Evaluation of JERS-1 SAR images from a coastal wind retrieval point of view

Wind retrieval from Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) using an L-band model function in coastal regions is evaluated. It is known that JERS-1 SAR has excessive ambiguities. This paper also gives a quantitative evaluation of excessive ambiguities in coastal scenes of JERS-1 SAR. First, focusing on the cases where wind blows from the shore in Sagami Bay, we investigate phenomena of wind speed increase with offshore distance using European Remote sensing Satellite-1 (ERS-1) SAR-derived wind speeds. The relation between wind speed and offshore distance is well formulated, which indicates the transition of the atmospheric boundary layer from land to sea surface. Wind speeds derived from JERS-1 SAR should be overestimated due to the excessive ambiguity. Then, for observation time of each JERS-1 SAR capturing the cases that wind blows from the shore in Sagami Bay, the expected wind speed growth profile is derived from the wind speed growth formula and an in situ wind observation of Hiratsuka Experiment Station. We convert the wind-speed profile into the sigma-0 profile by an L-band model function. Finally, the profiles of JERS-1 SAR-observed and the estimated sigma-0 are compared, and the excessive ambiguity is estimated as the difference between them. As a result, the dynamic range of first azimuth ambiguity is as large as that of the wind-relating signal from the ocean surface. Moreover, higher order azimuth ambiguities and range ambiguity also may have a significant impact on near-shore wind retrieval.     

A continental-scale mosaic of the Amazon basin using JERS-1 SAR

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.     

JERS-1 SAR image quality and interferometric potential

As part of the JERS-1 investigation program, the authors evaluate the duality of images from the Japanese radar and validate its interferometric capabilities. JERS-1 interferometry has the potential to derive digital terrain models and measure ground displacements     

Sea ice monitoring by L-band SAR: an assessment based on literature and comparisons of JERS-1 and ERS-1 imagery

Spaceborne single-polarization C-band synthetic aperture radar (SAR) imagery is widely used to gather information about the state of the sea ice cover in the polar regions. C-band is regarded as a reasonable choice for all-season monitoring capabilities. For specific mapping tasks, however, other frequency bands can be more suitable. In the first part of this paper, the summary of a literature study dealing with the utilization of L-band SAR imagery for sea ice monitoring is presented. Investigations reveal that if deformation features such as ice ridges, rubble fields, and brash ice are to be mapped, L-band radar is superior in a number of cases. The second part of this paper addresses the comparison of JERS-1 and ERS-1 SAR images that were acquired over sea ice east of Svalbard and along the east coast of Greenland. The effects of the different frequencies, polarizations, and incidence angles of the two SAR systems are discussed. It is demonstrated that the images of both sensors complement one another in the analysis of ice conditions, resulting in a more detailed view of the sea ice cover state.     

Polarimetric microwave wind radiometer model function and retrieval testing for WindSat

A geophysical model function (GMF), relating the directional response of polarimetric brightness temperatures to ocean surface winds, is developed for the WindSat multifrequency polarimetric microwave radiometer. This GMF is derived from the WindSat data and tuned with the aircraft radiometer measurements for very high winds from the Hurricane Ocean Wind Experiment in 1997. The directional signals in the aircraft polarimetric radiometer data are corroborated by coincident Ku-band scatterometer measurements for wind speeds in the range of 20-35 m/s. We applied an iterative retrieval algorithm using the polarimetric brightness temperatures from 18-, 23-, and 37-GHz channels. We find that the root-mean-square direction difference between the Global Data Assimilation System winds and the closest WindSat wind ambiguity is less than 20/spl deg/ for above 7-m/s wind speed. The retrieval analysis supports the consistency of the Windrad05 GMF with the WindSat data.     

A phase signature for detecting wet subsurface structures using polarimetric L-band SAR

In this paper, we investigate the ability of L-band synthetic aperture radar (SAR) systems to penetrate soils to retrieve information about subsurface wet structures. Our experiment site, the Pyla dune, is a bare sandy area allowing high radar penetration and known to have large wet subsurface structures (paleosoils) at varying depths. Buried paleosoils, which act as moisture tanks, are detectable with radar, since they present a high permittivity due to their water content. By analyzing airborne polarimetric SAR data, we established that a phase signature is correlated to the buried wet palesoils: a phase difference of 23/spl deg/ between the horizontal (HH) and vertical (VV) channels was clearly observed. It allows detection of the paleosoil down to a larger depth (5.2 m) than when only considering HH and HV amplitude signals (3.5 m). In order to confirm this result, field measurements were performed that led to the same observed phase difference. We could fit our observations to the semiempirical model proposed by Oh and Sarabandi, and we reproduced the observed phenomenon using a two-layer integral equation method (IEM) model of the Pyla dune, which was completed by finite-difference time-domain (FDTD) numerical simulations. We show that the soil moisture significantly influences the radar response in terms of phase difference between the copolarized modes. Our study also shows that the single-scattering IEM model reproduces the observed phase difference fairly well for a natural outdoor site when combined to FDTD simulation results. This phase signature could be used as a new tool to map subsurface moisture in arid regions.     

星载合成孔径雷达海面风速反演技术研究

CMOD系列地球物理模式函数作为业务化应用的风速反演模式,已由CMOD4（1997）发展到CMOD5.N（2010）,且该系列模式函数还在不多的发展完善。     

Validating a scatterometer wind algorithm for ERS-1 SAR

The ocean surface wind field is observed from space operationally using scatterometry. The European Space Agency's (ESAs) ERS-1 satellite scatterometer routinely produces a wind product that is assimilated into forecast models. Scatterometry cannot give accurate wind estimates close to land, however, because the field of view of a spaceborne scatterometer is on the order of 50 km. Side lobe contamination, due to the large contrast in backscatter between land and water, compounds the problem. Synthetic aperture radar (SAR) can provide wind speed and direction estimates on a finer scale, so that high-resolution wind fields can be constructed near shore. An algorithm has been developed that uses the spectral expression of wind in SAR imagery to estimate wind direction and calibrated backscatter to estimate wind strength. Three versions, based on C-band scatterometer algorithms, are evaluated for accuracy in potential operational use. Algorithm estimates are compared with wind measurements from buoys in the Gulf of Alaska, Bering Strait, and off the Pacific Northwest coast by using a data set of 61 near-coincident buoy and ERS-1 SAR observations. Representative figures for the accuracy of the algorithm are ±2 m/s for wind speed and ±37° for wind direction at a 25-km spatial resolution     

Simultaneous wind and rain retrieval using SeaWinds data

The SeaWinds scatterometers onboard the QuikSCAT and the Advanced Earth Observing Satellite 2 measure ocean winds on a global scale via the relationship between the normalized radar backscattering cross section of the ocean and the vector wind. The current wind retrieval method ignores scattering and attenuation of ocean rain, which alter backscatter measurements and corrupt retrieved winds. Using a simple rain backscatter and attenuation model, two methods of improving wind estimation in the presence of rain are evaluated. First, if no suitable prior knowledge of the rain rate is available, a maximum-likelihood estimation technique is used to simultaneously retrieve the wind velocity and rain rate. Second, when a suitable outside estimate of the rain rate is available, wind retrieval is performed by correcting the wind geophysical model function for the known rain via the rain backscatter model. The new retrieval techniques are evaluated via simulation and validation with data from the National Centers for Environmental Prediction and the Tropical Rainfall Measuring Mission Precipitation Radar. The simultaneous wind/rain estimation method yields most accurate winds in the "sweet spot" of SeaWinds' swath. On the outer-beam edges of the swath, simultaneous wind/rain estimation is not usable. Wind speeds from simultaneous wind/rain retrieval are nearly unbiased for all rain rates and wind speeds, while conventionally retrieved wind speeds become increasingly biased with rain rate. A synoptic example demonstrates that the new method is capable of reducing the rain-induced wind vector error while producing a consistent (yet noisy) estimate of the rain rate.     

An emissivity-based wind vector retrieval algorithm for the WindSat polarimetric radiometer

The Naval Research Laboratory WindSat polarimetric radiometer was launched on January 6, 2003 and is the first fully polarimetric radiometer to be flown in space. WindSat has three fully polarimetric channels at 10.7, 18.7, and 37.0 GHz and vertically and horizontally polarized channels at 6.8 and 23.8 GHz. A first-generation wind vector retrieval algorithm for the WindSat polarimetric radiometer is developed in this study. An atmospheric clearing algorithm is used to estimate the surface emissivity from the measured WindSat brightness temperature at each channel. A specular correction factor is introduced in the radiative transfer equation to account for excess reflected atmospheric brightness, compared to the specular assumption, as a function wind speed. An empirical geophysical model function relating the surface emissivity to the wind vector is derived using coincident QuikSCAT scatterometer wind vector measurements. The confidence in the derived harmonics for the polarimetric channels is high and should be considered suitable to validate analytical surface scattering models for polarized ocean surface emission. The performance of the retrieval algorithm is assessed with comparisons to Global Data Assimilation System (GDAS) wind vector outputs. The root mean square (RMS) uncertainty of the closest wind direction ambiguity is less than 20/spl deg/ for wind speeds greater than 6 m/s and less than 15/spl deg/ at 10 m/s and greater. The retrieval skill, the percentage of retrievals in which the first-rank solution is the closest to the GDAS reference, is 75% at 7 m/s and 85% or higher above 10 m/s. The wind speed is retrieved with an RMS uncertainty of 1.5 m/s.     

基于多时相Sentinel-1 SAR数据的喀斯特石漠化区地表土壤水分反演研究

土壤水分是地球表层水循环、能量循环和生物地球化学循环中的重要组成部分,是研究喀斯特石漠化地区生态系统的关键参数。基于多时相的Sentinel-1 SAR数据与Alpha 近似模型构建土壤水分观测方程组,反演喀斯特石漠化地区地表土壤水分并对其时空变化特征及误差影响因素展开分析。研究发现观测周期内区域土壤水分总体变化趋势与降雨量变化趋势高度一致,石漠化地区土壤水分高值与空间异质性程度明显高于非石漠化地区。精度验证结果显示土壤水分反演结果的均方根误差为0.059 cm³/cm³,平均误差为0.026 cm³/cm³,该方法在区域地表土壤水分反演中表现出一定的适用性,分析认为地表土壤因周边的复杂生境条件产生的混合像元问题是导致反演误差的主要影响因素。研究可为利用短时间周期重复遥感观测方法获取复杂山区环境下的土壤水分提供参考,为喀斯特石漠化地区生态系统修复和生态产业发展提供支撑。     

QuikSCAT geophysical model function for tropical cyclones andapplication to Hurricane Floyd

The QuikSCAT radar measurements of several tropical cyclones in 1999 have been studied to develop the geophysical model function (GMF) of Ku-band radar σ₀ values (normalized radar cross section) for extreme high wind conditions. To account for the effects of precipitation, the authors analyze the co-located rain rates from the Special Sensor Microwave/Imager (SSM/I) and propose the rain rate as a parameter of the GMF. The analysis indicates the deficiency of the NSCAT2 GMF developed for the NASA scatterometer, which overestimates the ocean σ₀ for tropical cyclones and ignores the influence of rain. It is suggested that the QuikSCAT σ₀ is sensitive to the wind speed of up to about 40-50 m s^-1. The authors introduce modifications to the NSCAT2 GMF and apply the modified GMF to the QuikSCAT observations of Hurricane Floyd. The QuikSCAT wind estimates for Hurricane Floyd in 1999 was improved with the maximum wind speed reaching above 60 m s^-1. The authors perform an error analysis by comparing the QuikSCAT winds with the analyses fields from the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division (HRD). The reasonable agreement between the improved QuikSCAT winds and the HRD analyses supports the applications of scatterometer wind retrievals for hurricanes     

Estimation of bare surface soil moisture and surface roughnessparameter using L-band SAR image data

An algorithm based on a fit of the single-scattering integral equation method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by the Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well-managed watershed in southwest Oklahoma. Prior to its application for soil moisture inversion, a good agreement was found between the single-scattering IEM simulations and the L-band measurements of SIR-C and AIRSAR over a wide range of soil moisture and surface roughness conditions. The sensitivity of soil moisture variation to the co-polarized signals were then examined under the consideration of the calibration accuracy of various components of SAR measurements. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively     

ERS-1 scatterometer measurements. II. An algorithm forocean-surface wind retrieval including light winds

For pt.I see ibid., vol.36, no.2, p.603-22 (1998). An algorithm for retrieving European Remote Sensing Satellite (ERS-1) scatterometer winds, denoted the Rufenach-Bates-Tosini (RBT) algorithm, is developed and used to retrieve winds collocated within ±25 km of buoy measurements in two oceanic regions, equatorial and midlatitude. An improvement in the retrieved RBT winds over the European Space Agency (ESA) winds is due mainly to a geophysical model employing the full available wind-speed range, including the lightest winds. This model, denoted BMOD5, is tuned by using the scatterometer and buoy measurements, resulting in two different models for the midlatitude and equatorial regions. The RBT retrieved winds exhibit (1) a larger number of solutions (wind vectors) and (2) smaller biases in wind speed than the ESA wind product. The increase in the number of retrieved winds is primarily due to lighter winds employed, 0.2 m/s to 18 m/s; whereas, the ESA winds are truncated near 3 m/s. The ESA winds underestimate the highest winds significantly, by about 20%, and overestimate the lightest winds. The RBT wind bias is less than a few percent at the highest winds and a few tenths of a m/s at the lowest winds. Both algorithms retrieve 180° ambiguous directions almost as often as the true direction. Regression fits to the winds using the RBT algorithm produce standard deviations of 1 m/s and 25° near the equator for winds varying from 0.2-10 m/s and 1.2 m/s and 250 at midlatitudes for winds varying from 0.2-18 m/s, provided that the ambiguities are removed     

An occupancy model for image retrieval and similarity evaluation

The analysis of visual information often involves the manipulation of enormous volumes of data. If some tolerance is allowed in the results, orders of magnitude improvement in efficiency can be achieved in such analysis by appropriate selective processing, without necessarily considering all the data features. To guarantee that the error introduced does not exceed the allowed limit, a certain minimum proportion of the data must be involved in the analysis. This proportion cannot be determined arbitrarily. It should be chosen based on some formal methods, with a consideration of the error inherent in the data. This paper presents some techniques for improving the retrieval efficiency in image-based information systems, with performance guarantees on the reliability of results. Using the statistical theory of occupancy, it develops a model for the formal selection of the minimal subset of image features to be involved in histogram-based similarity evaluation. This guarantees that decisions based on the minimum proportion are always the same as (or close to) the one that would have been reached by considering all the features. Results on real and simulated data show the performance of the model on speedup, robustness, scalability, and performance guarantees.     

Stand age retrieval in production forest stands in New Zealand using C- and L-band polarimetric Radar

Regressions of single-, dual-, quad-, and full-polarization L- and C-band synthetic aperture radar (SAR) against stand age from 403 radiata pine stands in Kaingaroa Forest, New Zealand have been carried out, using the National Aeronautics and Space Administration's Airborne SAR instrument. The regressions attempted to find products suitable for the separation of young (two years or less) from old stands (25 years or older), and for the estimation of stand age. Local incidence angle had no significant effect for C-band, but was always significant for L-band, giving a standard error reduction of 13% to 32% for log stand age. Stand density was highly significant for both bands, giving standard error reductions of 7% to 47%. Single- and dual-polarization products were severely biased, and it was impossible to separate young and old stands, except L-band horizontal-(HH)-plus-horizontal-vertical (HV). C-band quad-polarization gave less bias and lower error than for that L-band, when local incidence angle and stand density were excluded. C-band full-polarization using covariance magnitudes gave no improvement over C-band quad-polarization, but L-band did give a significant improvement. The C- and L-band full-polarization products with six polarimetric indices gave significant improvements in the standard error. The results show that regressions of SAR data with stand age are possible with full- and quad-polarization L- and C-band datasets, although the prediction limits increase rapidly with stand age. The smallest error in estimated stand age, with an RMS of 3.22 years, was for L-band full-polarization with six polarimetric indices, calculated from a validation dataset. Separation of young and old forest stands was only possible for full-, quad-polarization, and the L-band HH-plus-HV products.     

A new wind vector algorithm for C-band SAR

Ocean wind speed and wind direction are estimated simultaneously using the normalized radar cross sections /spl sigma//sup 0/ corresponding to two neighboring (25-km) blocks, within a given synthetic aperture radar (SAR) image, having slightly different incidence angles. This method is motivated by the methodology used for scatterometer data. The wind direction ambiguity is removed by using the direction closest to that given by a buoy or some other source of information. We demonstrate this method with 11 ENVISAT Advanced SAR sensor images of the Gulf of Mexico and coastal waters of the North Atlantic. Estimated wind vectors are compared with wind measurements from buoys and scatterometer data. We show that this method can surpass other methods in some cases, even those with insufficient visible wind-induced streaks in the SAR images, to extract wind vectors.     

Simple algorithm for soil moisture retrieval with co-polarized SAR data

In this paper, an empirical methodology to retrieve bare soil moisture by Synthetic Aperture Radar (SAR) is developed. The model is based on Advanced Integral Equation Model (AIEM). Since AIEM cannot express cross-polarized backscattering coefficients accurately, we propose an empirical model to retrieve soil moisture for bare farmland only with co-polarized SAR data. The soil moisture can be obtained by solving an equation of HH and VV polarized data without any field measurements. Both simulated and real SAR data are used to validate the accuracy of the model. This method is especially effective in a large area where the surface roughness is difficult to be completely measured.