, 2015, a phreatic eruption occurred in Owakudani, the largest fumarole area in Hakone volcano, Japan. In this study, an interferometric synthetic aperture radar (InSAR) time series analysis of the Advanced Land Observing Satellite‐2 (ALOS‐2)/Phased Array type L‐band Synthetic Aperture Radar‐2 (PALSAR‐2) data was performed to measure deformation after the eruption. The results show that the central cones of the volcano have subsided since the eruption and its deflation source is located beneath the previously estimated bell‐shaped conductor, which is considered as a sealing layer confining a pressurized hydrothermal reservoir. Therefore, the InSAR results demonstrate the deflation of the hydrothermal system beneath the volcano. One possible cause of this deflation is compaction due to a decrease in pore pressure caused by rupture and fluid migration during and after the eruption. Plain Language Summary: From June 29 to July 1, 2015, an eruption occurred in Owakudani, the largest steaming area in Hakone volcano, Japan. Our analysis using satellite radar demonstrates that the central part of Hakone volcano has subsided since the eruption and that the deflation source is located in the reservoir of hot water beneath the volcano. One possible cause of this deflation is compaction due to a pressure drop produced by rupture and fluid migration during and after the eruption. Key Points: Posteruptive deflation beneath the central cones of Hakone volcano was detected by radar interferometry after the 2015 phreatic eruptionOur model inversion suggests that deflation of a hydrothermal system confined by a sealing layer beneath the volcano has been taking placeThe hydrothermal system deflation is likely attributable to rupture of the sealing layer and system depressurization due to the eruption [ABSTRACT FROM AUTHOR]

Thermokarst is a typical process that indicates widespread permafrost degradation in yedoma landscapes. The Lena-Aldan interfluvial area in Central Yakutia in eastern Siberia is now facing extensive landscape changes with surface subsidence due to thermokarst development during the past few decades. To clarify the spatial extent and rate of subsidence, multiple spatial datasets, including GIS and remote sensing observations, were used to analyze the Churapcha rural locality, which has a typical yedoma landscape in Central Yakutia. Land cover classification maps for 1945 and 2009 provide basic information on anthropogenic disturbance to the natural landscape of boreal forest and dry grassland. Interferometric synthetic aperture radar (InSAR) with ALOS-2/PALSAR-2 data revealed activated surface subsidence of 2 cm/yr in the disturbed area, comprising mainly abandoned agricultural fields. Remote sensing with an unmanned aerial system also provided high-resolution information on polygonal relief formed by thermokarst development at a disused airfield where InSAR analysis exhibited extensive subsidence. It is worth noting that some historically deforested areas have likely recovered to the original landscape without further thermokarst development. Spatial information on historical land-use change is helpful because most areas with thermokarst development correspond to locations where land was used by humans in the past. Going forward, the integrated analysis of geospatial information will be essential for assessing permafrost degradation.

Abstract Although the 2015 Hakone Volcano eruption was a small-scale phreatic eruption with a discharged mass of only about 100 tons, interferometric synthetic aperture radar successfully detected surface deformations related to the eruption. Inversion model of the underground hydrothermal system based on measured ground displacements by ALOS-2/PALSAR-2 images showed that a crack opened at an elevation of about 530–830 m, probably at the time of the eruption. A geomorphological analysis detected several old NW–SE trending fissures, and the open crack was located just beneath one of the fissures. Thus, the crack that opened during the 2015 eruption could have been a preexisting crack that formed during a more voluminous hydrothermal eruption. In addition, the inversion model implies that a sill deflation occurred at an elevation of about 225 m, probably at the time of the eruption. The deflation of sill-like body represents a preexisting hydrothermal reservoir at an elevation of 100–400 m, which intruded fluid in the open crack prior to eruption. The volume changes of the open crack and the sill were calculated to be 1.14 × 105 m3 (inflation) and 0.49 × 105 m3 (deflation), respectively. A very local swelling (about 200 m in diameter) was also detected at the eruption center 2 months before the eruption. The local swelling, whose rate in satellite line-of-sight was 0.7–0.9 cm/day during May 2015 and declined in June, had been monitored until the time of the eruption, when its uplift halted. This was modeled as a point pressure source at an elevation of about 900 m (at a depth of about 80–90 m from the ground surface) and is considered to be a minor hydrothermal reservoir just beneath the fumarolic field. Our analysis shows that the northernmost tip of the open crack reached within 200 m of the surface. Thus, it is reasonable to assume that the hydrothermal fluid in the open crack found a way to the surface and formed the eruption.Graphical abstractXXXX

In this article, ALOS-2/PALSAR-2 dual-polarized [horizontal-transmit–horizontal-receive and horizontal-transmit–vertical-receive (HH/HV)] ScanSAR mode L-band synthetic aperture radar (SAR) imagery over an Arctic study area was evaluated for their suitability for operational sea ice (SI) monitoring. The L-band SAR data are studied for the estimation of different SI parameters: SI concentration, SI thickness, SI type, and SI drift. Also, some comparisons with nearly coincident C-band data over the same study area have been made. The results indicate that the L-band SAR data from ALOS-2/PALSAR-2 are very useful for estimating the studied SI parameters and equally good or better than using the conventional operational dual-polarized C-band SAR satellite data. [ABSTRACT FROM AUTHOR]

I have been conducting a study of postseismic deformation following the 2016 Kumamoto earthquake using ALOS-2/PALSAR-2 acquired till 2018. I apply ionospheric correction to interferograms of ALOS-2/PALSAR-2. L-band SAR gives us high coherence enough to reveal surface deformation even in vegetated or mountainous area for pairs of images acquired more than 2 years. Postseismic deformation following the Kumamoto earthquake exceeds 10 cm during 2 years at some spots in and around Kumamoto city and Aso caldera. Westward motion of ~ 6 cm/year was dominant on the southeast side of the Hinagu fault, while westward shift was detected on both sides of the Futagawa fault. The area of latter deformation seems to have correlation with distribution of pyroclastic flow deposits. Significant uplift was found around the eastern Futagawa fault and on the southwestern frank of Aso caldera, whose rate reaches 4 cm/year. There are sharp changes across several coseismic surface ruptures such as Futagawa, Hinagu, and Idenokuchi faults. Rapid subsidence between Futagawa and Idenokuchi faults also found. It is confirmed that local subsidence continued along the Suizenji fault, which newly appeared during the mainshock in Kumamoto City. Subsidence with westward shift of up to 4 cm/year was also found in Aso caldera. Time constant of postseismic decay ranges from 1 month to 600 days at selected points, but that postseismic deformation during the first epochs or two is dominant at point in the Kumamoto Plain. This result suggests multiple source of deformation. Westward motion around the Hinagu fault may be explained with right lateral afterslip on the shallow part of this fault. Subsidence along the Suizenji fault can be attributed to normal faulting on dipping westward. Deformation around the Hinagu and Idenokuchi faults cannot be explained with right lateral afterslip of Futagawa fault, which requires other sources. Deformation in northern part of Aso caldera might be the result of right lateral afterslip on a possible buried fault. [ABSTRACT FROM AUTHOR]

Coeruptive deformation helps to interpret physical processes associated with volcanic eruptions. Because phreatic eruptions cause small, localized coeruptive deformation, we sometimes fail to identify plausible deformation signals. Satellite synthetic aperture radar (SAR) data allow us to identify extensive deformation fields with high spatial resolutions. Herein, we report coeruptive crustal deformation associated with the 2018 Kusatsu-Shirane phreatic eruption detected by time series analyses of L-band satellite SAR (ALOS-2/PALSAR-2) data. Cumulative deformation maps derived from SAR time series analyses show that subsidence and eastward displacement dominate the southwestern side of an eruptive crater with a spatial extent of approximately 2 km in diameter. Although we were unable to identify any significant deformation signals before the 2018 eruption, posteruptive deformation on the southwestern side of the crater has been ongoing until the end of 2019. This prolonged deformation implies the progression of posteruptive physical processes within a confined hydrothermal system, such as volcanic fluid discharge, similar to the processes observed during the 2014 Ontake eruption. Although accumulated snow and dense vegetation hinder the detection of deformation signals on Kusatsu-Shirane volcano using conventional InSAR data, L-band SAR with various temporal baselines allowed us to successfully extract both coeruptive and posteruptive deformation signals. The extracted cumulative deformation is well explained by a combination of normal faulting with a left-lateral slip component along a southwest-dipping fault plane and an isotropic deflation. Based on the geological background in which the shallow hydrothermal system develops across Kusatsu-Shirane volcano, the inferred dislocation plane can be considered as a degassing pathway from the shallow hydrothermal system to the surface due to the phreatic eruption. We reconfirmed that SAR data are a robust tool for detecting coeruptive and posteruptive deformations, which are helpful for understanding shallow physical processes associated with phreatic eruptions at active volcanoes. [ABSTRACT FROM AUTHOR]

In this study, the micro-topographical classification on the basis of extraction of geomorphological feature information such as sand dune, terrace and former river bed was carried out through the integrated analysis of LANDSAT-8/OLI mosaic data, ALOS-2/PALSAR-2 data, and the existing DSM in the delta area along the Ili River of Balkhash Lake. The study result showed the possibilities of the classification with six categories including the oldest delta in the object area and its vicinity through the analysis of the sand dune distribution by filtering operation for DSM, the PALSAR-2 ScanSAR intensity data, and the vegetation covering and high soil moisture area distribution by NDVI and NDWI derived from OLI data. [ABSTRACT FROM AUTHOR]

Abstract Detailed wave-like spatial patterns of atmospheric propagation delay signals associated with mountain lee waves were detected in Hokkaido and Tohoku by synthetic aperture radar (SAR) interferometry (InSAR) with the ScanSAR mode observation data of a Phased Array-type L-band Synthetic Aperture Radar 2 on board the Advanced Land Observing Satellite 2. Both cases occurred under stable atmosphere conditions. The InSAR-observed peak-to-trough line of sight changes in the mountain wave signals was 4 and 5 cm with the horizontal wavelengths of 9 and 15 km in Hokkaido and Tohoku, respectively. Locations of positive phase maxima in the mountain wave signals coincides with locations of cloud streets observed by visible satellite imagery, indicating that crests of mountain waves contain relatively much water vapor compared with wave troughs. Numerical weather simulations with the horizontal grid spacing of 1 km were performed to reproduce InSAR phase variations, and as a result those simulations could reasonably reproduce observed wave amplitudes and wavelengths in both cases. On the other hand, numerical simulations tended to overestimate wave attenuation rates: simulated mountain waves decreased as the wave propagated faster than those of observed signals. Because the simulated wave attenuation rate is sensitive to physics in the planetary boundary layer (PBL), we investigated the reproducibility of five PBL schemes implemented in the WRF model. As a result, all the PBL schemes showed little attenuation except for the Yonsei University scheme (YSU), while the wavelength in the YSU was most close to the observation. Our study demonstrated the uniqueness and usefulness of InSAR for meteorological application as the ability to map the detailed water vapor distribution regardless of cloud cover. In addition, the reasonable reproducibility of the water vapor delay signal due to lee waves by the numerical weather model encourages researchers who tackle the correction of the tropospheric propagation delay, increasing the accuracy in detecting surface deformations. Graphical abstract .

Applications of fully polarimetric synthetic aperture radar (POLSAR) have increased in the past few decades. The potential of model-based decompositions is coupled with polarimetric information extraction from the POLSAR data for target identification and classification. The coherency matrix $[T]$ with nine independent parameters, and associated with some physical scattering models, serves as input to these decompositions. This paper attempts to assign one such physical scattering model to the real part of $T_{23}$ ($\text {Re}\{{T}_{23}\}$) and develop a new scattering power decomposition model, called as the seven-component scattering decomposition (7SD). Previously developed scattering power models have eliminated $\text {Re}\{T_{23}\}$ , assuming the orientation angle compensation condition, to reduce the number of independent $[T]$ parameters. The proposed 7SD model has been tested on fully polarimetric SAR data sets acquired by the spaceborne Advanced Land Observing Satellite-2/Phased Array type L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2) and airborne F-SAR, and the results are compared with the existing scattering power decompositions. The physical scattering model for $\text {Re}\{T_{23}\}$ is derived from a particular configuration of dipoles (referred to as “mixed dipole” configuration), which gives rise to compound scattering. The mixed-dipole scattering occurs in urban areas that are highly oriented to the radar illumination direction as well as in vegetation areas. 7SD also delivers an additional mixed dipole scattering power compared to the previous six-component scattering model. The mixed-dipole scattering model reduces the contribution of volume scattering power in double-bounce predominant areas (such as oriented urban blocks), thereby imparting improved understanding of the polarimetric information contained in the coherency matrix. [ABSTRACT FROM AUTHOR]

Compact polarimetry (CP) offers a tradeoff with fully polarimetric modes in terms of swath width, power budget, and polarimetric information content. In this letter, a classification comparison is made among real CP, simulated CP (SCP), and quad polarimetric (QP) data acquired from the L-band SAR system onboard the ALOS-2 satellite. The Wishart supervised classification scheme is used to compare data modes over two regions of a mixed test site in India. The quantitative classification assessment indicates that the QP data have higher classification accuracy than any other polarimetric combinations for both regions. The comparative classification accuracy of real versus SCP data is different for the two regions. The overall accuracy of the real CP data is slightly higher ~1% than SCP for region 1, which is dominated by urban and rice classes, whereas it is lower by ~9% for the agricultural crop dominated region 2. [ABSTRACT FROM AUTHOR]

We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborne ALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-day baseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low (≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e.,

Seasonal changes control the development of salt crust over the Salar de Aguas Calientes Sur located in Andes Highlands, Chile. Precipitations throughout the Altiplanic winter (December to March) and austral winter (June to September) caused ponds to enlarge and surface salt crusts to dissolve driving roughness and dielectric features of the salar surface change over time. A four-year time series backscattering coefficient analysis, obtained by Sentinel 1 and ALOS-2/PALSAR-2 with 10 m of spatial resolution, demonstrated the capability of microwaves to discriminate seasonal patterns illustrated in this paper. Both sensors showed to be sensitive to changes in the surface crust due to weather conditions. Backscattered power gradually increased during the driest months as the rough salt crusts develop and decreased rapidly due to precipitations or flooding events, which lead to a smoothing appearance to radar. The high temporal frequency of acquisition in Sentinel 1 (5−13 scenes/month) allowed the discrimination among climate and annual seasonality and episodic events in the C-band backscatter coefficient. On the other hand, ALOS-2/PALSAR-2 showed subsurface changes at L-band since the salinity of the brine in the soil reduces the penetration depth of backscattered power for shorter wavelengths. Results might be useful to monitor salars with geographic and weather conditions similar to Salar de Aguas Calientes Sur.

Detailed wave-like spatial patterns of atmospheric propagation delay signals associated with mountain lee waves were detected in Hokkaido and Tohoku by synthetic aperture radar (SAR) interferometry (InSAR) with the ScanSAR mode observation data of a Phased Array-type L-band Synthetic Aperture Radar 2 on board the Advanced Land Observing Satellite 2. Both cases occurred under stable atmosphere conditions. The InSAR-observed peak-to-trough line of sight changes in the mountain wave signals was 4 and 5 cm with the horizontal wavelengths of 9 and 15 km in Hokkaido and Tohoku, respectively. Locations of positive phase maxima in the mountain wave signals coincides with locations of cloud streets observed by visible satellite imagery, indicating that crests of mountain waves contain relatively much water vapor compared with wave troughs. Numerical weather simulations with the horizontal grid spacing of 1 km were performed to reproduce InSAR phase variations, and as a result those simulations could reasonably reproduce observed wave amplitudes and wavelengths in both cases. On the other hand, numerical simulations tended to overestimate wave attenuation rates: simulated mountain waves decreased as the wave propagated faster than those of observed signals. Because the simulated wave attenuation rate is sensitive to physics in the planetary boundary layer (PBL), we investigated the reproducibility of five PBL schemes implemented in the WRF model. As a result, all the PBL schemes showed little attenuation except for the Yonsei University scheme (YSU), while the wavelength in the YSU was most close to the observation. Our study demonstrated the uniqueness and usefulness of InSAR for meteorological application as the ability to map the detailed water vapor distribution regardless of cloud cover. In addition, the reasonable reproducibility of the water vapor delay signal due to lee waves by the numerical weather model encourages researchers who tackle the correction of the tropospheric propagation delay, increasing the accuracy in detecting surface deformations. [ABSTRACT FROM AUTHOR]

This paper presents that temporal variations in the flow velocity for Shirase Glacier in Antarctica were studied using synthetic aperture radar (SAR) images obtained by the Japanese Earth Resources Satellite-1 (JERS-1) in 1996-1998, the Advanced Land Observing Satellite (ALOS) in 2007-2010 and the ALOS-2 in 2014-2015. The scenes were analyzed using image correlation. The obtained ice-flow velocity increases rapidly from the upstream region to the coast, but its velocity is roughly constant over a region, 10 km long about the grounding line (GL), then gradually tends to increase again downstream from the GL. This trend has continued largely unchanged over the 20 years since 1996, judging from the flow velocity profiles obtained from JERS-l/SAR, ALOS/PALSAR and ALOS-2/PALSAR-2 observations. The flow velocity on the central streamline near the GL is 2.29 ± 0.02~0.03 km a-1 (mean of 1996-2015). Between the GL and the point 30 km downstream, ice velocities tended to be lower in 2007-2010 or 2014-2015 than in 1996-1998. Upstream from the GL, the velocities were higher in 2007-2010 and 2014-2015 than in 1996-1998, increasingly so with distance upstream, reaching a maximum of approximately 0.57 km a-1 (differences of mean velocity between 2007 and 2010) and 0.72 km a-1 (differences of mean velocity between 2014 and 2015) at around 18 km upstream from the GL before decreasing again to a respective value of ~0.25 and ~0.35 km a-1 at 30 km upstream. [ABSTRACT FROM AUTHOR]

In paddy soils in the Mekong Delta, soil archaea emit substantial amounts of methane. Reproducing ground flux data using only satellite-observable explanatory variables is a highly transparent method for evaluating regional emissions. We hypothesized that PALSAR-2 (Phased Array type L-band Synthetic Aperture RADAR) can distinguish inundated soil from noninundated soil even if the soil is covered by rice plants. Then, we verified the reproducibility of the ground flux data with satellite-observable variables (soil inundation and cropping calendar) and with hierarchical Bayesian models. Furthermore, inundated/noninundated soils were classified with PALSAR-2. The model parameters were successfully converged using the Hamiltonian–Monte Carlo method. The cross-validation of PALSAR-2 land surface water coverage (LSWC) with several inundation indices of MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-2 (Advanced Microwave Scanning Radiometer-2) data showed that (1) high PALSAR-2-LSWC values were detected even when MODIS and AMSR-2 inundation index values (MODIS-NDWI and AMSR-2-NDFI) were low and (2) low values of PALSAR-2-LSWC tended to be less frequently detected as the MODIS-NDWI and AMSR-2-NDFI increased. These findings indicate the potential of PALSAR-2 to detect inundated soils covered by rice plants even when MODIS and AMSR-2 cannot, and show the similarity between PALSAR-2-LSWC and the other two indices for nonvegetated areas.

Series of earthquakes including three M > 6 earthquakes occurred in Kumamoto prefecture in the middle of the Kyushu island, Japan. In order to reveal the associated crustal deformation signals, we applied offset tracking technique to ALOS-2/PALSAR-2 data covering three M > 6 earthquakes and derived the 3D displacements around the epicenters. We could identify three NE-SW trending displacement discontinuities in the 3D displacements that were consistent with the surface location of Futagawa and Hinagu fault system. We set three-segment fault model whose positions matched the displacement discontinuities, and estimated the slip distributions on each segment from the observed pixel-offset data. Whereas right-lateral slip was dominant in the shallower depth of the larger segments, normal fault slip was more significant at a greater depth of the other segment. The inferred configuration and slip distribution of each segment suggest that slip partitioning under oblique extension stress regime took place during the 2016 Kumamoto earthquake sequence. Moreover, given the consistent focal mechanisms derived from both the slip distribution model and seismology, the significant non-double couple components in the focal mechanism of the main shock are due to simultaneous ruptures of both strike-slip and normal faulting at the distinct segments. [ABSTRACT FROM AUTHOR]

We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission's applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborne ALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR's 12-day baseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low (≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e., <90 Mg/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is <60% (or <100 Mg/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg/ha to 47.4 Mg/h with UAVSAR and from 57.1 Mg/ha to 46 Mg/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions. [ABSTRACT FROM AUTHOR]

Seasonal changes control the development of salt crust over the Salar de Aguas Calientes Sur located in Andes Highlands, Chile. Precipitations throughout the Altiplanic winter (December to March) and austral winter (June to September) caused ponds to enlarge and surface salt crusts to dissolve driving roughness and dielectric features of the salar surface change over time. A four-year time series backscattering coefficient analysis, obtained by Sentinel 1 and ALOS-2/PALSAR-2 with 10 m of spatial resolution, demonstrated the capability of microwaves to discriminate seasonal patterns illustrated in this paper. Both sensors showed to be sensitive to changes in the surface crust due to weather conditions. Backscattered power gradually increased during the driest months as the rough salt crusts develop and decreased rapidly due to precipitations or flooding events, which lead to a smoothing appearance to radar. The high temporal frequency of acquisition in Sentinel 1 (5–13 scenes/month) allowed the discrimination among climate and annual seasonality and episodic events in the C-band backscatter coefficient. On the other hand, ALOS-2/PALSAR-2 showed subsurface changes at L-band since the salinity of the brine in the soil reduces the penetration depth of backscattered power for shorter wavelengths. Results might be useful to monitor salars with geographic and weather conditions similar to Salar de Aguas Calientes Sur. [ABSTRACT FROM AUTHOR]

• L-band provided slightly less accurate SSM estimates than the C-band. • For NDVI < 0.7, the RMSE on SSM estimation was 4.6 and 5.3 vol.% for C- and L-bands. • L-band enabled SSM estimation under a well-developed vegetation cover (NDVI > 0.7). • C-band became completely attenuated for some crops when the NDVI value was > 0.7. • Error on SSM estimation for L and C bands strongly depends on Hrms and SSM values. Surface soil moisture (SSM) estimation is of great importance in several areas, such as hydrology, agriculture and risk assessment. C-band SAR (synthetic aperture radar) data have been widely used to estimate SSM, whereas few studies have been performed using L-band SAR due to the low availability of L-band SAR data. In this context, the objective of the present paper is to compare the SSM estimation potentials of the C- (Sentinel-1) and L-bands (PALSAR) for wheat and grassland plots. The inversion approach developed in this study uses neural networks to invert the SAR signal and estimate the SSM. For each radar frequency, the developed neural networks were trained using the following as an input vector: SAR incidence angle, SAR polarization (VV for the C-band and HH for the L-band), and NDVI from optical images. Artificial Neural networks (ANNs) were developed and validated using synthetic and real databases. The results showed that the L-band provided slightly less accurate SSM estimates than the C-band. Moreover, the results showed that the accuracies of the SSM estimates for both frequencies strongly depended on the soil roughness (Hrms) and SSM values. From the synthetic database at SSM values less than 25 vol.%, the ANNs underestimated the SSM for Hrms values less than 1.5 cm and overestimated the SSM for Hrms values greater than 1.5 cm. In addition, the ANNs underestimated the SSM value regardless of the Hrms value when the SSM value was greater than 25 vol.%. An RMSE analysis of the SSM estimates showed that the highest RMSE values were observed for the L-band regardless of the SSM value, and high RMSE values were observed for the C-band only in very wet soil conditions (SSM>25 vol.%). From the real database at NDVI values less than 0.7, the RMSE (root mean square error) of the SSM estimates was 4.6 vol.% for the C-band and 5.3 vol.% for the L-band. Most importantly, the L-band enabled the estimation of the SSM under a well-developed vegetation cover (NDVI > 0.7) with an RMSE of 6.7 vol.%, whereas the C-band SAR signal became completely attenuated for some crops when the NDVI value was greater than 0.7, and thus the estimation of SSM was impossible using the C-band. [ABSTRACT FROM AUTHOR]

In paddy soils in the Mekong Delta, soil archaea emit substantial amounts of methane. Reproducing ground flux data using only satellite-observable explanatory variables is a highly transparent method for evaluating regional emissions. We hypothesized that PALSAR-2 (Phased Array type L-band Synthetic Aperture RADAR) can distinguish inundated soil from noninundated soil even if the soil is covered by rice plants. Then, we verified the reproducibility of the ground flux data with satellite-observable variables (soil inundation and cropping calendar) and with hierarchical Bayesian models. Furthermore, inundated/noninundated soils were classified with PALSAR-2. The model parameters were successfully converged using the Hamiltonian–Monte Carlo method. The cross-validation of PALSAR-2 land surface water coverage (LSWC) with several inundation indices of MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-2 (Advanced Microwave Scanning Radiometer-2) data showed that (1) high PALSAR-2-LSWC values were detected even when MODIS and AMSR-2 inundation index values (MODIS-NDWI and AMSR-2-NDFI) were low and (2) low values of PALSAR-2-LSWC tended to be less frequently detected as the MODIS-NDWI and AMSR-2-NDFI increased. These findings indicate the potential of PALSAR-2 to detect inundated soils covered by rice plants even when MODIS and AMSR-2 cannot, and show the similarity between PALSAR-2-LSWC and the other two indices for nonvegetated areas. [ABSTRACT FROM AUTHOR]