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Emmanuel Dinnat

Publications and source records attributed to Emmanuel Dinnat.

10 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

The Dielectric Constant at P-Band for Salinity from 0 to 150 pss

Measurements have been made at P-band (0.707 GHz) to construct a model for the dielectric constant of sea water and extend the model for the dielectric constant to high salinity (50-150 pss). The measurements are part of research to develop a model for the dielectric constant suitable for future wide-bandwidth remote sensing of salinity and for application to water bodies such as the Great Salt Lake with salinity significantly above that found in the open ocean. Measurements have been made at temperatures from 2 to 30 °C and salinity from 0 to 138 pss. The data have been fit to a Debye model for the dielectric constant with a single resonance as has been employed at L-band (1.413 GHz) where remote sensing of salinity is currently done. Comparison with contemporary models developed from data at L-band indicates that the L-band model and new P-band model do well at both frequencies for salinity less than 50 pss but at higher values of salinity the L-band models diverge from the data. The data has also been used to test at high salinity the mathematical relationship between salinity and conductivity which is the basis for the practical salinity scale (pss).

Dielectric Constant↗
NASA NTRS2023

New Seawater Dielectric Constant Parametrization and Application to SMOS Retrieved Salinity

The accuracy of the Sea Surface Salinity (SSS) retrieved from L-Band radiometer measurements is strongly dependent on the reliability of the dielectric constant model. Two new parametrizations were recently developed based on one hand on the Soil Moisture and Ocean Salinity (SMOS) satellite multi-angular brightness temperature measurements by Boutin et al. (2021) (BV), and on the other hand, on new George Washington University laboratory measurements by Zhou et al. (2021) (GW2020). These two approaches are fully independent. For most SSS and Sea Surface Temperature (SST) conditions commonly observed over the open ocean, the relative variations of brightness temperatures Tb simulated through the BV and GW2020 parametrizations agree particularly well, and better than with earlier parametrizations previously used in the SMOS, Soil Moisture Active Passive (SMAP) and Aquarius SSS retrievals. Nevertheless, uncertainty remains, especially below 10 °C where a ∼ 0.1 K relative difference between the two models is observed. This motivates the development of a revised parameterization, BVZ, based on a methodology similar to that used to derive BV but using GW2020 instead of SMOS measurements. Compared to the GW2020 parameterization, BVZ is derived with a reduced number of degrees of freedom, it relies on the TEOS10 PSS78 conductivity-salinity relationship, and on the previously derived static permittivity of fresh water. One month per season of SMOS data have been reprocessed in 2018 using BV, GW2020, and BVZ. We find the best overall agreement between SMOS SSS and Argo SSS with BVZ parametrization, with noticeable improvement in the 5 °C–15 °C SST range.

sea measurements↗
NASA NTRS2022

The Fourth Stokes Parameter for Geolocation in Passive Microwave Remote Sensing from Space

Polarimetric microwave radiometers such as SMAP are capable of measuring the fourth Stokes parameter in brightness temperature over the Earth surface. The value of this parameter is normally small but exhibits sharp spikes when the scene includes large differences in emission from the surface, such occur at land/water boundaries. In this manuscript, it is shown that these spikes can be used to accurately locate coastlines with potential application to geolocation in passive microwave remote sensing from space. Examples are presented using the L-band radiometer on SMAP, first with theory using calculations with the SMAP antenna pattern and orbit and then with SMAP measurements of the fourth Stokes parameter over Madagascar. Using the SMAP data, the coastline is located with a standard deviation less than 2 km. The results are consistent with the conventional approach used for geolocation of the SMAP radiometer footprint.

Passive Microwave Remote Sensing↗
NASA NTRS2022

FOAM Emissivity Modelling with Foam Properties Tuned by Frequency and Polarization

We model the sea foam emissivity as a part of the work done by an international science team on developing a radiative transfer model of reference quality for the ocean surface emissivity from L band to infrared frequencies. The focus here is on the foam emissivity at frequencies from 1 to 89 GHz. Sensitivity study for different foam properties(foam layer thickness and upper limit of the foam void fraction)guided the effort to tune the model by frequency and polarization. The results show that the differences between simulated and observed brightness temperatures decrease when using tuned foam model.

Magdalena D Anguelova↗
NASA NTRS2021

Sensitivity of Wide Bandwidth Radiometer for Remote Sensing of Ocean Salinity

Passive microwave remote sensing of sea surface salinity from space is currently being done with measurements in the 27 MHz wide spectral window at 1.413 GHz (L-band) protected for passive use. Modern L-band instruments, such as the radiometers on SMOS and Aquarius, have demonstrated the feasibility of monitoring surface salinity from space, and they have also demonstrated the need for better accuracy, especially in cold water. Proposals to improve accuracy have largely involved adding measurement at more frequencies. For example, adding lower frequencies to improve the sensitivity to salinity in cold water and adding higher frequencies to enable simultaneous retrieval of sea surface temperature which is needed in the retrieval of salinity. These strategies involve trade-offs, some obvious such as the effects of interference from anthropogenic sources of radio frequency radiation (RFI) when operating outside the protected band and the loss of spatial resolution at lower frequencies. But, some are more subtle and arise because of the dependence of the retrieval of salinity on other parameters of the ocean surface, in particular, water temperature and roughness (wind speed). The effect of these interdependencies on the potential accuracy of salinity remote sensing in the frequency range 0.3 – 3.0 GHz is examined here to gain insight into the potential for future wide bandwidth instruments for remote sensing of salinity and the optimization of their design. There is benefit including the low frequencies, especially for cold water, but a danger of increased error including frequencies above 1.5 - 2.0 GHz depending on temperature.

Microwave Remote Sensing↗
NASA NTRS2021

Debye Dielectric Model Function for Seawater Based on Expanded L-Band Measurement Data Set

New seawater dielectric measurements have recently been made over a broad range of salinities and temperatures at the George Washington University (GW). These measurements have been used to refine the existing dielectric model function of seawater for salinity retrieval. The mathematical structure of the new model function has been investigated to: (a) look for a physical basis for the mathematical form; and (b) to optimize the accuracy of data fitting. The Debye model has been chosen to represent the dielectric constant of seawater as a function of salinity (S), temperature (T) and frequency (f). The retrieved salinity using the Debye model has good agreement with the in-situ data collected by Argo floats. The global differences in retrieved salinity and in-situ data will be presented at the meeting.

ocean salinity↗
NASA NTRS2021

Seawater Debye Model Function at L-Band and Its Impact on Salinity Retrieval From Aquarius Satellite Data

A model function of seawater, which specifies the dielectric constant of seawater as a function of salinity, temperature, and frequency, is important for the retrieval of sea surface salinity using satellite data. In 2017, a model function has been developed based on measurement data at 1.4134 GHz using a third-order polynomial expression in salinity ( S ) and temperature ( T ). Although the model showed improvements in salinity retrieval, it had an inconsistent behavior between partitioned salinities. To improve the stability of the model, new dielectric measurements of seawater have been made recently over a broad range of salinities and temperatures to expand the data set used for developing the model function. The structure of the model function has been changed from a polynomial expansion in S and T to a physics-based model consisting of a Debye molecular resonance term plus a conductivity term. Each unknown parameter is expressed in S and T based on the expanded measurement data set. Physical arguments have been used to limit the number of unknown coefficients in these expressions to improve the stability of the model function. The new model function has been employed in the retrieval algorithm of the Aquarius satellite mission to obtain a global salinity map. The retrieved salinity using a different model function is compared with in situ data collected by Argo floats to evaluate the impact and the performance of model functions. The results indicate that the new model function has significant improvements in salinity retrieval compared with other existing models.

Seawater↗
NASA NTRS2020

Spurious Signal in SMAP Fourth Stokes Parameter

The radiometer on the NASA Soil Moisture Active/Passive (SMAP) mission is a fully polarimetric instrument that operates at L-band in the spectrum window at 1400-1427 MHz protected for passive use only. A unique feature of the radiometer is the fully digital back-end which permits direct computation of the third and fourth Stokes parameters: the real and imaginary part of the correlation of signal at horizontal and vertical polarizations, respectively. In particular, the SMAP conical scanning geometry provides the opportunity to look at the global distribution of the fourth Stokes parameter, TA4, at constant incidence angle (40°). A striking feature of TA4 is the existence of a strong (±10 K) spurious signal at coastlines. This article provides examples of the spikes and an explanation of the cause. Simulations have shown that the spurious signal is associated with antenna imperfections, such as cross polarization coupling and phase mismatch between polarizations.

radiometry↗
NASA NTRSDate not supplied

A Reference Ocean Surface Emission and Backscatter Model from Microwaves to Infrared

Satellite observations are vital for the initialization of Numerical Weather Prediction models, and very important for climate monitoring and prediction, as well as other applications such as hydrology and flood awareness prediction. Knowledge of radiative contributions from the Earth's surface is needed to sound the lower troposphere from space. The lack of a reference quality ocean emission and backscatter model is a major gap in our ability to provide absolute calibration of the satellite based observing system. Uncertainty in emissivity models is not well characterized and different models are used for different spectral bands, for active and passive instruments. An International Space Science Institute (ISSI) team was put together [4] to address these issues. The objectives of the team are to provide a reference model as a community software (i.e., documented and freely available code), that is maintained and supported, has traceable uncertainty estimations, and that enables new science from microwaves to infrared with bidirectional reflectance distribution function (BRDF) capability. We will present the model and its various components, discussing the choices between various parameterizations, building on the LOCEAN model of [2]. The model predictions will be evaluated at various frequencies, including comparisons to radiometric observations by SMAP, AMSR2 and GMI (e.g., [5]). We will discuss early model evaluation in the infrared and for active microwave sensors. Areas of ongoing research include improving the foam parametrization (coverage and emissivity) to provide consistent performances across frequencies, building on [1], and the azimuthal dependence of the active and passive signals. The model will be used to generate training data for fast models e.g., Fastem, [3], that are used in operational data assimilation and climate re-analysis.

Emmanuel Dinnat↗