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This paper presents measurements using a dual-polarised radiometer operating at 93 GHz to detect ice or water on asphalt in laboratory conditions. The brightness temperatures of both H and V polarizations were measured for a dry surface, liquid water, and ice on asphalt at observation angles of 50° and 56°. The results presented in this paper demonstrate that the studied road conditions can be identified by the radiometer. The measurements are compared with a model and surface parameters, such as dielectric constant and roughness are fitted and compared to reference values. The experiments and results, described in this article, are the first steps towards the future installation of a polarimetric sensor on a moving vehicle for traffic safety. [ABSTRACT FROM AUTHOR]

Temporal and spatial variation of sea ice type in the Arctic is an indicator of regional and global change. Arctic sea ice can be classified into two major categories: multiyear ice (MYI) and first-year ice. In this paper, classification method based on machine learning is established and applied to produce daily sea ice classification data set during the winter (November–April) from 2002 to 2017 using active microwave data from QuikSCAT and Advanced Scatterometer as well as passive microwave data from Advanced Microwave Scanning Radiometer for EOS, Special Sensor Microwave Imager/Sounder, and Advanced Microwave Scanning Radiometer 2 radiometer. First, the open water area is flagged out using brightness temperature (Tb) from the passive microwave sensor. Then, K-means algorithm is applied to identify the clusters of the two ice types in the Tb/backscatter parameter space and finally assign pixels to each class. Two optimization methods based on the movement of MYI and marginal ice zone are used to correct the misclassification of MYI. The results have shown a decrease of MYI in winter from 2002 to 2017, especially in 2008 and 2013 with a remarkable recovery in 2014. The classifications are consistent with results by visual interpretation from synthetic aperture radar images in the Canadian Arctic Archipelago with overall classification accuracy over 93%. Comparison with classifications from previous studies and products shows that our method could reflect more differences in MYI declining trend interannually and less anomalous fluctuations in certain years. [ABSTRACT FROM AUTHOR]

In the present paper, permittivity maps of equatorial lunar surface are generated using brightness temperature ( T B ) data obtained from Microwave Radiometer (MRM) of Chang'e-1 and physical temperature ( T P ) data obtained from Diviner of Lunar Reconnaissance Orbiter (LRO). Here, permittivity mapping is not carried out above 60° latitudes towards the lunar poles due to large anomaly in the physical temperature obtained from the Diviner. Microwave frequencies, which are used to generate these maps are 3 GHz, 7.8 GHz, 19.35 GHz and 37 GHz. Permittivity values are simulated using T B values at these four frequencies. Here, weighted average of physical temperature obtained from Diviner are used to compute permittivity at each microwave frequencies. Longer wavelengths of microwave signals give information of more deeper layers of the lunar surface as compared to smaller wavelength. Initially, microwave emissivity is estimated using T B values from MRM and physical temperature ( T P ) from Diviner. From estimated emissivity the real part of permittivity ( ε ), is calculated using Fresnel equations. The permittivity maps of equatorial lunar surface is generated. The simulated permittivity values are normalized with respect to density for easy comparison of simulated permittivity values with the permittivity values of Apollo samples as well as with the permittivity values of Terrestrial Analogue of Lunar Soil (TALS) JSC-1A. Lower value of dielectric constant ( ε ′) indicates that the corresponding lunar surface is smooth and doesn't have rough rocky terrain. Thus a future lunar astronaut can use these data to decide proper landing site for future lunar missions. The results of this paper will serve as input to future exploration of lunar surface. [ABSTRACT FROM AUTHOR]

L-band brightness temperature (TB) has been shown to provide the best sensitivity to soil moisture (SM) although C- and X-band based products offer a longer time-series from satellite-based measurements. Currently, global coverage SM is routinely produced from spaceborne measurements using all three frequency bands, but despite continued validation efforts of the products, the relative characteristics and performance of these observations have not been fully established. Therefore, this study focused on the parametrization of SM retrieval algorithms at L-, C- and X-bands using TB observations from the L-band radiometer on NASA's SM Active Passive (SMAP) mission and the C- and X-band channels of JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite. These can be applied in global SM retrieval algorithms using either one of the frequencies or a combination of them. The reference in situ SM data was obtained from 12 core validation sites across various land cover types around the world. The investigation highlighted the known challenges of retrieving SM from C- and X-band data compared to the higher sensitivity of the L-band data. Even with a site-specific retrieval algorithm parameterization, the mean correlation of the C- and X-band retrievals for the core validation site SM measurement were much lower than that for L-band, being 0.52 (0.54) and 0.45 (0.47) for vertical (horizontal) polarization, respectively, while for the L-band retrieval the corresponding values were 0.81 (0.77). The parameterization exercise showed that matching the C- and X-band TB measurements with an emission model was not difficult; the problem was relating the observations to SM under the influence of large roughness and vegetation effects. As a result, parameter optimization produced values for some sites that were not realistic or did not allow any practical sensitivity to SM at C- and X-band. Considering the L-band observations, the parameter optimization resulted in superior bias performance as compared to the operational SMAP product parameterization, but the sensitivity to SM changes (R and unbiased root mean square difference) did not improve markedly, or in some cases degraded at the expense of a smaller bias. • Spaceborne radiometer observation at L/C/X-band over 12 soil moisture core sites. • Calibration of soil moisture retrieval algorithm for L/C/X-band using the core sites. • L-band retrieval achieves correlation of 0.8 while C/X-band achieves only 0.5. • Retrieval capability of C/X-band questionable over some of the sites. • Calibration of L-band improves bias and RMSD over SMAP product but not sensitivity. [ABSTRACT FROM AUTHOR]

This paper considers some of the issues of radiometer brightness image formation and reconstruction for use in the NASA-sponsored Calibrated Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature Earth System Data Record project, which generates a multisensor multidecadal time series of high-resolution radiometer products designed to support climate studies. Two primary reconstruction algorithms are considered: the Backus–Gilbert approach and the radiometer form of the scatterometer image reconstruction (SIR) algorithm. These are compared with the conventional drop-in-the-bucket (DIB) gridded image formation approach. Tradeoff study results for the various algorithm options are presented to select optimum values for the grid resolution, the number of SIR iterations, and the BG gamma parameter. We find that although both approaches are effective in improving the spatial resolution of the surface brightness temperature estimates compared to DIB, SIR requires significantly less computation. The sensitivity of the reconstruction to the accuracy of the measurement spatial response function (MRF) is explored. The partial reconstruction of the methods can tolerate errors in the description of the sensor measurement response function, which simplifies the processing of historic sensor data for which the MRF is not known as well as modern sensors. Simulation tradeoff results are confirmed using actual data. [ABSTRACT FROM AUTHOR]

Tipping calibration is an important technique for the absolute calibration of ground-based microwave radiometers. The standard tipping calibration is usually used in clear atmosphere. The spatial variations of liquid water in cloudy atmosphere may lead to great calibration errors in the process of standard tipping calibration. A new method of tipping calibration for ground-based microwave radiometers is proposed, which uses an accurate channel to calibrate another inaccurate channel in cloudy atmosphere. The simulation and measurements show that the method proposed here can eliminate the radiation of cloud liquid water and calibrate the radiometer under cloudy-sky conditions effectively. A relative tipping calibration is also introduced to retrieve the parameters in cloudy atmosphere. It has been proven that the relative tipping calibration can reduce the retrieval error significantly in cloudy atmosphere even though neither of the two channels is accurate. [ABSTRACT FROM PUBLISHER]

A prerequisite for the successful retrieval of geophysical parameters from remote sensing measurements is the development of an accurate forward model. The European Space Agency Soil Moisture and Ocean Salinity (SMOS), carrying onboard an L-band interferometric radiometer (Microwave Interferometric Radiometer using Aperture Synthesis), was launched on November 2009. Due to the lack of L-band passive ocean measurements from space, several prelaunch forward models were developed and initially used in the SMOS ocean salinity operational processor. In this paper, an update of the prelaunch semi-empirical forward model is presented, using for the first time, real SMOS data. In particular, the ocean surface emissivity modulation at L-band due to rough sea surface is reviewed and reanalyzed. A new model definition is provided with the help of a simple neural network. The improvement is quantified in terms of retrieved salinity accuracy compared with the climatology and concerns essentially the range of wind speeds higher than 12 m·s-1. [ABSTRACT FROM AUTHOR]

Accurate prediction of sea surface emission is the key for sea surface salinity retrieval from satellite microwave radiometer. In order to retrieve salinity from satellite observation, several sea surface microwave emission models have been developed based on theoretical or empirical methods and validated by in-situ measurements in different regions. However, their performances are still unclear in the Chinese coastal waters. In this study, based on two cruises measurements in the East China Sea (ECS), including the brightness temperature measured by a shipborne microwave radiometer and other auxiliary data (sea surface salinity, sea surface temperature and wind speed), the performances of different sea surface emission models are tested. The results showed that the developed models provide fairly good accuracy in predicting brightness temperature; for example, the accuracy of small perturbance/small scale approximation model (SPM/SSA), two-scale model (TSM) and empirical model is in the range from 0.6 K to 3 K. Moreover, the TSM and empirical models are further improved by optimizing the model parameters in the ECS. Finally, the sea surface salinity were retrieved from shipborne measured data based on the improved models, and the results show that the root mean square (rms) differences between retrieved and in-situ sea surface salinity is about 0.4 psu, indicating the significant improvement by the regional model parameters. [ABSTRACT FROM AUTHOR]