Simon H. Yueh

Aquarius: An Instrument to Monitor Sea Surface Salinity From Space

Aquarius is a combined passive/active L-band microwave instrument that is being developed to map the salinity field at the surface of the ocean from space. The data will support studies of the coupling between ocean circulation, global water cycle, and climate. Aquarius is part of the Aquarius/Satelite de Aplicaciones Cientiflcas-D mission, which is a partnership between the U.S. (National Aeronautics and Space Administration) and Argentina (Comision Nacional de Actividades Espaciales). The primary science objective of this mission is to monitor the seasonal and interannual variation of the large-scale features of the surface salinity field in the open ocean with a spatial resolution of 150 km and a retrieval accuracy of 0.2 psu globally on a monthly basis.

Modeling of wind direction signals in polarimetric sea surface brightness temperatures

There has been an increasing interest in the applications of polarimetric microwave radiometers for ocean wind remote sensing. Aircraft and spaceborne radiometers have found a few Kelvins wind direction signals in sea surface brightness temperatures, in addition to their sensitivities to wind speeds. However, it was not clear what physical scattering mechanisms produced the observed brightness dependence on wind direction. To this end, polarimetric microwave emissions from wind-generated sea surfaces are investigated with a polarimetric two-scale scattering model, which relates the directional wind-wave spectrum to passive microwave signatures of sea surfaces. Theoretical azimuthal modulations are found to agree well with experimental observations for all Stokes parameters from near nadir to 65/spl deg/ incidence angles. The upwind and downwind asymmetries of brightness temperatures were interpreted using the hydrodynamic modulation. The contributions of Bragg scattering by short waves, geometric optics scattering by long waves and sea foam are examined. The geometric optics scattering mechanism underestimates the directional signals in the first three Stokes parameters, and predicts no signals in the fourth Stokes parameter (V). In contrast, the Bragg scattering was found to dominate the wind direction signals from the two-scale model and correctly predicted the phase changes of the upwind and crosswind asymmetries in T/sub /spl upsi// and U from middle to high incidence angles. The phase changes predicted by the Bragg scattering theory for radiometric emission from water ripples is corroborated by the numerical Monte Carlo simulation of rough surface scattering. This theoretical interpretation indicates the potential use of polarimetric brightness temperatures for retrieving the directional wave spectrum of short gravity and capillary waves.

Modelling of wind direction signals in polarimetric sea surface brightness temperatures

A preliminary geophysical model function, relating the sea surface brightness temperatures to ocean surface wind speed and direction, was developed using the data acquired at 45/spl deg/, 55/spl deg/, and 65/spl deg/ incidence angles by Jet Propulsion Laboratorys (JPL) aircraft 19- and 37-GHz polarimetric radiometers in 1994 and 1995. Radiometric temperatures from all polarization channels under cloud-free conditions showed clear dependence on surface wind direction. When there were stratus or scattered clouds, T/sub /spl nu// and T/sub h/ were significantly influenced by the radiation from cloud water, but the polarimetric channel U was found to be insensitive to clouds. The Fourier harmonic coefficients of the wind direction signals were derived from experimental data and related to the wind speed and direction, incidence angle and frequency. In general, all harmonic coefficients increase from low to moderate wind speeds, except the sin 2/spl phi/ component of U at 65/spl deg/ incidence, which peaked at low winds with a peak-to-peak amplitude of 0.6 to 1 Kelvin at about 3 m/s winds. At moderate wind speeds, 45/spl deg/ incidence angle exhibits larger second harmonic signals, but smaller first harmonic signals, than higher incidence angles. Wind direction signals were similar in 19 and 37 GHz channels, but the 37 GHz channel showed a slightly stronger wind direction sensitivity than the 19 GHz channel. The results suggest promising applications of passive microwave radiometers to ocean wind vector measurements.

Symmetry properties in polarimetric remote sensing

This paper presents the relations among polarimetric backscattering coefficients from the viewpoint of symmetry groups. Symmetry of geophysical media encountered in remote sensing due to reflection, rotation, azimuthal, and centrical symmetry groups is considered for both reciprocal and nonreciprocal cases. On the basis of the invariance under symmetry transformations in the linear polarization basis, the scattering coefficients are related by a set of equations which restrict the number of independent parameters in the polarimetric covariance matrix. The properties derived under these transformations are general and apply to all scattering mechanisms in a given symmetrical configuration. The scattering coefficients calculated from theoretical models for layer random media and rough surfaces are shown to obey the derived symmetry relations. Use of symmetry properties in remote sensing of structural and environmental responses of scattering media is discussed. As a practical application, the results from this paper provide new methods for the external calibration of polarimetric radars without the deployment of man-made calibration targets.

Error sources and feasibility for microwave remote sensing of ocean surface salinity

A set of geophysical error sources for the microwave remote sensing of ocean surface salinity have been examined. The error sources include the sea surface temperature, sea surface roughness, atmospheric gases, ionospheric Faraday rotation, and solar and Galactic emission sources. It is shown that the brightness temperature effects of a few kelvin can be expected for most of these error sources. The key correction requirements for accurate salinity measurements are the knowledge accuracy of 0.5/spl deg/C for the sea surface temperature (SST), 10 mbar for the surface air pressure, 2/spl deg/C for the surface air temperature, 0.20 accuracy for the Faraday rotation, and surface roughness equivalent to 0.3 m s/sup -1/ for the surface wind speed. We suggest the use of several data products for corrections, including the AMSR-type instruments for SST and liquid cloud water, the AMSU-type product for air temperature, the scatterometer products or numerical weather analysis for the air pressure, coincidental radar observations with 0.2 dB precision for surface roughness, and on-board polarimetric radiometer channel for Faraday rotation. The most significant sky radiation is from the Sun. A careful design of the antenna is necessary to minimize the leakage of solar radiation or reflection into the antenna sidelobes. The narrow-band radiation from Galactic hydrogen clouds with a bandwidth of less than 1 MHz is also significant, but can be corrected with a radio sky survey or minimized with a notched (band-rejection) filter centered at 1.421 GHz. The other planetary and Galactic radio sources can also be flagged with a small data loss. We have performed a sampling analysis for a polar-orbiting satellite with 900 km swath width to determine the number of satellite observations over a given surface grid cell during an extended period. Under the assumption that the observations from different satellite passes are independent, it is suggested that an accuracy of 0.1 psu (practical salinity unit) is achievable for global monthly 10 latitude by 10 longitude gridded products.

Observations of soil moisture using a passive and active low-frequency microwave airborne sensor during SGP99

Data were acquired by the Passive and Active L- and S-band airborne sensor (PALS) during the 1999 Southern Great Plains (SGP99) experiment in Oklahoma to study remote sensing of soil moisture in vegetated terrain using low-frequency microwave radiometer and radar measurements. The PALS instrument measures radiometric brightness temperature and radar backscatter at L- and S-band frequencies with multiple polarizations and approximately equal spatial resolutions. The data acquired during SGP99 provide information on the sensitivities of multichannel low-frequency passive and active measurements to soil moisture for vegetation conditions including bare, pasture, and crop surface cover with field-averaged vegetation water contents mainly in the 0-2.5 kg m/sup -2/ range. Precipitation occurring during the experiment provided an opportunity to observe wetting and drying surface conditions. Good correlations with soil moisture were observed in the radiometric channels. The 1.41-GHz horizontal-polarization channel showed the greatest sensitivity to soil moisture over the range of vegetation observed. For the fields sampled, a radiometric soil moisture retrieval accuracy of 2.3% volumetric was obtained. The radar channels showed significant correlation with soil moisture for some individual fields, with greatest sensitivity at 1.26-GHz vertical copolarized channel. However, variability in vegetation cover degraded the radar correlations for the combined field data. Images generated from data collected on a sequence of flight lines over the watershed region showed similar patterns of soil moisture change in the radiometer and radar responses. This indicates that under vegetated conditions for which soil moisture estimates may not be feasible using current radar algorithms, the radar measurements nevertheless show a response to soil moisture change, and they can provide useful information on the spatial and temporal variability of soil moisture. An illustration of the change detection approach is given.

Polarimetric measurements of sea surface brightness temperatures using an aircraft K-band radiometer

Presents the first experimental evidence that the polarimetric brightness temperatures of sea surfaces are sensitive to ocean wind direction in the incidence angle range of 30 to 50/spl deg/. The experimental data were collected by a K-band (19.35 GHz) polarimetric wind radiometer (WINDRAD) mounted on the NASA DC-8 aircraft. A set of aircraft radiometer flights was successfully completed in November 1993. The authors performed circle flights over National Data Buoy Center (NDBC) moored buoys deployed off the northern California coast, which provided ocean wind measurements. The results indicate that passive polarimetric radiometry has a strong potential for global ocean wind speed and direction measurements from space. >

Estimates of Faraday rotation with passive microwave polarimetry for microwave remote sensing of Earth surfaces

A technique based on microwave passive polarimetry for the estimates of ionospheric Faraday rotation for microwave remote sensing of Earth surfaces is described. Under the assumption of azimuth symmetry for the surfaces under investigation, it is possible to estimate the ionospheric Faraday rotation from the third Stokes parameter of microwave radiation. An error analysis shows that the Faraday rotation can be estimated with an accuracy of better than 1/spl deg/ with a space-based L-band system, and the residual correction errors of linearly polarized brightness temperatures can be less than 0.1 K. It is suggested that the estimated Faraday rotation angle can be further utilized to derive the ionospheric total electron content (TEC) with an accuracy of about 1 TECU=10/sup 16/ electrons-m/sup -2/ which will yield 1 mm accuracy for the estimate of an ionospheric differential delay at the Ku-band. Therefore, this technique can potentially provide accurate estimates of ionospheric Faraday rotation, TEC and differential path delay for applications including microwave radiometry and scatterometry of ocean salinity and soil moisture as well as satellite altimetry at sea surface height. A conceptual design applicable to real aperture and aperture synthesis radiometers is described for the measurements of the third Stokes parameter.

Assessment of the SMAP Passive Soil Moisture Product

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

Application of neural networks to radar image classification

A number of methods have been developed to classify ground terrain types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are often grouped into supervised and unsupervised approaches. Supervised methods have yielded higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. In this paper, a new terrain classification technique is introduced to determine terrain classes in polarimetric SAR images, utilizing unsupervised neural networks to provide automatic classification, and employing an iterative algorithm to improve the performance. Several types of unsupervised neural networks are first applied to the classification of SAR images, and the results are compared to those of more conventional unsupervised methods. Results show that one neural network method-Learning Vector Quantization (LVQ)-outperforms the conventional unsupervised classifiers, but is still inferior to supervised methods. To overcome this poor accuracy, an iterative algorithm is proposed where the SAR image is reclassified using a maximum likelihood (ML) classifier. It is shown that this algorithm converges, and significantly improves classification accuracy. >