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.

Deep neural network (DNN), equipped with powerful distinctive generalization ability, is one of the most popular retrieval tools for processing ocean remote sensing data from spaceborne microwave radiometers. Considering the signal received by microwave radiometers at different frequencies changes with atmospheric effects, such as rainfall and water vapor in both horizontal (H) and vertical (V) polarization (pol.), and differential signals between H and V pol, these physical factors cannot be ignored in high wind speed retrieval. This study focuses on the integration of physically driven statistical functions with a residual neural network (ResNet) to derive a specialized type of DNN framework for high wind speed retrieval. Specifically, a dynamic contribution-matrix ResNet (DCM-ResNet) model is proposed, which dynamically adjusts the contribution (C) matrix coefficients within the model. These coefficients, reflecting the physical information of radiation transfer, are used to automatically regulate the model’s estimation of high wind speeds. Experiments are conducted using brightness temperatures (TBs) from the Advanced Microwave Scanning Radiometer-2 and wind speeds from the Stepped-Frequency Microwave Radiometer. The experimental results show that the maximum wind speed can reach up to 60 m/s with a root mean square error (RMSE) of 2.92 m/s. In addition, the wind speed RMSE remains stable within 6 m/s as the rainfall rate increases. The contributions of different frequency bands to the C-matrix results are closely related to radiative transfer.

The Interferometric Imaging Radar Altimeter (InIRA) carried on the Tiangong-2 Space Laboratory can observe three-dimensional sea surface topography over a swath tens of kilometers wide. The InIRA represents a test mission that will provide valuable experience for the design and data processing technology of future wide-swath altimeters. In addition to the tropospheric delay, ionospheric delay, and sea state bias that affect traditional altimeters, InIRA was also affected by the cross-track error derived from roll error, baseline length error, and interference phase error, which had to be properly corrected. In this paper, range error correction based on correction models and cross-track error correction based on the reference topography data method were applied to a 15-day InIRA dataset and evaluated using the Jason-2, Jason-3, Saral/AltiKa, and Sentinel-3A. The results showed that the standard deviation between corrected InIRA and the combined dataset was 7.96 cm after eliminating the influence of tides and dynamic atmospheric correction, which was comparable to nadir altimeter measurement accuracy.

Wet troposphere path delay (WPD) is a crucial error source for wide-swath altimeters with high-resolution observations. This study generated a simulated wide-swath through the nadir microwave radiometer (MWR) track and evaluated four wide-swath wet tropospheric correction (WTC) methods based on multisource data from 2017 with strict spacetime windows. The Numerical Weather Model (NWM) method can provide a basic correction for the wide-swath altimeter. The MWR method, based on the extension of nadir 1-beam MWR, could not provide a reliable correction. The residual error increased rapidly after deviating from the nadir profile. The overall root-mean-square error (RMSE) of the combination method was approximately 0.58 cm and had high uniformity within the swath. Even at the far end of the swath, the correction was better than that on the NWM and MWR methods. The objective analysis (OA) method using linear spatiotemporal objective analysis had the best result among the four correction methods. The overall RMSE was only 0.49 cm, which was 34% lower than the NWM method. High consistency was maintained at each cross-track position of the swath. The last two wide-swath WTC methods provided in this study had high performance. For the wide-swath altimeter, the combination method is a scheme worthy of consideration, as it can provide WTC results conveniently, quickly, and accurately. Although the operation of the OA method is complex and depends on multisource observation data, it promises to be a high-quality scheme for the wide-swath altimeter WTC when a more accurate WTC value is required.

A novel voltage equalizer called Multilevel Architecture is proposed for series-connected photovoltaic (PV) system to prevent negative influence of mismatch issues. This undesirable mismatch triggers multiple power maxima in PV string P-V characteristics, encumbering maximum power points tracking (MPPT) algorithms. This paper designed a control scheme for isolated-port flyback converter operating in discontinuous conduction mode (DCM). The synchronous strategy is also investigated and discussed for the equalizer to compensate the mismatched PV elements efficiently. Furthermore, Simulation and experimental results for four PV elements connected in series are included to demonstrate the benefits of this approach, with biasing currents of 5A, 4A, 3A, 2A, respectively. With the proposed voltage equalizer, local MPPs were successfully eliminated and the total generated power is significantly increased.