The NASA ISRO Synthetic Aperture Radar (NISAR) is scheduled for launch early in 2024 from the Satish Dhawan Space Centre (SDSC), at Sriharikota, near Chennai, India. This mission is the result of a collaboration between NASA and Indian Space Research Organization (ISRO), where NASA has contributed elements of the mission such as an L-band SAR, and ISRO has contributed other elements, such as an S-band SAR. After successful launch, the NISAR mission will collect left-looking L-band SAR data over most of the Earth’s land areas twice during every 12-day exact repeat orbit. (once while in an ascending orbit direction and once while in a descending orbit direction). NASA and ISRO have individual and joint requirements on the mission that include the performance of the imaging radars onboard the spacecraft. For example, NASA must demonstrate that this L-band SAR will achieve a set of identified science measurement accuracy requirements that span Ecosystem science, Solid Earth science, and Cryosphere science disciplines. Likewise, ISRO has several applications objectives on both the L-band and S-band data from NISAR that the ISRO science team and project will be developing and testing. Pre-launch and post-launch activities have been planned to validate that these requirements are met. Here, we will discuss how the NASA plans are being executed and will present any initial results at the conference.

In preparation for the NISAR launch and data collection in 2024, the NISAR Project Science Team is building workflows for each Science Team discipline (Ecosystems, Cryosphere, and Solid Earth). This abstract focuses on the Ecosystem disciplines and the development of on-demand cloud-processing workflows for wetlands inundation, forest biomass, agricultural active crop area, and forest disturbance. The workflow simulates NISAR data using UAVSAR or ALOS-2 Single Look Complex data, which are processed to Level 2 geocoded polarimetric covariance matrix products using InSAR Scientific Computing Environment 3.0 software and to Level 3 science products using the Algorithm Theoretical Basis Documents. In this presentation, we describe these workflows and efforts to improve efficiency and data accessibility by using a cloud processing system. We present preliminary sample products from each Ecosystem discipline: inundation, forest biomass, crop area, and forest disturbance..

Globally, wetlands are a critical habitat of numerous plants and animal species and play a major role in maintaining Earth’s biodiversity. Wetlands ecosystems are particularly challenging to characterize and monitor because many tend to be in remote regions that are difficult to access. As optical satellite remote sensing instruments observe only the top of the canopy, they are limited in they cannot directly observe surface inundation beneath vegetation canopies. The NISAR mission is tasked with mapping surface inundation in wetlands. NISAR’s unique capability enables consistent and reliable observations of wetlands inundation extent and dynamics. This capability supports characterization of changes in wetlands systems driven by a changing climate and will support a better understanding of the impacts to carbon fluxes, ecosystems services and biodiversity in these critical systems.Focusing on our efforts in Pacaya Samiria Nation Reserve, Peru, we provide an overview of NISAR’s capability to study wetlands ecosystems. Pacaya Samiria has been selected to be the NISAR calibration-validation site for tropical wetlands. Here, we have established a network of in situ sensors for validation of inundation state and extent in a complex tropical wetlands environment. Our sensor network is supported by ancillary measurements of vegetation structure drawn from drone and ground LiDAR and photogrammetry. We also describe the calibration validation approach that will be implemented as part of supporting the NISAR objectives, and we present preliminary results using ALOS PALSAR-2 data and possibly first light images from NISAR.

The NISAR mission which in its most recent round of launch preparations was set to launch in the spring of 2024, and now delayed until later in the fall or early spring of 2025, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The two frequency, L- and S-band will full-polarimetric capability over a 250 km wide swath using the SweepSAR technique [1] will collect reliable set of observations (60 per year; 30 each for ascending and descending passes) on a continuing basis that will allow for the modeling and observation of time-varying processes that are prevalent in the living environment broadly described as Ecosystems. Among the prime science goals of the NISAR Ecosystems disciplines are in the characterization of agriculture, disturbance, biomass, forest structure and water dynamics seen in the world’s rivers, coasts, and permafrost regions. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

We establish a workflow for validating NISAR Solid Earth Science (SES) products based on UAVSAR measurements of secular velocities and coseismic displacements across earthquake faults. UAVSAR is an L-band synthetic aperture radar capable of measuring solid Earth deformations through repeat pass interferometry. High spatial resolution makes UAVSAR especially sensitive to surface deformation at short spatial wavelengths (e.g., near a crustal fault). Furthermore, UAVSAR acquisition schemes can provide a 3D picture of deformation. For NISAR validation, secular (interseismic) deformation will be measured across the creeping section of the San Andreas fault, where fault creep presents a well-defined tectonic signal and a rich UAVSAR data archive exists. The UAVSAR measurements will be quantitatively compared to measurements based on satellite InSAR data. The workflow developed herein is similar to that in the SES algorithm theoretical basis document (ATBD) for validating NISAR products, with changes made to account for the peculiarities of UAVSAR data.

We present the validation methodology and results of Dynamic Surface Water eXtent from Harmonized Landsat Sentinel-2 (DSWx-HLS). The DSWx-HLS product is the first of the DSWx suite, comprised of products each which map water from Earth Observation optical and SAR satellites. We detail the generation of high-resolution (3 m) validation datasets from a globally-stratified sample of dry, moderate, and wet sites. We provide the precise accounting of the classification metrics used to verify the Observational Products for End-users from Remote Sensing Analysis (OPERA) project requirements. We also report broader classification metrics across the validation datasets considered. OPERA performs validation in the public domain to ensure that the validation activities are transparent and reproducible. The resulting validation datasets and provisional OPERA products are publicly available; the software used for validation is also open-source.

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) project at the Jet Propulsion Laboratory (JPL) will provide a near-global Radiometric Terrain Corrected synthetic aperture radar (SAR) backscatter from Sentinel-1 (RTC-S1) product. The OPERA RTC-S1 product will deliver map-projected burst-based radar images with a geographic scope that includes all land masses excluding Antarctica, and with temporal sampling coincident with the availability of Sentinel-1 interferometric wide (IW) single-look complex (SLC) data. This paper presents the OPERA RTC-S1 product, providing details about its layers, static layers, and metadata; describing the product’s processing workflow, based on the ISCE3 framework and using the same algorithms developed for the NASA-ISRO Synthetic Aperture Radar (NISAR) mission; and outlining the algorithm verification and the product validation plan. We also present a global mosaic of preliminary OPERA RTC-S1 products generated from a global end-to-end test run from a Sentinel-1A orbit cycle. The OPERA RTC-S1 product will be publicly distributed through the Alaska Satellite Facility (ASF) Distributed Active Archive Center (DAAC) free of charge, with a release date scheduled for September 2023 with forward stream production.

Flooding is one of the deadliest and costliest natural disasters. Climate change-induced flooding events are increasing worldwide, disproportionately impacting low-income and developing communities. While early warning systems save lives, satellite-based observation systems are vital for the disaster relief and recovery phases. Current satellite-based operational flood products are largely based on either optical remote-sensing methods, which exhibit a limited ability to detect water through clouds and vegetation, or microwave remote sensing, which provides relatively low spatial and temporal resolution. New small satellite constellations using radar or GNSS reflectometry (GNSS-R) have been shown to enhance our ability to overcome these deficiencies. In this work, we quantify the performance of using GNSS-R measurements from the NASA Cyclone Global Navigation Satellite Systems (CYGNSS) satellite constellation to map surface water in South Sudan and the Sudd wetland in comparison with a set of representative operational products. We make quantitative comparisons of our results with operational flood products based on Visible Infrared Imaging Radiometer Suite (VIIRS) and MODIS and with C-band Sentinel-1 synthetic aperture radar. We find that our method detects 35.4% more surface water than Sentinel-1, while the VIIRS- and MODIS-based products underestimate by 4.8% and 83.7%, respectively. We use several metrics commonly used to evaluate classification performance: precision, true positive rate (TPR), true negative rate (TNR), F2-score, and the Matthews correlation coefficient (MCC) and assess the comparisons in this statistical framework. We discuss the consequences of our findings, including ways CYGNSS data may enhance current flood products and assist decision-makers and emergency managers.

The NASA ISRO Synthetic Aperture Radar (NISAR) Mission plans to generate >40TB of raw data daily to support open access and operational L-band science. This includes Ecosystems applications for agriculture. To further prepare, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) platform was used to observe cropland sites across the southern United States to support the development of L-band prototype science products. Major crops include corn, cotton, pasture, peanut, rice, and soybean. A suite of cropland classification experiments applied a set of strategic algorithms to synergistically assess performance, scattering mechanisms, and limitations. SAR terms with sensitivity to volume scattering performed well and consistently across mapping experiments achieving accuracy greater than 80% for cropland vs not cropland. Volume scattering and cross-pol terms were most useful across the different ML techniques with overall accuracy and Kappa consistently over 90% and. 85, respectively, for crop type by late growth stages for both L-band observations.

Identifying wetland's spatial distribution is essential for their restoration and management due their significant role in the global water and carbon cycles. This study aims to assess the ability of upcoming NISAR data for delineating similar wetland classes using machine learning techniques. In particular, we investigated the synergistic use of several polarimetric features for efficient classification of wetland types. To this end, 84 polarimetric features from 11 polarimetric decompositions were extracted from full-polarimetry simulated NISAR data. The mean-shift algorithm was employed to segment the imagery for importing to the object-based machine learning classifiers. Post-classification feature importance analysis using the Gini index suggests that H/A/ALPHA, Freeman-Durden, and Aghababaee decomposition parameters have the highest contribution to the overall accuracy. Further, overall accuracies of 74.33% and 81.93% obtained by SVM and RF, respectively, demonstrated a great capability of NISAR data for wetland mapping and monitoring using limited available training data. Overall, the proposed approach for manipulating the upcoming NISAR data will provide some insight on the efficiency of an upcoming trend in using multi-frequency and full-polarimetry NISAR data with Sweep-SAR architect for producing high-resolution land cover maps on a global scale.

The sensitivity of remote sensing instruments for measuring inundation extent can vary widely. Many sensors are suitable for accurate delineation of open water extent, but in vegetated environments the vegetation canopy can obscure the presence of standing water from detection. Detecting inundation extent in these vegetated environments is especially critical for identifying flooding extent where excess surface water extends into the forests surrounding lakes and streams. In addition, cloud cover can impede timely acquisition of imagery by optical sensors. Here, we examine sensitivity of L-band Global Navigation Satellite Systems Reflectometry (GNSS-R) to flooded conditions relative to the well-known signatures of inundation by L-band SAR, and confirm that there is noticeable sensitivity of GNSS reflected signal to inundated areas, including wetlands covered by vegetation, captured by the strong response of the specular reflection by the underlying water surface.

This paper describes the concepts and recent activities of the HydroSAR project, a collaborative effort between the University of Alaska Fairbanks, the NASA Goddard and Marshall Space Flight Centers, and the Jet Propulsion Laboratory, focused on the automatic production of SAR-derived information for weather-related hazards. This paper will introduce the main processing algorithms as well as the broad product portfolio of the HydroSAR project, which includes information on surface water extent and surface water depth, in addition to capturing agriculture activity and other event-related changes. We will also highlight the cloud-based implementation of HydroSAR, which enables the system to generate data quickly and over large spatial scales. To demonstrate the performance of the service, we present results from a recent event response, where HydroSAR was used to support mapping activities related to the 2020 South Asia monsoon floods, inundating at least 25% of Bangladesh and large areas of the Bihar Province, India. In-region partner ICIMOD supported the performance assessment of the delivered products and algorithms.

The NISAR mission, an L-band and S-band 12-day repeat-pass InSAR, currently scheduled to be launched in January 2023, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The reliable set of 30 ascending and 30 descending 250 km swath of observations on a continuing basis will allow for the modeling and observation of hydrologic processes that serves as a forcing function and mode of energy transport for most living things, as well as changes in the landcover that are associated with agriculture, river and coastal dynamics, and disturbance. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

In the rainy season, Southeastern Mexico flooding is a recurrent phenomenon affecting the rural and services sector with important economic loss. The objective of this study was to detect and monitor flooding areas occurring during the rainy season using Radarsat-2 and Sentinel-1 SAR in Tabasco, Mexico. The project was carried out in 2017, a year when registered precipitation levels attained an accumulation of 2,013 mm, when September and October were the rainiest months. Cartographic information was produced concerning flooded areas, water bodies and wetlands before the waters receded. This information was compared for the same areas during the dry season. This methodology can be used to estimate the flood extents that occur frequently in this region of Mexico, especially because the phenomenon manifests itself year by year due to global warming, which is admitted, to cause greater intensity and more frequent rain events.

The NASA Arctic Boreal Vulnerability Experiment (ABoVE) [1] has been collecting extensive spaceborne, airborne, and field-based measurements in the boreal regions of western North America to develop a better understanding of the vulnerability and resilience of these ecosystems. Now half-way through this ambitious 10-year experiment, unique time series measurements of various kinds (airborne sensor, in situ field measurements) are now available. The NASA-ISRO Synthetic Aperture Radar (NISAR) mission [3] has a requirement to measure inundation extent in wetland areas from the L-band SAR data that it will be collecting worldwide at 12-day intervals, after it is launched in 2022. The NISAR project is evaluating Cal/Val sites for validating its science requirements, including the wetlands in the Tanana River floodplain in the Bonanza Creek Experimental Forest (BCEF), 20 km southwest of Fairbanks [2]. This area includes a long-term research project funded by NSF and USGS (the Alaska Peatland Experiment - APEX) where measurements have been collected since 2004 to study processes controlling carbon storage in this moderately rich fen. In this paper, we examine the L-band UAVSAR airborne SAR data that has been acquired over wetlands in this region since 2017, compare these data against in situ estimates of inundation extent for this area, and classify the UAVSAR data into inundation-related classes.

SAR tomographic methods have proven extremely adept at measuring vegetation vertical structure at a variety of wavelengths including L and P-bands. The three dimensional structure of vegetation and its changes resulting from either natural or anthropogenic causes are key parameters in monitoring ecosystems. The NASA/JPL UAVSAR collected data at L and P-bands at Delta Junction, Alaska in September of 2017 whereas the NASA/JPL UAVSAR and DLR F-SAR acquired data at the BERMS site near Saskatoon, Canada on August 19 and 23 of 2018 respectively. Tomographic data sets were collected at L-band and P-band by the NASA/JPL UAVSAR at Delta Junction and at L-band at BERMS and DLR F-SAR acquired data at L-band and S-band. Ground truth data sets and lidar data from the NASA LVIS system were also acquired at BERMS. We compare L and P tomography at Delta Junction and L-band and S-band tomography from the two systems to each other and to the lidar data sets at BERMS. These data are then used to estimate biomass and assess spatial gradients in the canopy vertical structure. We also compare our data with simulated boreal forest data to assess the sensitivity to the data collection geometry and canopy parameters.

The forthcoming launch of the NASA-ISRO Synthetic Aperture Radar (NISAR) mission will open the path to a new type of L-band measurements constituted by dense time-series of polarimetric backscatter and interferometric coherence with unprecedented spatial and temporal sampling. Here, we start the development of a theoretical framework that links L-band backscatter time-series with interferometric coherence measurements. The water-cloud-model (WCM) and an extended version of the random-motion-over-ground (RMoG) model are adopted to express radar measurements in terms of forest above-ground biomass and tree height. Time-series data collected during the 2019 UAVSAR AM-PM campaign in Southeastern United States are used to evaluate the correlation of various PolSAR- and InSAR-derived parameters with field-measured above-ground biomass.

The ecosystem science requirements for the NASA ISRO Synthetic Aperture Radar (NISAR) will need to be validated after its launch in 2021 [1]. Out of all disciplines that are encompassed by the NISAR mission, ecosystems are the one in most need of pre-launch proxy data, consisting of repeated L-band observations over an extended period of time. The solid earth, cryosphere, and hazards research communities have been able to use historical and contemporary spaceborne data available from ERS-1/2, Radarsat, TerraSAR, Sentinel-1, and others for developing and evaluating products similar to what NISAR would be able to provide. This has been possible, in part, because of the focus of these disciplines on sparsely vegetated surfaces and the fairly straight-forward correspondence of surface scattering properties at both L- and C-band (wavelength of 24 cm and 5 cm respectively). Time series data from L-band sensors of value for ecosystem science disciplines, in contrast, have been sporadic and irregular. Ecosystems targets are almost always vegetated, with the scattering components and volume scattering nature of the target giving different scattering responses at the different wavelength regimes. For this reason, the use of C-band observations as a proxy for NISAR's L-band, as is often done for other disciplines, is not possible for the development and testing of NISAR algorithms.In 2018, a plan was developed for a field/airborne/spaceborne campaign to acquire data in 2019 for NISAR pre-launch ecosystem algorithm development and for evaluation of NISAR ecosystem Cal/Val protocols. This plan includes the acquisition of not just L-band SAR data from the NASA/JPL UAVSAR airborne SAR, but also the acquisition of spaceborne data, airborne data, and field measurements to fully exercise the NISAR protocols for validation of its ecosystem science measurement requirements. Included in the plan is the processing of the field, airborne, and spaceborne data into validation products and the generation of NISAR-like level 3 science products.

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.

The ecosystem science requirements for the NASA ISRO Synthetic Aperture Radar (NISAR) will need to be validated after its launch in 2021 [1]. NASA has also been conducting a major field campaign in the boreal region of North America called the Arctic Boreal Vulnerability Experiment (ABoVE) [2]. ABoVE presents an opportunity to establish critical Cal/Val sites for the NISAR mission, as requirements for estimating biomass under 100 t/ha, detecting disturbance in forested areas, delineation of wetland inundation, and measuring permafrost deformation must be validated in the boreal region. Here, we describe preliminary analysis of field measurements collected as part of ABoVE against a time series of C-band SAR data from the ESA Sentinel-1 satellites.

SAR tomographic methods have proven extremely adept at measuring vegetation vertical structure at a variety of wavelengths including L and P-bands [2]. Measuring the three dimensional structure of vegetation and its changes resulting from either natural or anthropogenic causes are key parameters in monitoring ecosystems. The NASA/JPL UAVSAR system has deployed to multiple sites including Alaska over the last several years to conduct tomographic SAR observations at L-band and P-band. This talk will provide a brief overview of a tomographic SAR experiment conducted in the boreal forests of Alaska in August and September of 2017 at both L and P-bands. This site consists mostly of relatively short vegetation with mean height less than 20 m and maximal height less than 25 m. It is sparse compared with previous temperate and tropical forest tomographic observations made by UAVSAR. These observations provides a unique data set to compare tomographic data at L and P-bands for this type of biome.

This paper presents an L-band Single Antenna Polarimetric Active Radar Calibrator (SAPARC) designed and constructed at the University of Michigan in collaboration with the suborbital radar group of Jet Propulsion Laboratory. The SAPARC is intended for radiometric and polarimetric calibration of both airborne and spaceborne L-band radars, including the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and NASA-ISRO Synthetic Aperture Radar (NISAR). The presented Polarimetric Active Radar Calibrator (PARC) is designed to have a specific scattering matrix response (equal entries) and a very large and stable radar cross section (RCS) value which is significantly higher than its passive counterparts for providing a much higher signal-to-clutter ratio. The specific scattering matrix response enables the PARC to evaluate all the radar channels' radiometric calibration constants as well as cross-talk and channel imbalances, which cannot be estimated by the passive calibration targets. The much higher signal-to-clutter ratio provided by the PARC further reduces the residual errors of calibration and allows them to be easily deployed at different target scenes during radar data acquisitions.

On September 21 and 22, 2016, the L-band UAVSAR airborne Synthetic Aperture Radar (SAR) imaged the SpaceX rocket debris field at Kennedy Space Center, Cape Canaveral Florida. This debris field was the result of an explosion of a SpaceX Falcon 9 rocket during its launch preparations on September 1. The data collected by UAVSAR allowed us to investigate methods for the detection of small ground targets in a complex wetland environment using multi-aspect, fully polarimetric, and high resolution SAR data. We developed a new time domain processor to focus the SAR data directly to a predefined map grid instead of traditional radar coordinates. This approach allowed us to form co-registered SAR images acquired from 8 flight headings to a common map grid without additional resampling. We computed various polarimetric observables for each observed aspect angle, then calculated statistical deviations to identify candidate locations of debris.

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.

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.

This paper investigates the ability of radar image segmentation to produce meaningful, structurally homogenous objects with respect to lidar-derived forest metrics. A comparative approach is taken to determine if radar-derived segments perform better in this respect than arbitrary, square segments or landcover-derived segments. It is found that segmentation of UAVSAR co- and cross-polarization backscatter magnitudes results in increased lidar homogeneity on the segment level relative to the arbitrary and landcover segmentations.

Soil Moisture Active Passive (SMAP), a proposed mission in support of the Earth Science Decadal Survey, conducted afield campaign in June 2010 to support algorithm development. As part of the experiment in situ soil moisture measurements were made over a two week period in which multiple UAVSAR flights were conducted. Repeat-pass polarimetric-interferometric data generated from these flights were analyzed to see if phase changes could be correlated with soil moisture changes. Also, we compared the data to that predicted by simple surface scattering models and showed moderate agreement with the Oh model [4].

The National Research Council's decadal survey recommended DESDynI as one of the high priority missions for NASA. The mission envisions an InSAR/Lidar instrument for observing ecosystem structures on global scales with high spatial resolutions. Consistent and highly resolved global maps of biomass and carbon stocks require highly accurate observations of vegetation, in fact it is expected that such accuracies would require a combination of the high vertical precision of Lidar observations and the large spatial extent of SAR/InSAR measurements. Here we analyze radar backscatter data along with biomass estimates from a field campaign conducted in the Harvard forest in Massachusetts, USA.

We have been developing high-resolution thematic maps of wetlands throughout the North American boreal regions. We assemble a wetlands map for each region based on data collected during the late 1990s, then construct a second map based on data collected during the late 2000s. Comparison of the two maps then makes it possible to assess changes that have occurred over the course of the intervening decade..

We have been developing a continental-scale map of the North American boreal wetlands based on L-Band SAR imagery collected in 1997-1998 by the Japanese Earth Resources Satellite (JERS). The map currently covers the entire state of Alaska, identifying up to nine wetlands classes and two uplands classes. We have also recently obtained and classified a region of L-Band SAR imagery collected in 2007 by the Advanced Land Observing Satellite (ALOS) Phased Array L-Band SAR (PALSAR). Herein, we compare the results of the PALSAR classification to those of the JERS classification in order to detect changes in wetlands type or extent during the decade-long interval between the two sets of SAR imagery.

Field measurements of dielectric properties of hypersaline deposits were realized over an arid site located in Death Valley, CA. The dielectric constant of salt and water mixtures is usually high but can show large variations, depending on the considered salt. We confirmed values observed on the field with laboratory measurements and used these results to model both the amplitude and phase behaviors of the synthetic aperture radar (SAR) signal at C- and L-bands. Our analytical simulations allow reproducing specific copolar signatures observed in both Airborne SAR (AIRSAR) and Spaceborne Imaging Radar (SIR-C) data, corresponding to the saltpan of the Cottonball Basin. More precisely, the main objective of the present paper is to understand the influence of soil salinity as a function of soil moisture on the dielectric constant of soils and then on the backscattering coefficients recorded by airborne and spaceborne SAR systems. We also propose the copolarized backscattering ratio and phase difference as indicators of moistened and salt-affected soils. More precisely, we show that these copolar indicators should allow monitoring of the seasonal variations of the dielectric properties of saline deposits at both C- and L-bands. Because of the frequency dependence of the ionic conductivity, we also show that L-band SAR systems should be efficient tools for detecting both soil moisture and salinity, while C-band SAR systems are more suitable for the monitoring of soil moisture only. Through the study of terrestrial evaporitic environments by means of spaceborne SAR systems, our results could also be of great interest for defining future planetary missions, particularly for the exploration of Mars.

The SAR/InSAR component of the NASA DesdynI mission for measuring vertical vegetation structure from space consists of four possible approaches. These include the use of radar backscatter to estimate biomass, to employ PolInSAR relative phase for measuring the vertical extent, the use of interferometric phase and a ground reference, or the use of interferometric correlation magnitude alone. Temporal decorrelation is a significant contributor to decorrelation of interferometric echoes and is not always separable from volumetric decorrelation hence contributing to uncertainties in vegetation parameter estimates obtained using just correlation magnitude. In this text we analyze data that is close to the best case scenario for isolating temporal decorrelation. With almost zero baseline and a repeat pass of one day, SIR-C data over the eastern US serves as our case study of temporal decorrelation.

In this paper, two data sets consisting of co-located full-waveform lidar and InSAR observations are discussed, one over the Duke Forest, near Durham, North Carolina, and the other, the Harvard Forest, located in Western Massachusetts. Data for the Duke forest consists of AIRSAR and GeoSAR (both airborne sensors) interferometric SAR observations spanning in frequency from X-band down to P-band, and data from the GSFC's SLICER instrument. For the Harvard Forest, spaceborne data from JAXA's ALOS/PALSAR mission is used in conjunction with GSFC's LVIS instrument. Early work with SLICER and GeoSAR data has used a lookup table approach for generating a table that correlates the InSAR observables of differential height between X-and P-band observations, and X-band correlation magnitude to lidar derived height. This table was then used for estimating heights over the remaining swath, where lidar data was not available. A similar technique can be used for spaceborne data, in this case, over the Harvard Forest. In this paper, the comparison between lidar observations and the InSAR Duke observations are shown, and then followed by a preliminary treatment highlighting relationships in the ALOS/PALSAR Harvard data that can be exploited for similar purposes.

We consider the exploitation of dielectric properties of saline deposits for the detection and mapping of moisture in arid regions on both Earth and Mars. We present simulated and experimental study in order to assess the effect of salinity on the complex permittivity of geological materials and, therefore, on the radar backscattering coefficient in the [1–7 GHz] frequency range. Laboratory measurements are performed on sand/sodium chloride aqueous mixtures using a vectorial network analyzer coupled to an open-ended coaxial dielectric probe. We aim at calibrating and validating semiempirical dielectric mixing models. In particular, we evaluated the dependence of the real and imaginary parts of complex permittivity on the microwave frequency, water content, and salinity. Our results confirm that if the real part is mainly affected by the moisture content, the imaginary part is more sensitive to salinity. In addition to the classic formulas of mixing models, the ionic-conductivity losses, which are due to mobile ions in the saline solution, are taken into account in order to better assess the effect of salinity on the dielectric properties of mixtures. Dielectric mixing models are then used as input parameters for the simulation of the radar backscattering coefficients by means of an analytical model: the integral equation model. Simulation results show that salinity should have a significant impact on the radar backscattering recorded in synthetic aperture radar data in terms of the magnitude of the backscattering coefficient. Moreover, our results suggest that VV polarization provides a greater sensitivity to salinity than HH polarization.

This paper deals with the exploitation of dielectric properties of saline deposits for the detection and mapping of moisture in arid regions on both Earth and Mars. We then present a simulation and experimental study in order to assess the effect of salinity on the permittivity of geological materials and therefore on the radar backscattering coefficient in the [1-7 GHz] frequency range. Dielectric mixing models were first calibrated by means of experimental measurements before being used as input parameters of analytical scattering models (IEM, SPM). Simulation results will finally be compared to field measurements (Pyla dune, Death Valley, Mojave Desert) and will be used for the interpretation of SAR data (AIRSAR, PALSAR).

Vertical profiles of forest density potentially are robust indicators of forest biomass, fire susceptibility and ecosystem function. Tropical forests, which are among the most dense and complicated targets for remote sensing, contain about 45% of the world's biomass. Remote sensing of tropical forest structure is therefore an important component to global biomass and carbon monitoring. As in radio astronomy, which uses multibaseline radio interferometry to measure the structure of celestial objects, so multibaseline interferometric SAR (InSAR) can be used to estimate the vertical structure of forests. Vegetation density profiles, along with radar backscattering characteristics and attenuation, determine the radar brightness profile "seen" by InSAR. This paper will describe an experiment at La Selva Biological Station in Costa Rica (~3m rainfall/year) in which we flew 18 effective fixed baselines over tropical forests at C-band (0.056 m wavelength) and L-band (0.25 m). Preliminary inversions for radar brightness profiles will be compared to extensive lidar profiles measured in the same area. They will also be compared to field-measured profiles.

This paper focuses on the cross-track interferometric data calibration results and height accuracy analysis. We also present the key elements of the calibration techniques for cross-track interferometric SAR processed with the AIRSAR Integrated Processor.

In this paper, we present an integrated, end-to-end synthetic aperture radar (SAR) processing system that accepts data processing requests, submits processing jobs, performs quality analysis, delivers and archives processed data. This fully automated SAR processing system utilizes database and internet/intranet web technologies to allow external users to browse and submit data processing requests and receive processed data. It is a cost-effective way to manage a robust SAR processing and archival system. The integration of these functions has reduced operator errors and increased processor throughput dramatically.

Boreal ecosystems play an essential role in global climate regulation. Forests constitute pools of terrestrial carbon and are generally considered as global sinks of atmospheric CO/sub 2/, contributing to attenuating the greenhouse effect. Large amounts of carbon are also stored in boreal lakes, bogs and wetlands, partially released as CH/sub 4/ and other trace gases to the atmosphere during the spring and summer months. Human activities in the forest zone are however reducing the size of the carbon pool and climate change is triggering shorter winters and earlier thaw onset, changing the natural equilibrium. Given its global importance, there is a need to map and monitor the boreal zone, and as the changes occur on all from local, regional to global scales, fine resolution information over vast areas is required. The Global Boreal Forest Mapping (GBFM) project is an international collaborative undertaking initiated by NASDA in 1996, as a follow-on to the tropical-focused Global Rain Forest Mapping (GRFM) project [A. Rosenqvist et al., (2000)]. Utilising the L-band Synthetic Aperture Radar (SAR) on the Japanese Earth Resources Satellite (JERS-1). one of the main objectives of the GBFM project is the generation of extensive, pan-boreaL SAR image mosaics, to provide snap-shots of the forest wetland and open water status in the mid-1990's. Mosaics over Canada, Alaska. Siberia and Europe have been generated, available on the Internet and on DVD free of charge for research and educational purposes. The GBFM project also entails research activities in North America, Siberia and northern Europe, aimed at advancing scientific applications of L-band SAR data in the boreal zone.

A methodology, example and accuracy assessment are given for a continental-scale mosaic of the Amazon River basin at 100 m resolution using the JERS-1 satellite. This unprecedented resource of L-band SAR data collected by JERS-1 during the low-flood season of the river amounts to a collection of 57 orbits of the satellite and a total of some 1500 1 k/spl times/1 kB images. Interscene overlap both in the along-track and cross-track directions allows common reference points to be used to correct individual scene geolocation inaccuracies that have been derived from the satellite ephemeris. The set of common reference points is assembled into a matrix formulation that is used to solve for individual scene geometric offsets. By correcting for these offsets, each scene is placed within a global coordinate system, which can then be used as the basis for creating a final, visually seamless mosaic. The methodology employed in this approach allows for a mathematical foundation to be applied to the mosaicking process as well as providing a unique, traceable solution for correctly geolocating satellite imagery.

The SIR-C/X-SAR imaging radar took its first flight on the Space Shuttle Endeavour in April 1994 and flew for a second time in October 1994. This multifrequency radar has fully polarimetric capability at L- and C-band, and a single polarization at X-band (X-SAR). The Endeavour missions were designated the Space Radar Laboratory-1 (SRL-1) and -2 (SRL-2). Calibration of polarimetric L- and C-band data for all the different modes SIR-C offers is an especially complicated problem. The solution involves extensive analysis of pre-flight test data to come up with a model of the system, analysis of in-flight test data to determine the antenna pattern and gains of the system during operation, and analysis of data from over fourteen calibration sites distributed around the SIR-C/X-SAR orbit track. The SRL missions were the first time a multifrequency polarimetric imaging radar employing a phased array antenna has been flown in space. Calibration of SIR-C data products involved some unique technical problems given the complexity of the radar system. In this paper, the approach adopted for calibration of SIR-C data is described and the calibration performance of the data products is presented.>

During normal synthetic aperture radar (SAR) processing, a flat Earth is assumed when performing radiometric corrections such as antenna pattern and scattering area removal. The authors examine the effects of topographic variations on these corrections. Local slopes will cause the actual scattering area to be different from that calculated using the flat Earth assumption. It is shown that this effect may easily cause calibration errors larger than a decibel. Ignoring the topography during antenna pattern removal may also introduce errors of several decibels in the case of airborne systems. The effect of topography on antenna pattern removal is expected to be negligible for spaceborne SARs. The authors show how these effects can be taken into account if a digital elevation model is available for the imaged area. The errors are quantified for two different types of terrain, a moderate relief area near Tombstone, AZ, and a high relief area near Oetztal in the Austrian Alps. The authors show errors for two well-known radar systems, the C-band ERS-1 spaceborne radar system and the three frequency NASA/JPL airborne SAR system (AIRSAR).>