The interferometric microwave radiometer (IMR) utilizes an interferometric synthetic aperture technique to achieve high spatial resolution in low-frequency microwave remote sensing, addressing the challenges of deploying large-scale passive sensors in space. IMR measures spatial harmonics of scene brightness temperature, known as visibility, which are then used in inversion algorithms to reconstruct the target brightness temperature. In 1-D IMR, the interferometric synthetic aperture technique is applied only in the cross-track direction, resulting in higher resolution compared with the coarser along-track direction determined by the real antenna aperture. Current research focuses on cross-track inversion, which has yielded promising results; however, the low along-track resolution remains a significant limitation for its overall application. This article introduces the Backus-Gilbert (BG)-inspired 1-D IMR resolution enhancement method, inspired by real aperture microwave radiometer techniques, to address along-track resolution limitation. The study utilizes the L-band 1-D IMR of the Microwave Imager Combined Active and Passive (MICAP) aboard the Chinese Ocean Salinity Satellite as an example. Results from synthetic test images and hardware-in-the-loop simulation demonstrate that the proposed method enhances along-track resolution and provides the flexibility to optimize for either higher image quality or radiometric resolution comparable to the traditional 1-D IMR inversion method. Additionally, it improves the accuracy of salinity measurements in coastal areas.

Synthetic Aperture Radar (SAR) has unique advantages in sea surface wind field retrieval due to its all-weather observation capability and high spatial resolution. However, the effectiveness of wind measurement is vulnerable to limitations such as saturation of backscattering signals from the sea surface and attenuation of heavy rainfall under extreme weather conditions. Considering that the response of physical factors (e.g., radar backscattering coefficient at different polarizations) to wind varies in different wind speed ranges, in this study, based on 36 scenes of dual-polarized Sentinel-1 SAR images of hurricanes from 2016 to 2023, and using wind speed observations from the airborne Stepped-Frequency Microwave Radiometer (SFMR) as ground truth values, we proposed a partition and fusion residual transfer network (PFRTNet) model. In the training process, two separate residual neural networks were pretrained under low-to-medium wind speeds (< 30 m/s) and high winds (≥ 30 m/s), and combined with the idea of transfer learning. An attention mechanism was then introduced to achieve adaptive feature fusion. The optimal model inputs were determined by feature importance analysis and sensitivity experiments, which consists of 14 features including SAR measured physical parameters, image texture features, hurricane morphological information, as well as environmental and geographic factors. The PFRTNet model can effectively alleviate the underestimation of high wind speed caused by sample imbalance, and significantly improve the accuracy of hurricane wind retrieval. Independent evaluation results based on an additional 9 SAR images demonstrate that the algorithm achieves a root mean square error (RMSE) of 2.81 m/s under high wind conditions. Compared with other machine learning methods and traditional empirical algorithms, PFRTNet has significant performance advantages, with a RMSE reduction of approximately 2.0 m/s or more, confirming its robustness in retrieving hurricane wind speeds and finer-scale structural features.

Single-beam synthetic aperture radar (SAR) Doppler frequency observations have been widely used for retrieving ocean surface radial current velocities. However, the Doppler frequency contains multiple components, among which the systematic shift caused by electronic pointing error (EPE) is difficult to accurately model and remove. This issue is particularly prominent in open-ocean regions without land echo references, where it can significantly affect the accuracy of current retrieval. To address this problem, this study proposes a Bayesian framework–based method for radial current velocity retrieval, which innovatively incorporates the systematic Doppler shiftbcaused by EPE as a key parameter into the state vector, enabling its joint estimation with the ocean surface radial current velocity. Empirical analysis of 1,800 SAR sub-swath images demonstrates high consistency between the estimated Doppler shiftband land-derived true values, with a standard deviation (STD) of 6.45 Hz and a correlation coefficient (R) exceeding 93%. This validates the method’s capability for accurate EPE estimation in remote ocean regions. Performance comparisons against HF radar observations and drifting buoy measurements confirm that the proposed Bayesian retrieval method significantly outperforms conventional direct approaches: it reduces radial current velocity STD by 0.23 m/s and improves R by 35.60%. Additionally, it effectively corrects systematic biases in the ocean model background field, lowering the STD of the retrieved radial velocity relative to the model by 13.33%. Even in complicated dynamic contexts, the approach retains great accuracy and physical consistency, as demonstrated by case studies in unique regions. In conclusion, the suggested Bayesian retrieval method significantly improves the precision, resilience, and usefulness of radial current velocity retrieval while successfully resolving the technical difficulty of EPE correction in SAR data over open oceans.

Synthetic Aperture Radar (SAR), with its ability to function under any weather conditions and at any time of day, along with multi-polarization and frequent revisit capabilities, plays a crucial role in flood monitoring. However, SAR images face challenges such as coherent speckle noise, feature mixing, terrain undulation, and adverse weather, making flood monitoring difficult. To address these challenges, this paper proposes a high-precision flood mapping method from Sentinel-1 dual-polarization SAR data. We begin by generating false-color images through polarization combination and apply them to a multiscale segmentation approach, overcoming the limitations of single-polarization scattering and effectively reducing speckle noise. Digital elevation model and reference water datasets are integrated into the segmentation process to mask terrain shadowing and permanent water. To reduce feature mixing effects, the optimal SAR image with minimal feature mixing is selected for flood mapping using the Gaussian Mixture Model. In the subsequent two-step classification process, fuzzy sets of texture features are incorporated to assist in categorizing uncertain regions, further reducing interference from feature mixing and enhancing flood recognition accuracy. Additionally, integrating pixel-level and object-level analyses minimizes errors caused by improper segmentation. The proposed method is compared with several well-established algorithms, and the results demonstrate that our method outperforms the others in flood mapping accuracy. Analysis of years of flooding on the Leizhou Peninsula shows that Sentinel-1 SAR has the potential to effectively monitor the occurrence and development of floods.

Sea surface temperature (SST) measurements are crucial in the context of climate change. Microwave SST measurements are currently provided by radiometers operating in the C- and X-bands. In-orbit K- and Ka-band payloads lack the commonly used C- and X-bands for SST retrieval. We present the K-KaSSTNet, a residual neural network (NN) that, for the first time, uses the K and Ka microwave bands with much weaker SST response than C- and X-bands for SST retrieval. Despite training on a limited dataset from 2020 to 2021, K-KaSSTNet consistently achieves reasonable accuracy SST retrievals for data spanning 2017–2022. Moreover, by using deep learning (DL) interpretability methods, we have unveiled the underlying mechanisms driving K-KaSSTNet. When extended to the Special Sensor Microwave Imager/Sounder (SSMIS) and Calibration Microwave Radiometers (CMRs)—payloads typically not used for SST retrieval—the K-KaSSTNet model maintains SST retrievals with reasonable accuracy compared with Advanced Microwave Scanning Radiometer-2 (AMSR-2). This extension broadens the spatiotemporal coverage of microwave SST products and enhances the temporal sampling frequency and continuity of microwave SST measurements.

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.