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The Soil Moisture Active Passive (SMAP) mission includes a unique combination of instruments intended to provide daily global soil moisture data with high accuracy and resolution. Due to radar instrument failure, the default resolution of the data product decreased from the intended 9 km to 36 km shortly after the mission started to return data. To improve this, we employed the Scatterometer Image Reconstruction algorithm in its radiometer form (rSIR) to enhance the resolution of the radiometer brightness temperature measurements from which the soil moisture was derived. This paper compares the soil moisture estimates created from the rSIR-enhanced brightness temperatures with SMAP project radiometer L2_SM_SP and SMAP-Sentinel L2_SM_P products reported on 9 km and 3 km grids, respectively. We find that the difference of the rSIR-enhanced passive soil moisture product is generally within 0.020 cm 3 cm − 3 RMS of the 9 km SMAP radiometer L2_SM_SP and 0.045 cm 3 cm − 3 RMS of the 3 km SMAP-Sentinel L2_SM_P soil moisture products. The accuracy of the rSIR soil moisture can be improved by including better antenna pattern correction methods applied to the input TB measurements. [ABSTRACT FROM AUTHOR]

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]

Active and passive microwave signals over land co-vary depending on their shared scattering and emission characteristics by soil and vegetation media. Estimates of this covariation can be used beneficially to downscale coarse-resolution brightness temperatures with high-resolution backscatter for enhanced-resolution Earth observations. In this study, a forward model of covariation for vegetated soil is derived by combining two well-established models of active and passive microwave interactions. The covariation model is inverted to obtain a single-pass observation-driven estimation of active-passive microwave covariation (β) based on multi-channel radiometer and Synthetic Aperture Radar (SAR) scenes. A key feature of the estimation approach is that it is applicable to co-located spatial data scenes and does not rely on temporal information. We present applications of the estimation with combinations of SMAP (L-band) radiometry and both SMAP (L-band) and multi-angular Sentinel-1 (C-band) backscatter data. We first show that for the period of available SMAP L-band radiometer and radar data, the estimation approach for β , introduced in this study, yields similar results as the statistical time-series approach originally designed for SMAP. The new single-pass approach also allows estimation of active-passive covariation where the statistical approach cannot be applied because dynamic range of brightness temperature and backscatter are too limited to allow regression. We then apply the developed estimation method to SMAP L-band radiometer and multi-angular Sentinel-1 C-band SAR data. Here, the study quantifies the effects of microwave frequency and look-angles on the covariation by applying the estimation globally and analyzing the results as a function of vegetation cover – a key determinant of the strength of the covariation. • A single-pass estimation of active-passive microwave covariation is established. • The approach allows estimation of covariation without the need for time series. • It is observation-driven and does not require site-specific calibration. • It enables global downscaling of SMAP radiometer with Sentinel-1 radar data. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an analysis of the experimental measurements of the azimuthal anisotropy of the microwave emissions of a rough water surface. The experimental data were obtained using microwave radiometers with operating frequencies of ~37 GHz (a wavelength of ~8 mm) on the oceanographic platform of the Black Sea Hydrophysical Experimental Facility of the Russian Academy of Sciences in the period from 2005 to 2016. This paper describes the azimuthal anisotropy effect, experimental studies, measurement methods, and data processing. A qualitative comparison of the measurement results with the model calculations and other experimental studies of anisotropy was performed. It was shown that there is a strong sensitivity of the variation in the radio emissions of a rough water surface, both to the wind speed and to its direction at different incidence observation angles. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the functional dependence of the microwave emission of a rough water surface at a frequency of 37.5 GHz (wavelength of ~8 mm). The MiROSE model (Microwave Rough Ocean Surface Emission model) is based on the experimental studies of the own thermal radio emission of the water surface, which were carried out in 2005 and 2007 on the oceanographic platform of the Black Sea Hydrophysical Experimental Facility of the Russian Academy of Sciences. This paper demonstrates the steps of the simulation for selecting the optimal functions to describe the incident angle, wind, and temperature dependences of the increment of the own water surface emission. The following parameters can be calculated on the basis of the proposed model: the increment of the brightness temperature, radio-brightness contrast, and radio-brightness temperature for horizontal and vertical polarizations of the received emission. The model is applicable to water temperatures ranging from 12.5 to 25°C, wind speeds of 3-13 m/s, and incident angles of 30°-80° measured from the nadir. [ABSTRACT FROM AUTHOR]

The paper describes a method of simultaneous measurements of kinematic parameters of a high-speed process by a microwave radio interferometer with a 3-mm range of wavelengths and the brightness temperature of the same process by the microwave radio interferometer operating in the radiometer mode. The methods of radiometer calibration and analysis of radiometric data are described. Results of experimental investigations are reported by an example of measuring the detonation velocity and estimating the brightness temperature of the detonation front in TNT. [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]

Microwave radiometer is an effective instrument to monitor the atmosphere continuously in different weather conditions. It measures brightness temperatures at different frequency bands which are subjected to standard retrieval methods to obtain real time profiles of various atmospheric parameters such as temperature and humidity. But the retrieval techniques used by radiometer have to be adaptive to changing weather condition and location. In the present study, three retrieval techniques have been used to obtain the temperature and relative humidity profiles from brightness temperatures, namely; piecewise linear regression, feed forward neural network and neural back propagation network. The simulated results are compared with radiosonde observations using correlation analysis and error distribution. The analysis reveals that neural network with back propagation is the most accurate technique amongst the three retrieval methods utilized in this study. [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]

In the present study, the effectiveness of nowcasting convective activities using a microwave radiometer has been examined for Kolkata (22.65° N, 88.45° E), a tropical location. It has been found that the standard deviation of brightness temperature (BT) at 22 GHz and instability indices like Lifting Index (LI), K Index (KI) and Humidity Index (HI) has shown definite changes before convective events. It is also seen that combination of standard deviation of BT at 22 GHz and LI can be most effective in predicting convection. A nowcasting algorithm is prepared using 18 isolated convective events of 2011 and in all cases, a marked variation of these parameters has been seen an hour before the event. Accordingly, a prediction model is developed and tested on convective events of 2012 and 2013. It is seen that the model gives reasonable success in predicting convective rain about 7075 min in advance with a prediction efficiency of 80%. [ABSTRACT FROM AUTHOR]

Issues of the formation and recording of the spatial and angular distributions of thermal radio radiation of rainfall in the microwave range are discussed. The thermal radio-frequency radiative transfer in a threedimensional rain cell is simulated numerically with a different rainfall rate, taking into account the nonspherical shape of falling raindrops and their size distribution. The role of the three-dimensional inhomogeneity of rainfall fields in the formation of a field of their inherent thermal radio radiation in the microwave range is revealed. [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]

We present a new high-fidelity method of calibrating a cross-track scanning microwave radiometer using Global Positioning System (GPS) radio occultation (GPSRO) measurements. The radiometer and GPSRO receiver periodically observe the same volume of atmosphere near the Earth's limb, and these overlapping measurements are used to calibrate the radiometer. Performance analyses show that absolute calibration accuracy better than 0.25 K is achievable for temperature sounding channels in the 50-60-GHz band for a total-power radiometer using a weakly coupled noise diode for frequent calibration and proximal GPSRO measurements for infrequent (approximately daily) calibration. The method requires GPSRO penetration depth only down to the stratosphere, thus permitting the use of a relatively small GPS antenna. Furthermore, only coarse spacecraft angular knowledge (approximately one degree rms) is required for the technique, as more precise angular knowledge can be retrieved directly from the combined radiometer and GPSRO data, assuming that the radiometer angular sampling is uniform. These features make the technique particularly well suited for implementation on a low-cost CubeSat hosting both radiometer and GPSRO receiver systems on the same spacecraft. We describe a validation platform for this calibration method, the Microwave Radiometer Technology Acceleration (MiRaTA) CubeSat, currently in development for the National Aeronautics and Space Administration (NASA) Earth Science Technology Office. MiRaTA will fly a multiband radiometer and the Compact TEC/Atmosphere GPS Sensor in 2015. [ABSTRACT FROM PUBLISHER]

In the TOPSAR acquisition mode, Sentinel-1 sensors acquire some data in passive mode, with a periodicity of approximately one second. These data provide valuable information about scene brightness and Radio Frequency pollution. This paper reviews the method to extract these passively sensed data and identify Radio Frequency Interferences (RFI). Two sets of measurements are generated by back-projecting passively sensed data on the ground, generating Earth Brightness Temperature (BT) and RFI equivalent temperature. A proper calibration has been implemented thanks to AMSR-2 data. The paper shows the result of one year of data collection from Sentinel 1 A and B regarding RFI and BT. We discuss the stability of the systems and the accuracy of the measurements by cross-comparing results from the two sensors, and we show how this information can be exploited for precise SAR data denoising and RFI mitigation. • Sentinel-1 TOPSAR mode senses Earth Brightness and Radio Frequency Interferences. • Brightness maps can be generated over most landmasses and nearby sea-ocean. • C-band Radio Frequency emissions can be detected with a sensitivity of hundred of K and continuously monitored World-Wide. • A geometric resolution of kilometers can be achieved by back-projecting ascending and descending passes. • These maps are used to enhance SAR products quality by denoising and removing RFI. [ABSTRACT FROM AUTHOR]

Continuous estimates of the vertical integrated precipitable water vapor content from the tropospheric delay of the signal received by the antennas of the global positioning system (GPS) are used in this paper, in conjunction with the measurements of the Meteosat Second Generation (MSG) spinning enhanced visible and infrared imager (SEVIRI) radiometer and with the lightning activity, collected here by the ground-based lightning detection network (LINET), in order to identify links and recurrent patterns useful for improving nowcasting applications. The analysis of a couple of events is shown here as an example of more general behavior. Clear signs appear before the peak of lightning activity on a timescale from 2 to 3 h. In particular, the lightning activity is generally preceded by a period in which the difference between SEVIRI brightness temperature (TB) at channel 5 and channel 6 (i.e., ∆TB) presents quite constant values around 0 K. This trend is accompanied by an increase in precipitable water vapor (PWV) values, reaching a maximum in conjunction with the major flash activity. The results shown in this paper evidence good potentials of using radiometer and GPS measurements together for predicting the abrupt intensification of lightning activity in nowcasting systems. [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]

Soil Moisture Active Passive (SMAP) mission of NASA was launched in January 2015. Currently, SMAP has an L-band radiometer and a defunct L-band radar with a rotating 6-m mesh reflector antenna. On July 7th, 2015, the SMAP radar malfunctioned and became inoperable. Consequently, the production of high-resolution active-passive soil moisture product got hampered, and only ~2.5 months (April 15th, 2015 to July 7th, 2015) of data remain available. Therefore, during the SMAP post-radar phase, many ways were examined to restart the high-resolution soil moisture product generation of the SMAP mission. One of the feasible approaches was to substitute the SMAP radar with other available SAR data. Sentinel-1A/Sentinel-1B SAR data was found most suitable for combining with the SMAP radiometer data because of its nearly similar orbit configuration that allows overlapping of their swaths with a minimal time difference, a key feature/requirement for the SMAP active-passive algorithm. The Sentinel interferometric wide swath (IW) mode acquisition also provides the co-polarized and cross-polarized observations required for the SMAP active-passive algorithm. However, some differences do exist between the SMAP and Sentinel SAR data. They are mainly: 1) Sentinel has a C-band SAR whereas SMAP operates at L-band; 2) Sentinel has multiple incidence angles within its swath, and SMAP has one single incidence angle; and 3) Sentinel 1A/B Interferometric Wide (IW) swath width is ~250 km as compared to SMAP with 1000 km swath width. On any given day, the narrow swath width of the Sentinel observations significantly reduces the overlap spatial coverage between SMAP and Sentinel as compared to the original SMAP radar and radiometer swath coverage. Hence, the temporal resolution (revisit interval) suffers due to narrow overlapped swath width and degrades from 3 days to 12 days. One advantage of using very high-resolution resolution Sentinel-1A/Sentinel-1B data in the SMAP active-passive algorithm is the potential of obtaining the disaggregated brightness temperature and thus soil moisture at a much finer spatial resolution of 3 km and 1 km at global extent. The assessment of high-resolution product at 3 km and 1 km using the soil moisture calibration and validations sites shows reasonable accuracy of ~0.05 m3/m3. The SMAP-Sentinel1 active-passive high-resolution product is now available to the public (new version released in October 2018) through NSIDC (NASA DAAC). The duration of this product is from April 2015 to current date. • The article is about the high resolution (1 km and 3 km) soil moisture data from the SMAP mission. • The soil moisture is produced using the SMAP L-band TB and Sentinel 1A/B C-band Sigma0. • The high resolution soil moisture has reasonable accuracy (RMSE) of ~0.05 m3/m3. • The SMAP-Sentinel1 high resolution soil moisture product is ready for geophysical applications. • The temporal frequency of the this product is ~12 days, however, in Europe it is 6 days. [ABSTRACT FROM AUTHOR]

Accurate representation of the ocean-atmosphere coupling in weather, wave and climate models requires reliable estimates of air-sea surface fluxes of momentum, heat and mass. Whitecap fraction (W) usually quantifies the enhancement of the surface fluxes due to wave breaking. Satellite-based passive remote sensing of W from ocean surface brightness temperatures ( T B s) observes open ocean surface fluxes at low spatial resolution. Radiometric surface observations at higher resolution are necessary to monitor the complex environment in the coastal zone and in polar regions. We assess the feasibility of using the millimeter-wave frequencies (89 to 150 GHz) to observe whitecaps. We evaluate the derivative of the T B with respect to W as a measure for the observation of W. We describe the models and data used to evaluate the T B sensitivity to W for different instrumental and environmental conditions. Atmospheric absorption limits the ability to observe the surface at millimeter-wave frequencies. We find that the T B sensitivity to W at 89 GHz may be sufficient to support limited W retrieval from observations at altitudes below 1 km and that the T B sensitivity at 113 and 150 GHz is not sufficient. Clear skies, and low to moderate atmospheric humidity favor whitecap observations. [ABSTRACT FROM AUTHOR]