This study investigates the potential of a statistical-based, adaptive approach to unwrapping sequences of differential synthetic aperture radar (SAR) interferograms that cover a large swath of the terrain. The proposed method adopts a multiscale decomposition strategy to identify efficiently and then process sets of coherent points at different spatial scales. The coherent point selection process is performed considering the statistical properties of the stack of wrapped multilooked SAR interferograms generated at various scales. Overall, the adopted procedure allows automatically recognizing the areas in large swath interferograms where significant and reliable phase changes occur while moving from neighboring spatial scales. Over these regions, multiscale phase unwrapping (PhU) operations are performed efficiently, with a computational improvement and without losing significant information. To this aim, the implementation of a conditioned space-time PhU scheme that operates sequentially at different spatial grids is detailed. Then, the unwrapped interferograms are inverted to generate ground displacement time series through advanced multitemporal interferometric SAR (MT-InSAR) approaches, recovering information at different scales (from local to regional/continental). Experimental results have been obtained by applying the developed scheme to large-swath SAR datasets collected at the C band by Sentinel-1 sensors. The results demonstrate the feasibility and soundness of the developed multiscale PhU method.

Coastal wetlands play a crucial role in supporting diverse ecosystems and providing numerous ecosystem services. The monitoring of wetland hydrodynamics is essential for understanding and assessing their vulnerability to environmental stressors. In recent years, InSAR (Interferometric Synthetic Aperture Radar) time series analysis has emerged as a valuable tool for studying wetland hydrodynamics. However, accurate wetland water level change monitoring is occasionally hindered by the presence of high amounts of atmospheric water vapor over coastal areas, which mislead the interpretation of InSAR retrievals.In this paper, we present a methodological approach based on Independent Component Analysis (ICA) combined with image segmentation as a blind source separation technique to discriminate between Water Level Change (WLC) related features and wet tropospheric delay features here referred to as 'cloud-induced features' in a UAVSAR WLC time series. Our findings provide a specific methodological case study towards addressing the challenges associated with wet tropospheric delay in Airborne InSAR, and a potential alternative solution for improved and more accurate water level change monitoring in coastal wetlands.

AirSWOT is an airborne Ka-band synthetic aperture radar, capable of mapping water surface elevation (WSE) and water surface slope (WSS) using single-pass interferometry. AirSWOT participated in the NASA EVS-3 Delta-X campaign in 2021, which combined remote sensing from multiple instruments with an extensive coincident field data collection in the Mississippi River Delta, Louisiana, USA. As part of Delta-X, AirSWOT flew a greater number of flight lines than in previous AirSWOT campaigns, collecting a significant volume of data which can provide insight into the dynamics and quantity of water in the Atchafalaya and Terrebonne basins of the Mississippi River Delta. AirSWOT data has been processed into publicly available data products at a number of processing levels, depending on user needs and application, including a new Level-3 water surface product developed specifically for Delta-X. The Level-3 water surface product uses water masking and spatial averaging to produce a science-ready point data product, using the Level-2 GeoTIFF raster products as input. The Level-3 data allows profiles of WSE and WSS within designated channels to be easily calculated. AirSWOT estimates of WSE from Delta-X have been compared to in situ water level data with root mean square error (RMSE) of 9 cm, excluding data from two flights in September, 2021 which were adversely affected by poor weather conditions that affected the instrument hardware. Including all data, the RMSE increases to 12 cm. We have also used AirSWOT to help estimate the vertical datum for water level gauges without accurate vertical reference information. AirSWOT is capable of mapping WSE and WSS at high resolution in spatially complex coastal environments, making it a valuable instrument for studying these regions.

NISAR will provide data that addresses many topics within the ecosystems, solid Earth, and cryosphere science disciplines because of the mission’s near-global land coverage; PolSAR and InSAR suitability; fixed, continual, and regular acquisition strategy; and free and open data policy. The science community is well prepared to use the data, but that is not the case for all applications communities. Nevertheless, the information that can be derived from SAR is needed by many agencies to meet their mission mandates through improved or additional knowledge of ground conditions, providing data for models, and supporting disaster response and recovery. To facilitate extensive usage of NISAR’s data beyond the scientific community, the NISAR project and science team members have actively engaged the applications communities since the mission was in formulation. Here the activities and some of the outcomes are described.

Use of Synthetic Aperture Radar (SAR) data has been steadily increasing over the past decade as data from sensors become freely available. In this article, our focus is on the NASA-ISRO SAR (NISAR), the Observational Products for End-Users from Remote Sensing Analysis (OPERA), and Surface Deformation and Change (SDC), and how their user engagement activities combine to build a cohesive user engagement effort for SAR remote sensing. All of NASA remote sensing data is openly and freely available through its Distributed Active Archive Centers (DAAC), and SAR data is no exception. Data going back to SEASAT in 1978 can be accessed through the Alaska Satellite Facility, NASA’s SAR DAAC. The NISAR mission is expected to significantly increase the volume of SAR data available to the end users after its launch in early 2024. OPERA project is already underway preparing high-level data products suited for end-users and will be leveraging NISAR data when it becomes available. Finally, NASA is already conducting the SDC mission study to define a mission architecture for the next decade.

We compare in-situ water volume measurements of mineral oil emulsion sampled from an oil slick in Santa Barbara, California, to acquisitions of airborne UAVSAR data acquired in June 2022. Estimating the water-to-oil fraction using the UAVSAR imagery, we find that low SNR in the co- and cross-polarimetric channels limits this capability above a certain oil-to-water volumetric threshold. Higher SNR regions of the slick had water volume fractions below 20%, while lower SNR regions had water volume fractions above 20%. Calculated damping ratio values align with the noise analysis, indicating that a lower SNR corresponds to higher damping values, while a higher SNR corresponds to lower damping ratio values. For the high SNR case, water fractions calculated using the co-polarimetric ratio (VV/HH) and a theoretical backscattering model were slightly underestimated when compared with in-situ measurements. This observation could be due to potential sampling bias during the collection of in-situ samples, favoring thicker oil with a higher water cut.

Hydrodynamic models of the exchange of water between the ocean, rivers, and wetlands require high resolution maps of the water channel network and information on areas with overland flow. Furthermore, understanding the processes benefits from maps showing how the exchange changes during a tidal cycle, i.e., on an hourly-to-daily timescale, and with river discharge, which varies seasonally. Conventional water masks are not updated regularly and they do not always include seasonally active narrow channels in the inland areas which are required for the modelling. In this study, we use high resolution (~6m) airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images to map water channels and areas with discernible water level increase using data acquired for the Delta-X mission. The SAR intensity difference in multiple polarization channels and interferometric coherence are used to differentiate water channels from land areas, and the water channel masks produced are able to identify most channels with width >20m. We also developed a method combining interferometric amplitude and phase to identify areas with significant water depth change along the edges of small channels. Geocoded channel masks were produced for UAVSAR tracks covering two deltas (Wax Lake and Terrebonne) in coastal Louisiana for both low tide and high tide acquisitions. The methods developed can be extended to other deltas worldwide.

In this study, we model electromagnetic scattering from a realistic ocean surface to assess through simulation the effect of varying key slick properties on backscatter at microwave frequencies of L-, C-, and X-band for both thin and emulsified mineral oil. An ocean surface model is implemented by generating randomly rough ocean surface instances from ocean wave spectra corresponding to a variety of slick properties and different wind speeds. The finite difference time domain method (FDTD), based on Maxwell’s equations, is used to calculate the normalized radar cross section (NRCS) from the ocean surfaces, which we validate with radar observations. Results show that the effect on the NRCS does not scale linearly with the spectral damping caused by the oil layer. By changing various layer properties, we determine that the surface elasticity and oil kinematic viscosity most strongly impact the NRCS. The model is run with different oil layer thicknesses to evaluate the capability of SAR to determine absolute or relative slick thickness. We find that the thickness cannot be accurately determined from SAR backscatter alone in the absence of information about the key slick properties or calibration against known thicknesses in the given environmental conditions. The simulations indicate that ocean wave spectral components outside the expected Bragg scattering regime contribute significantly to the backscatter in some cases. Furthermore, the presence of an emulsion layer under certain conditions and for certain radar frequencies creates constructive interference that causes the NRCS to be enhanced rather than reduced when the layer thickness increases.

This paper described the ongoing effort and the scientific benefits of close coordination between more than a dozen ongoing and planned spaceborne SAR missions. Specific Illustrative examples of the scientific and applications benefits are included and described.

The damping ratio is a calculated feature that measures the contrast between oil-slicked water and the open ocean in SAR data. To implement the damping ratio, the current literature suggests estimating the open water backscatter by taking strips of undefined width across the range direction, obtaining the damping ratio as a function of incidence angle. We show in this paper that the method proposed in the literature can be improved by instead sampling open water pixels randomly. The method is tested on RADARSAT-2 quad-polarimetric SAR imagery of a verified oil slick acquired during the 2013 NOFO oil-on-water exercise conducted in the North Sea. The results suggest that deviations in the derived damping ratio encountered by implementing the method proposed in the literature can be reduced from of order 100– 10−1to 10−3.

Here, we present an enhanced algorithm to correct interferometric synthetic aperture radar (InSAR) phase unwrapping errors by incorporating iterative spatial bridging between islands and phase closure among interferograms. We use rapid repeat airborne synthetic aperture radar acquisitions from NASA’s airborne uninhabited aerial vehicle synthetic aperture radar (UAVSAR) instrument to estimate short-term changes in water level within coastal wetlands from a stack of consecutive interferograms acquired with very short temporal separation (~30 min). The algorithm is applied to six consecutive UAVSAR images collected in tidal wetlands of the Wax Lake Delta, Louisiana, USA. Validation of our water level change retrievals within situfield observations was conclusive with high correlation and an RMSE generally smaller than 3 cm. Comparison of our algorithm with other phase unwrapping error correction methods shows significant improvement (30%–35% increase in the number of correctly unwrapped pixels) when applied to rapid changes in water level. The set of corrections presented in this work enables measurement of water level change in deltas and other areas where tides drive highly dynamic flooding of inland vegetated areas. Although demonstrated for water level change, the method is applicable to other InSAR datasets with large spatial gradients or observed discontinuities between coherent but spatially isolated areas.

In the above article[1], Table I(b) cited an incorrect reference number. Reference [12] should have been given as [13], provided here as[2].

The NISAR mission will provide data covering nearly all the Earth's land areas and sea ice designed specifically to meet requirements of the NISAR science disciplines. However, for each science use case, the same data could potentially be used by one or more applications, often with little or no change in the core observables. Here we describe NISAR's capabilities for supporting a wide range of applications and the activities undertaken to inform, engage, and train the community that will increase the mission's societal impact.

The U.S. National Aeronautics and Space Administration (NASA) and Indian Space Research Organisation (ISRO) are working jointly to launch a major new Earth observing mission in the 2022–2023 timeframe. The NASA-ISRO Synthetic Aperture Radar (NISAR) mission will carry an L-band synthetic aperture radar (SAR) instrument provided by NASA and an S-band SAR instrument provided by ISRO. Both instruments are designed for surface deformation and change measurements that support natural hazard science and applications. Here we describe the instruments, mission operations, and science and applications requirements, and relate how the NISAR mission will benefit natural hazard monitoring.

In this paper we provide a brief review of the Earth observation requirements for a number key science applications for which spaceborne Synthetic Aperture Radar sensors can contribute with critical measurements. We outline the current state of the science and identify information gaps associated with each application, and subsequently, provide recommendations on how these gaps can be mitigated in the 2020's time-frame by coordination of current and already planned missions, and for the next decade, with a vision for a comprehensive constellation system that would address the outstanding scientific requirements.

The joint NASA/ISRO SAR (NISAR) satellite mission is anticipated to provide routine L-band coverage of most of the Earth's land surface every 12-days for both ascending and descending orbits. In terms of impact on solid earth science (SES), the primary measurement will be Interferometric SAR (InSAR) observations of ground deformation in two satellite line-of-sight (LOS) directions. Key observation characteristics include acquisitions with small interferometric baselines to maximize interferometric coherence and decrease sensitivity to topography, wide bandwidth allowing for split-band processing to model out the impacts of the ionosphere, and joint L- and S-band observations in selected regions. We describe here the key measurement requirements for solid earth science, as well as our approach to validating these requirements once the mission is underway.

Studies of oil slicks in the ocean environment with synthetic aperture radar (SAR) have found that one of the most complex challenges to oil spill detection is the separation of mineral oil spills from slicks that are biogenic in origin. The possible occurrence of multiple scattering mechanisms beyond Bragg scattering for the sea surface, with or without biogenic or mineral oil slicks, and even under low to moderate wind conditions, has also been a subject of debate because the measured signals from these radar-dark surfaces can be contaminated easily by noise. Therefore, the use of noise-uncontaminated data is required for oil spill study in order to avoid significant alteration in the measured radar backscatter, which can lead to misinterpretation and misclassification of the scattering mechanisms involved. To this end, this study uses uninhabited aerial vehicle SAR data, with a noise-equivalent sigma zero as low as −53 dB, to investigate slick classification within a deep learning framework in order to assess deep architectures’ capabilities for providing a reliable and accurate three-state classifier capable of separating mineral oil films from biogenic slicks and from the clean sea. The study exploits parameters with sensitivity to the dielectric constant and ocean wave damping properties, and convolutional neural networks’ (CNNs’) capability for learning nonlinear features, shapes, and textural and statistical patterns, in order to obtain significant classification accuracy. Very high accuracy results have been achieved, with values up to 0.91, 0.94, 0.98, and 0.99 under the most probable real-world spill acquisition conditions.

In this article, we present an innovative space–time adaptive multilooking technique that operates on a sequence of multitemporal, differential synthetic aperture radar interferograms. The developed approach relies on the application of the fundamentals of directional statistics theory. At variance with other methods that identify the set of statistically homogenous pixels (SHPs) within a multilooking (complex averaging) window based on the statistics of the single-look-complex (SLC) SAR images, the proposed method is exclusively based on the analysis of the multitemporal sequence of full resolution DInSAR interferograms. The SHPs are then used to generate spatially adaptive multilooked interferograms both at the native, full-scale grid of the SLC images and at the multilooked resolution scale. The algorithm is effective and simple to implement, only requiring the availability of a sequence of full-scale differential SAR interferometry (DInSAR) interferograms. The interferograms can then be used to generate ground displacement time-series through advanced multitemporal interferometric SAR (MTInSAR) approaches. Experimental results obtained by applying the adopted technique to two SAR data sets acquired at X- and L-band, respectively, demonstrate the validity of the developed method.

The effects of both system additive and multiplicative noise on the X-, C-, and L-band synthetic aperture radar (SAR) data covering oil slicks are examined. Prior studies have attempted to characterize such oil slicks, primarily through analysis of polarimetric SAR data. In this article, we factor in system noise that is added to the backscattered signal, introducing artifacts that can easily be confused with random and volume scattering. This confusion occurs when additive and/or multiplicative system noise dominates the measured backscattered signal. Polarimetric features used in this article are shown to be affected by both additive and multiplicative system noise, some more than others. This article highlights the importance of considering specifically multiplicative noise in the estimation of the signal-to-noise ratio (SNR). The SNR based on additive noise should at least be above 10 dB and the SNR involving both additive and multiplicative noise should at least be above 0 dB. The SNR from TerraSAR-X (TS-X) and Radarsat-2 (RS-2) is below 0 dB for the majority of the oil slick pixels when considering both the additive and multiplicative noise, rendering these data unsuitable for any analysis of the scattering properties and characterization. These results are in contrast to the reduced impact of noise on oil slicks detected by the L-band UAVSAR system. In particular, we find that there is no need to invoke exotic scattering mechanisms to explain the characteristics of the data. We also recommend a noise subtraction for any polarimetric scattering analysis.

The unique capabilities of imaging radar to penetrate cloud cover and collect data in darkness over large areas at high resolution makes it a key information provider for the management and mitigation of natural and human-induced disasters such as earthquakes, volcanoes, landslides, floods, sinkholes, and wildfires. In 2018 we demonstrated the utility of NASA/JPL's airborne Ka-band single-pass interferometric radar (GLISTIN-A) to monitor the growth of lava flow thickness during the surprisingly extensive Kilauea volcano eruption that lasted 3 months. We also deployed UAVSAR's L-band polarimetric repeat-pass interferometric radar at the request of the Federal Emergency Management Agency (FEMA) to monitor flood extent in heavily vegetated areas of North and South Carolina in the aftermath of Hurricane Florence.

Remote sensing technology is an essential link in the global monitoring of the ocean surface and radars are efficient sensors for detecting marine pollution. When used operationally, a tradeoff must usually be made between the covered area and the quantity of information collected by the radar. To identify the most appropriate imaging mode, a methodology based on Receiver Operating Characteristic (ROC) curve analysis has been applied to an original dataset collected by an airborne system, SETHI, characterized by a very low instrument noise floor. The dataset was acquired during an oil spill clean-up exercise carried out in 2015 in the North Sea. Various polarization-dependent quantities are investigated and a relative ordering of the main polarimetric parameters is reported. VV offers the best tradeoff between the benefit of detection performance and the instrument and data requirements. When the sensor has a sufficiently low noise floor, HV is also recommended because it provides strong slick-sea contrast. Among all the investigated quad-polarimetric settings, no significant added value compared to single-polarized data was found.

Remote sensing technology is an essential link in the global monitoring of the ocean surface, and radars are efficient sensors for detecting marine pollution. When used operationally by authorities, a tradeoff must usually be made between the covered area and the quantity of information collected by the radar. To identify the most appropriate imaging mode, a methodology based on receiver operating characteristic curve analysis has been applied to an original data set collected by two airborne systems operating at L-band, both characterized by a very low instrument noise floor. The data set was acquired during controlled releases of mineral and vegetable oil at sea. Various polarization-dependent quantities are investigated, and their ability to detect slick-covered areas is assessed. A relative ordering of the main polarimetric parameters is reported in this paper. When the sensor has a sufficiently low noise floor, HV is recommended because it provides the strongest slick-sea contrast. Otherwise, VV is found to be the most relevant parameter for detecting slicks on the ocean surface. Among all the investigated quad-polarimetric settings, no significant added value compared to single-polarized data was found. More specifically, it is demonstrated, by increasing the instrument noise level, that the studied polarimetric quantities which combine the four polarimetric channels have performances of detection mainly driven by the instrument noise floor, namely, the noise equivalent sigma zero. This result, obtained by progressively adding noise to the raw synthetic aperture radar (SAR) data, indicates that the polarimetric discrimination between clean sea and polluted area results mainly from the differentiated behavior between single-bounce scattering and noise. It is thus demonstrated, using SAR data collected with a low instrument noise floor, that there is no deviation from Bragg scattering for radar scattering from ocean surface covered by mineral and vegetable oil.

The objectives of this research are to (1) address remote sensing strategies and requirements for estimating snow depth and snow water equivalent (SWE) using existing L-Band interferometric data sets in coordination with field-based observations and modeling frameworks and, with this information, (2) inform the Next Generation Cold Land Processes Experiment (SnowEx) toward articulating the appropriate science and research questions for a single motivating science plan. As proposed, SnowEx is a multi-year airborne snow campaign with a primary goal of exploring multimodal sensor observations in coordination with field campaigns to inform the next generation snow remote sensing satellite platform. Based on limitations of satellite-based optical and LiDAR instruments operating in regions of the globe with consistent cloud-cover, the fact that many snow-dominated regions are at more northerly latitudes (limited solar illumination in the middle of winter), and these snow-dominated regions often experience periods of prolonged cloud cover (due to synoptic precipitation events), a microwave remote sensing platform may be the most viable path to space for a dedicated snow remote sensing mission. Specifically, L-Band radar interferometry has shown some unique promise with an archive of historical and contemporary satellite collections from JAXA's PALSAR-1 and PALSAR-2 instruments, respectively. Moreover, with the expected NISAR (NASA-ISRO Synthetic Aperture Radar) mission launch in 2020 and the unprecedented availability of dedicated global interferometric L-Band products every 12-days, as well as what is in essence a NISAR airborne simulator in JPL's UAVSAR platform, the L-Band interferometric approach to estimating snow depth and snow water equivalent (SWE) requires further investigation within the context of in-situ observations and modeling frameworks.

It has long been known from the synthetic aperture radar (SAR) data record that marine slicks can be identified as zones of reduced backscatter separately from the surrounding wind-roughened waters. The composition and properties of marine slicks dampens the shortwave field by both suppression of wave growth and increase in wave dissipation, through an increase in surface tension and a reduction in wind friction. There are, however, clear complications related to SAR marine slick detection. The first problem is that marine slicks may be confused on SAR imagery with areas of low wind and other low-backscatter ocean features such as air-sea temperature differences, rain, and freshwater plumes. The second problem is that there are two primary forms of marine slicks, one composed of biogenic material and the other related to mineral oils from natural seeps or oil spills largely from anthropogenic-related hazards. Biogenic oils, often called surface active agents or surfactants and/or natural films, occur in a thin monolayer and are highly viscoelastic oils that are a byproduct of ocean plant and animal growth. Surfactants readily accumulate in convergent zones by internal waves and current/eddy fields, but are mixed into the upper ocean and rapidly disperse and disappear under windy conditions. The conundrum of detecting and isolating spilled mineral oil from biogenic films and other ocean forms of low backscatter areas has long been recognized by Werner Alpers, his students, and colleagues, and continues to this day. This rich experimental and theoretical legacy, largely focused on the use of radar and SAR, took on steam in the 1980s and continues to this day. This includes laboratory experiments, detailed examination of the properties of both mineral and biogenic slicks and the dampening effects on radar scattering, collection of radar and SAR imagery from ocean platforms, aircraft, the space shuttle, and satellites. The complications and difficulties of this seemingly relative simple notion of separating biogenic films and mineral oil, including as these materials interact with wind, waves, and currents and the use of all sorts of radar frequencies and polarizations collecting data from all over the world, continues to prove challenging in identifying a reliable methodology. In this study, we will review the past efforts of Professor Alpers and his continued influence on current efforts to clearly identify mineral oil and its properties within the vagaries of the ocean environment.

During 2013-2017, the State of California experienced a major drought that impacted water management practices, water delivery, and groundwater extraction, any of which had the potential to impact the critical infrastructure used for water conveyance. The use of synthetic aperture radar interferometry (InSAR) and multi-polarization SAR images to monitor levees and aqueducts in California during the period 2009-2016, which overlapped the drought, is described. The study used data acquired with UAVSAR, an L-band airborne SAR instrument operated by NASA. An overview is given of the methodology adopted to measure subsidence rates of ≥ 2 mm/yr of earthen levees and aqueduct embankments. Results are presented showing the most significant identified hazards to the structures from ongoing land use practices, natural hazards, and groundwater withdrawal.

We improved the repeat-pass InSAR processing capability for the L-band UAVSAR airborne synthetic aperture radar in order to support time-series analysis of repeat zero-baseline observations as well as multiple baseline observations for TomoSAR imaging. This new capability enabled us to conduct tomographic experiments in Gabon during the AfriSAR deployment in support of vegetation structure studies. For the GLISTIN-A Ka-band radar, we streamlined the radar operations and implemented a robust production processor that will routinely generate topographic data products in order to support large-scale science campaigns. The new capabilities were put to test in support of the Oceans Melting Glacier Greenland campaign in March 2016.

Oil spill detection using a time series of images acquired off Norway in June 2015 with the uninhabited aerial vehicle synthetic aperture radar is examined. The relative performance of a set of features derived from quad-polarization versus hybrid-polarity (HP) modes in detection of various types of slicks as they evolve on a high wind driven sea surface is evaluated. It is shown that the HP mode is comparable with the full-polarimetric mode in its ability to distinguish the various slicks from open water (OW) for challenging conditions of high winds (9-12 m/s), small release volumes (0.2-0.5 m3), and during the period 0-9 h following release. The features that contain the cross-polarization component are better for distinguishing the various slicks from open water at later and more developed stages. Although these features are not available in the HP mode, we identify alternative features to achieve similar results. In addition, a clear correlation between the results of individual features and their dependence on particular components within the two-scale Bragg scattering theory is identified. The features that show poor detectability of the oil slicks are those that are independent of the small-scale roughness, while the features resulting in good separability were dependent on several factors in the two-scale Bragg scattering model. We conclude that the HP mode is a viable alternative for SAR-based oil spill detection and monitoring that provides comparable results to those from the quad-polarimetric SAR.

This letter discusses cross-correlation features derived from near-coincident RADARSAT-2 quad-polarimetric and RISAT-1 hybrid-polarity (HP) measurements collected during the NOrwegian Radar oil Spill Experiment in 2015 (NORSE2015). We show that the imaginary part of the cross-correlation between RH and RV is an HP parallel to the real part of the cross-correlation between HH and VV earlier proposed for oil spill characterization. We compared the RADARSAT-2 and RISAT-1 scenes, separated in time by less than an hour, and the results show a clear difference between the slicks across these acquisitions. The development of the oil spills was closely monitored during NORSE2015. Due to the evolving nature of the oil spills and the weathering processes acting upon the spills, our results also indicate an importance of a high synthetic aperture radar sampling rate during an actual oil spill event.

Uninhabited aerial vehicle synthetic aperture radar (UAVSAR) is a reconfigurable polarimetric L-band SAR that operates in quad-polarization mode and is specifically designed to acquire airborne repeat-track SAR data for interferometric measurements. In this paper, we present details of the UAVSAR radar performance, the radiometric calibration, and the polarimetric calibration. For the radiometric calibration, we employ an array of trihedral corner reflectors, as well as distributed targets. We show that UAVSAR is a well-calibrated SAR system for polarimetric applications, with absolute radiometric calibration bias better than 1 dB, residual root-mean-square (RMS) errors of ~0.7 dB, and RMS phase errors ~5.3°. For the polarimetric calibration, we have evaluated the methods of Quegan and Ainsworth et al. for crosstalk calibration and find that the method of Quegan gives crosstalk estimates that depend on target type, whereas the method of Ainsworth et al. gives more stable crosstalk estimates. We find that both methods estimate leakage of the copolarizations into the cross-polarizations to be on the order of -30 dB.

Rapid response to natural disasters resulting from events such as earthquakes, volcanoes, floods, and tsunamis or anthropogenically induced events such as oil spills often requires response time measured in hours to days. The type of information required spans information on the magnitude and location of damage needed by immediate response teams to longer time scale information to monitor recovery efforts. Airborne radar can play an important role in response to disasters given its day/night and all weather imaging capability coupled with its unique set of measurement observables such as millimeter level surface deformation from radar interferometry and polarimetric scattering data. The properties a radar sensor must possess to have a useful role, e.g., frequency, resolution, swath width, etc., depends on the intended application. In this paper we discuss the potential for radar like the NASA/JPL UAVSAR system to respond to disaster management and provide examples from existing UAVSAR data collection to illustrate its potential.

Quantification of the various components of the carbon cycle budget is key to improved climate modeling and projecting anthropogenic affects on climate in the future. Estimating the levels of above ground biomass contained in the world's forests that comprise 86% of the planet's above ground carbon and monitoring the rate of change to these standing stocks resulting from both natural and anthropogenic disturbances is necessary to solving the carbon cycle sink. Remote sensing is the only viable means of obtaining a global inventory of forest biomass at the hectare scale. The most promising means of obtaining remotely sensed biomass measurements involve using either lidar or radar measurements of vegetation structure coupled with allometric relationships. We have collected repeat-pass L-band fully polarimetric radar data at multiple spatial and temporal baselines to investigate the tree height and structure measurements using polarimetric interferometry techniques. This paper will discuss this experiment and comparison with lidar data.

We analyze the fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired on June 23, 2010, from two adjacent, overlapping flight tracks that imaged the main oil slick near the Deepwater Horizon (DWH) rig site in the Gulf of Mexico. Our results show that radar backscatter from both clean water and oil in the slick is predominantly from a single surface scatterer, consistent with the tilted Bragg scattering mechanism, across the range of incidence angles from 26° to 60°. We show that the change of backscatter over the main slick is due both to a damping of the ocean wave spectral components by the oil and an effective reduction of the dielectric constant resulting from a mixture of 65-90% oil with water in the surface layer. This shows that synthetic aperture radar can be used to measure the oil volumetric concentration in a thick slick. Using the H/A/α parameters, we show that surface scattering is dominant for oil and water whenever the data are above the noise floor and that the entropy (H) and α parameters for the DWH slick are comparable to those from the clean water. The anisotropy, A, parameter shows substantial variation across the oil slick and a significant range-dependent signal whenever the backscatter in all channels is above the instrument noise floor. For slick detection, we find the most reliable indicator to be the major eigenvalue of the coherency matrix, which is approximately equal to the total backscatter power for both oil in the slick and clean sea water.

The islands of the Sacramento-San Joaquin Delta have been subject to subsidence since they were first reclaimed from the estuary marshlands starting over 100 years ago, with most of the land currently lying below mean sea level. This area, which is the primary water resource of the state of California, is under constant threat of inundation from levee failure. Since July 2009, we have been imaging the area using the quad-polarimetric UAVSAR L-band radar, with eighteen data sets collected as of April 2011. Here we report results of our polarimetric and differential interferometric analysis of the data for levee deformation and land surface change.

We report here an analysis of the polarization dependence of L-band radar backscatter from the main slick of the Deepwater Horizon oil spill, with specific attention to the utility of polarimetric decomposition analysis for discrimination of oil from clean water and identification of variations in the oil characteristics. For this study we used data collected with the UAVSAR instrument from opposing look directions directly over the main oil slick. We find that both the Cloude-Pottier and Shannon entropy polarimetric decomposition methods offer promise for oil discrimination, with the Shannon entropy method yielding the same information as contained in the Cloude-Pottier entropy and averaged in tensity parameters, but with significantly less computational complexity.