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Roger Lang

Publications and source records attributed to Roger Lang.

7 records
All sourcesNASA NTRSDOE OSTI
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

A Cavity System for Seawater Dielectric Measurements at P-Band

This paper describes a new system for measuring the dielectric constant of seawater near 700 MHz. The purpose of this research is to obtain data at P-band for the dielectric constant of seawater that can be combined with previous L-band data for developing a model function valid from 500 MHz to 2000 MHz. A transmission-type cylindrical cavity has been constructed with the TM010 mode resonance occurring near 700 MHz. The seawater is introduced via a capillary quartz tube. The dimensions of the cavity have been determined by electromagnetic modelling. The model takes the cavity wall loss into account and can provide accurate estimation of resonance condition for both the empty cavity and cavity with seawater. The errors between the theory and measurement are about 0.07% for the resonant frequency and 0.4% for the quality factor, Q. The cavity design, experimental setup and measurement schedule will be presented in the paper.

Roger Lang↗
NASA NTRS2022

P- and L-Band Retrieval of Subsurface Soil Moisture and Temperature Profiles as First-Order Polynomial Function

This paper demonstrates the potential use of P and L band passive measurements to determine root zone soil moisture (SM) and soil temperature(ST). SM and ST data have been taken as a function of depth during the NASA GSFC PLEX19 experiment in the summer of 2019 at Beltsville, MD, USA. Using these data, a coherent model has been used to compute H and V brightness temperatures at frequencies of 0.8 and 1.4 GHz with an observation angle of 35 degrees. These synthetic brightness data are then used to estimate the SM and ST profiles which are represented by linear polynomials. The inversion problem is formulated as a least square problem that is solved by a global optimization method known as the Adaptive Simulated Annealing(ASA) method. Four inversion examples having different SM and ST profiles are presented. Selected results show that the standard deviation between the retrieved and measured data is less than 0.077 cm3/cm3 for SM, and 2.245 °C for ST.

Ming Li↗
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 NTRSDate not supplied

Active/Passive Remote Sensing of a Mature Soybean Canopy at L-band

This paper reports on development of a scattering model of a soybean canopies at L-band. The scattering model will be used for active and passive remote sensing of soybean plants with an emphasis on mature soybeans where the presence of pods is important to the observed microwave signal. The active problem is treated using the Distorted Born Approximation (DBA) and the passive problem is analyzed using Peake's method. These results are compared with field measurements made in 2012 by the truck-mounted ComRAD system, which is a combined radar and radiometer instrument operating at L-band. Investigation of backscatter from the soybean canopy shows good agreement between the radar measurement data and modeling results. The results also indicate that the L-band radar backscatter can be used to monitor both the soil moisture and the growth of the soybean pods. Past passive modeling efforts have not considered soybeans late in the growing season when pods are important. A new passive radiation model of soybean canopy will be developed based on the backscatter model.

Yiwen Zhou↗