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The Chang E’-1 and E’-2 (CE-1 and CE-2) orbital sounders provided high resolution global maps of lunar microwave brightness temperatures (TB) at wavelengths of 0.8, 1.55, 3.85, and 10 cm covering approximately two years of data over the 2007-2010 time frame. The four channel microwave radiometers (MRMs) effectively sampled the upper ~2 meters of the lunar regolith and are diagnostic of thermal and electrical properties including mineralogy, rock abundance and, potentially, interior heat flow. Early comparisons of co-located data between the two instruments revealed ~10-20 K offsets between the CE-1 and CE-2 measurements that required in-flight recalibration efforts. These have included comparisons with model predictions at Apollo sites as well as adjustments of the cold sky horn reference temperatures to include contamination from lunar surface emissions. This paper proposes an in-flight recalibration methodology that focuses on correction of the pre-flight transfer coefficients that determine the relative importance of component losses along the radiometer hardware paths connecting the antennas to the detectors. It is shown that for each channel a single parameter, representing the ratio of the cold sky and main antenna transfer coefficients, can be constrained by knowledge of the stable regolith physical temperatures below the diurnal-varying layer, and is sufficient to establish values for the complete set of hardware transfer coefficients. The in-flight comparisons proposed for recalibration are based on backside highland models of regolith thermal properties. Potential remaining offsets of the recalibrated MRM data are evaluated in terms of parameter uncertainties of our chosen nominal thermal model.

The Microwave Radiation Imager (MWRI) sensors aboard on the Chinese FengYun-3 (FY-3) series satellites have a great potential for long-term study of sea ice distribution. This study corrected the newly released FY-3D MWRI brightness temperature (TB) data with the Advanced Microwave Scanning Radiometer 2 (AMSR2) TB data and used an Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie points algorithm to derive the sea ice concentration (SIC) from these corrected MWRI TB data. We assessed the accuracy of our MWRI-ASI SIC product by comparing with the published SIC products at different spatial resolution and by validating with the Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-1 data. We find that MWRI-ASI SIC has the smallest difference with AMSR2-ASI SIC with the mean absolute difference (MAD) of 6.8%, and the differences mainly occur at the marginal ice zone regions. The MWRI-ASI displays the highest difference with Sea Ice Index and OSI-430-b at 25 km with the mean MAD of 11.8%. MADs between MWRI-ASI SIC and SIC products from other sensors are below 10% except for late June to early October, when those are higher. Compared with the MODIS SIC, MWRI-ASI SIC outperforms other SIC products at the same resolution, with the mean bias of -2.1% and mean RMSD of 13.5%. Although MWRI-ASI tends to under-estimate high SIC, it can capture details of large leads, ice edge, and fragmented ice area. Our MWRI-ASI SIC product at 12.5 km is promising to integrate into long-term sea ice records.

Estimating total precipitable water vapour (TPWV) from advanced microwave scanning radiometer 2 (AMSR2) measurements with reasonable accuracy is challenging. Therefore, this study aims to improve t...

FengYun-3C (FY-3C) is the second-generation polar-orbiting meteorological satellite in China. As one of the most important microwave payloads deployed onboard FY-3C, microwave radiation imager (MWRI) has been continuously observing radiance originating from land and sea surface and supports numerical weather predictions at regional and global scales. With a long-term monitoring of the cold-end reference, MWRI onboard FY-3C was found suffering from discontinuous observations due to its temporary power-OFF/power-ON in the years of 2015 and 2018, resulting in anomalous brightness temperature (TB) jump up to 2–3 K in magnitude at some channels. Analysis of the operating status of FY-3C MWRI indicated that deviation of the receiver system from its optimal operating status affects the nonlinearity characteristics significantly. A correction algorithm, which relates nonlinearity to the autogain control (AGC) operating voltage of the sensor, is thus proposed on the basis of on-ground thermal/vacuum (T/V) test results in the prelaunch phase in order to mitigate the calibration anomalies of FY-3C MWRI. Nonlinearity correction of FY-3C MWRI is further corroborated by comparisons of simultaneously overlapping observations of global precipitation measurement (GPM) microwave imager (GMI). The results demonstrated that after nonlinearity correction the performance of FY-3C MWRI has been improved at all channels except for the 36-V channel where technical failures have been detected since October 2016.

Across spatial and temporal scales, sea-ice motion has implications on ship navigation, the sea-ice thickness distribution, sea ice export to lower latitudes and re-circulation in the polar seas, among others. Satellite remote sensing is an effective way to monitor sea-ice drift globally and daily, especially using the wide swaths of passive microwave missions. Since the late 1990s, many algorithms and products have been developed for this task. Here, we investigate how processing sea-ice drift vectors from the intersection of individual swaths of the Advanced Microwave Scanning Radiometer 2 (AMSR2) mission compares to today's status-quo (processing from daily averaged maps of brightness temperature). We document that the swath-to-swath (S2S) approach results in many more (two orders of magnitude) sea-ice drift vectors than the daily-maps (DM) approach. These S2S vectors also validate better when compared to trajectories of on-ice drifters. For example, the RMSE of the 24 hour Arctic sea-ice drift is 0.9 km for S2S vectors, and 1.3 km for DM vectors from the 36.5 GHz imagery of AMSR2. Through a series of experiments with actual AMSR2 data and simulated Copernicus Imaging Microwave Radiometer (CIMR) data, we study the impact that geo-location uncertainty and imaging resolution have on the accuracy of the sea-ice drift vectors. We conclude by recommending that a swath-to-swath approach is adopted for the future operational Level-2 sea-ice drift product of the CIMR mission. We outline some potential next steps towards further improving the algorithms, and making the user community ready to fully take advantage of such a product.

Objectives: To predict meteorological phenomena such as rain events from sky status during rainy and non-rainy periods. Methods: The method used here is based on brightness temperature ratio measurement at two different frequencies, namely 23.8 GHz and 30 GHz respectively, using ground based dual frequency radiometric data. The ratios of brightness temperature readings obtained by the dual-frequency radiometer at the two above mentioned frequencies are calculated for each simultaneously-taken pair of measurements. Data obtained by the authors for the year 2009 at Cachoeira Paulista in Brazil has been used for analysis. Findings: The major results obtained from the analysis of data collected over a continuous period of seven months are used to construct corresponding histograms and cumulative count graphs of brightness temperature ratios. The histograms and graphs clearly show three peak values that could be interpreted as thresholds between clear sky, cloudy sky and rainy sky conditions respectively. Novelty: The study implements detection of rain events from sky status during rainy and non-rainy periods using peak brightness temperature values obtained from graphs generated using the observation data. The outlined technique can therefore be used to clearly determine sky conditions and accurately predict rain phenomena. The ratio of brightness temperatures at the two frequencies is a unique parameter which is critical to the successful estimation of rain from sky status. The results agree well with multi-channel radiometric data obtained by other researchers at lower frequencies. Keywords: sky status; microwave; millimeter wave; dual frequency radiometer; brightness temperature; rainfall

An algorithm for ice/water detection of ice less than 30 cm in thickness (thin ice) using dual-polarized synthetic aperture radar (SAR) and passive microwave data has been developed based on insights from previous work. The frequency chosen from those available was in the C-band. For the radiometer layer ice concentrations, land contaminated pixels were removed. For the SAR layers, a novel approach to their use in ice detection was developed using the coefficient of variation (COV), defined as the ratio of the standard deviation of the normalized radar cross section, $\sigma _{0}$ , to the mean $\sigma _{0}$ . The COV reduced the complicated signatures of breaking waves and thin ice to bright ice pixels on a dark water background. The co-pol and cross-pol COV layers and the radiometer layer were merged according to physics-based rules, except in regions adjacent to land or the ice edge where only the SAR layers were valid. The detection algorithm was applied to 233 Sentinel 1a and 1b dual-polarization images of the freeze-up from 2015, 2016, 2018, and 2019 in the Beaufort and Chukchi Seas. The synergistic ice retrievals showed improved agreement with manual operational analyses in the marginal ice zone over other automated operational products.

Temporal Experiment for Storms and Tropical Systems—Demonstration (TEMPEST-D) is a 6U CubeSat satellite with a cross-track scanning millimeter-wave radiometer measuring at five frequencies from 87 to 181 GHz. It employs a direct-detection architecture with InP HEMT monolithic microwave integrated circuit (MMIC) low-noise amplifiers and related new technologies. An end-to-end two-point external calibration is performed every 2-s rotation of the scanning mirror, based on observations of the cosmic microwave background and an internal blackbody calibration target, with three thermistors to monitor the target physical temperature. Corrections for antenna pattern effects and cross-scan biases based on prelaunch measured values were updated using data from an on-orbit calibration pitch maneuver. Validation of the observed brightness temperatures ( $T_{\text {B}}$ ) is performed by comparing to coincident nonprecipitating ocean observations from five well-calibrated on-orbit instruments, including Global Precipitation Measurement (GPM) mission Microwave Imager (GMI) and four Microwave Humidity Sounder (MHS) sensors on board NOAA-19, MetOp-A, MetOp-B, and MetOp-C satellites. Absolute calibration accuracy is within 1 K for all channels, well within the 4-K requirement. Calibration precision, or stability over time, is within 0.6 K for all channels, also well within the 2-K requirement. The intrinsic noise of TEMPEST-D is lower than MHS, resulting in similar on-orbit noise equivalent differential temperatures (NEDTs), even though TEMPEST-D has a much shorter integration time of 5 ms as compared to 18 ms for MHS. As a result, although the TEMPEST-D radiometer is substantially smaller, lower power, and lower cost than similar current operational radiometers, it has comparable or better performance in terms of instrument noise, calibration accuracy, and calibration stability or precision.

Microwave technology is very sensitive to Radio Frequency Interferences (RFI). Works previously done within this Master by Marc Jou [1] showed the impossibility to retrieve measurements using L-band radiometers on-board drones. After detecting such issue, Balamis first tried to solve it by hardware: a new antenna design and the extensive use of shielding on the drone were tried without success. Balamis started the development of its first digital radiometer based on the use of Software Defined Radio architecture on 2017, partially funded with the support of CDTI. The resulting minimum viable digital radiometer was ready by June 2019, but it did not include any RFI mitigation capability. Developments done my Master student Ahmad Daoud [2] demonstrated the identification of RFI using Fast Fourier Transform (FFT) over RAW data but could not provide any efficient implementation of its mitigation on-board the L-band radiometer. The proposed solution is the implementation of the FFT and the RFI filters using Field Programmable Gate of Array (FPGA) for the input signals, and its concurrent performance. Filtering an analog signal by introducing in-system FFT of ZYNQ7000 FPGA is implemented in this project. Additionally, the power consumption of FPGA, and the need to dissipate it, forces the development of a temperature control system with cooling capabilities. It is done to improve the previous heating-only thermal control of Balamis radiometer. Such more advanced thermal control will be also used for the Interferometric Ground-based Synthetic Aperture Radar that Balamis is developing. Solving these two goals are therefore the purpose of this Master Thesis.

The ocean X-band microwave emission model for modeling measurements of satellite radiometers over the cold Arctic seas at an observation angle of 65° is proposed. The model is based on the experimental geophysical model function (GMF) of microwave emission dependence on surface wind speed for an angle of 55°, that was developed from the Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements and the two-scale theory of the ocean microwave radiation. The experimental GMF is derived from the comparison of AMSR2 measurements over the Arctic seas with surface wind speeds retrieved from these data. The model is limited by wind speed of 15 m/s and does not take into account the foam emission. The model allows discriminating between longwave and shortwave wind-induced microwave radiation and using the presented approach to proceed to the observation angle of the MTVZA-GYa (temperature and humidity atmospheric sounding unit) microwave radiometer on board the Meteor-M Russian polar orbiting satellites.

Compared with traditional real aperture microwave radiometers, one-dimensional synthetic aperture microwave radiometers have higher spatial resolution. In this paper, we proposed to retrieve sea surface temperature using a one-dimensional synthetic aperture microwave radiometer that operates at frequencies of 6.9 GHz, 10.65 GHz, 18.7 GHz and 23.8 GHz at multiple incidence angles. We used the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts and a radiation transmission forward model to calculate the model brightness temperature. The brightness temperature measured by the spaceborne one-dimensional synthetic aperture microwave radiometer was simulated by adding Gaussian noise to the model brightness temperature. Then, a backpropagation (BP) neural network algorithm, a random forest (RF) algorithm and two multiple linear regression algorithms (RE1 and RE2) were developed to retrieve sea surface temperature from the measured brightness temperature within the incidence angle range of 0°–65°. The results show that the retrieval errors of the four algorithms increase with the increasing Gaussian noise. The BP achieves the lowest retrieval errors at all incidence angles. The retrieval error of the RE1 and RE2 decrease first and then increase with the incidence angle and the retrieval error of the RF is contrary to that of RE1 and RE2.

Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (−1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.

Calibration is a key issue for quantitative application of meteorological satellite data. The complex space environment may cause many uncertainties in data calibration. A highly stable and reliable calibrator in flight is needed. Because the Moon has no atmosphere and no environmental variation, the physical and chemical properties of its surface are stable in the long term. The Moon might be an ideal candidate for in-flight thermal calibration. In advanced satellite-borne microwave remote sensing such as NOAA-18, the deep space view (DSV) of the microwave humidity sounder (MHS) has viewed the Moon many times every year. Using the thermal-physical properties of the lunar regolith derived from the Diviner infrared (IR) brightness temperature (TB) data, we solve the one-dimensional heat conduction equation to obtain the temperature profile of the near side of the lunar regolith medium. The loss tangents of the regolith medium are retrieved from microwave TB data of the Chinese satellite Chang’e-2. The integrated radiative transfer equation is used to simulate the weighted disk-average TB of the lunar surface for the MHS channels at 89, 157, and 183 GHz for the year 2011. The Moon is taken as an extended circular target. The simulated TBs are used to correct the full width at half maximum (FWHM) fitted with the MHS counts. We analyze the influences of the distance between the satellite and the Moon, the lunar phase angle, and the FWHM of the radiometer on the inverted FWHM. The corrected TB data are compared with the simulation. This paper presents a new method for thermal calibration of spaceborne in-flight microwave and millimeter-wave radiometers with the weighted disk-average TB of the lunar surface.

This study proposes the use of a Spectral Linear Mixing Model (SLMM) on passive microwave data for mapping the concentration and area of young and/or first-year ice in the oceanic region located in...

In this contribution, we present a comparison of three digital real-time spectrometers used in passive remote sensing of ozone and other trace gases in the middle atmosphere. During a period of six months, we connected the spectrometers to the same radiometric front-end to perform parallel observations of the ozone emission line at 110.836 GHz. This allowed us to better characterize a bias previously observed on the integrated spectra of the Acqiris AC240, a widely used digital spectrometer which has been used for more than a decade in many operational microwave radiometers. We investigated the bias under different atmospheric conditions and found that it is caused by multiple sources. Nonlinearities in the calibration are responsible for part of the bias, but a larger contribution stems from a second effect in the AC240. Although this error source is still partly unexplained, we found that a simple correction scheme simulating a spectral leakage can be applied to the integrated spectra of the AC240 and worked well on our range of observations. We also show that by applying our bias correction to the spectra, we can correct the bias in the ozone retrievals. There is still a need for further measurements to validate this approximate correction, but it could help to correct the numerous time series of ozone and other atmospheric constituents recorded by the AC240.

Sea surface salinity (SSS) in coastal zones with high temporal and spatial resolutions is also needed for scientific research and marine economy. However, one of the limitations of airborne L -band real-aperture microwave radiometers and synthesis aperture interferometric radiometers is high mass and volume or the high complexity of the system. To overcome the limitations, an L -band phased array radiometer is proposed as an alternative solution to monitor SSS in coastal zones from a small unmanned aerial vehicle (UAV). The L -band phased array radiometer is introduced, which only comprises a phased array and a single receiver. In addition, the calibration methods of the L -band phased array, radiometer that the external calibration method and the internal calibration method, are also introduced. A series of experiments was performed to assess the performance of the L -band phased array radiometer. The results indicate that the L -band phased array radiometer show a good performance in low pointing angles and low scanning angles. As a conclusion, the L -band phased array radiometer can be an alternative solution to monitor SSS in coastal zones with high temporal and spatial resolutions from a small UAV.

This letter presents a novel background calibration technique for microwave radiometers in indoor applications such as security sensing for static targets where unknown noise sources may add to background noise and degrade measurement accuracy. Such a background correction is important for absolute measurement of radiometric apparent temperature as opposed to relative measurement of contrast. The technique is validated by measuring the error in apparent temperature of hot water using a 23.55 GHz radiometer in an uncontrolled environment where the background noise is artificially increased using a diode-based noise source. The calibration technique reduced the prediction error introduced due to incorrect assumptions about the background by approximately 1 to 3 K.

The MIRS microwave radiometer–spectrometer is described. The instrument is being developed using a conceptual design that was the first stage of ground preparations for the Convergence Space Experiment (SE). At the next stage of SE implementation, it is planned to use the proposed technical solutions as design documentation for manufacturing test and flight prototypes. MIRS is a multi-frequency scanner and sounder intended to record the characteristic radiothermal radiation emitted by the system of the Earth’s surface and atmosphere. The set of frequencies of the received radiation is in the range of 10 to 200 GHz. The device is to be installed on the Russian Orbital Segment (ROS) of the International Space Station (ISS). Conical scanning of the Earth’s surface is done by rotating an antenna unit mounted on a fixed base. With a 1.3 s period of rotation and a constant Earth incidence angle of 53.1 deg, continuous coverage of the Earth’s surface is ensured in a swath 800 km wide with spatial resolution of 10 km for the highest-frequency channels. Nine radiometer–polarimeters laid out as a full-power radiometer are used as radiation receivers. The MIRS antenna system is an offset parabolic antenna with separated irradiators. The optimum design solutions allowing the main problems of the SE to be solved and the MIRS parameters to be maintained under operating conditions on board the ISS are chosen for the conceptual design.

The results of the cloud liquid water path (LWP) “land–sea” gradient retrieval from ground-based measurements of the downwelling microwave radiation near the Gulf of Finland coastline in the suburbs of Saint-Petersburg are presented. The measurements were carried out at the Department of Physics, St. Petersburg State University, by an RPG-HATPRO radiometer operating in the angular scanning mode. The inverse problem is solved by linear regression with the use of different statistical models of cloudiness for training the algorithm. Seven-year average values of the gradient of LWP for summer and winter have been obtained. The results demonstrate the presence of a positive “land–sea” gradient of LWP (larger values over the land and smaller values over the sea) in both periods, which qualitatively agrees with available satellite data.

Microwave remote sensing can be used to monitor the time evolution of some key parameters over land, such as land surface temperature or surface water extent. Observations are made with instruments, such as the Scanning Microwave Multichannel Radiometer (SMMR) before 1987, the Special Sensor Microwave/Imager (SSM/I) and the subsequent Special Sensor Microwave Imager/Sounder (SSMIS) from 1987 and still operating, and the more recent Global Precipitation Measurement Microwave Imager (GMI). As these instruments differ on some of their characteristics and use different calibration schemes, they need to be inter-calibrated before long-time-series products can be derived from the observations. Here an inter-calibration method is designed to remove major inconsistencies between the SMMR and other microwave radiometers for the 18 and 37 GHz channels over continental surfaces. Because of a small overlap in observations and a ∼6 h difference in overpassing times between SMMR and SSM/I, GMI was chosen as a reference despite the lack of a common observing period. The diurnal cycles from 3 years of GMI brightness temperatures are first calculated and then used to evaluate SMMR differences. Based on a statistical analysis of the differences, a simple linear correction is implemented to calibrate SMMR on GMI. This correction is shown to also reduce the biases between SMMR and SSM/I, and can then be applied to SMMR observations to make them more coherent with existing data records of microwave brightness temperatures over continental surfaces.

Forward models are a key tool to generate synthetic observations given knowledge of the atmospheric state. In this way, they are an integral part of inversion algorithms that aim to retrieve geophysical variables from observations or in data assimilation. Their application for the exploitation of the full information content of remote sensing observations becomes increasingly important when these are used to evaluate the performance of cloud-resolving models (CRMs). Herein, CRM profiles or fields provide the input to the forward model whose simulation results are subsequently compared to the observations. This paper introduces the freely available comprehensive microwave forward model PAMTRA (Passive and Active Microwave TRAnsfer), demonstrates its capabilities to simulate passive and active measurements across the microwave spectral region for upward- and downward-looking geometries, and illustrates how the forward simulations can be used to evaluate CRMs and to interpret measurements to improve our understanding of cloud processes. PAMTRA is unique as it treats passive and active radiative transfer (RT) in a consistent way with the passive forward model providing upwelling and downwelling polarized brightness temperatures and radiances for arbitrary observation angles. The active part is capable of simulating the full radar Doppler spectrum and its moments. PAMTRA is designed to be flexible with respect to instrument specifications and interfaces to many different formats of input and output, especially CRMs, spanning the range from bin-resolved microphysical output to one- and two-moment schemes, and to in situ measured hydrometeor properties. A specific highlight is the incorporation of the self-similar Rayleigh–Gans approximation (SSRGA) for both active and passive applications, which becomes especially important for the investigation of frozen hydrometeors.

The relative phase and amplitude calibration between individual receiving elements are one of the key challenges in interferometric microwave radiometry. For monolithic interferometers, for example the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) on board the Soil Moisture and Ocean Salinity (SMOS) mission, the calibration of these two parameters is accomplished by correlated noise injection, where a noise signal from a central noise source is distributed to multiple elements in phase, and any measured phase and amplitude differences can be measured and compensated. As new multisatellite interferometers are being proposed, a new strategy capable of calibrating these errors between the free-flying antennas as well as any antenna position errors is required. In this article, a new calibration scheme is proposed which uses a set of microwave beacons in place of the central noise source, placed at known locations within the interferometer’s field of view. The visibility function produced by these point-sources can be precalculated, and any errors between the calculated and the measured visibility samples can be attributed to the errors to be determined. Since the phase of the measured visibility is a function of phase and antenna position errors, this technique is capable of calibrating these two parameters simultaneously. Calibration equations for far- and near-field beacons are presented. Using these expressions, five interferometric calibration routines are proposed and examined for geostationary formation flight microwave radiometers. Although this technique is ideal for multisatellite interferometers with variable antenna positions, it is also applicable to monolithic interferometers that can undergo substantial array deformation.

Calibration plays an important role in improving the accuracy of the microwave and millimeter-wave radiometric measurements. Several calibration techniques have been used in radiometers including external calibration targets, vicarious sources, and internal calibrators such as noise diodes or matched reference load. A new calibration technique based on deep learning has recently been developed to calibrate microwave and millimeter-wave radiometers. The deep-learning calibrator has been previously demonstrated on a computer noise-wave modeled Dicke-switching radiometer. This article applies the new deep-learning calibration technique for the calibration of the high-frequency airborne microwave and millimeter-wave radiometer (HAMMR) instrument. A deep-learning neural network model is built to calibrate the 2014 West Coast Flight Campaign antenna temperature measurements of the HAMMR. The deep-learning calibrator antenna temperature estimates are obtained from the radiometric measurements. The deep-learning calibration results are compared with the existing conventional calibration techniques used in HAMMR 2014 field campaign. The results have shown that the deep-learning calibrator is in agreement with the conventional calibration techniques. In this article, it is demonstrated that the deep-learning calibrator can be employed for calibrating the radiometers with high accuracy.

Microwave thermometry provides noninvasive detection of thermal inhomogeneity in biological tissues, early diagnosis of oncological diseases, and correction of treatment based on changes in the parameters of the body’s electromagnetic radiation. We present an analytical review of developments in medical radiometers. Challenges hindering the development of radiometry are identified, as are proposed solutions.

The rapid development of miniaturized satellite instrument technology has created a new opportunity to deploy constellations of passive microwave (PMW) radiometers to permit retrievals of the same Earth scene with very high temporal resolution to monitor cloud evolution and processes. For such a concept to be feasible, it must be shown that it is possible to distinguish actual changes in the atmospheric state from the variability induced by making observations at different Earth incidence angles (EIAs). To this end, we present a flexible and physical optimal estimation-based algorithm designed to retrieve profiles of atmospheric water vapor, cloud liquid water path, and cloud ice water path from cross-track PMW sounders. The algorithm is able to explicitly account for the dependence of forward model errors on EIA and atmospheric regime. When the algorithm is applied to data from the Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) CubeSat mission, its retrieved products are generally in agreement with those obtained from the similar but larger Microwave Humidity Sounder instrument. More importantly, when forward model brightness temperature offsets and assumed error covariances are allowed to change with EIA and sea surface temperature, view-angle-related biases are greatly reduced. This finding is confirmed in two ways: through a comparison with reanalysis data and by making use of brief periods in early 2019 during which the TEMPEST-D spacecraft was rotated such that its scan pattern was along track, allowing dozens of separate observations of any given atmospheric feature along the satellite’s ground track.

Objective : Near-field microwave radiometry has emerged as a tool for real-time passive monitoring of local brain activation, possibly attributed to local changes in blood flow that correspond to temperature and/or conductivity changes. The aim of this study is to design and evaluate a prototype system based on microwave radiometry intended to detect local changes of temperature and conductivity in depth in brain tissues. A novel radiometric system that comprises a four port total power Dicke-switch sensitive receiver that operates at 1.5 GHz has been developed. Methods and Results : The efficacy of the system was assessed through simulation and experiment on brain tissue mimicking phantoms under different setup conditions, where temperature and conductivity changes were accurately detected. In order to validate the radiometer's capability to sense low power signals occurring spontaneously from regions in the human brain, the somatosensory cortices of one volunteer were measured under pain-inducing psychophysiological conditions. The promising results from the initial in vivo measurements prove the system's potential for more extensive investigative trials. Conclusion and Significance : The significance of this study lies on the development of a compact and sensitive radiometer for totally passive monitoring of local brain activation as a potential complementary tool for contributing to the research effort for investigating brain functionality.

This work addresses the intercalibration of the Microwave Radiation Imager (MWRI) on the Chinese second-generation polar-orbiting meteorological satellite Fengyun 3C (FY-3C) against the Microwave Imager (GMI) on the Global Precipitation Measurement (GPM) Core Observatory, in which a modified Double Difference (DD) method is developed and the Brightness Temperatures (TBs) in FY-3C MWRI and GMI channels are simulated using the ocean microwave Radiative Transfer Model (RTM) fed with the fifth generation of European Centre for Medium-range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. With the modified DD method, the intercalibration of FY-3C MWRI in 2017 are obtained. The results show that the MWRI observations are underestimated, especially for the low frequency channels. The calibration biases (mean of DDs) in FY-3C MWRI channels are temperature dependent, and decrease with the frequency increment. The in-orbit calibration of the MWRI descending (MWRID) data is 1~2 K worse than that of the MWRI ascending (MWRIA) data. At the TBs of standard scene defined by the Global Space-based Inter-Calibration System (GSICS), the calibration errors (mean of DDs ± standard deviation at the mean) of MWRIA data in January 2017 are -6.7±0.4 K, -8.3±0.8 K, -3.0±0.7 K, -1.9±1.0 K, -2.5±1.1 K, -3.9±0.8 K, -2.1±1.5 K, -1.5±1.0 K and -0.4±2.3 K in the 10V/H, 18V/H, 23V, 36V/H and 89V/H channels, respectively, while the calibration errors of MWRID data in January 2017 are -7.6±0.8 K, -9.1±1.2 K, -4.4±0.8 K, -2.9±1.3 K, -3.6±1.2 K, -5.1±0.8 K, -2.6±1.4 K, -2.5±1.1 K and -1.2±2.5 K in the nine channels, respectively. Although calibration biases exist, the in-orbit calibration of FY-3C MWRI is generally stable in 2017. The results of the modified DD method are consistent with that of the DD method. The modified DD method is promising to be applied to the intercalibration with both target and reference radiometers on polar-orbiting satellites.

The advanced microwave radiometer-climate quality (AMR-C) is a part of the European Sentinel-6A/B series, a collaboration between ESA and NASA, of two Earth-observing satellites, which will be launched in 2020 and 2025. Compared to its predecessor, Jason-3, the two AMR-C radiometer instruments have an external calibration system which enables higher radiometric stability accomplished by moving the secondary mirror between well-defined targets. Sentinel-6 allows continuing the study of the ocean circulation, climate change, and sea-level rise for at least another decade. Besides the external calibration for the AMR heritage radiometer (18.7, 23.8, and 34 GHz channels), the AMR-C contains a high-resolution microwave radiometer (HRMR) with radiometer channels at 90, 130, and 168 GHz. This subsystem allows for a factor of 5× higher spatial resolution at coastal transitions. This article presents a brief description of the instrument and the measured performance of the completed AMR-C-A and AMR-C-B instruments.

Land surface temperature (LST) is a key parameter and plays an important role in hydrology, ecology, environment, and biogeochemistry. It is difficult for the existing satellites to acquire global LST with spatial and temporal consistency. The Moon-based Earth observation platform with a long life, large coverage can observe continuously the Earth, and obtain the global-scale LST. At present, various approaches for retrieving LST from passive microwave remote sensing data have been developed for the satellite remote sensing data with small and constant viewing zenith angle, however, the Moon-based Earth observation platform located outside the Earth's ionosphere has the viewing zenith angle of 0-90°. In this study, a modified physical-based method of LST retrieval from passive microwave data was developed for wide viewing zenith angles, and the LST and emissivity can be simultaneously estimated through the analysis of various atmospheric and ionosphere parameters. Three types of data, including the FengYun-3B satellite microwave radiation imager data, the multichannel Advanced Microwave Scanning Radiometer data, and Moon-based microwave radiation simulation data with the viewing zenith angles of 52-53°, 55°, and 0-90°, respectively, were used to retrieve LST. Results show that the estimation accuracy of LST decreases with the increase of viewing zenith angle. The 23.8 and 36.5 GHz brightness temperature is optimum for the LST estimation under a large-scale viewing zenith angle, and the root mean square errors of the LST are 5.18, 5.44, and 4.79 K, respectively.

In a traditional spaceborne microwave radiometer system with a scanning antenna there is often a conflict between spatial and radiometric resolution. Integration over many beams per frequency might be necessary to improve radiometric resolution. Many beams may be generated using many classical feed horns or by a Focal Plane Array (FPA) system. At C-band horns are bulky and replacing several such horns with a FPA is an interesting option. The FPA concept uses many small antenna elements, many receivers, and powerful on-line digital processing. A focal plane array system that can replace 4 horns is evaluated, especially concerning power consumption.

A two-dimensional variational retrieval (2D-Var) is presented for a passive microwave imager. The overlapping antenna patterns of all frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are explicitly simulated to attempt retrieval of near-surface wind speed and surface skin temperature at finer spatial scales than individual antenna beams. This is achieved, with the effective spatial resolution of retrieved parameters judged by analysis of 2D-Var averaging kernels. Sea surface temperature retrievals achieve about 30 km resolution, with wind speed retrievals at about 10 km resolution. It is argued that multi-dimensional optimal estimation permits greater use of total information content from microwave sensors than other methods, with no compromises on target resolution needed; instead, various targets are retrieved at the highest possible spatial resolution, driven by the channels' sensitivities. All AMSR2 channels can be simulated within near their published noise characteristics for observed clear-sky scenes, though calibration and emissivity model errors are key challenges. This experimental retrieval shows the feasibility of 2D-Var for cloud-free retrievals and opens the possibility of stand-alone 3D-Var retrievals of water vapour and hydrometeor fields from microwave imagers in the future. The results have implications for future satellite missions and sensor design, as spatial oversampling can somewhat mitigate the need for larger antennas in the push for higher spatial resolution.

Scatterometers provide very stable ocean vector wind data records. This is because they measure the ratio of backscattered to incident microwave signal over the ocean surface as opposed to an absolute quantity (e.g., emitted microwave signal). They provide an optimal source of observations for building a long ocean vector wind Climate Data Record (CDR). With this objective in mind, observations from different satellite platforms need to be assessed for high absolute accuracy versus a common ground truth and for fine cross-calibration during overlapping periods. Here we describe the methodology for developing a CDR of ocean surface winds from the C-band ASCAT scatterometers onboard MetOp-A, -B, and -C. This methodology is based on the following principles: a common Geophysical Model Function (GMF) and wind algorithm developed at Remote Sensing Systems (RSS) and the use of in situ and satellite winds to cross-calibrate the three scatterometers within the accuracy required for CDRs, about 0.1 m/s at the global monthly scale. Using multiple scatterometers and radiometers for comparison allows for the opportunity to isolate sensors that are drifting or experiencing step-changes as small as 0.05 m/s. We detected and corrected a couple of such changes in the ASCAT-A wind record. The ASCAT winds are now very stable over time and well cross-calibrated with each other. The full C-band wind CDR now covers 2007-present and can be easily extended in the next decade with the launch of the MetOp Second Generation scatterometers.

A dataset of microphysical cloud parameters from optically thin clouds, retrieved from infrared spectral radiances measured in summer 2017 in the Arctic, is presented. Measurements were conducted using a mobile Fourier-transform infrared (FTIR) spectrometer which was carried by the RV Polarstern. This dataset contains retrieved optical depths and effective radii of ice and water, from which the liquid water path and ice water path are calculated. These water paths and the effective radii are compared with derived quantities from a combined cloud radar, lidar and microwave radiometer measurement synergy retrieval, called Cloudnet. Comparing the liquid water paths from the infrared retrieval and Cloudnet shows significant correlations with a standard deviation of 8.60 g · m−2. Although liquid water path retrievals from microwave radiometer data come with a uncertainty of at least 20 g · m−2, a significant correlation and a standard deviation of 5.32 g · m−2 between the results of clouds with a liquid water path of at most 20 g · m−2 retrieved from infrared spectra and results from Cloudnet can be seen. Therefore, despite its large uncertainty, the comparison with data retrieved from infrared spectra shows that optically thin clouds of the measurement campaign in summer 2017 can be observed well using microwave radiometers within the Cloudnet framework. Apart from this, the dataset of microphysical cloud properties presented here allows to perform calculations of the cloud radiative effects, when the Cloudnet data from the campaign are not available, which was from the 22nd July 2017 until the 19th August 2017. The dataset is published at Pangaea (Richter et al., 2021).

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.

Recent lunar observations have concluded that significant water ice deposits may exists in the regolith, especially in the permanently shadowed regions near the lunar poles. Detection and utilization of these reserves is very important to sustain the continuous human presence on the Moon. This paper, using analytical models and numerical simulations, demonstrates that wideband microwave radiometers are promising instruments for exploring underground water ice deposits on the Moon as their brightness temperature spectra are highly sensitive to water presence in the regolith.

Distributed from L to high frequency band, Radio frequency interferences (RFIs) are contaminating the geophysical parameters estimated from the measurements of microwave radiometers. Chinese researchers are developing series of Earth observation satellites focusing on the marine dynamic environment for numerical weather prediction and climate monitoring. The scanning microwave radiometer onboard HY-2B (Haiyang-2B) has been monitoring sea surface temperature observations since 2018. After more than 2 years in orbit, an overview of RFI scenario worldwide has been provided and lessons have been learnt from HY-2B RFI activities. The Interferometric Microwave Radiometer (IMR) will be a main payload onboard the Chinese Ocean Salinity Satellite. Preliminary studies have been carried out on RFI detection and localization to improve the performance of this mission.

Microwave Imager Combined Active and Passive (MICAP), which is a package of active and passive microwave instruments including L/C/K-band radiometers and L-band scatterometer, has been approved to be taken onbord the Chinese Ocean Salinity Mission. The L-band one-dimensional synthetic aperture radiometer (L-Rad) is the key part of MICAP to measure sea surface salinity (SSS). Since radio frequency interference (RFI) is reported as a serious threat to L-band radiometry, the RFI detection and mitigation techniques must be carefully designed before launch. However, these techniques need to be developed based on the knowledge of how RFI affects complex correlation, visibility function, and reconstructed brightness temperature. This paper presents a time-domain signal modeling method for the simulation of interferometric measurement under RFI’s presences, and a simulation system for L-Rad is established accordingly. Several RFI cases are simulated with different RFI types, parameters, and positions, and the RFI characteristics upon L-Rad’s measurement are discussed. The proposed simulation system will be further dedicated to the design of RFI processing strategy onboard MICAP.

The launch of L-band passive microwave radiometer satellites in the last decade (SMOS, Aquarius, and SMAP) started a new era in Earth Observation at low frequency. On the trails of these missions, recent studies showed that microwave radiometers operating at even lower frequencies (P-band) can provide new information for cryospheric and ocean science and improve the current understanding of Earth processes. In particular, experimental airborne campaigns were conducted in both Artic and Antarctica, within the NASA's UWBRAD project, by using a wide band radiometer operating in the range 0.5-2 GHz, aimed at investigating the temperature profiles of ice sheets and the sea ice thickness. By leveraging the results of UWBRAD system, a new mission concept was proposed to ESA Earth Explorer 10 and 11 (called CryoRad) and NASA EVI-6 (called PolarRad) featuring a spacebome 0.4-2 GHz nadir-looking microwave radiometer for the monitoring of polar regions. Even though the main goals of all these sensors are related to observables such as soil moisture, ocean salinity, and geophysical parameters of the cryosphere, all of them suffer from the emission due the atmosphere. In the spectral range comprised between 0.5 and 2 GHz the atmosphere is considered almost transparent, being the major contributions to the emission due to the water vapor (in both physical phases) and to the molecular oxygen, however a clear assessment is still lacking in literature. The aim of this paper is to provide an accurate estimate of the atmospheric effect on measured T B from space at low frequency for different climate scenario by using a combination of in-situ data (i.e. radio-sounding) and microwave emission model.

The NASA Soil Moisture Active Passive (SMAP) mission design includes two instruments that make coincident active and passive measurements in the low frequency microwave L-band range. In the design, the two instruments share the large (6 meter) light-weight mesh rotating reflector and some of the antenna subsystems. The passive radiometer measurements provide measurements that are highly sensitive to surface soil moisture but at coarse resolution. The active radar measurements provide high-resolution measurements but less sensitive to soil moisture variations because of two-way attenuation through the overlying vegetation canopy and more complex rough soil surface scattering. The synergy between the active and passive measurements allows retrieval of surface soil moisture at intermediate scales and with intermediate accuracy. The SMAP radar failed after three months, thus allowing only about three months of active-passive products. The SMAP project switched to using the Copernicus Sentinel 1-A and 1-B C-band SAR measurements for its active-passive product. The disadvantage of the switch-over is the greater vegetation attenuation and more complex rough-surface scattering of C-band when compared to L-band. The advantages are greater resolution of the C-band Synthetic Aperture Radar (SAR). The revisit times are also affected since data from two platforms with different swath widths have to be combined. In this presentation we explore the algorithm issues associated with the switch and compare the products during the period when both the SMAP radar and Sentinel-1 SARs were operating.

Low frequency microwave measurements have been used to gain insight into what happens deep inside ice sheets for some time now. In this paper, we used a deep neural network to classify each pixel within SMAP radiometer footprints over the Antarctica ice sheet as melt or no-melt. NASA's SMAP mission offers a valuable additional set of observations. The SMAP L-band (1.4 GHz) radiometer retrievals also cover virtually the entire Antarctica ice sheet twice a day. Consistent morning and evening sampling are provided by 6 AM/PM equator-crossings of the satellite ascending and descending polar orbits. The spatial resolution of the instrument is about 40 km. The cross-entropy loss function is used in our network. To make training and test sets, we used air temperature records from available weather stations to distinguish melt and no-melt ice sheet conditions. Our results show that the ice sheet experienced extensive surface melting during the 2015–2016 melt season, and also intensive melting in 2019–2020, particularity on the West Antarctic Ice Sheet.

The Special Sensor Microwave Imager/Sounder (SSMIS) provides vital long-term microwave radiometer observations as a member of the Global Precipitation Measurement (GPM) mission constellation. There are four SSMIS sensors that have been launched with three still in operation. In preparation for the GPM Version 7 (V07) data release, previous SSMIS calibration corrections are reexamined and updated using the latest data and recent knowledge gained from calibrating similar microwave radiometers. These calibration updates are presented here and include pointing parameters that affect geolocation, along-scan bias adjustments to correct for fall-off at the edge of the scan, and thermal heating issues caused by an emissive reflector and solar intrusions. The new updates greatly improve the accuracy of the SSMIS observations that will be released as part of the GPM V07 dataset.

Modern microwave radiometers have demonstrated the feasibility of monitoring surface salinity from space and also the need for better accuracy in cold water. Accuracy could be improved by adding measurements at lower frequencies (lower than the measurement at 1.4 GHz currently used) and closer to the peak in sensitivity of brightness temperature to changes in salinity. Proposals to accomplish this have focused on wide bandwidth receivers which include at the low end frequencies close to the peak in sensitivity. This strategy involves trade-offs, some obvious such as radio frequency interference (RFI) when operating outside the protected band at 1.4 GHz and the loss of spatial resolution at lower frequencies. Others stemming from the interdependence of the retrieval of salinity on water temperature and surface roughness are more subtle. The objective of this manuscript is to examine this interdependence and its implications to future wide bandwidth instruments for remote sensing of salinity from space.

An advanced method for sea ice concentration (SIC) retrieval, based on measurements of the Advanced Microwave Scanning Radiometer 2 (AMSR2), is validated. An advancement relates to tie point choice for the polarization difference in satellite measurements over open water. The approach is based on the results of physical modeling of the sea ice - ocean - atmosphere system and analysis of the AMSR2 measurements in the Arctic region. The validation is done for summer months using MODIS Sea Ice Extent data as a source of ground-truth information. Areas of 100% open water and 100% sea ice are compared for the MODIS Sea Ice Extent and the AMSR2 retrieved SIC. It is found that the AMSR2 algorithm systematically underestimates 100% SIC as compared with the MODIS 100% SIC in summer months.

The Chinese Ocean Salinity Satellite is part of the Chinese ocean dynamic satellite series, and is dedicated to global SSS observation with two payloads onboard. These two payloads, named the Interferometric Microwave Radiometer (IMR) and the Microwave Imager Combined Active and Passive (MICAP), both adopt interferometric aperture synthesis technology. IMR is a two-dimensional L-band interferometric radiometer system with a Y -shaped antenna array. MICAP is a one-dimensional passive/active combined system, where the L-, C-, and K-band interferometric radiometers' antenna arrays are arranged in a line, and the active device is an L-band scatterometer. Based on the payload configurations, an end-to-end simulation shows that, with the assistance of the C- and K-band radiometers and the L- band scatterometer, the combined retrieval accuracy improves compared with the SSS retrieved using only L-band TB and auxiliary parameters. The best accuracy of SSS is achieved when considering all the available measurements from two payloads together in a combined retrieval.

Passive microwave radiometers provide brightness temperature (TB) measurements in a large spectral range with relatively high temporal resolution. Because of their significant role in environmental studies, it is imperative to have a higher temporal resolution and more consistent datasets. This research aims to build a TB diurnal cycle at the Global scale. The shape (amplitude and phase) of the diurnal cycle for each month is obtained by merging several days of Global Precipitation Measurement (GPM) Microwave Imager (GMI) TB measurements because the acquisition times of GMI vary from day to day. This preliminary shape will later be improved by using the other sensors with daily fixed acquisition times such as three Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer 2 (AMSR2). The final product is a highly frequent diurnal cycle that can help prediction of the time of the freeze thaw transition states.