Uninhabited Aerial Vehicle Synthetic Aperture Radar Data Research Articles

Quantification of wetland vegetation communities features with airborne AVIRIS-NG, UAVSAR, and UAV LiDAR data in Peace-Athabasca Delta

Arctic-boreal wetlands, important ecosystems for biodiversity and ecological services, are experiencing hydrological changes including permafrost thaw, earlier snowmelt, and increased wildfire susceptibility. These changes are affecting wetland productivity, species diversity, and biogeochemical cycles. However, given the diverse forms and structures of wetland vegetation communities, traditional wetland maps generated from lower spatial and spectral resolution satellite imagery lack community-level vegetation classification and miss spatially complex patterns. In this study, we built a cloud-based workflow to map wetland vegetation community of the Peace-Athabasca Delta (PAD), Canada, by leveraging high-resolution (5-m) airborne multi-sensor datasets, namely NASA's Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), and a historical LiDAR archive. Validation of our classifications using ground references indicates that classifications derived from AVIRIS-NG have higher accuracies (≥87.9%) than either UAVSAR (65.6%) or LiDAR (75.9%) for mapping wetland vegetation communities. We also show improved classification accuracy when combining information from multiple sensors. In particular, incorporating AVIRIS-NG and UAVSAR datasets substantially reduced omission errors of wet graminoid and wet shrub classes from 29.6% to 20.5% and from 10.8% to 7.5%, respectively. Combining AVIRIS-NG and LiDAR datasets further improves overall accuracy (+2.2%) for most classifications, especially emergent vegetation, wet graminoid, and wet shrub. The best performing model, using features derived from all three sensors, achieved an overall accuracy of 93.5%. The framework established here can be used to leverage extensive airborne AVIRIS-NG and UAVSAR datasets collected across Alaska and northwest Canada to understand the spatial distribution of Arctic-Boreal wetland vegetation communities.

The Importance of Lake Emergent Aquatic Vegetation for Estimating Arctic‐Boreal Methane Emissions

AbstractAreas of lakes that support emergent aquatic vegetation emit disproportionately more methane than open water but are under‐represented in upscaled estimates of lake greenhouse gas emissions. These shallow areas are typically less than ∼1.5 m deep and can be detected with synthetic aperture radar (SAR). To assess the importance of lake emergent vegetation (LEV) zones to landscape‐scale methane emissions, we combine airborne SAR mapping with field measurements of vegetated and open‐water methane flux. First, we use Uninhabited Aerial Vehicle SAR data from the NASA Arctic‐Boreal Vulnerability Experiment to map LEV in 4,572 lakes across four Arctic‐boreal study areas and find it comprises ∼16% of lake area, exceeding previous estimates, and exhibiting strong regional differences (averaging 59 [50–68]%, 22 [20–25]%, 1.0 [0.8–1.2]%, and 7.0 [5.0–12]% of lake areas in the Peace‐Athabasca Delta, Yukon Flats, and northern and southern Canadian Shield, respectively). Next, we account for these vegetated areas through a simple upscaling exercise using paired methane fluxes from regions of open water and LEV. After excluding vegetated areas that could be accounted for as wetlands, we find that inclusion of LEV increases overall lake emissions by 21 [18–25]% relative to estimates that do not differentiate lake zones. While LEV zones are proportionately greater in small lakes, this relationship is weak and varies regionally, underscoring the need for methane‐relevant remote sensing measurements of lake zones and a consistent criterion for distinguishing wetlands. Finally, Arctic‐boreal lake methane upscaling estimates can be improved with more measurements from all lake zones.

Flood Extent Mapping During Hurricane Florence With Repeat‐Pass L‐Band UAVSAR Images

AbstractExtreme precipitation events are intensifying due to a warming climate, which, in some cases, is leading to increases in flooding. Detection of flood extent is essential for flood disaster response, management, and prevention. However, it is challenging to delineate inundated areas through most publicly available optical and short‐wavelength radar data, as neither can “see” through dense forest canopies. In 2018, Hurricane Florence produced heavy rainfall and subsequent record‐setting riverine flooding in North Carolina, USA. NASA/JPL collected daily high‐resolution full‐polarized L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data between September 18th and 23rd. Here, we use UAVSAR data to construct a flood inundation detection framework through a combination of polarimetric decomposition methods and a Random Forest classifier. Validation of the established models with compiled ground references shows that the incorporation of linear polarizations with polarimetric decomposition and terrain variables significantly enhances the accuracy of inundation classification, and the Kappa statistic increases to 91.4% from 64.3% with linear polarizations alone. We show that floods receded faster near the upper reaches of the Neuse, Cape Fear, and Lumbee Rivers. Meanwhile, along the flat terrain close to the lower reaches of the Cape Fear River, the flood wave traveled downstream during the observation period, resulting in the flood extent expanding 16.1% during the observation period. In addition to revealing flood inundation changes spatially, flood maps such as those produced here have great potential for assessing flood damages, supporting disaster relief, and assisting hydrodynamic modeling to achieve flood‐resilience goals.

The NASA AfriSAR campaign: Airborne SAR and lidar measurements of tropical forest structure and biomass in support of current and future space missions

In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies.During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30,000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1-hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community.

Performance Evaluation of UAVSAR and Simulated NISAR Data for Crop/Noncrop Classification Over Stoneville, MS.

Synthetic Aperture Radar (SAR) data are well‐suited for change detection over agricultural fields, owing to high spatiotemporal resolution and sensitivity to soil and vegetation. The goal of this work is to evaluate the science algorithm for the NASA ISRO SAR (NISAR) Cropland Area product using data collected by NASA's airborne Uninhabited Aerial Vehicle SAR (UAVSAR) platform and the simulated NISAR data derived from it. This study uses mode 129, which is to be used for global‐scale mapping. The mode consists of an upper (129A) and lower band (129B), respectively having bandwidths of 20 and 5 MHz. This work uses 129A data because it has a four times finer range resolution compared to 129B. The NISAR algorithm uses the coefficient of variation (CV) to perform crop/noncrop classification at 100 m. We evaluate classifications using three accuracy metrics (overall accuracy, J‐statistic, Cohen's Kappa) and spatial resolutions (10, 30, and 100 m) for crop/noncrop delineating CV thresholds (CVthr) ranging from 0 to 1 in 0.01 increments. All but the 10 m 129A product exceeded NISAR's mission accuracy requirement of 80%. The UAVSAR 10 m data performed best, achieving maximum overall accuracy, J‐statistic, and Kappa values of 85%, 0.62, and 0.60. The same metrics for the 129A product respectively are: 77%, 0.40, 0.36 at 10 m; 81%, 0.55, 0.49 at 30 m; 80%, 0.58, 0.50 at 100 m. We found that using a literature recommended CVthr value of 0.5 yielded suboptimal accuracy (65%) at this site and that optimal CVthr values monotonically decreased with decreasing spatial resolution.

Deep Learning for Mineral and Biogenic Oil Slick Classification With Airborne Synthetic Aperture Radar Data

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.

Internal kinematics of the Slumgullion landslide (USA) from high-resolution UAVSAR InSAR data

Landslides represent one of the most damaging natural hazards and often lead to unexpected casualties and property damage. They also continually modify our natural environment and landscapes. Knowledge of landslide systems is largely restricted by the stochastic nature, subjective interpretation and infrequent or spatially sparse surveying of landslides. Characterized by persistent daily movements of a couple of centimeters over multi-centennial timescales and a long narrow shape as long as ~4 km, the Slumgullion landslide in Colorado, USA represents an ideal natural laboratory to study slow-moving landslides. Here we demonstrate the capability of the highly accurate, spatially continuous airborne Synthetic Aperture Radar (SAR) system of the NASA Uninhabited Aerial Vehicle SAR (UAVSAR) to characterize the kinematic details of internal deformation of the Slumgullion landslide using SAR interferometry (InSAR). We develop a phase-based approach to automatically extract the boundaries of the mobile geological structures without unwrapping. Comparison with historic field observations from 1991 reveals the 40-m advance of the frontal toe and shift of an internal fault. The UAVSAR data also resolve an internal minislide (100 by 70 m), which moves more southerly than the main body at 5 mm/day in the lower part of the landslide. A Light Detection and Ranging (LiDAR) Digital Elevation Model (DEM) shows that the minislide is associated with the opening of a 30 by 10 m pull-apart basin and bounding strike-slip faults. These extensional structures, nearby incised streams, and steepened local slopes helped establish the kinematic environment for the formation of the secondary minislide. The disparity between the UAVSAR InSAR-derived horizontal moving directions and the LiDAR DEM-derived slope aspects suggest that while the surface topography governs the first-order orientation, the local kinematics is also subject to the variable nature of heterogeneous landslide materials and the irregular basal bedrock surface. The landslide velocity and precipitation show similar multi-annual variations. Our study demonstrates that the freely available, high-resolution UAVSAR data, have great potential for characterizing landslide kinematics and other small-scale geological and geomorphological processes.

Rheology of a Debris Slide From the Joint Analysis of UAVSAR and LiDAR Data

AbstractLandslide rheology governs the deformation and flow behavior of sliding masses. As rheology strongly varies as a function of the composition and environment of landslides, a wide range of viscosities have been suggested based on very limited experimental or observational constraints. Here, we introduce a novel method to quantify the landslide rheology from remote sensing data. We focus on an ideal natural laboratory, the Slumgullion landslide, Colorado, which has moved at tens of millimeters per day for centuries. A joint analysis of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Interferometric Synthetic Aperture Radar (InSAR)‐derived surface displacements and Light Detection and Ranging (LiDAR) Digital Elevation Model (DEM)‐derived landslide thickness at its frontal toe allows us to invert for the intrinsic viscosity (109–1011.5 Pa·s under different degrees of plasticity) based on the Bingham plastic model. Detailed displacement measurements also elucidate local variations in magnitude and orientation. Our method presents the capability of remote sensing data to understand the rheology of quasi‐static debris slides in general.

Can Mineral Oil Slicks Be Distinguished From Newly Formed Sea Ice Using Synthetic Aperture Radar?

In this feasibility study discriminating oil slicks and newly formed sea ice using synthetic aperture radar (SAR) imagery is investigated, using imagery from the L-band high-resolution uninhabited aerial vehicle synthetic aperture radar (UAVSAR) airborne and the satellite C-band RADARSAT-2 (RS-2) systems. To determine the separability of these two varying but similar appearing low backscatter ocean surfaces, multipolarization features are utilized from both SAR datasets. The discrimination is evaluated using the Kolmogorov–Smirnov separability test. All imagery was obtained during several sea ice campaigns in the Arctic Ocean and separate oil spill campaigns in Norway and the Gulf of Mexico, with each campaign collecting in situ observations. We observe that the polarization difference (VV-HH) reliably separates the mineral oil slicks and newly formed sea ice areas using UAVSAR images, due to the low noise floor and subsequent high signal-to-noise ratio (SNR) radiometric performance of the airborne system. The comparably higher noise floor and related lower SNR hampers the separability in the RS-2 images. Simulated noise floors were generated by adding white Gaussian noise to the UAVSAR data, which show that discrimination between the two low backscatter phenomena using multipolarization features is possible, provided that both datasets are still well above the noise floor. The pixel resolution has a limited effect on the separability. The results of this study provide an approach to distinguish oil slicks from newly formed sea ice, which might be of special interest should an oil spill occur within the marginal ice zone.

Oil Slick Characterization Using a Statistical Region-Based Classifier Applied to UAVSAR Data

During emergency responses to oil spills on the sea surface, quick detection and characterization of an oil slick is essential. The use of Synthetic Aperture Radar (SAR) in general and polarimetric SAR (PolSAR) in particular to detect and discriminate mineral oils from look-alikes is known. However, research exploring its potential to detect oil slick characteristics, e.g., thickness variations, is relatively new. Here a Multi-Source Image Processing System capable of processing optical, SAR and PolSAR data with proper statistical models was tested for the first time for oil slick characterization. An oil seep detected by NASA`s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in the Gulf of Mexico was used as a study case. This classifier uses a supervised approach to compare stochastic distances between different statistical distributions (fx) and hypothesis tests to associate confidence levels to the classification results. The classifier was able to detect zoning regions within the slick with high global accuracies and low uncertainties. Two different classes, likely associated with the thicker and thinner oil layers, were recognized. The best results, statistically equivalent, were obtained using different data formats: polarimetric, intensity pair and intensity single-channel. The presence of oceanic features in the form of oceanic fronts and internal waves created convergence zones that defined the shape, spreading and concentration of the thickest layers of oil. The statistical classifier was able to detect the thicker oil layers accumulated along these features. Identification of the relative thickness of spilled oils can increase the oil recovery efficiency, allowing better positioning of barriers and skimmers over the thickest layers. Decision makers can use this information to guide aerial surveillance, in situ oil samples collection and clean-up operations in order to minimize environmental impacts.

Full year crop monitoring and separability assessment with fully-polarimetric L-band UAVSAR: A case study in the Sacramento Valley, California

Spatial and temporal information on plant and soil conditions is needed urgently for monitoring of crop productivity. Remote sensing has been considered as an effective means for crop growth monitoring due to its timely updating and complete coverage. In this paper, we explored the potential of L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data for crop monitoring and classification. The study site was located in the Sacramento Valley, in California where the cropping system is relatively diverse. Full season polarimetric signatures, as well as scattering mechanisms, for several crops, including almond, walnut, alfalfa, winter wheat, corn, sunflower, and tomato, were analyzed with linear polarizations (HH, HV, and VV) and polarimetric decomposition (Cloude–Pottier and Freeman–Durden) parameters, respectively. The separability amongst crop types was assessed across a full calendar year based on both linear polarizations and decomposition parameters. The unique structure-related polarimetric signature of each crop was provided by multitemporal UAVSAR data with a fine temporal resolution. Permanent tree crops (almond and walnut) and alfalfa demonstrated stable radar backscattering values across the growing season, whereas winter wheat and summer crops (corn, sunflower, and tomato) presented drastically different patterns, with rapid increase from the emergence stage to the peak biomass stage, followed by a significant decrease during the senescence stage. In general, the polarimetric signature was heterogeneous during June and October, while homogeneous during March-to-May and July-to-August. The scattering mechanisms depend heavily upon crop type and phenological stage. The primary scattering mechanism for tree crops was volume scattering (>40%), while surface scattering (>40%) dominated for alfalfa and winter wheat, although double-bounce scattering (>30%) was notable for alfalfa during March-to-September. Surface scattering was also dominant (>40%) for summer crops across the growing season except for sunflower and tomato during June and corn during July-to-October when volume scattering (>40%) was the primary scattering mechanism. Crops were better discriminated with decomposition parameters than with linear polarizations, and the greatest separability occurred during the peak biomass stage (July-August). All crop types were completely separable from the others when simultaneously using UAVSAR data spanning the whole growing season. The results demonstrate the feasibility of L-band SAR for crop monitoring and classification, without the need for optical data, and should serve as a guideline for future research.

Biomass retrieval from L-band polarimetric UAVSAR backscatter and PRISM stereo imagery

The forest above-ground biomass (AGB) and spatial distribution of vegetation elements have profound effects on the productivity and biodiversity of terrestrial ecosystems. In this paper, we evaluated biomass estimation from L-band Synthetic Aperture Radar (SAR) data acquired by National Aeronautics and Space Administration (NASA) Uninhabited Aerial Vehicle SAR (UAVSAR) and the improvement of accuracy by adding canopy height information derived from stereo imagery acquired by Japan Aerospace Exploration Agency (JAXA) Panchromatic Remote Sensing Instrument for Stereo Mapping (PRISM) on-board the Advanced Land Observing Satellite (ALOS). Various models for prediction of forest biomass from UAVSAR data were investigated at pixel sizes of 1/4ha (50m×50m) and 1 ha. The variance inflation factor (VIF) was calculated for each of the explanatory variables in multivariable regression models to assess the multi-collinearity between explanatory variables. In addition, the t- and p-values were used to interpret the significance of the coefficients of each explanatory variables. The R2, Root Mean Square Error (RMSE), bias and Akaike information criterion (AIC), and leave-one-out cross-validation (LOOCV) and bootstrapping were used to validate models. At 1/4-ha scale, the R2 and RMSE of biomass estimation from a model using a single track of polarimetric UAVSAR data were 0.59 and 52.08Mg/ha. With canopy height from PRISM as additional independent variable, R2 increased to 0.76 and RMSE decreased to 39.74Mg/ha (28.24%). At 1-ha scale, the RMSE of biomass estimation based on UAVSAR data of a single track was 39.42Mg/ha with a R2 of 0.77. With the canopy height from PRISM, R2 increased to 0.86 and RMSE decreased to 29.47 Mg/ha (20.18%). The models using UAVSAR data alone underestimated biomass at levels above ~150Mg/ha showing the saturation phenomenon. Adding canopy height from PRISM stereo imagery significantly improved the biomass estimation and elevated the saturation level in estimating biomass. Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation. Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation at 1/4-ha scale, R2 increased from 0.59 to 0.66 and RMSE reduced from 52.08 to 48.57Mg/ha. Averaging multiple acquisitions of UAVSAR data from the same look azimuth direction did not improve biomass estimation. A biomass map derived from NASA's LVIS (Laser Vegetation Imaging System) waveform data was used as a reference for evaluation of the biomass maps from these models. The study has also shown that the errors decreased when deciduous, evergreen, and mixed forests were modeled separately but the improvement was not significant.

Use of time-series L-band UAVSAR data for the classification of agricultural fields in the San Joaquin Valley

The study of agriculture and agricultural production is an important part of societal planning for allocation of resources and economic forecasting. The prediction of crop output requires collecting accurate crop data throughout the growing season, in locations with a wide range of climatic conditions. Repeat observations from synthetic aperture radar (SAR) have been shown to be a reliable way of gathering frequent crop measurements, even in perpetually cloudy regions.In this work, repeat coverage of an area in California's San Joaquin Valley, with images taken by NASA's L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system approximately once a month during 2010 and 2012, was used to examine a time-history of backscatter signatures and polarimetric H/A/alpha (Entropy, Anisotropy, and Polarization angle) decomposition values of alfalfa, corn, and winter wheat with the objective of improving classification of individual crops. Distinguishable signatures were observed for all three crops. The signature was dominated by the growth stage and physical structure of the crops during the mature part of the growing season, and by weather events, planting practices, and harvesting procedures during other parts of the year. These data support previous findings that multiple images throughout the year capture the full growth pattern, allowing for more accurate identification of agricultural crops than what can be determined by a single image. The overall accuracies for this time-series classification were 75% using HV-polarized backscatter, 79% using the alpha decomposition, and 83% using the entropy decomposition, which were the three polarizations or decompositions with greatest separability between crops. It is shown that the full-year time series of images builds a more comprehensive model for crop backscatter than previous works.

Polarimetric Decomposition for Monitoring Crop Growth Status

This letter investigates the polarimetric decomposition for monitoring the crop growth status over agricultural fields. Based on an existing polarimetric decomposition, the vegetation volume scattering component is removed from the full polarimetric synthetic aperture radar (SAR). Then, the estimated crop orientation is combined with the dominant scattering mechanism in the remaining ground coherency matrix to define the vegetation growth indicators for the crop growth monitoring from SAR. The proposed method is evaluated on the time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data and the extensive ground truth measurements collected in the framework of the Soil Moisture Active Passive (SMAP) Validation Experiment in 2012. The results show that the scattering characteristics vary with the crop types and the phenological development stage. The random orientation is the most important case during the crop development period considered in this letter. For canola, corn, and wheat, the dihedral scattering is significant in the remaining ground coherency matrix at the early growth stage, and then, the surface scattering becomes important in the further growth. For soybean and pasture, the surface scattering dominates the remaining ground coherency matrix during the considered crop development stage. The vegetation growth indicators derived from UAVSAR data are well correlated with the ground measurements of crop height and biomass. This letter demonstrates, for the first time, the crop growth monitoring by using polarimetric decomposition via the vegetation orientation and scattering mechanisms.

Monitoring of subsidence with UAVSAR on Sherman Island in California's Sacramento–San Joaquin Delta

Sherman Island, the westernmost island in the Sacramento–San Joaquin Delta in California, plays a crucial role in maintaining the water flux between saline ocean water from the San Francisco Bay to its west and the rest of the Delta to its east. Land elevation below mean sea level and continuous subsidence over the past century has made this island a high priority area for investigations of subsidence and restoration in the Delta. This study reports the results of successful application of Interferometric Synthetic Aperture Radar (InSAR) data and technique to measure subsidence in the Delta, which is a coherence-challenged non-urban area. We carried out a time-series interferometric analysis of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band (23.8 cm) data, collected from July 2009–August 2014, to assess both the spatial and temporal variation of subsidence on Sherman Island. We report both large-scale (island-wide) subsidence trends and small-scale (levee/farm scale) subsidence features in Sherman Island. Assuming the subsidence is linear during the five years of UAVSAR data acquisition, subsidence rates across the island range from 0–5 cm/yr, with an average of 1.3 ± 0.2 cm/yr. We estimate our systematic uncertainty to be 0.3 cm/yr. Overall, the central region in the island has subsided at a faster rate than the rest of the island. We find our results to be consistent with previous measurements of subsidence rates at electric transmission line towers scattered throughout the island. The results of this study provide insights into several factors influencing subsidence, including soil type, water table depth, land use, land elevation and the location and time of levee repairs. Subsidence monitoring on Sherman Island is essential for maintaining a reliable water supply for the state of California and for protecting the Delta ecosystem.

Evaluation of Simplified Polarimetric Decomposition for Soil Moisture Retrieval over Vegetated Agricultural Fields

This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It aims to retrieve the soil moisture by using only the surface scattering component, once the volume scattering contribution is removed. Evaluation of the proposed simplified algorithm is performed using extensive ground measurements of soil and vegetation characteristics and the time series of UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) data collected in the framework of SMAP (Soil Moisture Active Passive) Validation Experiment 2012 (SMAPVEX12). The retrieval process is tested and analyzed in detail for a variety of crops during the phenological stages considered in this study. The results show that the performance of soil moisture retrieval depends on both the crop types and the crop phenological stage. Soybean and pasture fields present the higher inversion rate during the considered phenological stage, while over canola and wheat fields, the soil moisture can be retrieved only partially during the crop developing stage. RMSE of 0.06–0.12 m3/m3 and an inversion rate of 26%–38% are obtained for the soil moisture retrieval based on the simplified polarimetric decomposition.

Detection of Durable and Permanent Changes in Urban Areas Using Multitemporal Polarimetric UAVSAR Data

Change detection using synthetic aperture radar (SAR) data is useful in emergency situations and unfavorable weather conditions. In this letter, change detection using multitemporal polarimetric Uninhabited Aerial Vehicle SAR data is investigated in an urban environment. The most robust polarimetric parameters are determined, and change detection techniques using a maximum likelihood ratio and a hyperbolic tangent model function are applied to the selected parameter. The model function was introduced to quantify the change characteristics and to rule out seasonal changes or those related to mobile features, and thus to only detect durable and permanent changes in urban environments. A comparison of results with historical Google Earth images showed a good level of agreement. Fitting of the hyperbolic tangent function to the multitemporal polarimetric parameters significantly reduces the false detection rate and indicates whether a building was constructed or destroyed, as well as when the detected changes occurred.

Modeling Bare Soil L-Band Polarimetric - Values Using a Second-Order SPM Model

Polarimetric soil moisture retrieval is among the main objectives of leading synthetic-aperture-radar satellite missions since it allows to systematically analyze costly-to-obtain polarization information to increase retrieval accuracy. In this letter, we present the results of modeling the L-band entropy $(H)$ and alpha $(\alpha)$ values as a function of soil dielectric constant and roughness using a second-order small-perturbation model to simulate the polarimetric soil backscattering. Modeling results are then compared to bare soil uninhabited aerial vehicle synthetic aperture radar (UAVSAR) data acquired simultaneously to in situ field campaigns in Canada during SMAPVEx12. Our model is able to correctly predict observed ranges of $H$ and $\alpha$ and to consistently model dielectric constant and roughness changes. Nevertheless, a systematic overestimation of $\alpha$ is observed when compared with the analyzed UAVSAR data set. Taking UAVSAR data as benchmark, theoretical reasons for this mismatch are analyzed.

Advantages to Geoscience and Disaster Response from QuakeSim Implementation of Interferometric Radar Maps in a GIS Database System

High-resolution maps of earth surface deformation are available in public archives for scientific interpretation, but are primarily available as bulky downloads on the internet. The NASA uninhabited aerial vehicle synthetic aperture radar (UAVSAR) archive of airborne radar interferograms delivers very high resolution images (approximately seven meter pixels) making remote handling of the files that much more pressing. Data exploration requiring data selection and exploratory analysis has been tedious. QuakeSim has implemented an archive of UAVSAR data in a web service and browser system based on GeoServer (http://geoserver.org). This supports a variety of services that supply consistent maps, raster image data and geographic information systems (GIS) objects including standard earthquake faults. Browsing the database is supported by initially displaying GIS-referenced thumbnail images of the radar displacement maps. Access is also provided to image metadata and links for full file downloads. One of the most widely used features is the QuakeSim line-of-sight profile tool, which calculates the radar-observed displacement (from an unwrapped interferogram product) along a line specified through a web browser. Displacement values along a profile are updated to a plot on the screen as the user interactively redefines the endpoints of the line and the sampling density. The profile and also a plot of the ground height are available as CSV (text) files for further examination, without any need to download the full radar file. Additional tools allow the user to select a polygon overlapping the radar displacement image, specify a downsampling rate and extract a modest sized grid of observations for display or for inversion, for example, the QuakeSim simplex inversion tool which estimates a consistent fault geometry and slip model.

Incidence Angle Normalization of Radar Backscatter Data

The National Aeronautics and Space Administration's (NASA) proposed Soil Moisture Active Passive (SMAP) satellite mission ( ~ 2014) will include a radar system that will provide L-band multi-polarization backscatter at a constant incidence angle of 40 °. During the pre-launch phase of the project, there is a need for observations that will support the radar-based soil moisture algorithm development and validation. A valuable resource for providing these observations is the NASA Jet Propulsion Laboratory Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). However, SMAP will observe at a constant incidence angle of 40 °, and UAVSAR collects data over a wide range of incidence angles (25 °-60°). In this investigation, a technique was developed and tested for normalizing UAVSAR data to a constant incidence angle. The approach is based on a histogram matching procedure. The data used to develop and demonstrate this approach were collected as part of the Canadian Soil Moisture Experiment 2010 (CanEx-SM10). Land cover in the region included agriculture and forest. Evaluation was made possible by the acquisition of numerous overlapping UAVSAR flight lines that provided multiple incidence angle observations of the same locations. Actual observations at a 40° incidence angle were compared to the normalized data to assess performance of the normalization technique. An optimum technique should be able to reduce the systematic error (Bias) to 0 dB and to lower the total root mean square error (RMSE) computed after correction to the level of the initial residual error (RMSEres) present in the data set. The normalization approach developed here achieved both of these. Bias caused by the incidence angle variability was minimized to ~ 0 dB, whereas the residual error caused by instrument related random errors and amplitude fluctuations due to ground variability was reduced to approximately 3 dB for agricultural areas and 2.6 dB for forests; these values were consistent with the initial RMSEres estimated using the un-corrected data. The residual error can be reduced further by aggregating the radar observations to a coarser grid spacing. The technique adequately adjusted the backscatter over the full swath width irrespective of the original incidence angle, polarization, and ground conditions (vegetation cover and soil moisture). In addition to providing a basis for fully exploiting UAVSAR (or similar aircraft systems) for SMAP algorithm development and validation, the technique could also be adapted to satellite radar systems. This normalization approach will also be beneficial in terms of reducing the number of flight lines required to cover a study area, which would eventually result in more cost-effective soil moisture field campaigns.