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.

The Aperture Synthetic Radiometer (ASR) system consists of an antenna array along with subsequent receiving channels and data processing modules, and it has been widely applied in Earth observation fields. The arrangement of the antenna array directly affects the system’s sampling coverage in the spatial frequency, which in turn determines its imaging quality. Therefore, we propose a cooperative optimization method for antenna arrays based on deep reinforcement learning, aiming to improve the sampling coverage of the ASR system, thereby enhancing its imaging performance. Experimental results show that the final stable coverage value of the proposed method is 10.545%, representing a 41.2% improvement over the lowest value. Furthermore, it outperforms other comparative methods in both coverage performance and reconstructed image quality, demonstrating its effectiveness in optimizing antenna array layout and enhancing the imaging quality of the system.

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.

The experimental detection of the changes in microwave radiation of rocks under pressure is key to identifying earthquake anomalies through satellite passive microwave remote sensing. However, such changes have not been comprehensively characterized due to the considerable differences in crustal lithology, weak microwave radiation signals, and strong environmental noise. Considering the intrinsic and significant diversity of different polarized microwave radiations of any materials, this study investigated the responses of different polarized microwave radiations during loading the rock materials. Thus, a synchronized detection system including multiple sensors was constructed at outdoor to reveal the stress-induced changes in C-band microwave brightness temperature (MBT) of diorite specimen. Experimental results show that both the horizontal and vertical MBT varied regularly with the changes of pressure; however, the trends of changes of MBT were greatly influenced by the polarization modes. Specifically, a positive correlation was illustrated between the change in vertical polarization MBT and cyclically varied pressure, during which the MBT changed with a rate of 0.033 K/MPa about. In contrast, the changes in horizontal polarization MBT exhibited a negative correlation with the varied pressure, and the MBT change rate was approximately −0.031 K/MPa. Based on the radiative transfer theory, it was found that the opposite MBT changes with respect to h- and v-polarizations are supposed to be caused by the dielectric anisotropy under uniaxial compression conditions. This study illustrates the significant and discernible MBT changes of diorite induced by the stress, which is helpful to identify the detectable microwave radiation anomalies before large earthquake occurrence.

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.

Pressure is a critical factor affecting the dielectric properties of rock materials. Previous studies have shown that the microwave dielectric properties of mineral rocks undergo regular changes under uniaxial pressure. In this study, diorite was selected as the specimen, and an open coaxial resonant microwave dielectric testing system was employed to investigate the microwave dielectric (2–18.3 GHz) properties of rock specimens under uniaxial and biaxial loading modes, respectively. Experimental results indicate that under uniaxial pressure, the dielectric constant at all frequency bands decreased gradually with the increase of compressive stress, with an average decrease amplitude of 4.5%; nevertheless, under the biaxial loading process, the change in dielectric constant exhibited a more complex pattern, which was closely related to the relationship between the axial stress and the lateral stress. When the axial stress was lower than the lateral stress, the dielectric constant increased significantly with the rise of axial stress; however, when the axial stress increased and got greater than the lateral stress, the dielectric constant began to decrease. The theoretical analysis based on dielectric physics indicates that both the changes in the number of polarization units and the polarization abilities of these polarization units influenced the dielectric properties of rocks under different loading conditions. This study is greatly important for revealing the change patterns and mechanisms of microwave dielectric properties of rocks, which implies that stress-induced disturbances are an important factor to be considered in applying radar investigation and microwave remote sensing for geological exploration and geohazard perception.

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.

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.

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.

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.

The arrangement of the antenna array of the mirrored aperture synthesis (MAS) determines the transformation matrix for solving the cosine visibilities. When the transformation matrix is rank deficient, the solved cosine visibilities will introduce the rank-deficient error. For the two-dimensional mirrored aperture synthesis (2-D MAS), the existing literature has discussed the relationship between the arrangement of the antenna array and the rank-deficient error but has not proposed an array that can achieve a full rank transformation matrix, which is call the rank-full array (RFA). Based on a new method for calculating the rank of the transformation matrix and the literature on one-dimensional (1-D) RFAs, this paper proves that 2-D RFA cannot be obtained when the reflectors are all metal, and by introducing a reflector made of magnetic conductor, 2-D RFA can be obtained. Besides, this paper obtained the existence conditions for 2-D RFA. Based on those conditions, two methods are proposed to obtain 2-D RFAs.

The antenna array arrangement determines the rank of the transformation matrix in 1-D mirrored aperture synthesis (1-D MAS). Only when the transformation matrix is of full rank can cosine visibilities be precisely solved from the transformation equations. However, the existing literature states that full rank cannot be realized with the default perpendicular polarization of antennas. In this letter, the transformation matrices of arrays with various polarizations of antennas are discussed, and a type of full array with a full-rank transformation matrix is found. Based on these results, the existence conditions for an array with a full-rank transformation matrix are obtained. In addition, a search algorithm with a shorter search time than the existing algorithm is designed to find low-redundancy arrays with a full-rank transformation matrix. The simulations verify that cosine visibilities are precisely solved from the transformation equations for the array with a full-rank transformation matrix.

Setting two reflectors perpendicular to each other and an array produces mirrored aperture synthesis (MAS), which obtains a higher spatial resolution than aperture synthesis (AS) for the same antenna array. The proposal of the MAS theory and the experimental verification suggest that the spatial resolution can be improved by setting the reflector. This paper proposes a method in which four tilted planar reflectors are set around an antenna array. This method is named two-dimensional (2-D) mirrored aperture synthesis with four tilted planar reflectors (2-D MAS-T). Because 2-D MAS-T uses more reflectors than MAS, the spatial resolution is further improved. The principle of 2-D MAS-T with an antenna array is given in this paper. The relationship between the size of the reflectors, the spatial resolution and the field of view (FOV) is obtained. The simulation and experimental results demonstrate that 2-D MAS-T can achieve a higher spatial resolution with the same antenna array than 2-D AS and 2-D MAS.

In 2-D mirrored aperture synthesis (2-D MAS), the array configuration determines the rank of the transformation matrix, which can affect the accuracy of the solved cosine visibilities. Ultimately, errors in solved cosine visibilities degrade the quality of reconstructed brightness temperature images. In this letter, the maximum rank of the transformation matrix is analyzed and proven by mathematical induction. An array optimization model of 2-D MAS based on the “maximum-rank” criterion is established, and optimal arrays are presented. Finally, the simulations and experiments are carried out to demonstrate the effectiveness of the optimization model.

In two-dimensional mirrored aperture synthesis (2-D MAS), the rank of the transformation matrix can affect the accuracy of the solved cosine visibilities; therefore, it has an impact on the accuracy of the reconstructed brightness temperature image. In this article, the influence of the rank deficient on the accuracy of the reconstructed brightness temperature image of 2-D MAS is discussed. An analysis of the rank-deficient error is performed by computing its impact on the radiometric accuracy for a reference scene. Two correction methods based on multiple measurements are proposed to correct the rank-deficient error. The simulations and experiments are carried out to demonstrate the effectiveness of the proposed methods.

The mirrored aperture synthesis radiometer (MASR) has been proposed as a promising technique for Earth observation. However, considering the system imperfections, the cosine visibility function and the brightness temperature distribution do not constitute a cosine transform pair. The method to improve the quality of reconstructed brightness temperature images for a practical MASR has not yet been developed. In this article, the complete formulation of cosine visibility functions in a practical MASR is derived to better understand the measurement of a MASR with non-negligible effects of the system imperfections. The array factor of a practical MASR is analyzed. Then, the alias-free field of view (FOV) for a MASR is presented, and an iterative method is proposed to reconstruct the brightness temperature image in the alias-free FOV to reduce the computational burden. The numerical simulation and experimental results demonstrate that the proposed method can be applied to practical MASRs and that the reconstruction quality is significantly improved by the proposed method. Finally, the H-matrix inversion method is discussed as an alternative reconstruction method for a practical MASR. Compared with the H-matrix inversion method, the proposed method is more convenient for practical applications.

Aperture synthesis technique is able to provide high spatial resolution without requiring very large and massive real aperture. Recently, in addition to the well-known aperture synthesis radiometer, the concept of mirrored aperture synthesis has been proposed, and the experiments has been carried out to validate this technique. However, the field of view (FOV) of a mirrored aperture synthesis radiometer (MASR) has not been analyzed. In this paper, the FOV of an one-dimensional MASR is analyzed based on the symmetric convolution. A simulation and experiment are carried out to validate the derived FOV. The FOV of a two-dimensional MASR can be analyzed in the same way.

The concept, principle, and method of microwave radiometric imaging with mirrored aperture synthesis (MAS) were proposed by the Huazhong University of Science and Technology (HUST), Wuhan, China. MAS can achieve higher spatial resolution without extending the size of the antenna array. However, the MAS principle has not been sufficiently validated by practical experiments. To solve this problem, an innovative experiment system of MAS at the V-band (MAS-V) has been developed by HUST. Experiments on the interference fringe pattern, spatial resolution, as well as the imaging of an electric warmer and the absorbing material in the styrofoam box filled with liquid nitrogen are performed. The initial experimental results demonstrate that MAS can achieve higher spatial resolution with the same antenna array compared with the conventional AS. For MAS, the experimental results are consistent with theory and simulation results.

Mirrored aperture synthesis (MAS) has been proposed to utilize a few antennas to provide high spatial resolution for atmospheric remote sensing. In order to verify the fundamentals of mirrored aperture synthesis (MAS) further, a prototype working at V band was developed in the past two years. In this paper, the prototype is briefly introduced, and then some indoor experiments and the experimental results for one-dimensional MAS based on the prototype are presented, mainly including the fringe pattern experiment for verifying the correlation output and the imaging experiment based on two point sources for evaluating the spatial resolution of MAS.

Mirrored Aperture Synthesis (MAS) was proposed to improve spatial resolution for passive microwave remote sensing. However, the MAS theory needs experimental verification. In this paper, an Experimental System of MAS (MAS-V) at V frequency band is introduced, which is developed for verifying the principle and performance of MAS. MAS-V is composed of an antenna array, reflectors, a receiving channel array, a synchronous acquisition subsystem, a computing server, and a rotatory platform. The initial experimental results demonstrate that the principle of MAS is valid. The experimental spatial resolutions are consistent with the theoretic spatial resolutions.

In this letter, 1-D mirrored aperture synthesis with a rotating reflector (1-D MAS-R) is proposed to improve the spatial resolution and reduce the number of required antennas for passive microwave remote sensing. The principle of the 1-D MAS-R with an antenna array is given, and from the principle, the 1-D MAS-R with only one antenna can also reconstruct the image of the scene. Simulation results demonstrate the validity of the 1-D MAS-R even with only one antenna, and the spatial resolution is improved by increasing the distance between the reflector and the antenna or antenna array.

Mirrored aperture synthesis (MAS) can improve spatial resolution compared with conventional aperture synthesis (AS). In this paper, for 1D MAS and 2D MAS, the array optimization models with the autocorrelation are established. The optimal array configurations based on the models are presented. The simulation results demonstrate that the optimal array configurations have good performance in imaging and can achieve higher spatial resolution than other MAS array configurations with the same antenna number.

Mirrored Aperture Synthesis (MAS) was proposed to improve spatial resolution with fewer antennas for passive microwave remote sensing. In this paper, an Experiment System of MAS (ESMAS) at 36.4GHz is introduced, which is designed for verifying the principle and performance of MAS. ESMAS is composed of an antenna array, four reflectors, a receiving channel array, a synchronous acquisition subsystem, a processor, and a rotatory platform. The initial experiment results are given.

Aperture synthesis with high spatial resolution would seriously increase the hardware requirements and system complexity. To reduce the system complexity, Rotating mirrored aperture synthesis (RMAS) that combines the antenna array rotation with mirrored aperture synthesis was proposed to reduce the number of antennas. In this paper, the sieving method is presented to optimize the array configuration for RMAS. The initial simulation results demonstrate the validity of the proposed method and the optimized array of RMAS can reconstruct the brightness temperature of the observed scene with fewer antennas.

Aperture synthesis technique can provide high spatial resolution without requiring very large and massive real aperture. However, large aperture synthesis systems are very complicated. In this paper, Rotating Mirrored Aperture Synthesis (RMAS) is proposed, which combines rotating and mirrored aperture synthesis to improve spatial resolution with fewer antennas. The principle of RMAS is presented. The initial simulation result shows the RMAS system can approximately reproduce the brightness temperature image of the observed scene.