The fully polarimetric aperture synthesis radiometer (FPASR) is capable of acquiring the fully polarimetric brightness temperature (BT), which has become increasingly significant in remote sensing. Antenna pattern errors can introduce significant errors to the reconstructed image of the FPASR. Analyzing and correcting the antenna pattern errors is crucial for obtaining high-quality BT images. In this paper, the antenna pattern errors are analyzed and classified into additive and multiplicative errors. A two-step correction method is proposed to reduce the influence of antenna pattern errors on the reconstructed BT. An end-to-end simulator for FPASR has been developed to assess both the antenna pattern errors and the effectiveness of the correction method. The simulation results show that the two-step correction method can reduce the brightness temperature error caused by the antenna pattern errors by over 70%. The successful image of the flight experiment validates the correction method as well.

Unlike the traditional multi-channel synthetic aperture radar (SAR) three-dimensional (3-D) reconstruction technology, the single-channel SAR with two synthetic apertures in the vertical and horizontal planes, realized by curved trajectory, has advantages of requiring less acquisition data, lower device cost, and fewer limitations on trajectories. However, due to the complex aerodynamic configuration and flight characteristics, critical challenges are faced including the mathematical model establishment, signal property analyses and reconstruction approach design. In this paper, to address these issues, a 3-D vector geometry model of curvilinear SAR (CLSAR) system is presented and its properties, including elevation bandwidth and resolution, are studied in detail. Then, a novel 3-D reconstruction approach for single-channel CLSAR system based on azimuth pre-focusing is presented, in which the cylindrical symmetry of SAR system is utilized to provide accurate azimuth information of targets in observing scene. With the assisted azimuths, the scene imaged in vertical plane can be performed, and the 3-D reconstruction of interested targets can be obtained. Numerical results using simulated data demonstrate the effectiveness of the proposed approach.

The space-borne microwave radiometers and scatterometers can effectively measure global sea surface winds under non-precipitation. However, the measurements in rainy conditions significantly degrade, which are usually flagged as poor quality or invalidated for some scientific purposes. This paper develops a combined active–passive wind vector retrieval model for rainy conditions based on the HY-2B radiometer and scatterometer measurements. In our model, the polarization ratio of brightness temperatures at 6.925 GHz (PR06) is used as an indicator to implicitly represent the rain effect. For wind speed retrieval, a statistical regression model is trained as a function of PR06 and brightness temperatures of the radiometer. Moreover, two new geophysical model functions, including rain effect, are developed for wind direction inversion. Comparisons between HY-2B retrieval results and ERA5 wind products indicate that the retrieval model performs well under all rainy conditions. The overall root mean squared errors (RMSEs) of wind speed and direction retrievals are 1.60 m/s and 20.60°, respectively. With an increase in the rain rate, the wind retrieval performance degrades slightly and still provides a reliable retrieval result.

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, cloud liquid water, and wind direction have significant crosstalk effects on SST retrieval, which increases exponentially when the cloud liquid water exceeds 0.1mm. The crosstalk effect can be significantly reduced by incorporating wind speed fitting in the SST retrieval algorithm. By fitting iQuam SST data, the multiple localizing sea surface wind speed (WS) and latitude algorithms are developed, significantly reducing the impact of crosstalk parameters on SST retrieval and improving the SST retrieval accuracy. And the algorithm also performed well in correcting the non-linear response of TB and SST. The results reveal that the retrieval algorithm eliminates bias across different wind speeds, water vapor, and cloud liquid water ranges and performs consistently on ascending and descending orbits. The overall bias of the SMR 4-year SST product is -0.004°C, with a standard deviation of 0.588 °C, posing a substantial improvement over the existing product with a bias of -0.022C and a standard deviation of 0.803°C. Moreover, the 4-year fluctuation of standard deviation is very small, indicating excellent stability of the retrieval algorithm.

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.

For an aperture synthesis radiometer (ASR), the visibility and the modified brightness temperature (BT) are related to the Fourier transform when the distance between the system and the source is in the far-field region. BT reconstruction can be achieved using G-matrix imaging. However, for ASRs with large array sizes, the far-field condition is not satisfied when performing performance tests in an anechoic chamber due to size limitations. Using far-field imaging methods in near-field conditions can introduce errors in the images and fail to correctly reconstruct the BT. Most of the existing methods deal with visibilities, converting near-field visibilities to far-field visibilities, which are suitable for point sources but not good for extended source correction. In this paper, two near-field imaging methods are proposed based on the near-field distance. These methods enable BT reconstruction in near-field conditions by generating improved resolving matrices: the near-field G-matrix and the F-matrix. These methods do not change the visibility measurements and can effectively image both the point source and the extended source in the near field. Simulations of point sources and extended sources in near-field conditions demonstrate the effectiveness of both methods, with F-matrix imaging outperforming near-field G-matrix imaging. The feasibility of both near-field imaging methods is further validated by carrying out experiments on a 10-element Y-array system.