WindSat radio-frequency interference signature and its identification over land and ocean

Radio-frequency interference (RFI) in the spaceborne multichannel radiometer data of WindSat and the Advanced Microwave Scanning Radiometer-EOS is currently being detected using a spectral difference technique. Such a technique does not explicitly utilize multichannel correlations of radiometer data, which are key information in separating RFI from natural radiations. Furthermore, it is not optimal for radiometer data observed over ocean regions due to the inherent large natural variability of spectral difference over ocean. In this paper, we first analyzed multivariate WindSat and Scanning Multichannel Microwave Radiometer (SMMR) data in terms of channel correlation, information content, and principal components of WindSat and SMMR data. Then two methods based on channel correlation were developed for RFI detection over land and ocean. Over land, we extended the spectral difference technique using principal component analysis (PCA) of RFI indices, which integrates statistics of target emission/scattering characteristics (through RFI indices) and multivariate correlation of radiometer data into a single statistical framework of PCA. Over ocean, channel regression of X-band can account for nearly all of the natural variations in the WindSat data. Therefore, we use a channel regression-based model difference technique to directly predict RFI-free brightness temperature, and therefore RFI intensity. Although model difference technique is most desirable, it is more difficult to apply over land due to heterogeneity of land surfaces. Both methods improve our knowledge of RFI signatures in terms of channel correlations and explore potential RFI mitigation, and thus provide risk reductions for future satellite passive microwave missions such as the NPOESS Conical Scanning Microwave Imager/Sounder. The new RFI algorithms are effective in detecting RFI in the C- and X-band Windsat radiometer channels over land and ocean.     

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 multifrequency algorithm for the retrieval of soil moisture on alarge scale using microwave data from SMMR and SSM/I satellites

The sensitivity of microwave emission at different frequencies to soil moisture in bare and vegetated soils has been investigated using experimental data. Since the best frequency for the measurement of soil moisture (L-band) is absent in current satellite sensors, it is necessary to seek alternative solutions. An algorithm is proposed for the retrieval of soil moisture based on the sensitivity to moisture of both the brightness temperature and the polarization index at C-band, one that is able to correct for the effect of vegetation by means of the polarization index at X-band. The algorithm has been tested by using experimental data collected with airborne microwave radiometers on agricultural areas and validated by using the data sets of special sensor microwave/imager (SMM/I) and scanning multichannel microwave radiometer (SMMR). These research activities are planned in view of coming new satellites: AQUA (NASA) and ADEOS-II (NASDA), which will be launched by the end of 2001. These will have new generation microwave radiometers (AMSR-E and AMSR) onboard, which show much better characteristics with respect to the previous sensors, in particular an enhanced spatial resolution     

A methodology for surface soil moisture and vegetation opticaldepth retrieval using the microwave polarization difference index

A methodology for retrieving surface soil moisture and vegetation optical depth from satellite microwave radiometer data is presented. The procedure is tested with historical 6.6 GHz H and V polarized brightness temperature observations from the scanning multichannel microwave radiometer (SMMR) over several test sites in Illinois. Results using only nighttime data are presented at this time due to the greater stability of nighttime surface temperature estimation. The methodology uses a radiative transfer model to solve for surface soil moisture and vegetation optical depth simultaneously using a nonlinear iterative optimization procedure. It assumes known constant values for the scattering albedo and roughness, and that vegetation optical depth for H-polarization is the same as for V-polarization. Surface temperature is derived by a procedure using high frequency V-polarized brightness temperatures. The methodology does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes and may be applied to other wavelengths. Results compare well with field observations of soil moisture and satellite-derived vegetation index data from optical sensors     

A Two Step Linear Statistical Technique Using Leaps And Bounds Procedure for Retrieval of Geophysical Parameters from Microwave Radiometric Data

A linear statistical technique using leaps and bounds procedure is developed for retrieving geophysical parameters from remote measurements. It is applied to the retrieval of sea surface temperature (SST) from the Scanning Multichannel Microwave Radiometer (SMMR) on the SEASAT Satellite. The technique involves the application of an efficient algorithm to select the best fixed-size subset, of the 10 SMMR channels for linearly retrieving a given geophysical parameter. The 5 channel subset [6.6 V, 6.6H, 10H, 18 V, 21H], where V and H refer to the vertical and horizontal polarizations, respectively and numbers are the channel frequencies in gigahertz, provides the minimum rms error in estimating SST. Comparison with ground truth indicates that the algorithm infers SST with an rms accuracy of better than 1.5K under most environmental conditions. A quality control procedure is proposed which should further improve the accuracy.     

Retrieval of Ocean Surface And Atmospheric Parameters from Multichannel Microwave Radiometric Measurements

This paper discusses the retrieval from Scanning Multichannel Microwave Radiometer (SMMR) data of ocean surface temperature, surface wind speed, rain rate, cloud height, and the amount of water vapor and nonprecipitating liquid water over the ocean. The sensitivity of the algorithms that retrieve the wind speed and seasurface temperature in the absence of rain to the (undetected) presence of rain, and the accuracy of a more general method that retrieves rain rate along with other meteorological parameters are discussed. These investigations are based on models of the microwave emission from the Earth's atmosphere over the ocean in the presence of rain. The modeling technique and the retrieval methods are also discussed.     

Observations of Oceanic Surface-Wind Fields from the Nimbus-7 Microwave Radiometer

Brightness temperatures from the fiIve-frequency (6.6, 10.7, 18, 21, and 37 GHz) dual-polarized scanning multichannel microwave radiometer (SMMR) on Nimbus 7 have been used to obtain surface wind fields over the ocean. The sateilite-derived wind field for 1200Z, February 19, 1979, in the eastern North Pacific has been compared with an operationally generated surface-wind analysis field. Previous point comparisons at selected locations have indicated that satellite winds are accurate to 3 ms-1. The results here, although of a preliminary nature, indicate that SMMR-derived winds may be used to determine large-scale wind fields over the ocean, particularly in areas of strong wind gradients such as found in cyclonic systems.     

A nonlinear optimization algorithm for WindSat wind vector retrievals

WindSat is a space-based polarimetric microwave radiometer designed to demonstrate the capability to measure the ocean surface wind vector using a radiometer. We describe a nonlinear iterative algorithm for simultaneous retrieval of sea surface temperature, columnar water vapor, columnar cloud liquid water, and the ocean surface wind vector from WindSat measurements. The algorithm uses a physically based forward model function for the WindSat brightness temperatures. Empirical corrections to the physically based model are discussed. We present evaluations of initial retrieval performance using a six-month dataset of WindSat measurements and collocated data from other satellites and a numerical weather model. We focus primarily on the application to wind vector retrievals.     

Design and test of the ground-based L-band Radiometer for Estimating Water In Soils (LEWIS)

In the framework of the preparation of the Soil Moisture and Ocean Salinity (SMOS) mission, several field experiments are required so as to address specific modeling issues. The goal is to improve current models and to test retrieval algorithms. However, adequate ground instrumentation is scarce and not readily available "off the shelf". In this context, a high-accuracy L-band radiometer was required for a specific long-term campaign for the preparation of the SMOS mission. For this purpose, a dual-polarized radiometer was designed and built to check algorithms for surface soil moisture retrieval from multiangular dual-polarized brightness temperatures. This radiometer has been tested in the field for 20 months and is operational since end of January 2003. The aim of this paper is to give details of the system architecture, calibration procedures, together with the performances obtained and some preliminary results.     

Flower Constellation of Millimeter-Wave Radiometers for Tropospheric Monitoring at Pseudogeostationary Scale

In this paper, the design of a minisatellite FLOwer constellation (FC), deploying millimeter-wave (MMW) scanning RADiometers, namely, FLORAD, and devoted to tropospheric observations, is analyzed and discussed. The FLORAD mission is aimed at the retrieval of thermal and hydrological properties of the troposphere, specifically temperature profile, water-vapor profile, cloud liquid content, and rainfall and snowfall rate. The goal of frequent revisit time at regional scale, coupled with quasi-global coverage and relatively high spatial resolution, is here called pseudogeostationary scale and implemented through a FC of three minisatellites in elliptical orbits. FCs are built on compatible (resonant) orbits and can offer several degrees of freedom in their design. The payload MMW channels for tropospheric retrieval were selected following the ranking based on a reduced-entropy method between 90 and 230 GHz. Various configurations of the MMW radiometer multiband channels are investigated, pointing out the tradeoff between performances and complexity within the constraint of minisatellite platform. Statistical inversion schemes are employed to quantify the overall accuracy of the selected MMW radiometer configurations.      

Microwave radiometric technique to retrieve vapor, liquid and ice.II. Joint studies of radiometer and radar in winter clouds

For pt.I see ibid., vol.35, no.2, p.224-36 (1997). A neural network-based retrieval technique is developed to infer vapor, liquid, and ice columns using two- and three-channel microwave radiometers. Neural network-based inverse scattering methods are capable of merging various data streams in order to retrieve microphysical properties of clouds and precipitation. The method is calibrated using National Oceanic and Atmospheric Administration (NOAA) results in a cloud-free condition. The performance of two- and three-channel neural network-based techniques is verified by independent NOAA estimates. The estimates of vapor and liquid agree with NOAA values. In the presence of ice, the liquid estimates deviated from NOAA's estimates. One of the major contributions of the three-channel radiometer is the estimation of ice in a winter cloud. The three-channel radiometer not only improves estimates of vapor and liquid, but also retrieves the ice column. Passive remote sensing can be ameliorated with the help of active remote sensing methods. The three-channel radiometer is used for estimating columnar contents of vapor, liquid, and ice in a cloud. It is shown that vertical profiles of median size diameter, number concentration, liquid water content, and ice water content can be inferred by combining radar reflectivity and radiometer observations. The combined remote sensor method is applied to Winter Icing and Storms Project (WISP) data to obtain detailed microphysical properties of clouds and precipitation. The authors also derived Z- Ice Water Content (IWC) and Z- Liquid Water Content (LWC) relationships and they are consistent with the earlier results     

Measurement of Global Oceanic Winds from Seasat-SMMR and Its Companson with Seasat-SASS and ALT Deinved Winds

Wind speed has been retrieved using different channel combinations of Seasat Scanning Multichannel Microwave Radiometer (SMMR) data. The sets of channel combinations were derived by applying a leaps and bounds procedure to a statistical data base of brightness temperature and geophysical parameters. Different best subsets of sizes two to five SMMR channels were obtained, based on R2, the coefficient of determination criterion. Wind speeds derived using the best two channel subsets (10.6 H and 18.0 V) were compared with Joint Air-Sea Interaction Experiment)(JASIN) in situ observations of winds and an RMS difference of 1.5 m/s was found. The retrieval accuracy of wind speed from different subsets of two to five channels agreed to within about 10 percent. Global maps of oceanic winds have been generated and compared with maps obtained from Seasat scatterometer and altimeter derived winds. The three sensors depictedthe general features of wind distribution in both the northern and southern hemispheres and co form with the prevailing ideas, but differed quantitatively over certain latitude belts requiring further research.     

Analysis and improvement of tipping calibration for ground-basedmicrowave radiometers

The tipping-curve calibration method has been an important calibration technique for ground-based microwave radiometers that measure atmospheric emission at low optical depth. The method calibrates a radiometer system using data taken by the radiometer at two or more viewing angles in the atmosphere. In this method, the relationship between atmospheric opacity and viewing angle is used as a constraint for deriving the system calibration response. Because this method couples the system with radiative transfer theory and atmospheric conditions, evaluations of its performance have been difficult. In this paper, first a data-simulation approach is taken to isolate and analyze those influential factors in the calibration process and effective techniques are developed to reduce calibration uncertainties. Then, these techniques are applied to experimental data. The influential factors include radiometer antenna beam width, radiometer pointing error, mean radiating temperature error, and horizontal inhomogeneity in the atmosphere, as well as some other factors of minor importance. It is demonstrated that calibration uncertainties from these error sources can be large and unacceptable. Fortunately, it was found that by using the techniques reported, the calibration uncertainties can be largely reduced or avoided. With the suggested corrections, the tipping calibration method can provide absolute accuracy of about or better than 0.5 K     

AIRS/AMSU/HSB on the Aqua mission: design, science objectives, data products, and processing systems

The Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Humidity Sounder for Brazil (HSB) form an integrated cross-track scanning temperature and humidity sounding system on the Aqua satellite of the Earth Observing System (EOS). AIRS is an infrared spectrometer/radiometer that covers the 3.7-15.4-/spl mu/m spectral range with 2378 spectral channels. AMSU is a 15-channel microwave radiometer operating between 23 and 89 GHz. HSB is a four-channel microwave radiometer that makes measurements between 150 and 190 GHz. In addition to supporting the National Aeronautics and Space Administration's interest in process study and climate research, AIRS is the first hyperspectral infrared radiometer designed to support the operational requirements for medium-range weather forecasting of the National Ocean and Atmospheric Administration's National Centers for Environmental Prediction (NCEP) and other numerical weather forecasting centers. AIRS, together with the AMSU and HSB microwave radiometers, will achieve global retrieval accuracy of better than 1 K in the lower troposphere under clear and partly cloudy conditions. This paper presents an overview of the science objectives, AIRS/AMSU/HSB data products, retrieval algorithms, and the ground-data processing concepts. The EOS Aqua was launched on May 4, 2002 from Vandenberg AFB, CA, into a 705-km-high, sun-synchronous orbit. Based on the excellent radiometric and spectral performance demonstrated by AIRS during prelaunch testing, which has by now been verified during on-orbit testing, we expect the assimilation of AIRS data into the numerical weather forecast to result in significant forecast range and reliability improvements.     

Radiometric profiling of temperature using algorithm based on neural networks

The retrieval of atmospheric temperature profiles from microwave radiometer brightness temperatures requires the solution of a nonlinear inversion problem. An inversion technique based on neural networks (NN) is developed. The NN technique, compared with the classical inversion methods, exhibits better results in terms of retrieval accuracy, vertical resolution and elaboration time.     

L波段微波辐射计周期脉冲式干扰时域检测方法研究

L波段微波辐射计是探测土壤湿度和海水盐度的有效遥感器。但是,全球定位系统(GPS)信号、雷达信号以及一些商用电子产品的电磁辐射造成的频谱污染都可以对微波辐射计的探测造成干扰,使得被动微波遥感对地观测结果具有一定的偏差,降低了地表参数的反演精度。该文通过实验模拟脉冲式噪声干扰,观测其在L波段(全功率接收型式)微波辐射计系统中的传输特性,分析输出信号特性与辐射计参数(积分时间、灵敏度)的相关性,获取其数字特征参数,结合脉冲检测法(APB),提出一种新的自相关检测(ACD)算法,能够有效用于周期性的脉冲式辐射干扰的检测,在微波辐射计系统积分时间1 ms的情况下,能够检测1.5 K的噪声干扰,满足卫星遥感探测反演地表参数精度的需求。     

Tests of sequential data assimilation for retrieving profile soilmoisture and temperature from observed L-band radiobrightness

Sequential data assimilation (Kalman filter optimal estimation) techniques are applied to the problem of retrieving near-surface soil moisture and temperature state from periodic terrestrial radiobrightness observations that update soil heat and moisture diffusion models. The retrieval procedure uses a time-explicit numerical model to continuously propagate the soil state profile, its error of estimation, and its interdepth covariances through time. The model's coupled soil moisture and heat fluxes are constrained by micrometeorology boundary conditions drawn from observations or atmospheric modeling. When radiometer data are available, the Kalman filter updates the state profile estimate by weighing the propagated state, error, and covariance estimates against an a priori estimate of radiometric measurement error. The Kalman filter compares predicted and observed radiobrightnesses directly, so no inverse algorithm relating brightness to physical parameters is required. The authors demonstrate Kalman filter model effectiveness using field observations and a simulation study. An observed 1 m soil state profile is recovered over an eight-day period from daily L-band observations following an intentionally poor initial state estimate. In a four-month simulation study, they gauge the longer term behavior of the soil state retrieval and Kalman gain through multiple rain events, soil dry-downs, and updates from radiobrightnesses     

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.     

Freeze/thaw classification for prairie soils using SSM/Iradiobrightnesses

Data from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) have been used to classify snow-free soils in the northern Great Plains as either frozen or thawed. The technique is based on differing sensitivities among SMMR radiobrightness frequencies to liquid moisture and volume scattering in the upper few millimeters of bare soil. The SMMR is no longer active. A current near-equivalent is the Special Sensor Microwave/Imager (SSM/I). The authors demonstrate that SSM/I radiobrightnesses also exhibit differential sensitivities to liquid water and volume scattering in frozen soil despite their higher frequencies. They find that the best classification discriminants for SSM/I data are a combination of the 37-GHz V-pol radiobrightnesses and the 19-to-37-GHz V-pol spectral gradients. They also examine the sensitivity of the classification to atmospheric emission and absorption and find little effect