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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.

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.

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.

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.

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.

Global warming is one of the most serious problems we are facing in the 21st Century. Sea ice has an important role of reflecting the solar radiation back into space. However, once sea ice started to melt, the ice-free water would absorb the solar radiation and amplify global warming in the Arctic region. Thus, importance of sea ice monitoring is increasing. Since longer wavelength microwave can penetrate clouds, passive microwave radiometers on-board satellites are powerful tools for monitoring the global distribution of sea ice on daily basis. The Advanced Passive Microwave Scanning Radiometer AMSR2 which was launched by JAXA in May 2012 on-board GCOM-W satellite provides brightness temperature data that are used to estimate sea ice concentration, the fundamental parameter that is used to monitor the sea ice cover. JAXA is providing AMSR2 sea ice concentration data, derived using ASMR2 Bootstrap Algorithm as a standard product of AMSR2, as a means to communicate how the sea ice cover is changing. This paper describes the advantages of AMSR2 in calculating sea ice concentration and evaluate the accuracy of the sea ice concentration in the Sea of Okhotsk by comparing the result with simultaneously collected MODIS data. The result suggested that under normal winter condition, the RMSE of the AMSR2 sea ice concentration could be less than 10%.

Ground-based microwave radiometers profilers (MWRPs) have been used in numerical weather prediction (NWP) systems and show different impacts on forecasts. Currently, there are around hundreds of ground-based MWPRs used in weather stations over China; however, the application of MWPRs in NWP systems is rather limited. In this work, two MWRP retrieved profiles were assimilated into the Weather Research and Forecasting(WRF) model for a rainstorm event that occurred in Beijing, China. The quality of temperature and humidity profiles retrieved from the MWRP was evaluated against radiosonde observations and showed the reliability of the two MWRP products. Then, comparisons between the measurements of ground-based rain gauges and the corresponding forecasted precipitation in different periods of the rainstorm were investigated. The results showed that assimilating the two MWRPs affected the distribution and intensity of rainfall, especially in the early stage of the rainstorm. With the development of the rainstorm, adding MWRP data showed only a slight influence on the precipitation during the stable and mature period of the rainstorm, since the two MWRP observations were too limited to affect the large area of heavy rainfall.

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.

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.

In this paper, a multiband noncontact temperature-measuring microwave radiometer system is developed. The system can passively receive the microwave signal of the core temperature field of the human body without removing the clothes of the measured person. In order to accurately measure the actual temperature of multilayer tissue in human core temperature field, four frequency bands of 4–6 GHz, 8–12 GHz, 12–16 GHz, and 14–18 GHz were selected for multifrequency design according to the internal tissue depth model of human body and the relationship between skin depth and electromagnetic frequency. Used to measure the actual temperature of human epidermis, dermis, and subcutaneous tissue, a small and highly directional multiband angular horn antenna was designed for the radiometer front end. After the error analysis of the full-power microwave radiometer, a novel hardware architecture of the microwave interferometric temperature-measuring radiometer is proposed, and it is proven that the novel interferometric microwave radiometer has less error uncertainty through theoretical deduction. The experimental results show that the maximum detection sensitivity of the novel interferometric microwave temperature-measuring radiometer is 215 mV/dBm, and the temperature sensitivity is 0.047 K/mV. Compared with the scheme of the full-power radiometer, the detection sensitivity is increased 7.45-fold, and the temperature sensitivity is increased 13.89-fold.

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.

Ground-based multichannel microwave radiometers (GMRs) can observe the atmospheric microwave radiation brightness temperature at K-bands and V-bands and provide atmospheric temperature and humidity profiles with a relatively high temporal resolution. Currently, microwave radiometers are operated in many countries to observe the atmospheric temperature and humidity profiles. However, a theoretical analysis showed that a radiometer can be used to observe solar radiation. In this work, we improved the control algorithm and software of the antenna servo control system of the GMR so that it could track and observe the sun and we use this upgraded GMR to observe solar microwave radiation. During the observation, the GMR accurately tracked the sun and responded to the variation in solar radiation. Furthermore, we studied the feasibility for application of the GMR to measure the absolute brightness temperature (TB) of the sun. The results from the solar observation data at 22.235, 26.235, and 30.000 GHz showed that the GMR could accurately measure the TB of the sun. The derived solar TB measurements were 9950 ± 334, 10,351 ± 370, and 9217 ± 375 K at three frequencies. In a comparison with previous studies, we obtained average percentage deviations of 9.1%, 5.3%, and 4.5% at 22.235, 26.235, and 30.0 GHz, respectively. The results demonstrated that the TB of the sun retrieved from the GMR agreed well with the previous results in the literature. In addition, we also found that the GMR responded to the variation in sunspots and a positive relationship existed between the solar TB and the sunspot number. According to these results, it was demonstrated that the solar observation technique can broaden the field usage of GMR.

A new configuration of a pseudo-correlation type radiometer is proposed for a microwave biomedical application, such as diabetic foot neuropathy. The new approach as well as its simulated performance are thoroughly assessed using commercial off-the-shelf components and custom designed subsystems. We configured a pseudo-correlation receiver, centred at 3.5 GHz, to validate the proposal, comparing its simulated response with a measured alternative based on a 90∘ hybrid coupler pseudo-correlation prototype. We custom designed a balanced Wilkinson power divider and a 180∘ hybrid coupler to fulfil the receiver’s requirements. The proposed configuration demonstrated an improved noise temperature response. The main advantage is to enable the recalibration of the receiver through simultaneous measurable output signals, proportional to each input signal, as well as the correlated response between them.

The purpose of this chapter is to offer an accurate treatment of relevant aspects of satellite remote sensing of precipitation using passive microwave (PMW) radiometers. Microwave observations of the Earth’s system differ substantially from those based on optical and infrared wavelengths. Visible (VIS) and infrared (IR) instruments essentially sense the cloud top properties by measuring reflected or emitted radiation. Microwave frequencies, on the contrary, possess greater penetrating capabilities than optical radiation and can thus be exploited to investigate cloud internal properties by retrieving the interaction of hydrometeors with the radiation field. This fact is particularly true in the remote sensing of precipitation, where the impact of the volume of raindrops on the radiative field is directly linked to the total extinction of incident radiation. The following paragraphs will be focused on one hand on theoretical considerations at the foundations of thermal radiation processes and on the other the attention will be centered on a treatment of practical aspects of retrieving precipitation in the microwave bands. Particular attention will be dedicated in the first part of the chapter to the radiative transfer theory in the microwaves by using the Rayleigh-Jeans formulation and a description of the absorption and scattering processes associated with the Mie theory with the approximation of extinction from poly-disperse media as proxies for natural media. This section will create a valid substrate to understand and theoretically evaluate the impact of atmospheric constituents such as precipitation types or hydrometeor phases and sizes on the natural radiation emitted from the Earth. Furthermore, the theoretical considerations of this section will be compared in the second part of the chapter with real measurements. Making use of a wide suite of microwave frequencies of a new generation of PMW sensors flying on board polar orbiting satellites the attenuation of the microwave signal due to rain clouds will be discussed possibly discerning the contribution to the total radiation of the emissivity from various surfaces falling into the satellite field of view. Finally, a new microwave highfrequency method to retrieve and classify precipitation types will be presented.

This study presents a simulated simultaneous retrieval of mass mean cloud ice particle effective diameter, ice water content, water vapor, and temperature profiles using a combination of a 94‐GHz cloud radar and multifrequency (118, 183, 240, 310, 380, 664, and 850 GHz) millimeter‐ and submillimeter‐wave radiometers from a space platform. The retrieval capabilities and uncertainties of the combined radar and microwave radiometers are quantified. We show that this combined active and passive remote sensing approach with SmallSat technologies addresses a gap in the current state‐of‐the‐art remote sensing measurements of ice cloud properties, especially deriving vertical profiles of ice cloud particle sizes in the atmosphere together with the ambient thermodynamic conditions. Therefore, this new approach can serve as a plausible candidate for future missions that target cloud and precipitation processes to improve weather forecasts and climate predictions.

Ocean evaporative fluxes are a critical component of the Earth's energy and water cycle, but their estimation remains uncertain. Near‐surface humidity is a required input to bulk flux algorithms that relate mean surface values to the turbulent fluxes. Several satellite‐derived turbulent flux products have been developed over the last decade that utilize passive microwave imager observations to estimate the surface humidity. It is known, however, that these estimates tend to diverge from one another and from in situ observations. Analysis of current state‐of‐the‐art satellite estimates provided herein reveals that regional‐scale biases in these products remain significant. Investigations reveal a link between the spatial coherency of the observed biases to atmospheric dynamical controls of water vapor vertical stratification, cloud liquid water, and sea surface temperature. This information is used to develop a simple state‐dependent bias correction that results in more consistent ocean surface humidity estimates. A principal conclusion is that further improvements to ocean near‐surface humidity estimation using microwave radiometers requires incorporation of prior information on water vapor stratification and sea surface temperature., Key Points Current remote sensing estimates of ocean surface humidity exhibit large‐scale and spatially coherent biasesThese biases are organized at the regional scale by atmospheric dynamics that control the vertical structure of water vapor and cloud amountAnalyses show that state‐dependent bias corrections can be developed to result in more consistent ocean surface humidity estimates

IEEE CubeSats with miniaturized microwave radiometers are now demonstrating the potential to provide science-quality weather measurements. For example, the Micro-Sized Microwave Atmospheric Satellite-2A (MicroMAS-2A) and Temporal Experiment for Storms and Tropical Systems-Demonstration (TEMPEST-D) CubeSats are both launched in 2018 and have demonstrated microwave atmospheric sounder data from orbit. The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is a follow-on constellation of six 3U CubeSats based on the MicroMAS-2 design that is scheduled for launch no sooner than 2021. The TROPICS sensors use internal noise diodes (NDs) for calibration. Although the NDs on TROPICS are similar to technology flown on GMI, they have not been tested on-orbit at TROPICS frequencies. In order to track and correct ND drift, we develop a novel method of calibration for CubeSat constellations, such as TROPICS, by incorporating periodic solar and lunar intrusions as an additional source of information to counter ND drift. These lunar intrusions also occur for existing satellites hosting microwave radiometers in sun-synchronous polar orbits but are much more infrequent than for the TROPICS constellation's scanning payload. In this work, we develop a solar/lunar calibration correction algorithm and test it using advanced technology microwave sounder (ATMS) lunar intrusion data. The mean bias and standard deviation between the solar/lunar calibration correction algorithm and actual ATMS data fall within the expected ATMS error budget of 0.6-3.9 K, validating our model., NASA (Award NNX16AM73H)

21 pages, 8 figures, In this project report, the main outcomes relevant to the Sino-European Dragon-4 cooperation project ID 32235 “Microwave satellite measurements for coastal area and extreme weather monitoring” are reported. The project aimed at strengthening the Sino-European research cooperation in the exploitation of European Space Agency, Chinese and third-party mission Earth Observation (EO) microwave satellite data. The latter were exploited to perform an effective monitoring of coastal areas, even under extreme weather conditions. An integrated multifrequency/polarization approach based on complementary microwave sensors (e.g., Synthetic Aperture Radar, scatterometer, radiometer), together with ancillary information coming from independent sources, i.e., optical imagery, numerical simulations and ground measurements, was designed. In this framework, several tasks were addressed including marine target detection, sea pollution, sea surface wind estimation and coastline extraction/classification. The main outcomes are both theoretical (i.e., new models and algorithms were developed) and applicative (i.e., user-friendly maps were provided to the end-user community of coastal area management according to smart processing of remotely sensed data). The scientific relevance consists in the development of new algorithms, the effectiveness and robustness of which were verified on actual microwave measurements, and the improvement of existing methodologies to deal with challenging test cases, This research was funded by the ESA–MOST Dragon-4 project “Microwave satellite measurements for coastal area and extreme weather monitoring”, grant number 32235. The APC was funded by the ESA and NRSCC, With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

This document constitutes the first Progress Report of the CryoRad-FO project, and is the project deliverable number 04.2, as required by the Technical Management Annex of the Contract between ASI and the National Research Council. The purpose of the document is to report the progress of the project in the 3 months of activity 23/06/2021 - 23/09/2021. The activities carried out, the critical issues addressed, the measures taken to mitigate them are described and the activities to be undertaken in the following quarter are indicated.

This document contains the Eighth Year Report for DOMEX-3 experiment, prepared by the Institute for Applied Physics "Nello Carrara" - Firenze in the framework of the ESA contract N. 4000105872/12/NL/NF. In the document the execution of the activity carried out in the eighth experimental year of the project is described (i.e. from January to December 2020). It includes also the studies conducted in Italy during the eighth winter campaign.

Sun-tracking microwave (ST-MW) radiometry is a ground-based technique where the Sun is used as a beacon source to infer the atmospheric path attenuation in all-weather conditions. ST-MW radiometry shows an appealing potential for overcoming the difficulties to perform satellite-to-Earth radiopropagation experiments in the unexplored millimeter-wave and submillimeter-wave frequency region, especially where experimental data from beacon receivers are not available. The theoretical framework, the ad hoc procedures and data processing will be presented, together with the estimate of the overall error budget. The applications and challenges during field deployments, such as the recent WRad campaign in Italy based on ST-MW data analysis, funded by ESA and carried out together with AFRL (NY, USA), will be discussed.

Microwave radiometers can be considerably affected by Radio Frequency Interference (RFI). These man-made interference conceal the underlying natural signal, preventing the retrieval of geophysical variables. Adopting on-board detection and mitigation techniques is a requirement to reduce the impact of RFI. Several families of RFI detection algorithms have been developed over the last years (e.g. [1]–[4]). In this work, a new detection technique is proposed and its performance analyzed. It is based in the distortion of the shape of the cross-correlation function at lags different from zero under the presence of RFI. Its performance is compared to other common RFI mitigation algorithms. Proposed methods' performance is found to surpass other common algorithms such as signal Kurtosis, while presenting some convenient properties for its practical application in correlation and synthetic aperture radiometers.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2020-11-01, 資料番号: PA2110066000

Increasing attention is being paid to the monitoring of global change, and remote sensing is an important means for acquiring global observation data. Due to the limitations of the orbital altitude, technological level, observation platform stability and design life of artificial satellites, spaceborne Earth observation platforms cannot quickly obtain global data. The Moon-based Earth observation (MEO) platform has unique advantages, including a wide observation range, short revisit period, large viewing angle and spatial resolution, thus, it provides a new observation method for quickly obtaining global Earth observation data. At present, the MEO platform has not yet entered the actual development stage, and the relevant parameters of the microwave sensors have not been determined. In this work, to explore whether a microwave radiometer is suitable for the MEO platform, the land surface temperature (LST) distribution at different times is estimated and the design parameters of the Moon-based microwave radiometer (MBMR) are analyzed based on the LST retrieval. Results show that the antenna aperture size of a Moon-based microwave radiometer is suitable for 120 m, and the bands include 18.7, 23.8, 36.5 and 89.0 GHz, each with horizontal and vertical polarization. Moreover, the optimal value of other parameters, such as the half-power beam width, spatial resolution, integration time of the radiometer system, temperature sensitivity, scan angle and antenna pattern simulations are also determined.

Radio-frequency-interference (RFI) signals have gradually become a more serious problem in active and passive microwave remote sensing. However, currently, there is no reliable RFI source distribution data to evaluate the accuracy of existing RFI identification methods. In this study, a simplified generalized RFI detection method (GRDM) is proposed to detect RFI applied to the ocean surface. Two RFI detection methods, the GRDM and the double-principal component analysis (DPCA) method, are used for cross-validation to obtain RFI recognition thresholds of DPCA in the Advanced Microwave Scanning Radiometer 2 (AMSR2) ocean data. In addition, in the present work the source and distribution characteristics of RFI over the ocean surface are also analyzed. The results show that the proposed scheme can effectively identify RFI signals from AMSR2 data, and only 7.3, 10.65, and 18.7 GHz channels are contaminated by RFI over the ocean surface. There are strong 7.3 GHz interference signals over the waters of East Asia (with the value of ΔTBH mostly between 5 and 30 K and ΔTBv mostly between 5 and 40 K), Europe (with the value of ΔTBH mostly between 5 and 40 K and ΔTBv mostly between 5 and 30 K), and North America (with the value of ΔTBH mostly between 5 and 50 K and ΔTBv mostly between 5 and 30 K). The RFI signals in 10.65 GHz data are mainly distributed over the Mediterranean and other European waters (with the value of ΔTBH mostly between 5 and 35 K and ΔTBv mostly between 5 and 20 K). The RFI signals at 18.7 GHz are mainly present over the offshore marine areas of North America (with the value of ΔTBH mostly between 5 and 50 K and ΔTBv mostly between 5 and 40 K), with the strongest RFI distributed near the Great Lakes of America, and the RFI magnitudes over the east and west coasts are stronger than over the south coast. Satellite-borne microwave observations over the ocean suffer from interference mainly from stationary communication/television satellites. Due to the reflection of the sea surface, the range and intensity of RFI are strongly dependent on the relative geometric positions of stationary satellites and space-borne passive instruments. Therefore, RFI coverage area changes every day over the ocean in one 16-day period, which is very different from that over the land.

International audience; Despite a lot of progress over the last decades, rain retrieval from spaceborne measurement has been a challenge since the first launch of a passive microwave radiometers on one of the NOAA Defense Meteorological satellites in the 70s. Deep-learning and convolutional U-Nets might be able to offer a breakthrough on the topic because they do take into account the topology of both the rain field and the measured brightness temperatures. The present paper offers the very first results on the application of such artificial neural networks on the rain retrieval problem.

The long-term stability of microwave radiometers (MWR) on-board altimetry missions is critical to reduce the uncertainty on the global mean sea level estimate. Harmonization and homogenization steps are applied to MWR observations in that perspective. The Sentinel-3 tandem phase provides a unique opportunity to quantify the uncertainties on the “zero-bias line” homogenization approach defined by Bennartz et al. (2020). Initially developed to improve the performance of the wet tropospheric correction retrieval, it is used here to provide a common reference for the inter-calibration between Sentinel-3A and Sentinel-3B MWR. A simplified version of the “zero-bias line” approach, a linear correction depending on brightness temperatures, allows to strongly reduce the bias between the two radiometers for both channels (about 0.5 K) and the standard deviation of the difference (0.3 K). The full version of the approach adding a dependency on wind speed has improved the quality of the WTC retrieval (Bennartz et al. 2020) but degrades the performance of the homogenization. It is thus recommended to apply the simplified version of this approach in the processing of fundamental data record. The quantification of the uncertainties on the homogenization approach is only possible due to the ideal configuration of the Sentinel-3 tandem phase. The same dataset and the same metrics could be used to assess other approaches.

In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency’s essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration’s (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China’s Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B’s contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product.

Ocean surface whitecaps manifest surface wave breaking. Most of the whitecap data reported in the literature are based on optical observations through photographic or video recording. The air in whitecaps modifies the dielectric properties of microwave emissions and scattering. Therefore, whitecap information is intrinsic to microwave signals. This paper discusses a method to retrieve the ocean surface whitecap coverage from microwave radiometer signals.

A new methodology has been described in Kilic et al. (Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer, Remote Sensing 2020, 12, 1060, Part 1 of this study) to estimate Sea Ice Concentration (SIC) from satellite passive microwave observations between 6 and 36 GHz. The Ice Concentration Retrieval from the Analysis of Microwaves (IceCREAM) algorithm is based on an optimal estimation, with a simple radiative transfer model derived from satellite observations at 0% and 100% SIC. Observations at low and high frequencies have different spatial resolutions, and a scheme is developed to benefit from the low errors of the low frequencies and the high spatial resolutions of the high frequencies. This effort is specifically designed for the Copernicus Imaging Microwave Radiometer (CIMR) project, equipped with a large deployable antenna to provide a spatial resolution of &sim, 5 km at 18 and 36 GHz, and &sim, 15 km at 6 and 10 GHz. The algorithm is tested with Advanced Microwave Scanning Radiometer 2 (AMSR2) observations, for a clear scene over the north polar region, with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) estimates and the Ocean Sea Ice&mdash, Satellite Application Facilities (OSI SAF) operational products. Several algorithm options are tested, and the study case shows that both high spatial resolution and low errors are obtained with the IceCREAM method. It is also tested for the full polar regions, winter and summer, under clear and cloudy conditions. Our method is globally applicable, without fine-tuning or further weather filtering. The systematic use of all channels from 6 to 36 GHz makes it robust to changes in ice surface conditions and to weather interactions.

An advanced algorithm for atmospheric water vapor column (WVC) retrieval from the Advanced Microwave Scanning Radiometer (AMSR) measurements over the Arctic sea ice (SI) and open ocean waters is presented. The algorithm is built on the physical modeling of the brightness temperature (BT) of the microwave radiation of the SI–open ocean–atmosphere system at the AMSR frequencies and polarizations. The BTs are calculated using a data set of the SI, atmospheric, and oceanic parameters changing in the range of their natural variability in the Arctic, and using the SI microwave emission coefficients varied according to the published experimental data. The inverse operator explores neural networks (NNs), trained on an ensemble of modeled BTs. The algorithm is applied both to the AMSR-E and to the AMSR2 measurement data. Validation of the algorithm is performed with radiosonde (r/s) WVC measurements from the four Arctic coastal stations at different SI conditions during 2014–2017. The results of the application of the new algorithm to satellite radiometer measurements are also compared with the Era-Interim reanalysis WVC, as well as with other satellite WVC products, based on the data of the Moderate Resolution Imaging Spectrometer (MODIS) and on the data of the Advanced Microwave Sounding Unit-B (AMSU-B) for 2008 and 2015. To justify the usage of the Era-Interim WVC as a reference data set for the algorithm accuracy estimation in the Arctic area, Era-Interim WVC is also compared with the r/s WVC measurements.

Melt occurrence in Antarctica is derived from L-band observations from the Soil Moisture and Ocean Salinity (SMOS) satellite between the austral summer 2010–2011 and 2017–2018. The detection algorithm is adapted from a threshold method previously developed for 19 GHz passive microwave measurements from the special sensor microwave imager (SSM/I) and special sensor microwave imager sounder (SSMIS). The comparison of daily melt occurrence retrieved from 1.4 and 19 GHz observations shows an overall close agreement, but a lag of few days is usually observed by SMOS at the beginning of the melt season. To understand the difference, a theoretical analysis is performed using a microwave emission radiative transfer model. It shows that the sensitivity of 1.4 GHz signal to liquid water is significantly weaker than at 19 GHz if the water is only present in the uppermost tens of centimetres of the snowpack. Conversely, 1.4 GHz measurements are sensitive to water when spread over at least 1 m and when present in depths up to hundreds of metres. This is explained by the large penetration depth in dry snow and by the long wavelength (21 cm). We conclude that SMOS and higher-frequency radiometers provide interesting complementary information on melt occurrence and on the location of the water in the snowpack.

This document contains the Seventh Year Report for DOMEX-3 experiment, prepared by the Institute for Applied Physics "Nello Carrara" - Firenze in the framework of the ESA contract N. 4000105872/12/NL/NF. In the document is described the execution of the activity carried out in the seventh experimental year of the project (i.e. from January to December 2019). It includes also the studies conducted during the seventh year of the winter campaign in Italy.

3 pages, 4 figures, A method for internally calibrating microwave total power radiometers by using only one level of noise injection is presented. It is based on having a previous accurate characterization of the receiver noise temperature, which used de facto as a second calibration standard. The method proves to be at least equivalent to the classical two level, as demonstrated through their intercomparison using the data provided by the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) on board the European Space Agency Soil Moisture and Ocean Salinity (SMOS) Satellite. The long-term stability in terms of retrieved brightness temperature using both methods has similar trends with a small advantage for the one-point approach proposed her, With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

A new method for detecting hailstorms by using all the MHS-like (MHS, Microwave Humidity Sounder) satellite radiometers currently in orbit is presented. A probability-based model originally designed for AMSU-B/MHS-based (AMSU-B, Advanced Microwave Sounding Unit-B) radiometers has been fitted to the observations of all microwave radiometers onboard the satellites of the Global Precipitation Measurements (GPM) constellation. All MHS-like frequency channels in the 150–170 GHz frequency range were adjusted on the MHS channel 2 (157 GHz) in order to account for the instrumental differences and tune the original model on the MHS-like technical characteristics. The novelty of this approach offers the potential of retrieving a uniform and homogeneous hail dataset on the global scale. The application of the hail detection model to the entire GPM constellation demonstrates the high potential of this generalized model to map the evolution of hail-bearing systems at very high temporal rate. The results on the global scale also demonstrate the high performances of the hail model in detecting the differences of hailstorm structure across the two hemispheres by means of a thorough reconstruction of the seasonality of the events particularly in South America where the largest hailstones are typically observed.