Phased Array L-band Synthetic Aperture Radar Research Articles

Fusion of Hyperspectral and L-Band SAR Data to Estimate Fractional Vegetation Cover in a Coastal California Scrub Community

A study was carried out to investigate the utility of airborne hyperspectral and satellite L-band Synthetic Aperture Radar (SAR) data for estimating fractional coverages of herbaceous, coastal scrub, and bare ground cover types on the central California coast. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery collected in September of 2008 and Phased Array L-band SAR (PALSAR) (HH- and HV-polarizations) captured in April and July of 2008 were combined for vegetation cover mapping. Hyperspectral features, computed as AVIRIS indices (NDVI, TCARI/OSAVI, and PRI), and textural information (energy, contrast, homogeneity, and fractal dimension) produced by L-band SAR were fused together to generate a new feature space. We used global Ordinary Least Squares (OLS) linear regression to integrate and decompose the new feature space for fractional vegetation mapping. Ground measurements of fractional cover were collected from plots located within the U.S. Forest Service’s Brazil Ranch study site for validation of the OLS model predictions. Significant linear relationships were found between fractional cover mapping from remote sensing and the ground-truth data. The estimation accuracy of fractional coverage mapping from remote sensing in terms of Root Mean Square Error (RMSE) was 17%, 12%, and 10%, for the herbaceous, coastal scrub, and bare ground covers, respectively. Decomposition results showed that textural information from L-band SAR strongly supported herbaceous and coastal scrub fractional mapping, while indices features from AVIRIS significantly improved mapping of herbaceous cover and bare ground.

Mapping of mangrove forests on the southern coast of São Paulo, Brazil, using synthetic aperture radar data from ALOS/PALSAR

Widespread in the coastal zones of (sub)tropical regions, mangroves are an essential habitat for many animal species and provide subsistence resources for many human coastal communities. Among remote sensing techniques, synthetic aperture radar is a particularly advantageous method to monitor mangroves: images are not dependent on cloud cover and can provide information from forest understory. This article mapped mangrove forests in the southern coast of São Paulo State (Brazil) using frequency-based contextual classification of incoherent attributes derived from a multi-polarized Phased Array L-Band Synthetic Aperture Radar (PALSAR) image. The use of 10 incoherent attributes and only 3 Synthetic Aperture Radar (SAR) vegetation indices as input for digital classification showed the best accuracies with kappa index values of 0.739 and 0.734, respectively. HH polarization and SAR vegetation indices were the attributes that contributed most to the mangrove mapping procedure. The use of L-Band SAR data was effective for mapping mangrove areas, and therefore it is recommended as a tool for coastal management.

A Generalized Radar Backscattering Model Based on Wave Theory for Multilayer Multispecies Vegetation

A generalized radar scattering model based on wave theory is described. The model predicts polarimetric radar backscattering coefficients for structurally complex vegetation comprised of multiple species and layers. Compared to conventional two-layer crown-trunk models, modeling of actual forests has been improved substantially, allowing better understanding of microwave interaction with vegetation. The model generalizes an existing single-species discrete scatterer model and, by including scattering and propagation effects through judiciously defined vegetation layers, enables its application to an arbitrary number of species types. The scatterers within each layer are modeled as finite cylinders or disks having arbitrary size, density, and orientation, as in the predecessor model. The distorted Born approximation is used to represent the propagation through each layer, while scattering from each is modeled as a linear superposition of scattering from its respective random collection of scatterers. Interactions of waves within and between each layer and direct scattering from the ground are accounted for. Validation of the model is presented based on its application to 23 wooded savanna sites located in Queensland, Australia, and comparison with Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) Airborne Synthetic Aperture Radar (AIRSAR) data. Results indicate good agreement between simulated and actual backscattering coefficients, particularly at HH and VV polarizations. More discrepancies are found at HV polarizations and can be explained by uncertainties in the knowledge of input parameters, such as inaccuracies in the surface model, surface roughness parameterization, and soil moisture.

Geo-referencing of continental-scale JERS-1 SAR mosaics based on matching homologous features with a digital elevation model: theory and practice

An effective method for a posteriori ortho-rectification of continental-scale synthetic aperture radar (SAR) mosaics using a digital elevation model (DEM) has been developed. The method is based on homologous feature matching between the DEM and a simulated SAR image. The simulated image is derived from the radar-viewing geometry, topographic information and contextual information provided by the Shuttle Radar Topography Mission (SRTM), shorelines and water bodies database (SWBD) and GeoCover Landsat mosaics. Two large L-band SAR mosaics (the global boreal forest mapping (GBFM) Siberia mosaic and the global rain forest mapping (GRFM) Africa mosaic), assembled from the Japanese Earth Resources Satellite-1 (JERS-1) data, were accurately geo-referenced and ortho-rectified. The GRFM Africa mosaic was also radiometrically corrected for topographic effects. The accurate co-registration with the DEM allows for improved classification methods based on the combination of SAR backscatter with terrain features. Comparison of the revised GBFM and GRFM mosaics with a forthcoming set of continental-scale mosaics assembled from the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data will offer a unique possibility for change detection studies over the Tropical and Boreal forest zones with a temporal spacing of some 10 years.

The K&C PALSAR Mosaic of the African Continent: Processing Issues and First Thematic Results

The Japan Space Exploration Agency Kyoto and Carbon (K&C) Initiative seeks to demonstrate the potential of the Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data for addressing regional applications relating to climate change, carbon cycle science, and environmental conservation. This paper outlines the generation of a regional dual-polarization (HH and HV) mosaic for the entire African continent at spatial resolution on the order of 100 m. The main computational and radar science issues undertaken to generate a seamless mosaic with good radiometric and geometric accuracy are summarized. Preliminary investigations into the thematic information provided by the K&C Africa mosaic and comparisons with the JERS-1 SAR mosaic generated as part of the Global Rain Forest Mapping Project are reported, with emphasis placed on characterizing and detecting change in forests and savannas.

Remote sensing detection of gold related alteration zones in Um Rus area, Central Eastern Desert of Egypt

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Phased Array L-band Synthetic Aperture Radar (PALSAR) images covering the Um Rus area in the Central Eastern Desert of Egypt were evaluated for mapping geologic structure, lithology, and gold-related alteration zones. The study area is covered by Pan-African basement rocks including gabbro and granodiorite intruded into a variable mixture of metavolcanics and metasediments. The first three principal component analyses (PCA1, PCA2, PCA3) in a Red–Green–Blue (RGB) of the visible through shortwave-infrared (VNIR + SWIR) ASTER bands enabled the discrimination between lithological units. The results show that ASTER band ratios ((2 + 4)/3, (5 + 7)/6, (7 + 9)/8) in RGB identifies the lithological units and discriminates the granodiorite very well from the adjacent rock units. The granodiorites are dissected by gold-bearing quartz veins surrounded by alteration zones. The microscopic examination of samples collected from the alteration zones shows sericitic and argillic alteration zones. The Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) supervised classification methods were applied using the reference spectra of the USGS spectral library. The results show that these classification methods are capable of mapping the alteration zones as indicated by field verification work. The PALSAR image was enhanced for fracture mapping using the second moment co-occurrence filter. Overlying extracted faults and alteration zone classification images show that the N30E and N–S fractures represent potential zones for gold exploration. It is concluded that the proposed methods can be used as a powerful tool for ore deposit exploration.

Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data

Conversion of tropical forests to oil palm plantations in Malaysia and Indonesia has resulted in large-scale environmental degradation, loss of biodiversity and significant carbon emissions. For both countries to participate in the United Nation’s REDD (Reduced Emission from Deforestation and Degradation) mechanism, assessment of forest carbon stocks, including the estimated loss in carbon from conversion to plantation, is needed. In this study, we use a combination of field and remote sensing data to quantify both the magnitude and the geographical distribution of carbon stock in forests and timber plantations, in Sabah, Malaysia, which has been the site of significant expansion of oil palm cultivation over the last two decades. Forest structure data from 129 ha of research and inventory plots were used at different spatial scales to discriminate forest biomass across degradation levels. Field data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) imagery to both discriminate oil palm plantation from forest stands, with an accuracy of 97.0% ( κ = 0.64) and predict AGB using regression analysis of HV-polarized PALSAR data ( R 2 = 0.63, p < .001). Direct estimation of AGB from simple regression models was sensitive to both environmental conditions and forest structure. Precipitation effect on the backscatter data changed the HV prediction of AGB significantly ( R 2 = 0.21, p < .001), and scattering from large leaves of mature palm trees significantly impeded the use of a single HV-based model for predicting AGB in palm oil plantations. Multi-temporal SAR data and algorithms based on forest types are suggested to improve the ability of a sensor similar to ALOS PALSAR for accurately mapping and monitoring forest biomass, now that the ALOS PALSAR sensor is no longer operational.

Evaluation of the altimetry from SRTM-3 and planimetry from high-resolution PALSAR FBD data for semi-detailed topographic mapping in the Amazon Region

The Brazilian Amazon has a deficit of 35% of coverage regarding topographic mapping at semi-detailed (1:100,000) scale. This paper presents an alternative to overcome this scenario using a combination of planialtimetric information from two orbital SAR (Synthetic Aperture Radar) missions. The altimetry was acquired from Shuttle Radar Topography Mission (SRTM), while the planimetry was provided from Fine Beam Dual (FBD) images of the Phased Array L-band Synthetic Aperture Radar (PALSAR) sensor. The research was carried out in the mountainous area of the Serra dos Carajás (Pará State), located on the Amazon region. The quality of the orbital topographic information was evaluated regarding precise planialtimetric measurements acquired from Global Positioning System (GPS) field campaigns. The evaluations were performed following two approaches: (1) the use of Root Mean Square Error (RMSE) and (2) tendency and precision hypothesis tests. The investigation has shown that the planialtimetric quality of the orbital products fulfilled the Brazilian Map Accuracy Standards requirements for 1:100,000 A Class map. Thus, the use of combination of information provided by PALSAR and SRTM-3 data can be considered a promising alternative for production and update of semi-detailed topographic mapping in similar environments of the Amazon region, where topographic information is lacking or presents low quality.

Monitoring Rice Agriculture in the Sacramento Valley, USA With Multitemporal PALSAR and MODIS Imagery

Rice agriculture is an important crop that influences land-atmosphere interactions and requires substantial resources for flood management. Multitemporal acquisition strategies provide an opportunity to improve rice mapping and monitoring of hydroperiod. The objectives of this study were to 1) delineate rice paddies with Phased Array L-band Synthetic Aperture Radar (PALSAR) fine-beam single/dual (FBS/D) mode measurements and 2) integrate multitemporal, ScanSAR Wide-Beam 1 (WB1)- and Moderate Resolution Imaging Spectroradiometer (MODIS)- observations for flood frequency mapping. Multitemporal and multiscale PALSAR and MODIS imagery were collected over the study region in the Sacramento Valley, California, USA. A decision-tree approach utilized multitemporal FBS (HH polarization) data to classify rice fields and WB1 measurements to assess paddy flood status. High temporal frequency MODIS products further characterized hydroperiod for each individual rice paddy using a relationship between the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI). Validation found the PALSAR-derived rice paddy extent maps and hydroperiod products to possess very high overall accuracies (95% overall accuracy). Agreement between MODIS and PALSAR flood products was strong with agreement between 85-94% at four comparison dates. By using complementing products and the strengths of each instrument, image acquisition strategies and monitoring protocol can be enhanced. The results highlight how the integration of multitemporal PALSAR and MODIS can be used to generate valuable agro-ecological information products in an operational context.

Assessment of ALOS PALSAR 50 m Orthorectified FBD Data for Regional Land Cover Classification by Support Vector Machines

From its launch in 2006, the phased array L-band synthetic aperture radar (PALSAR) onboard the advanced land observing satellite (ALOS) has acquired many dual-polarized (FBD) images with a 70-km swath width, aiming to produce spatially consistent coverage over tropical rainforest. This paper investigates the relevancy of PALSAR orthorectified FBD product at 50-m resolution for regional land cover classification by the support vector machines (SVM). Our test site is the Riau province, Sumatra island, Indonesia, known to hold vast area of natural peatland forest with an extreme biodiversity threatened by industrial deforestation. Since it is demonstrated the radiometric information (HH and HV channels) cannot be solely used to achieve a good classification, the spatial information in these orthorectified data is investigated. A new tool using the recursive feature elimination SVM-based process and the textural Haralick's parameters is introduced. The real contribution of textures within the land cover classification can be understood. A small set of textural parameters is determined at local scale while being optimal for the land cover discrimination. The SVM-based classifier is carried out across the whole Riau province and its results are compared with a Landsat-based estimation. The agreement is over 70% with six classes and 86% for the natural forest map. These results are remarkable since only one PALSAR FBD product is used and this assessment is performed on more than 40 million pixels. The results confirm the high potential of the PALSAR sensor for forest monitoring at regional, if not global scale.

An Evaluation of the ALOS PALSAR L-Band Backscatter—Above Ground Biomass Relationship Queensland, Australia: Impacts of Surface Moisture Condition and Vegetation Structure

Focusing on woody vegetation in Queensland, Australia, the study aimed to establish whether the relationship between Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) HH and HV backscattering coefficients and above ground biomass (AGB) was consistent within and between structural formations (forests, woodlands and open woodlands, including scrub). Across these formations, 2781 plot-based measurements (from 1139 sites) of tree diameters by species were collated, from which AGB was estimated using generic allometric equations. For Queensland, PALSAR fine beam dual (FBD) 50 m strip data for 2007 were provided through the Japanese Space Exploration Agency's (JAXA) Kyoto and Carbon (K&C) Initiative, with up to 3 acquisitions available for each Reference System for Planning (RSP) paths. When individual strips acquired over Queensland were combined, `banding' was evident within the resulting mosaics, with this attributed to enhanced L-band backscatter following rainfall events in some areas. Reference to Advanced Microwave Scanning Radiometer-EOS (AMSR-E) data indicated that strips with enhanced L-band backscatter corresponded to areas with increased effective vegetation water content (kg m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ) and, to a lesser extent, soil moisture (g cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> ). Regardless of moisture conditions, L-band HV topographically normalized backscattering intensities backscatter (σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> ) increased asymptotically with AGB, with the saturation level being greatest for forests and least for open woodlands. However, under conditions of relative maximum surface moisture, L-band HV and HH σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> was enhanced by as much as 2.5 and 4.0 dB respectively, particularly for forests of lower AGB, with this resulting in an overall reduction in dynamic range. The saturation level also reduced at L-band HH for forests and woodlands but remained similar for open woodlands. Differences in the rate of increase in both L-band HH and HV σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> with AGB were observed between forests and the woodland categories (for both relatively wet and dry conditions) with these attributed, in part, to differences in the size class distribution and stem density between non-remnant (secondary) forests and remnant woodlands of lower AGB. The study concludes that PALSAR data acquired when surface moisture and rainfall are minimal allow better estimation of the AGB of woody vegetation and that retrieval algorithms ideally need to consider differences in surface moisture conditions and vegetation structure.

SAR observation and model tracking of an oil spill event in coastal waters

Oil spills are a major contributor to marine pollution. The objective of this work is to simulate the oil spill trajectory of oil released from a pipeline leaking in the Gulf of Mexico with the GNOME (General NOAA Operational Modeling Environment) model. The model was developed by NOAA (National Oceanic and Atmospheric Administration) to investigate the effects of different pollutants and environmental conditions on trajectory results. Also, a Texture-Classifying Neural Network Algorithm (TCNNA) was used to delineate ocean oil slicks from synthetic aperture radar (SAR) observations. During the simulation, ocean currents from NCOM (Navy Coastal Ocean Model) outputs and surface wind data measured by an NDBC (National Data Buoy Center) buoy are used to drive the GNOME model. The results show good agreement between the simulated trajectory of the oil spill and synchronous observations from the European ENVISAT ASAR (Advanced Synthetic Aperture Radar) and the Japanese ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array L-band Synthetic Aperture Radar) images. Based on experience with past marine oil spills, about 63.0% of the oil will float and 18.5% of the oil will evaporate and disperse. In addition, the effects from uncertainty of ocean currents and the diffusion coefficient on the trajectory results are also studied.

On a Novel Approach Using MLCC and CFAR for the Improvement of Ship Detection by Synthetic Aperture Radar

Multilook cross correlation (MLCC) is a useful technique in extracting the images of ships embedded in heavy sea clutter by synthetic aperture radar (SAR). In the ship detection experiment in 2006 by Phased Array L-band Synthetic Aperture Radar (PALSAR) on board the Advanced Land Observing Satellite, we applied MLCC to PALSAR data in order to extract small fishing boats. The result was that some boats were detected by thresholding MLCC coherence images under favorable conditions. However, it was also found that the threshold method was not suitable to automatically determine the threshold levels corresponding to the desired false alarm rate (FAR) values. In order to overcome this problem and to improve the accuracy of ship detection by MLCC, we propose a new and simple technique of MLCC-constant FAR (CFAR) or gamma-CFAR. In this method, CFAR is applied to interlook coherence images produced by MLCC. We tested this method using simulation and PALSAR data and then found out substantial improvement in signal-to-noise ratio and FAR in comparison with the coherence image alone. In this letter, we summarize the MLCC-CFAR algorithm and the experimental results.

Using L-band SAR coherence to delineate glacier extent

Significant retreat has been seen in many glaciers worldwide. With increasing concern about the impact of global warming, there has been a concerted effort to monitor ongoing change. Projects such as Global Land Ice Measurements from Space (GLIMS) rely on the contributions of institutions worldwide to update a global database of digital glacier outlines. To a great extent, this effort relies on the manual extraction of glacier area from satellite optical instruments, such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Unfortunately, dependence on optical imagery poses two significant challenges. First, clouds preclude the viewing of many glaciers, making periodic measurements impossible. Second, many alpine glaciers have significant debris cover, additionally complicating delineation of glacier extent from optical data. The sensitivity of synthetic aperture radar (SAR) coherence to motion offers a method for detecting glacial extent that is independent of weather and allows for the discrimination of active glaciers, with and without debris cover. In this paper, we describe an automated method for delineating the surface area of two glaciers in Alaska, one with and one without a debris-covered terminus. We use the periodic global observations of the Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) instrument to generate coherence images. Low coherence serves as our primary indicator of glacial extent. However, coherence can be degraded by dense vegetation and the topography of mountainous areas. Hence we develop a decision tree that also considers phase gradients and mountain slope to identify glacier boundaries. The result is a robust technique for generating digital glacier outlines. Coupled with the comprehensive global acquisitions of ALOS, it suggests a new method for measuring glacier extents around the world.

ALOS PALSAR D-InSAR for land subsidence mapping in Jakarta, Indonesia

Differential synthetic aperture radar interferometry (D-InSAR) is a technique capable of detecting land surface deformation. In this research, we use Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) data to investigate land subsidence in Jakarta during 2007 and 2008. It is found that four northern areas in the city exhibit clear indications of land subsidence. The location of the centre of subsidence is estimated, and the subsidence volume is evaluated for each area using the unwrapping method as a further process of using D-InSAR results. The subsidence depth and volume around the centre are estimated to be 10–22 cm and 0.2 × 105 – 4.2 × 105 m3, respectively, in the study time period. Comparison with ground survey data indicates that the D-InSAR analysis gives reliable estimates of the subsidence in an urban environment.

Verification of Polarimetric Calibration Method Including Faraday Rotation Compensation Using PALSAR Data

The spaceborne Phased Array L-band Synthetic Aperture Radar (PALSAR) needs polarimetric calibration in order for PALSAR data to be utilized for various applications such as geophysical analysis. In case of a PALSAR system using L-band, the Faraday rotation (FR) effect, which rotates the polarization plane of radio waves, becomes a problem when full-polarimetric observation data are used, such as target classification using polarization synthesis. Therefore, we need to estimate and remove both antenna distortion matrices and the FR effect. In this paper, we propose a polarimetric calibration method taking both channel imbalance and crosstalk of receiving and transmitting antennas and FR effect into consideration using two reference reflectors, namely, polarization preserving reflector and polarization rotating one. Then, we apply our calibration method to PALSAR data and derive antenna distortion matrices and FR angle simultaneously. Our calibration results show that the estimated antenna distortion matrices are almost equal to the calibration results from the Japan Aerospace Exploration Agency and that the estimated FR angle has a reasonable value.

Operational Large-Area Forest Monitoring in Siberia Using ALOS PALSAR Summer Intensities and Winter Coherence

Focusing on Siberia, the feasibility of using Advanced Land Observing Satellite Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) summer-intensity and winter-coherence images for large-area forest monitoring was investigated. Fine beam dual horizontal/horizontal and horizontal/vertical (polarization) intensity strip images were acquired during the summer of 2007. The processing consisted of radiometric calibration, orthorectification, and topographic normalization. The coherence was estimated from interferometric pairs with 46-day repeat-pass intervals. The pairs were acquired during the winters of 2006/2007 and 2007/2008. During both winters, suitable weather conditions that allow for low temporal decorrelation had been reported. By using PALSAR intensities and winter-coherence data, areas of forest and nonforest were separated. By combining both data types, a minimal overlap of the class signatures was observed, even though the analysis was conducted at the pixel level and no speckle filter was applied. The study concludes that the operational delineation of forest cover using PALSAR data is feasible. By applying a segmentation-based classification, an accuracy of 93% was obtained.

PALSAR Radiometric and Geometric Calibration

This paper summarizes the results obtained from geometric and radiometric calibrations of the Phased-Array L-Band Synthetic Aperture Radar (PALSAR) on the Advanced Land Observing Satellite, which has been in space for three years. All of the imaging modes of the PALSAR, i.e., single, dual, and full polarimetric strip modes and scanning synthetic aperture radar (SCANSAR), were calibrated and validated using a total of 572 calibration points collected worldwide and distributed targets selected primarily from the Amazon forest. Through raw-data characterization, antenna-pattern estimation using the distributed target data, and polarimetric calibration using the Faraday rotation-free area in the Amazon, we performed the PALSAR radiometric and geometric calibrations and confirmed that the geometric accuracy of the strip mode is 9.7-m root mean square (rms), the geometric accuracy of SCANSAR is 70 m, and the radiometric accuracy is 0.76 dB from a corner-reflector analysis and 0.22 dB from the Amazon data analysis (standard deviation). Polarimetric calibration was successful, resulting in a VV/HH amplitude balance of 1.013 (0.0561 dB) with a standard deviation of 0.062 and a phase balance of 0.612deg with a standard deviation of 2.66deg .

A New Method for Correcting ScanSAR Scalloping Using Forests and Inter-SCAN Banding Employing Dynamic Filtering

The Scanning Synthetic Aperture Radar (ScanSAR) is very useful for Earth observation because of its wider imaging swath and shorter revisit time. However, ScanSAR is sometimes affected by the following three artifacts: (1) scalloping, which often appears as repeating weak azimuth stripes at both edges of the focused burst image; (2) azimuth ambiguity (i.e., a form of ghosting that appears over the adjacent uniform area when the pulse repetition frequency is below the Doppler bandwidth); and (3) radiometric discontinuity (i.e., banding) between two adjacent scans. This paper proposes three methods to correct these artifacts, which are, specifically, the proposal for scalloping correction using Amazon Rainforest data, band limitation, and the correction for the inter SCAN banding using the dynamic gain correction algorithm. Several corrected sample data sets of the Phased-Array L-band SAR onboard the Advanced Land-Observing Satellite are presented to demonstrate the validity of the proposed methods.

An L-Band Ocean Geophysical Model Function Derived From PALSAR

This paper examines L-band normalized radar cross section (NRCS) dependence on ocean surface wind. More than 90 000 match-ups, each consisting of the L-band HH NRCS, incidence angles, wind speeds, and wind directions, were collected from the Phased-Array L-Band Synthetic Aperture Radar (PALSAR) and scatterometer wind vectors. Based on the match-ups, the L-band HH NRCS dependence on incidence angle and wind vector is modeled for 0-20-m/s wind speeds and 17deg-43deg incidence angles. The derived relation indicates that the wind sensitivity of the L-band NRCS is less than that of the C-band at moderate winds and large incidence angles, whereas comparable at stronger winds ((>10 m/s) and small incidence angles. The upwind-crosswind difference is amplified in the 10-15-m/s range followed by an almost zero amplitude from 4 to 8 m/s, which represents a clear phase shift with the C-band VV and Ku-band HH models. Wind speeds are then estimated from the match-ups, based on the derived model function. A comparison with the reference scatterometer winds reveals a 0.05-m/s bias and a 1.85-m/s root mean square error, where crosswind data give rise to large errors due to low wind sensitivity at wind speeds of around 10 m/s, particularly at large incidence angles. The L-band NRCS behavior in strong winds (>20 m/s), at which the C-band is saturated, was not determined in the current model due to the limited number of the match-ups.