Derek Burrage

Deriving Sea Surface Salinity and Density Variations From Satellite and Aircraft Microwave Radiometer Measurements: Application to Coastal Plumes Using STARRS

Using brightness temperature Tb measurements from L-band airborne microwave radiometers, with independent sea surface temperature (SST) observations, sea surface salinity (SSS) can be remotely determined with errors of about 1 psu in temperate regions. Nonlinearities in the relationship between Tb, SSS, and SST produce variations in the sensitivity of salinity S to variations in Tb and SST. Despite significant efforts devoted to SSS remote sensing retrieval algorithms, little consideration has been given to deriving density D from remotely sensed SSS and SST. Density is related to S and T through the equation of state. It affects the oceans static stability and its dynamical response to forcings. By chaining together two empirical relationships (flat-sea emissivity and equation of state) to form an inversion algorithm for sea surface density (SSD) in terms of Tb and SST, we develop a simple L-band SSD retrieval algorithm. We use this to investigate the sensitivity of SSD retrievals to observed Tb and SST and infer errors in D for typical sampling configurations of the airborne Salinity, Temperature, And Roughness Remote Scanner (STARRS) and satellite-borne Soil Moisture and Ocean Salinity (SMOS) and Aquarius radiometers. We then derive D from observations of river plumes obtained using STARRS and demonstrate several oceanographic applications: the observations are used to study variations in T and S effects on D in the Mississippi plume, and the across-shelf density gradient is used to infer surface geostrophic shear and subsurface geostrophic current in the Plata plume. Future basin-scale applications of SSD retrievals from satellite-borne microwave radiometers such as SMOS and Aquarius are anticipated.

An overview of GNSS remote sensing

The Global Navigation Satellite System (GNSS) signals are always available, globally, and the signal structures are well known, except for those dedicated to military use. They also have some distinctive characteristics, including the use of L-band frequencies, which are particularly suited for remote sensing purposes. The idea of using GNSS signals for remote sensing - the atmosphere, oceans or Earth surface - was first proposed more than two decades ago. Since then, GNSS remote sensing has been intensively investigated in terms of proof of concept studies, signal processing methodologies, theory and algorithm development, and various satellite-borne, airborne and ground-based experiments. It has been demonstrated that GNSS remote sensing can be used as an alternative passive remote sensing technology. Space agencies such as NASA, NOAA, EUMETSAT and ESA have already funded, or will fund in the future, a number of projects/missions which focus on a variety of GNSS remote sensing applications. It is envisaged that GNSS remote sensing can be either exploited to perform remote sensing tasks on an independent basis or combined with other techniques to address more complex applications. This paper provides an overview of the state of the art of this relatively new and, in some respects, underutilised remote sensing technique. Also addressed are relevant challenging issues associated with GNSS remote sensing services and the performance enhancement of GNSS remote sensing to accurately and reliably retrieve a range of geophysical parameters.

An Advanced Roughness Spectrum for Computing Microwave L-Band Emissivity in Sea Surface Salinity Retrieval

The influence of sea surface roughness dominates the error budget of satellite sea surface salinity (SSS) retrieval from L-band radiometers; thus, accurate roughness correction models are needed. Semi-analytical SSS correction models, as used in the soil moisture and ocean salinity satellite Level 2 processor, combine an emissivity model with an ocean wave spectrum model that describes the rough sea surface. Previous findings indicate that the errors contributed by ocean roughness model exceed those of the emissivity model. In this paper, we compare the performance of three well-known spectrum models and a new one as inputs to the small slope approximation/small perturbation method emissivity model. The new spectrum model, which is developed from empirical parameterization of short water wave spectra measured in the ocean and incorporates swell effects, performs very well in comparison with the other spectrum models, and we propose its consideration for future SSS roughness correction models.

GNSS remote sensing

This special issue publishes several innovative ideas, methods, and applications in GNSS remote sensing, especially in GNSS-R. The issue comprises six research papers of both theoretical studies and practice, and one review article. The topics investigated include sea level measurement, snow depth estimation, snow water equivalent estimation, soil moisture estimation, buried object detection, and simplified GNSS signal processing techniques.

Aircraft and in Situ Salinity and Ocean Color Measurements and Comparisons in the Gulf of Mexico

We report here on aircraft measurements made in May, 2007, with the NRL STARRS (Salinity, Temperature and Roughness Remote Scanner), and optical multi-wavelength radiance and irradiance sensors (Satlantic OCR-507 at SEA-WIFS wavelength bands). These measurements were made in conjunction with in situ measurements of sea surface salinity (SSS), ocean color, and fluorescence in the Atchafalaya River outflow from the R/V Pelican. In this work we demonstrate the ability of the aircraft optical and L-Band measurements to (a) detect the location of salinity and color fronts as observed in the in situ measurements from the ship and (b) provide context for the in situ measurements by providing synoptic measurements over a wider area than the ship was able to cover. A multilinear regression for salinity, based on three of the optical channels, provides an excellent qualitatative proxy for large scale salinity in the Atchafalaya plume region. We believe this is the first simultaneous use of L-Band and optical instruments to measure salinity from an aircraft.

Relationship between sea surface salinity from L-band radiometer and optical features in the East China Sea

The East China Sea (ECS) is often obscured from space in the visible and near-visible bands by cloud cover, which prevents remote sensing retrieval of optical properties. However, clouds are transparent to microwaves, and satellites with L-band radiometers have recently been put into orbit to monitor sea surface salinity (SSS). Previous studies have used the mixing of fluvial colored dissolved organic matter (CDOM) near coasts, where the mixing is approximately conservative over short time scales, to estimate SSS. In this study, the usual relationship between CDOM and salinity in the ECS has been used in reverse to estimate CDOM from remotely sensed SSS in the ECS and compare that CDOM with MODIS data. The SSS data used are 7 day composites from NASA’s Aquarius/SAC-D satellite which has an L-band radiometer. The challenges in using this approach are that 1) Aquarius SSS has coarse spatial resolution (150 km), and 2) the ECS has numerous anthropogenic sources of radiofrequency interference which adds noise to the L-band signal for the SSS retrievals. Despite the limits in the method, CDOM distribution in the ECS can be estimated under cloudy conditions. In addition to all-weather retrievals, an additional advantage of the approach is that the algorithm provides an estimate of CDOM absorption that is unaffected by the spectrally similar detritus absorption that can confound optical remote sensing estimates of CDOM.

Sea surface signature of tropical cyclones using microwave remote sensing

Measuring the sea surface during tropical cyclones (TC) is challenging due to severe weather conditions that prevent shipboard measurements and clouds which mask the sea surface for visible satellite sensors. However, sea surface emission in the microwave L-band can penetrate rain and clouds and be measured from space. The European Space Agency (ESA) MIRAS L-band radiometer on the Soil Moisture and Ocean Salinity (SMOS) satellite enables a view of the sea surface from which the effects of tropical cyclones on sea surface emissivity can be measured. The emissivity at these frequencies is a function of sea surface salinity (SSS), sea surface temperature (SST), sea surface roughness, polarization, and angle of emission. If the latter four variables can be estimated, then models of the sea surface emissivity can be used to invert SSS from measured brightness temperature (TB). Actual measured TB from space also has affects due to the ionosphere and troposphere, which have to be compensated for, and components due to the galactic and cosmic background radiation those have to be removed. In this research, we study the relationships between retrieved SSS from MIRAS, and SST and precipitation collected by the NASA TMI sensor from the Tropical Rainfall Measuring Mission (TRMM) satellite during Hurricane Isaac, in August 2012. During the slower movement of the storm, just before landfall on the vicinity of the Louisiana Shelf, higher precipitation amounts were associated with lower SSS and slightly increased SST. This increased trend of SST and lower SSS under regions of high precipitation are indicative of inhibited vertical mixing. The SMOS Level 2 SSS were filtered by a stepwise process with removal of high uncertainty in TB under conditions of strong surface roughness which are known to create noise. The signature of increased SST associated with increasing precipitation was associated with decreased SSS during the storm. Although further research is required, this study shows that there is a TB signal from the sea surface beneath a tropical cyclone that provides information on roughness and salinity.

Effects of foam and wind waves on microwave ocean emission

Meissner and Wentz (M09) [4] report global WindSat measurements of wind-induced emissivity change at 6, 10, 18, 23 and 37 GHz in wind speeds up to about 50 m/s (Fig. 1). Superimposed in the figure are the SSA/SPM simulations accounting for both foam and roughness effects. The agreement between computation and measurements is generally very good. Both measurements and simulations show a monotonic increase of emissivity change with wind speed up to 50 m/s, and the rate of change seem to slow down somewhat in high winds. Fig. 2 shows the comparison of WindSat measurements and SSA/SPM simulations with the foam and roughness components displayed separately. The foam effect increases monotonically with wind stress. For the vertical polarization in the WindSat configuration (incidence angle about 53°), foam is the dominant contributor of wind-induced emissivity change. The roughness contribution is positive at 6, 10 and 18 GHz with decreasing magnitude toward higher frequency; it becomes negative at 23 and 37 GHz. The null roughness contribution occurs between 18 and 23 GHz, and this range represents the ideal frequency band for passive microwave remote sensing with minimal surface roughness contamination. For the horizontal polarization, roughness contribution dominates in all five frequencies except for a small range of wind speeds near 50 m/s at 6 GHz. For microwave frequencies less than about 10 GHz, the wind speed sensitivity of roughness contribution is less than that of the foam contribution; for higher microwave frequencies, the two contributions have similar wind speed sensitivity.

Airborne Observation of ocean surface roughness variations using a combination of microwave radiometer and reflectometer systems - The second Virginia offshore (Virgo II) experiment

Airborne and satellite retrieval of Sea Surface Salinity (SSS) using L-band microwave radiometers requires accurate corrections for the influence of wind-induced Sea Surface Roughness (SSR) on the retrievals. We describe an airborne experiment, Virgo II, that combined an L-band microwave radiometer for retrieving SSS, with L- and S-band reflectometer systems for retrieving SSR descriptors including Mean Square Slope (MSS) and Wind Speed (WS) under a range of surface wind and wave conditions. The research objective is to use the SSR descriptors derived from the reflectometers to correct the brightness temperatures observed by the L-band radiometer, and produce more accurate SSS retrievals. Here we describe our experimental investigations to assess the feasibility of this approach. Preliminary comparisons of WS data retrieved from the reflectometers with coincident WS data from in situ platforms and an atmospheric circulation model indicate that after correcting for apparent biases, the reflectometry-derived SSR descriptors could, indeed, provide reliable corrections for the L-band radiometer salinity retrieval.

Performance of roughness correction models for retrieval of Sea Surface Salinity from air- and satellite-borne L-band radiometers

The recent and imminent launch of the SMOS and Aquarius satellites carrying microwave L-band radiometers provides an opportunity to map Sea Surface Salinity (SSS) globally with an expected error < 0.2 psu. However, the accuracy of retrieved SSS depends critically on brightness temperature (Tb) corrections for sea surface roughness (SSR) effects. This paper assesses the performance of representative roughness correction models when compared with published data, and applied to recently-acquired airborne L-band radiometer data. One type of model currently being used to process SMOS data combines a wind-driven gravity wave spectrum that describes SSR, with an electromagnetic (EM) model that determines microwave emissivity, to predict the Tb roughness increment relative to the flat sea response. We find that selection of both the spectral and emissivity models strongly influences the resulting (∼1 K) Tb errors. We conclude that more accurate modeling of short wavelength spectral components and their EM influence is needed, to reduce these errors to acceptable levels.