Accurate inversion of low altitude atmospheric temperature and humidity is crucial for weather forecasting and climate monitoring. This letter introduces the MR-TH method, a deep learning approach that uses convolutional neural networks and Transformer architecture to invert low altitude three-dimensional atmospheric temperature and humidity distribution from airborne microwave radiometer data. By capturing nonlinear relationships and spatial correlations, MR-TH improves the inversion accuracy of traditional methods. This network is trained and validated using onboard flight data, reanalysis products, and radiosonde measurements. The results indicate that the mean square error (MSE) of temperature inversion for MR-TH is 0.3-1.5 K and the humidity MSE is 0.2-2.0 g/kg, with an accuracy improvement of over 15% compared to the BP neural network method within the range of 1-5 km altitude. MR-TH also shows a high correlation (>90%) with radiosonde data. MR-TH provides a feasible solution for improving the accuracy of atmospheric parameter inversion from airborne microwave radiometer observation data.

Accurately measuring and inverting air parameters, such as air temperature and humidity, is crucial for weather forecasting, climate research, and environmental monitoring. In this letter, we propose an inversion method based on the transformer model to accurately estimate the spatial distribution of air temperature and humidity. Compared with traditional methods, the transformer model demonstrates superior ability in capturing nonlinear relationships and spatial dependencies in observational data, thereby improving inversion accuracy. Experiments conducted on real observational data have shown that compared to traditional techniques, the proposed method achieves a reduction of over 4.8% in the root mean square error (RMSE) of air temperature and over 14.2% in humidity estimation, demonstrating its high accuracy and reliability in inverting air temperature and humidity. This method provides a new approach for advancing air parameter inversion technology.

The Chinese ocean salinity satellite mission is currently undergoing development and rigorous testing. The satellite will be equipped with an L-band Y-shaped Synthetic Aperture Interferometric Radiometer (SAIR), which is similar to the highly regarded MIRAS. Leveraging lessons learned from the SMOS mission, significant emphasis has been placed on addressing Radio Frequency Interference (RFI). A pre-launch ground experiment has been conducted specifically for the SAIR payload of the Chinese ocean salinity satellite. This experiment aims to research and verify the efficacy of RFI processing algorithms. Notably, our observations indicate that the radiometer detects Gaussian thermal radiation noise, whereas RFIs consist of non-Gaussian signals, such as radar and wireless communication signals. This fundamental difference in signal types results in distinct system responses, leading to biases in RFI suppression, particularly for powerful RFI sources. To mitigate the suppression residuals of strong RFI, we have introduced an innovative adaptive channel response adjustment strategy. The core concept involves decomposing the covariance matrix to isolate the signal space dominated by intense RFI signals. By projecting the steering vector onto this signal space, we can accurately estimate the channel response of the RFI and make precise adjustments accordingly. Our experimental and simulation results are encouraging, demonstrating a marked improvement in RFI suppression effectiveness. This advancement is crucial for enhancing the accuracy and reliability of ocean salinity measurements from our satellite, ultimately contributing to a deeper understanding of our planet’s vital ocean ecosystems.

Sea surface salinity (SSS) is a fundamental parameter for understanding ocean phenomena and plays a vital role in studying global climate change and weather prediction models. Following the earlier launch of Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) satellites, the Chinese Ocean Salinity and Soil Moisture Mission (COSM) was successfully launched on November 14, 2024. The launched satellite is equipped with the 2-D L-band Aperture Synthesis Microwave Radiometer (LASMR) and the Microwave Imager Combined Active and Passive (MICAP) components to gather high-precision SSS information. This paper presents the Airborne LASMR (ALASMR), which features a Y-shaped two-dimensional synthetic aperture microwave radiometer and contains 11 antenna units with a unit spacing of 0.82λ. The ground test investigations of the ALASMR have been conducted to evaluate antenna patterns, test the sensitivity of receiving channels, and conduct ocean aviation experiments. To examine the flight observation, uniform salinity is assumed for the sea area obtained from the calibration platform of National Satellite Ocean Application Service (NSOAS) whereas the salinity gradient is taken for the Laizhou Bay area. The ALASMR exhibits enhanced imaging performance, with a spatial resolution of about 0.35 km and a width of about 1.24 km at the flight altitude of 1.2 km. The retrieval of SSS in the sea area of the ocean calibration platform is also demonstrated in this work. This indicates that ALASMR can reliably execute salinity observation of near-shore with high precision and provide a cross-calibration data source for COSM after its launch.

This article proposes a method for calculating the millimeter-wave (MMW) radiation of chaff corridor-target-sea scene. This method is used to analyze the attenuation of target brightness temperature (TB) by chaff corridor. First, a numerical method is proposed to obtain the facet model of chaff corridor, which is then combined with the facet models of sea and target obtained by the traditional method. Second, the ray tracing method is carried out to obtain the MMW radiation TB of the scene. In ray tracing, the emissivity, reflectivity, and transmittance of chaff corridor facets are calculated by the generalized equivalent conductor (GEC) method and the calculation method of reflection and transmission by a layered medium (RTLM). The emissivity and reflectivity of sea and target facets are calculated by their permittivity. Finally, to verify the correctness of the MMW radiation calculation method, a method for measuring the emissivity, reflectivity, and transmittance of chaff corridor is proposed. The TB of chaff corridor-metal plate and chaff corridor-ship-sea scenes is simulated and measured to analyze the TB attenuation caused by the chaff corridor. The results show that the calculation method is effective and the attenuation by the chaff corridor decreases with the increase of TB.

The Chinese Ocean Salinity Mission (COSM) is dedicated to the global sea surface salinity (SSS) using the interferometric aperture synthesis radiometry. The airborne COSM was conducted during July to August 2023 at Qixia of Shandong Province. The introduction of airborne COSM experiment and the pre-simulation results based on the design of airborne instruments, which are the scaled-down model of spaceborne payloads, are presented. The preliminary sea surface salinity (SSS) retrieval results show that, the best accuracy of SSS is achieved when considering all the available measurements from two payloads together in a combined retrieval. The results from the airborne COSM experiment verify the interferometric synthetic aperture payloads performance and will also help to improve the data processing technique and the key error correction methods in the future in-orbit operational phase.

Sea surface temperature (SST) and wind speed (SSWS) are two significant parameters in the coupled ocean-atmosphere system. Nowadays, space-borne microwave radiometers are the main approaches to measuring global SST and SSWS with high coverage and high accuracy. For conventional processing of passive microwave data, radiometer calibration is conducted to convert the radiometer raw output voltage data into the top of atmosphere (TOA) brightness temperatures (TBs). After calibration, sea surface parameters are retrieved from TBs using statistically or physically based retrieval methods. Considering the complex procedures of instrument calibration and retrieval, a novel estimation method based on deep learning is proposed to obtain SST and SSWS directly from radiometer voltages. A comprehensive matchup dataset, including the data measured by the scanning microwave radiometer (SMR) onboard the Chinese HY-2B satellite, the European reanalysis 5 (ERA5) products and the WindSat measurements, is used to develop and test the deep learning models. Validation against ERA5 and WindSat products indicates that the deep learning models perform well. To compare with traditional methods, we also retrieve SST and SSWS from brightness temperature data of SMR using a statistical regression retrieval algorithm. The comparison suggests that the deep learning models provide results close to and sometimes better than the regression algorithm. Furthermore, the feature importance of deep learning models and the dependence of their performances on sea state are analyzed. In this paper, it is demonstrated that the deep learning method is a reliable and feasible tool for SST and SSWS estimation from the radiometer voltages with high accuracy, which can simplify the data processing procedure and improve the processing efficiency.

Researches have demonstrated that passive interferometric microwave sensors (PIMSs) can be used in the imagery acquired and target detections, especially in low visibility and/or high sea clutter environments. Target detection by PIMSs is based on the high contrast against the background in the brightness temperature (TB) maps, and it is found that the targets still exhibit a high contrast within the alias field of view (FOV) of the background cancellation TB maps obtained by a background canceling method in the target detection algorithm. As a result, the targets can also be detected within the alias FOV of PIMSs, and PIMSs exhibit a wider FOV for target detection than that for Earth’s remote sensing. However, the effective FOV of PIMSs has never been studied for target detection before. In this article, the effective FOV of PIMSs is first studied for target detection. The effective FOV of PIMSs is analyzed and discussed for target detection in theory, which is based on the FOV of PIMSs for Earth’s remote sensing and the background canceling method. Numerical simulations are performed to analyze the effective FOV of PIMSs in terms of the metallic with a “cold” TB characteristic and the island with a “hot” TB characteristic. A series of experiments have been performed to verify the effective FOV of the PIMSs for target detection in ships, islands, and aircrafts. Both numerical and experimental results have demonstrated that the effective FOV of the PIMSs for target detection based on the background canceling method can be extended to the central hexagonal regions (${H}$), and it exhibits a wider FOV than alias-free FOV (AF-FOV) and/or extended AF-FOV (EAF-FOV) in Earth’s remote sensing, as expected.

Correction for the impact of direct Sun contamination is a crucial task in the data processing of interferometric microwave radiometer (IMR). The evident presence of solar radiation is observed in the brightness temperature images derived from the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) payload onboard the Soil Moisture and Ocean Salinity (SMOS) satellite, significantly impacting the data quality to retrieve sea surface salinity (SSS). This article introduces a novel sinusoid correction method for correcting the direct Sun contamination. By leveraging the characteristics of the solar disk, the proposed method simulates and compensates for the contribution of direct solar impact on the spatial frequency domain based on the response pattern of small point sources within the solar disk. The proposed method exhibits a reduced dependency on the precise solar position information and demonstrates resistance to radio frequency interference (RFI), and validations through a simulated IMR and data from in-orbit SMOS confirm the reduction of the direct solar impact on brightness temperature images. The proposed sinusoid correction method outperforms the single and multiple source methods, used in the SMOS operational data processing, especially for the central regions around the location of direct Sun.

This letter pays attention to the mitigation of the Radio Frequency Interference (RFI) sources located at coastal regions for Synthetic Aperture Interferometric Radiometer (SAIR). To remove an RFI at the visibility level, it will be necessary to estimate the Brightness Temperature (BT) of the point source, which is currently done by computing the median of the surrounded pixels to get the background BT, and subtracting it from the original contaminated BT. However, this strategy is based on the assumption that the background BT distribution around the RFI is flat, and this is not reasonable for those RFIs from coastal cities, and could result in estimation deviation. A robust RFI mitigation method is proposed without introducing complex computing. Instead of focusing only on the local area, the basic idea of our method is to step into the wide area, and minimize the overall fluctuation of the pixels affected by the RFI in the BT image. The method tests based on SMOS visibility samples validate the correctness of the developed method.

For 1-D aperture synthesis (1-D AS) radiometers, truncated sampling occurs in the frequency domain due to the system baseline limitation. Therefore, there is an obvious Gibbs oscillation in the reconstructed image. To solve this problem, an imaging method based on a 1-D convolutional neural network (1-D CNN) is proposed in this article. Compared with deep learning methods based on 2-D convolutions, the 1-D convolution not only reduces the amount of computation but also produces further performance improvements. The input data of the network are the 1-D visibility function samples, and the output data are the 1-D brightness temperature (BT) samples. The network learns the mapping relationship from the training of the 1-D visibility function samples and 1-D BT samples to complete 1-D AS imaging without any prior knowledge. To verify the performance of this imaging method, simulations and experiments based on the airborne C-band 1-D microwave interferometric radiometer (ACMIR) system are implemented. The simulation and experimental results demonstrate that the proposed AS-CNN method achieves higher performance than the inverse fast Fourier transform (IFFT) method in terms of image quality and Gibbs phenomenon suppression. In the case of an antenna failure and missing baseline, the AS-CNN method proposed in this article can still obtain a BT image with high imaging quality, which shows that the robustness of the network is better than that of the IFFT method.

Airborne passive interferometric microwave sensors (PIMSs) were proposed as a powerful complementary tool for ship detection, especially in low visibility and high sea clutter environments. At present, there is only one algorithm for ship detection by airborne PIMSs, and this algorithm is only based on the “cold” brightness temperature (TB) characteristic of metal ships with wakes in passive interferometric microwave images (PIMIs). Moreover, it has been found that ship wakes always exhibit a “hot” TB characteristic in PIMIs. In this article, an improved algorithm is innovatively proposed for ship detection by airborne PIMSs to improve detection performance, and it is simultaneously based on the “cold” and “hot” TB characteristics of metal ships with wakes. The microwave TB model of metal ships with wakes is established to analyze its TB characteristics. Given the TB model and its characteristics, an improved algorithm is introduced, which comprises five steps: searching targets, improving TB maps, clustering targets, estimating ship headings, and estimating ship trajectories. The improved algorithm can detect ships with a low false-alarm probability (FAR) and accurately estimate ship headings using only a single snapshot and the ship trajectory. The practicability and detection performance of the improved algorithm is verified by airborne experiments using an X-band PIMS. The experimental results demonstrate that the improved algorithm can accurately detect ships, estimate ship headings using only a single snapshot, and estimate ship trajectories. The impact of the algorithm parameters on the detection performance is analyzed and discussed. Moreover, the detection performance of the improved algorithm is analyzed and compared with the performance of the only existing algorithm, and the experimental results indicate that the improved algorithm has a better detection performance with a lower FAR value than the only existing algorithm, as expected.

The critical advantage of passive microwave imaging is that it can be used in all-day and in low visibility conditions, such as hazes, fogs, clouds, smokes, and sandstorms. In this article, the ground-based passive interferometric microwave sensor (PIMS) is proposed to detect civil aircrafts, especially in low visibility conditions. The fundamental of civil aircraft detection by a ground-based PIMS is introduced and analyzed. Numerical simulations are performed to assess its feasibility and detection performance, and the impact of the low visibility weather is analyzed and discussed. To verify the practicability of civil aircraft detection by the ground-based PIMS, a series of experiments have been performed by a ground-based X-band PIMS, and a data processing procedure is introduced, which contains a target detection algorithm. Experimental results have demonstrated that the civil aircrafts can be accurately detected and consecutively tracked by the ground-based PIMS using the target detection algorithm. Meanwhile, a mirrored TB phenomenon, the TB change, and an application scheme are analyzed and discussed. As a conclusion, the ground-based PIMS can be considered as a powerful complementary tool for civil aircraft detection.

As a frontier technology in radar imaging, hypersonic vehicle-borne (HSV) synthetic aperture radar (SAR) has several practical issues to be dealt with, namely, ground resolution capability, pulse repetition frequency (PRF) selection, and beam pointing description, especially for the near-vertical diving trajectory because of the extremely small angle between the velocity and slant range vectors. Moreover, its focusing approach design is greatly challenged by very large cross-couplings and spatial variations. Considering these practical problems, the constraints between system performance and parameter selection are analyzed firstly to obtain the parameter optimization procedure and avoid system design deviation. Then, a frequency radius/angle algorithm (FRAA) is devised, which is an extension of the radius/angle algorithm (RAA) performed in two-dimensional (2-D) frequency domain. In the FRAA, new range equation and 2-D frequency interpolation function are reconstructed with high accuracy by quadratic fitting and 3-D expansion. Compared with RAA, FRAA is more suitable for the HSV SAR with near-vertical diving trajectory. Simulation results verify the effectiveness of the proposed approach.

In passive microwave remote sensing of the Earth, compared with real aperture radiometer, synthetic aperture radiometer (ASR) is a very powerful instrument with many advantages. However, the system design is more complex than the real aperture radiometer, and the hardware ideality is often not guaranteed. The nonideal characteristics of the system hardware will bring a variety of errors to the system, which will cause the Fourier transform relationship between the visibility function and the brightness temperature image to no longer be established, thus reducing the quality of microwave brightness temperature image reconstruction by traditional methods. In this article, a new microwave brightness temperature image reconstruction method for ASR by transformer is proposed. This method uses a specially designed transformer structure to extract the spectrum features in the visibility function. This method learns the mapping relationship between the visibility function and the original scene brightness temperature image through the supervised learning method, and learns as much as possible the spectrum information contained in the original scene brightness temperature image. Moreover, when there are missing baselines, this method will supplement the missing observation frequency information, so as to obtain better reconstructed image quality. With the above-mentioned advantages, this method can suppress the Gibbs oscillation, and greatly reduce the sidelobe. Compared with the existing reconstruction methods, whether missing baselines or not, the proposed image reconstruction method by transformer has advantages in image quality. We verify the performance of this brightness temperature image reconstruction method through simulation and experiment.

This study develops a new sea surface temperature (SST) retrieval algorithm based on statistical regression that utilizes WindSat satellite data and in situ SST quality monitor (iQuam) buoy data. The proposed algorithm introduces new rules to remove abnormal brightness temperatures (TB) due to land, sea ice, radio frequency interference (RFI), and sun glint. Besides, SST data from 2019 to 2022 are generated from the HY-2B satellite’s scanning microwave radiometer (SMR) using a two-step retrieval method. Our study reveals that wind speed (WS), cloud liquid water (LW), and wind direction have significant crosstalk effects on SST retrieval, which increases exponentially when the cloud LW exceeds 0.1 mm. The crosstalk effect can be significantly reduced by incorporating WS fitting in the SST retrieval algorithm. By fitting iQuam SST data, the multiple localizing sea surface WS and latitude algorithms are developed, significantly reducing the impact of crosstalk parameters on SST retrieval and improving the SST retrieval accuracy. Also, the algorithm performed well in correcting the nonlinear response of TB and SST. The results reveal that the retrieval algorithm eliminates bias across different WSs, water vapor (WV), and cloud LW ranges and performs consistently on ascending and descending orbits. The overall bias of the SMR four-year SST product is$-0.004\,\,^{\circ }\text{C}$, with a standard deviation (STD) of 0.588 °C, posing a substantial improvement over the existing product with a bias of$-0.022\,\,^{\circ }\text{C}$and an STD of 0.803 °C. Moreover, the four-year fluctuation of STD is very small, indicating excellent stability of the retrieval algorithm.

In this paper, Mirrored Aperture Synthesis with tilting reflectors (MAS-T) is proposed to improve spatial resolution with fewer antennas for passive microwave remote sensing. The principle of MAS-T is given. The initial experimental results demonstrate that MAS-T can achieve higher spatial resolution with the same antenna array compared with conventional aperture synthesis.

Radio-frequency interference (RFI) is an increasingly serious problem for passive microwave sensors, it affects greatly the quality of the data and retrieval products. Data from these sensors are likely to be increasingly contaminated by RFI as the spectrum becomes more crowded. The scanning microwave radiometer (SMR) is an important payload of the Haiyang-2B (HY-2B) satellite, its observations also reveal very strong RFI contaminations on the C-band, X-band and K-band data, two methods were used to identify the RFI. The RFI not only exists in the main beam, but also enters into the cold mirror, the C and X bands are all found RFI intrusion into the cold mirror.

The brightness temperature of Open Ocean is homogeneity and easy to be simulated, which is used to the microwave radiometer calibration often. To be better understanding the status of the scanning microwave radiometer (SMR), which is the payload of Haiyang-2B (HY-2B) satellite, the experiment was sponsored by the National Satellite Ocean Application Service. The C, X, K band of the ground based radiometer RPG-XCH-DP was used to observe the ocean and sky brightness temperature, the multi-frequency microwave radiometers MP-3000A was used for the troposphere temperature profiling. The auxiliary data of ocean was provided by the oceanographic buoys. Systematic measurements were operated from August 22th, 2020 to September 6th, 2020.

Mirrored aperture synthesis (MAS) has been proposed as a novel interferometry to achieve higher spatial resolution in passive microwave remote sensing. The phase and amplitude errors for MAS are a crucial problem that has yet to be analyzed. In this letter, a model for the phase and amplitude errors is established. A correction method based on an external source is proposed to correct the phase and amplitude errors. An analysis of the position of the external source is performed by computing its impact on the radiometric accuracy of a reference scene. The imaginary parts of the cross correlations for MAS are analyzed for the first time. Simulations and experiments are carried out to demonstrate the effectiveness of the correction method.

In this letter, 1-D mirrored aperture synthesis with two tilted reflectors (1-D MAS-T) is proposed to improve the spatial resolution of an antenna array without extending its size. The principle of 1-D MAS-T is given. The size of the reflector is analyzed, and detailed calculation formulae is derived. Numerical simulations and experiments are carried out to illustrate the performance of 1-D MAS-T. The results demonstrate that 1-D MAS-T can achieve a higher spatial resolution than conventional aperture synthesis with the same antenna array.

For synthetic aperture radiometer, radio frequency interference (RFI) seriously contaminates the observed data and affects the quality of the brightness temperature (BT) image. CLEAN algorithm is usually used to mitigate the Gibbs effect generated by RFI. However, the closely distributed RFI sources cannot be well mitigated by the original CLEAN algorithm. This letter proposes an improved adaptive CLEAN algorithm to enhance the performance of RFI mitigation, especially the closely distributed RFI sources. The loop factor is forced to synchronize with residual BT of RFI for every iteration to avoid over mitigation. Over mitigation can result in a BT vacancy area or an extra BT point generated at the position of RFI. Soil Moisture and Ocean Salinity (SMOS) L1B real data are applied to verify the effectiveness of the improved CLEAN algorithm. The simulation shows that the improved CLEAN algorithm can be competent in eliminating isolated and closely distributed RFIs better than the classic CLEAN algorithm, and the over mitigation problem is solved by the proposed method.

The scanning microwave radiometer (SMR) is an important payload of the Haiyang-2B (HY-2B) satellite. The main reflector of the SMR has its own emission due to the imperfect reflector material and surface roughness; however, the reflector’s emissivity was not measured or estimated during the prelaunch phase. During the post-launch phase, it was discovered that the radiation contribution from the reflector changed with its position and over time. This phenomenon affected the accuracy of the calibration by a few Kelvins. This report analyzes the main reflector’s emissivity based on the observed results when HY-2B pitched over to view the homogeneous and isotropic cold space. The emissivity of the reflector was approximately 3%, but owing to the larger field of view, the emissivity of 6.925 and 10.7 GHz were not well assessed. After the radiation from the reflector has been corrected, the calibration error caused by the reflector’s emissivity is reduced.

The medium- and large- scale global sea surface temperature (SSTs), which is an important ocean parameter, are mainly measured by the space-borne microwave radiometry. However, the resolution and sensitivity are greatly limited by antenna size, orbit altitude, and flight speed. In this paper, an airborne C-band one-dimensional (1-D) microwave interferometric radiometer (ACMIR) is developed to obtain the SSTs with a high resolution and sensitivity. The spatial resolution of the ACMIR ranges from 40.7m to 612m, corresponding to the flight height of 500m to 8000m, while the sensitivity varies from 0.25K to 0.36K, associating with the boresight to the edge of the field of view. In order to evaluate the performance of the ACMIR, an ocean aviation experiment was conducted in the coastal area of the Yellow Sea in September 2020. The land observation results indicate that typical ground objects can be clearly distinguished. Furthermore, thesea surface brightness temperatures acquired by the ACMIR are compared with the estimates based on the radiative transfer model, with SST retrieval error of 0.77°C. The ACMIR can offer a high-resolution and accuracy of SST especially in coastal areas, and can meet several application requirements related to SSTs.

The Chinese Ocean Salinity Satellite is dedicated to global sea surface salinity (SSS) mapping with two payloads onboard, which are the interferometric microwave radiometer (IMR) and the microwave imager combined active and passive (MICAP), both using the interferometric aperture synthesis radiometry. One of the payloads is an L-band interferometric radiometer system with a Y-shaped antenna array. The other one, MICAP, is a 1-D 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, a series of simulations is applied to analyze the payloads performance, including the brightness temperature (TB) characteristic, the SSS accuracy, and the effects of Sun and land contamination. The TB simulation results show that the IMR possesses a finer spatial resolution compared to the MICAP L-band radiometer, whereas the latter achieves a better TB radiometric resolution. 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. Finally, the results related to Sun and land contamination that endanger the interferometric radiometer measurements are presented and discussed. The performance simulation in this article has been used as a reference for the design phase of two payloads and will be updated with the development of payload manufacture.

In this work, an IF(intermediate frequency) module of a hyperspectral microwave radiometer based on a polyphase filter bank (PFB) and Discrete Fourier Transformation (DFT)is introduced. The IF module is designed with an 800MSPS sampling-rate ADC and a Xilinx Virtex-7 FPGA. The module can achieve 512 channels and a bandwidth of 400M and process all the sampled data in real-time. The test results of this module are given and analyzed, such as linearity, accuracy, etc. It can be used in various applications of microwave remote sensing. The system has strong expandability.

In this work, an IF (intermediate frequency) module of a hyperspectral microwave radiometer is introduced. The IF module is designed with a 5GSPS sampling-rate ADC and a Virtex-7 FPGA. The module can achieve 512 channels and process all the sampled data in real-time. The test results of this module are given and analyzed, such as linearity, sensitivity, etc. It can be used in various applications of microwave remote sensing.

Chinese ocean salinity satellite is a mission designed for global ocean salinity monitoring. Two main payloads equipped onboard the satellite are the Interferometric Microwave Radiometer (IMR) and the Microwave Imager Combined Active and Passive (MICAP) which both have L-band radiometer and adopt aperture synthesis technology. A simulation is designed according to the payloads' configurations and consists of three parts: the brightness temperature generator, the radiometer system and the parameters retrieval. Preliminary results, which contain IMR and MICAP's brightness temperature resolution, spatial resolution, and parameter retrieval accuracy, are presented.

To monitor sea surface salinity (SSS) in coastal zones with high temporal and spatial resolution, an L-band phased array radiometer is developed. However, it is well known that the L-band radiometers suffer from serious radio frequency interference (RFI) effects. To mitigate the RFI effects, an RFI detection and mitigation algorithm is proposed for the L-band phased array radiometer. The experiments in coastal zones are performed to assess the performance of the RFI algorithm. The results indicate that the detection and mitigation algorithm exhibits good performance and can effectively mitigate the RFI effects.

Ship detection is important for a wide range of applications. In this article, an airborne passive interferometric microwave sensor (PIMS) is proposed as a powerful complementary tool for ship detection. The fundamental of ship detection by the airborne PIMS is introduced. A specification called “the detection factor” is addressed to quantitatively assess the performance of the airborne PIMS for ship detection. The detection probability is discussed and addressed based on the constant false-alarm rate (CFAR). Numerical simulations are performed to demonstrate the feasibility of ship detection by the airborne PIMS. The impacts of the atmosphere and the sea wind on the performance of the airborne PIMS for ship detection are also addressed in terms of theoretical analyses and numerical simulations. The airborne experiments are also carried out by an X-band PIMS to verify the practicability of ship detection by the airborne PIMS in a sunny weather and a cloudy weather. The data processing procedure and a ship detection algorithm in the experiments are introduced. The airborne experimental results reveal that the ships can be effectively detected and tracked by the airborne X-band PIMS in a sunny weather and a cloudy weather. It demonstrates the practicability of ship detection by the airborne PIMS. The ship detection probability is also analyzed, which agrees well with the theoretical detection probability.

L\X-band Full Polarization Aperture Synthesis Microwave Radiometer (FPASMR) is a 2-D aperture synthesis radiometer working at L and X-band with full polarization measurement for obtaining high precise measurement of sea surface salinity. FPASMR consists of dual Y-shaped arrays containing 23 L-band antennas per arm plus one in the center in L-band array and the same numbers of X-band antennas in X-band array. The demonstrator prototype of FPASMR has been developed and the validation has also been fulfilled.

In this paper, a L-band phased array radiometer for sea surface salinity is developed. To reduce the mass and volume of real-aperture microwave radiometers, the radiometer employs a phased array and a receiver channel, which can observe target scenes with electric scanning. The L-band phased radiometer is analyzed using noise waves. The calibration method of the radiometer is introduced. A Radio Frequency Interference (RFI) detected algorithm is also given. A series of experiment is performed and the present results show that this phased array radiometer exhibits good performance in some beams.

This paper briefly discusses the working principle of synthetic aperture microwave radiometer system. Through the system sensitivity and repeatability test, The sensitivity and repeatability of the system are obtained. Based on the test, a correction algorithm for calibration error of calibration network sensitivity and receiver repeatability is proposed, which can be used to calibrate the data in different environments on the satellite and provide the basis for the future onboard calibration.

The calibration of aperture synthesis radiometers (ASRs) with large arrays by internal noise injection suffers from high increased mass and volume and is not capable to deal with the out-of-plane distortion of antenna arrays. In this letter, a hybrid calibration method is proposed, which combines the redundant baselines and reduced noise injection. The hybrid method needs only a small noise injection network and can deal with the out-of-plane distortion of antenna arrays. The performance of the hybrid method depends on the noise injection element distribution, which is analyzed and given based on minimizing the root mean square of all residual amplitude and phase errors. Simulation results demonstrate its performance. The proposed hybrid method is particularly helpful to the calibration of ASRs with large arrays in geostationary orbit.

Correlation radiometer is the basic unit of interferometer or full polarization radiometer which can obtain the correlation of two receiving channels. The paper presents a novel receiving configuration of correlation radiometer using the digital signal processing to fulfill the IQ demodulation and acquire the band of application requirement exactly. This new receiving configuration can eliminate the quadrature errors which can simplify the calibration procedure and acquire exact receiving band which can improve the consistence of multiple parallel receiving channels. This novel receiving configuration which has been applied in L\X-band Full Polarization Aperture Synthesis Microwave Radiometer (FPASMR) establishes the basis of spaceborne engineering application in the future.

L\X-band Full Polarization Aperture Synthesis Microwave Radiometer (FPASMR) is a 2-D aperture synthesis radiometer which can measure the sea salinity and soil moisture. As a key component of FPASMR, the digital correlator has been introduced in this paper from three abstract: the system design, the hardware design and the test results of the demonstrator. The results demonstrate that the digital correlator referred in this paper can ensure the realtime signal sampling and processing of 140 ADC channels which meet the practical requirements.

L\X-band Full Polarization Aperture Synthesis Microwave Radiometer (FPASMR) is a 2-D aperture synthesis radiometer working at L and X-band with full polarization measurement for obtaining high precise measurement of sea salinity and soil moisture. FPASMR consists of dual Y-shaped arrays containing 23 L-band antennas per arm plus one in the center in L-band array and the same numbers of X-band antennas in X-band array. Some early demonstration results of antenna, optical module and calibration are also present.