Polarimetric radar remote sensing of ocean surface wind

Experimental data are presented to support the development of a new concept for ocean wind velocity measurement (speed and direction) with the polarimetric microwave radar technology. This new concept has strong potential for improving the wind direction accuracy and extending the useful swath width by up to 30% for follow-on NASA spaceborne scatterometer mission to SeaWinds series. The key issue is whether there is a relationship between the polarization state of ocean backscatter and surface wind velocity at NASA scatterometer frequencies (13 GHz). An airborne Ku-band polarimetric scatterometer (POLSCAT) was developed for proof-of-concept measurements. A set of aircraft flights indicated repeatable wind direction signals in the POLSCAT observations of sea surfaces at 9-11 m/s wind speed. The correlation coefficients between co- and cross-polarized radar response of ocean surfaces have a peak-to-peak amplitude of about 0.4 and are shown to have an odd-symmetry with respect to the wind direction, unlike the normalized radar cross sections     

A Wind and Rain Backscatter Model Derived From AMSR and SeaWinds Data

The SeaWinds scatterometer was originally designed to measure wind vectors over the ocean by exploiting the relationship between wind-induced surface roughening and the normalized radar backscatter cross section. Rain can degrade scatterometer wind estimation; however, the simultaneous wind/rain (SWR) algorithm was developed to enable SeaWinds to simultaneously retrieve wind and rain rate data. This algorithm is based on colocating data from the Precipitation Radar on the Tropical Rainfall Measuring Mission and SeaWinds on QuikSCAT. This paper develops a new wind and rain radar backscatter model for SWR using colocated data from the Advanced Microwave Scanning Radiometer (AMSR) and SeaWinds aboard the Advanced Earth Observing Satellite II. This paper accounts for rain height in the model in order to calculate surface rain rate from the integrated rain rate. The performance of SWR using the new wind/rain model is measured by comparison of wind vectors and rain rates to the previous SWR algorithm, AMSR rain rates, and National Center for Environmental Prediction numerical weather prediction winds. The new SWR algorithm produces more accurate rain estimates and improved winds, and detects rain with a low false alarm rate.      

Vegetation studies of the Amazon basin using enhanced resolutionSeasat scatterometer data

The Seasat-A scatterometer (SASS) was designed to measure the near-surface wind field over the ocean by inferring the wind from measurements of the surface radar backscatter. While backscatter measurements were also made over land, they have been primarily used for the calibration of the instrument. This has been due in part to the low resolution of the scatterometer measurements (nominally 50 km). In a separate paper the present authors introduced a new method for generating enhanced resolution radar measurements of the Earth's surface using spaceborne scatterometry. In the present paper, the method is used with SASS data to study vegetation classification over the extended Amazon basin using the resulting medium-scale radar images. The remarkable correlation between the Ku-band radar images and vegetation formations is explored, and the results of several successful experiments to classify the general vegetation classes using the image data are presented. The results demonstrate the utility of medium-scale radar imagery in the study of tropical vegetation and permit utilization of both historic and contemporary scatterometer data for studies of global change. Because the scatterometer provides frequent, wide-area coverage at a variety of incidence angles, it can supplement higher resolution instruments which often have narrow swaths with limited coverage and incidence angle diversity     

The polarization-dependent relation between radar backscatter fromthe ocean surface and surface wind vector at frequencies between 1 and18 GHz

A series of airborne scatterometer measurements carried out with the DUTSCAT multifrequency airborne scatterometer are discussed. This study deals with the first results obtained from the analysis of these measurements. The objective of this activity is to establish a multifrequency dual-polarization radar signature database, and with it a multidimensional version of the current CMOD1 model. The main features of the data set are the following. The wind exponent of the upwind normalized radar cross section (NRCS) increases with frequency and incidence angle in the case of HH polarization. The upwind/downwind ratio is mainly negative at 20° of incidence angle, always at C-, X-, and Ku1-bands     

Aircraft measurements of the microwave scattering signature of the ocean

Microwave scattering signatures of the ocean have been measured over a range of surface wind speeds from 3 m/s to 23.6 m/s using the AAFE RADSCAT scatterometer in an aircraft. Normalized scattering coefficients are presented for vertical and horizontal polarizations as a function of incidence angle (nadir to55deg) and radar azimuth angle (0degto360deg) relative to surface wind direction. For a given radar polarization, incidence angle, and azimuth angle relative to the wind direction, these scattering data exhibit a power law dependence on surface wind speed. The relation of the scattering coefficient to azimuth angle obtained during aircraft circles (antenna conical scans) is anisotropic and suggests that microwave scatterometers can be used to infer both wind speed and direction. These results have been used for the design of the Seasat-A Satellite Scatterometer (SASS) to be flown in 1978 on this first NASA oceanographic satellite.     

Retrieving ocean surface wind speed from the TRMM Precipitation Radar measurements

Spaceborne scatterometery has been used for many years now to retrieve the ocean surface wind field from normalized radar cross-section measurements of the ocean surface. Though designed specifically for the measurement of precipitation profiles in the atmosphere, the Precipitation Radar (PR) of the Tropical Rainfall Measuring Mission (TRMM) also acquires surface backscattering measurements of the global oceans. As such, this instrument provides an interesting opportunity to explore the benefits and pitfalls of alternative radar configurations in the satellite remote sensing of ocean winds. In this paper, a technique was developed for retrieving ocean surface winds using surface backscattering measurements from the TRMM PR. The wind retrieval algorithm developed for TRMM PR makes use of a maximum-likelihood estimation technique to compensate for the low backscattering associated with the PR configuration. The high vertical resolution of the PR serves to filter-out rain-contaminated cells normally integrated into Ku-band scatterometer measurements. The algorithm was validated through comparisons of ocean surface wind speeds derived from PR with remotely measured winds from TMI and QuikSCAT, as well as in situ observations from oceanographic buoys, revealing good agreements in wind speed estimations.     

Dual-polarization measurements at C-band over the ocean: results from airborne radar observations and comparison with ENVISAT ASAR data

This paper presents an analysis of measurements of the normalized radar cross-section (NRCS) in vertical and horizontal polarizations over the ocean obtained from the C-band airborne radar STORM. The dataset was collected during the experiment called "Validation with a Polarimetric Airborne Radar of ENVISAT SAR over the Ocean (VALPARESO)", which took place during the calibration/validation phase of the ENVISAT Advanced Synthetic Aperture Radar (ASAR). From this dataset, the properties of the polarization ratio are discussed and in particular its dependencies with radar geometry (incidence and azimuth angle) as well as with meteorological conditions (wind and sea state). The polarization ratio is found to be dependent on incidence and azimuth angles. Its dependence with incidence angle is found to be significantly different from empirical models previously proposed in the literature. It also exhibits some correlation with surface conditions (wind and wave) with a more important correlation with significant wave steepness. Two new analytical formulations are proposed to model the polarization ratio, one as a function of incidence angle only, the second one with additional dependence with azimuth angle. It is shown that it is necessary to consider an azimuth-dependent polarization ratio for incidence angles larger than 30/spl deg/. Comparisons with the polarization ratio from ENVISAT ASAR images are used to assess this model.     

Comparisons of scatterometer models for the AMI on ERS-1: thepossibility of systematic azimuth angle biases of wind speed anddirection

There have been many different attempts to develop a model to relate the normalized radar backscatter values for the C-band radars of the active microwave instrument (AMI) on ERS-1 as a function of 10-m wind speed, azimuth angle, which is wind direction relative to radar-beam direction and incidence angle. The first two models, namely CMOD-1, Long (1985) and CMOD-2, are analyzed, and modifications of them are used to show by means of Monte Carlo methods that it is important to be able to define the backscatter model for all azimuth angles in addition to obtaining good agreement at upwind, downwind, and crosswind relative to the radar-beam direction. Methods are described to compare one model to another and to show how to systematic discrepancies, which are the result of model differences, can be found. These discrepancies are also expected when various models are employed to recover winds from real backscatter data. Discrepancies between a model and an unknown “true” model can introduce systematic biases in the recovered wind vectors as opposed to random errors, which result from sampling variability. The validation of the vector winds from scatterometer data requires a comparison of these winds with accurate conventionally measured winds. The data buoys deployed by various nations can serve as the appropriate data because ship reports are not accurate enough     

Evaluation of a compound probability model with tower-mountedscatterometer data

Six months of data from the YSCAT94 experiment conducted at the CCIW WAVES research platform on Lake Ontario, Canada, are analyzed to evaluate a compound probability model. YSCAT was an ultrawideband small footprint (≈1 m) microwave scatterometer that operated at frequencies of 2-18 GHz, incidence angles from 0° to 60°, both h-pol and v-pol, and which tracked the wind using simultaneous weather measurements. The probability distribution function of the measured instantaneous backscattered amplitude (p(a)) is compared to theoretical distributions developed from-the composite model and a simple wave spectrum. Model parameters of the resulting Rayleigh/generalized lognormal distribution probability density function (pdf) (C, a₁ , and a₂) are derived directly from the data and are found to demonstrate relationships with wind speed, incidence angle, and radar frequency     

On airborne measurement of the sea surface wind vector by a scatterometer (altimeter) with a nadir-looking wide-beam antenna

A pilot needs operational information about wind over sea as well as wave height to provide safety for a hydroplane landing on water. Near-surface wind speed and direction can be obtained with an airborne microwave scatterometer, a radar designed for measuring the scatter characteristics of a surface. Mostly narrow-beam antennas are applied for such wind measurement. Unfortunately, a microwave narrow-beam antenna has considerable size that hampers its placement on flying apparatus. In this connection, a possibility to apply a conventional airborne radar altimeter as a scatterometer with a nadir-looking wide-beam antenna in conjunction with Doppler filtering for recovering the wind vector over sea is discussed, and measuring algorithms of sea surface wind speed and direction are proposed. The obtained result can be interesting for many studies in oceanography, meteorology, air-sea interaction, and climate change and for creation of an airborne radar system for amphibious airplane safe landing on the sea surface, in particular for search and rescue operations in coastal areas.     

Microwave emission and reflection from the wind-roughened seasurface at 6.7 and 18.6 GHz

Microwave radiometric observations were made with specially designed microwave radiometers at 6.7 and 18.6 GHz, and the results were compared with those of other investigators, over the frequency range of 1-40 GHz. Dependences of sea surface emission and reflection on wind speed, frequency, incidence angle, and polarization type are discussed in detail, following discussions of the reflective processes of sky radiation and error estimation in the retrieval of mainlobe-averaged brightness temperature. The wind speed sensitivity of brightness temperature, emissivity, and reflectivity is formulated with respect to frequency and incidence angle in each polarization. The brightness temperature, emissivity and reflectivity at arbitrary wind speed are derived employing this formulation. Based on the results obtained it is suggested that the 10-19-GHz band may be optimal for satellite microwave radiometer observations of sea-surface wind     

Identifiability in wind estimation from scatterometer measurements

The problem of identifiability of a wind vector that is estimated from wind scatterometer measurements of the radar backscatter of the ocean's surface is addressed. The traditional wind estimation approach produces multiple estimates of the wind direction. A second processing step, known as dealiasing or ambiguity removal, is used to select a single wind estimate from these multiple solutions. Dealiasing is typically based on various ad hoc considerations. The traditional wind estimation approach results in multiple solutions associated with local minima in an objective function formed from the noisy backscatter measurements. The authors discuss the question of the uniqueness of the wind vector estimates resulting from this intuitive approach     

Comparisons between measured and theoretical results forC- and Ku-bands backscatter from naturally occurringinternal wave current patterns

Predicted and measured values of radar backscatter and of internal wave surface currents are compared using data obtained during the SCATTMOD internal wave experiment. Radar backscatter and surface truth measurements were obtained during six days in August 1985 and cover nine sets of tide-generated internal waves in the Georgia Strait, Canada. The radar portion of this data consists of approximately 75 sets each of C- and Ku-band fanbeam airborne scatterometer signals, each processed to 25 incidence angles and an along-track resolution of either 12.5 or 6.25 m. Aircraft navigation data were also recorded, simultaneous surface measurements, including wave slope, wave height, current, wind, and ship position, were obtained from the CFAV Endeavour. Current meter were located both fore and aft to allow internal wave-phase velocity estimates to be computed     

Calibration of spaceborne scatterometers using tropical rainforests

Wind scatterometers are radar systems designed specifically to measure the normalized radar backscatter coefficient (σ°) of the ocean's surface in order to determine the near-surface wind vector. Postlaunch calibration of a wind scatterometer can be performed with an extended-area natural target such as the Amazon tropical rain forest. Rain forests exhibit a remarkably high degree of homogeneity in their radar response over a very large area though some spatial and temporal variability exist. The authors present a simple technique for calibrating scatterometer data using tropical rain forests, Using a polynomial model for the rolloff of σ° with incidence angle, the technique determines gain corrections to ensure consistency between different antennas and processing channels. Corrections for the time varying instrument gain are made consistent with a seasonally fixed rain forest response; however, without ground stations or aircraft flights, it is difficult to uniquely distinguish between seasonal variations in the rain forest and slow variations of the system gain. Applying the corrections, the intrinsic variability of the σ° of the rain forest is estimated to be ±0.15 dB, which is the limit of the accuracy of calibration using the rain forest. The technique is illustrated with Seasat scatterometer (SASS) data and applied to ERS-1 Active Microwave Instrument scatterometer (Escat) data. Gain corrections of up to several tenths of a decibel are estimated for SASS. Corrections for Escat data are found to be very small, suggesting that Escat data is well calibrated     

An updated analysis of the ocean surface wind direction signal in passive microwave brightness temperatures

We analyze the wind direction signal for vertically (v) and horizontally (h) polarized microwave radiation at 37 GHz, 19 GHz, and 11 GHz; and an Earth incidence angle of 53/spl deg/. We use brightness temperatures from SSM/I and TMI and wind vectors from buoys and the QUIKSCAT scatterometer. The wind vectors are space and time collocated with the radiometer measurements. Water vapor, cloud water and sea surface temperature are obtained from independent measurements and are uncorrelated with the wind direction. We find a wind direction signal that is noticeably smaller at low and moderate wind speeds than a previous analysis had indicated. We attribute the discrepancy to errors in the atmospheric parameters that were present in the data set of the earlier study. We show that the polarization combination 2v-h is almost insensitive to atmospheric changes and agrees with the earlier results. The strength of our new signals agrees well with JPL aircraft radiometer measurements. It is significantly smaller than the prediction of the two-scale sea surface emission model for low and intermediate wind speeds.     

Joint Monitoring of Ground and Sky for Cereal Crops Based on Scatterometer Measurement and ASAR Images

The joint monitoring of the ground and sky for cereal crops based on microwave data has become a popular method for researches on earth surface objects. Focused on the sensitivity of backscatter from the scatterometer measurement and advanced synthetic aperture radar (ASAR) images to cereal parameters of rice, nine acquisitions, including rice parameters related eco-physiological variables and scattering coefficients, have been carried over the paddy field corresponding to rice growth stages. This paper analyzes the relationship between the corresponding backscatter to the cereal parameters based on the measurement at the interesting bands, polarizations, and incidence angels. Further, a modified water cloud model is built based on the ground measurement and advanced integrated equation model (AIEM), and then cereal parameters from ASAR images are retrieved and verified. The research results show that the sensitivity of backscatter to cereals from the sensor of the radar scatterometer could be helpful to build the retrieve model for synthetic aperture radar (SAR) images, which can achieve the scientific goals of the joint monitoring of ground and sky for cereal crops.     

The Dutch ROVE Program

In the Netherlands the ROVE team (Radar Observation of VEgetation) investigates the possibilities of radar remote sensing in agriculture. It is an interdisciplinary working group in which five institutes collaborate. Each year, from 1975 to 1980, noncoherent backscatter measurements have been made on different types of vegetations, crops, and bare soils using groups of test fields laid out on a test farm of one of the participating institutes. This approach warrants adequate control over botanical, soil, and surface parameters. For the radar backscatter measurements a short-range FM/CW scatterometer is used mounted on a carriage which is moved along the fields. The combination of the FM/CW principle with movement of the system guarantees a sufficient number of independent observations in a measurement where the illuminated patch is large enough to contain an adequate number of scatterers. So the radar return parameter ? (or ?°) is determined with sufficient accuracy as a function of grazing angle and of time through the growing season. An accurate X-band side-looking airborne radar (SLAR) with digital recording is available for airborne verification experiments. The program is a continuation of the experiments described by the authors at the URSI meeting in Berne in 1974.     

Coherent laser radar at 2 μm using solid-state lasers

The development of a coherent laser radar system using 2-μm Tm and Tm, Ho-doped solid-state lasers, which is useful for the remote range-resolved measurement of atmospheric winds, aerosol backscatter, and differential absorption lidar (DIAL) measurements of atmospheric water vapor and CO₂ concentrations, is described. Measurements made with the 2-μm coherent laser radar system, advances in the laser technology, and atmospheric propagation effects on 2-μm coherent lidar performance are discussed. Results include horizontal atmospheric wind measurements to >20 km. vertical wind measurements to >5 km, near-horizontal cloud returns to 100 km, and hard target (mountainside) returns from 145 km     

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 advanced ambiguity selection algorithm for SeaWinds

SeaWinds on QuikSCAT, a spaceborne Ku-band scatterometer, estimates ocean winds via the relationship between the normalized radar backscatter and the vector wind. Scatterometer wind retrieval generates several possible wind vector solutions or ambiguities at each resolution cell, requiring a separate ambiguity selection step to give a unique solution. In processing SeaWinds on QuikSCAT data, the ambiguity selection is "nudged" or initialized using numerical weather prediction winds. We describe a sophisticated new ambiguity selection approach developed at Brigham Young University (BYU) that does not require nudging. The BYU method utilizes a low-order data-driven Karhunen-Loeve wind field model to promote self-consistency. Ambiguity selected winds from the BYU method and standard SeaWinds processing are compared over a set of 102 revs. A manual examination of the data suggests that the nonnudging BYU method selects a more self-consistent wind field in the absence of cyclonic storms. Over a set of cyclonic storm regions, BYU performs better in 9% of the cases and worse in 20% of the cases. Overall, the BYU algorithm selects 93% of the same ambiguities as the standard dataset. This comparison helps validate both nonnudging and nudging techniques and indicates that SeaWinds ambiguity selection can be generally accomplished without nudging.