ABSTRACT The flight test plays an important role in the development of an aircraft. Currently, with the increasing complexity and higher validation requirements for aircraft, there is a crucial need to generate high‐quality flight test task schedules in an efficient way. This paper proposes a flight test task scheduling problem (FTTSP), which involves assigning suitable aircraft and executing the flight test tasks in a given order. Generally, the flight test duration (FTD) is the primary optimisation objective for the flight test task schedule, as it has a direct impact on aircraft development costs and the time to enter the market. In this study, the FTTSP not only considers FTD but also takes into account task transfer consumption (TTC). A mixed‐integer linear programming mathematical model is first formulated to describe the FTTSP characteristics with the optimisation of the FTD and the TTC in a sequential manner. Then, a memetic algorithm with lexicographic optimisation (MALO) is proposed, which can efficiently obtain a high‐quality solution and ensure that the most critical metric can be fully optimised. In MALO, a two‐vector encoding and a task logic relationship repair mechanism based on the binary tree are established. An idle time insertion decoding method is designed to improve the aircraft utilisation rate. In addition to the selection, crossover and mutation operators, a local search operator is designed to enhance the solution quality. Finally, the full‐scale test instances are generated for the FTTSP to evaluate the algorithm's performance. The numerical results demonstrate the effectiveness and competitiveness of the MALO in generating a high‐quality schedule for flight test tasks.

This study develops a machine learning-based approach to retrieve significant wave height (SWH) from Soil Moisture Active Passive (SMAP) radiometer data under tropical cyclone (TC) conditions, accounting for limited fetch effects. Using over 1000 SMAP measurements within 400 km of the eye from 400 TCs in 2018-2024, we employed the Symmetric Hurricane Estimates for Wind (SHEW) model to determine TC eye locations and maximum wind radii (RMW). The three machine learning methods (i.e., eXtreme Gradient Boosting (XGBoost), Random Forest (RF) and Convolutional Neural Networks (CNNs)) were trained to establish the relationship between SMAP wind speeds and WAVEWATCH-III (WW3)-hindcasted SWH, incorporating key parameters including wind speed, distance to TC eye, RMW, and TC translation speed/direction. Validation against both WW3 hindcasts and Haiyang-2 (HY-2) altimeter measurements in 2023-2024 demonstrated the superior performance of the XGBoost model compared to empirical wave-growth, RF and CNNs models, i.e., lower root mean square error (RMSE) of 0.50 m (WW3) and 0.71 m (HY-2), along with higher correlation (Cor) of 0.96 (WW3) and 0.92 (HY-2).

This letter aims to assess the applicability of synchronous wind and wave observations using Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) during tropical cyclones (TCs) in the North West Pacific (NWP). From 2023 to 2024, 28 GF-3 images featuring visible TC eyes were available for this study. The well-known machine learning method, denoted as eXtreme Gradient Boosting (XGBoost), is implemented to retrieve sea surface wind and significant wave height (SWH) from dual-polarized GF-3 images acquired in Wide ScanSAR (WSC) mode. The inversion is supported by 20 GF-3 images collocated with re-constructed winds based on European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) data and Japan Meteorological Agency (JMA) best-track data along with SWH simulations from the WAVEWATCH-III (WW3). Wind speeds inverted from additional 8 GF-3 images are validated against Soil Moisture Active Passive (SMAP) measurements, yielding a root mean squared error (RMSE) of 3.31 m/s, a correlation (COR) of 0.80, and a scatter index (SI) of 0.29. The SWHs inverted from 8 images are compared with Haiyang-2 (HY-2) altimeter data and WW3 simulations, showing an RMSE of 0.74 m, a COR of 0.83, and an SI of 0.25. Our study demonstrates the robustness of GF-3 SAR in monitoring extreme sea states.

The inversion of the one-dimensional wave spectrum from dual-polarized synthetic aperture radar (SAR) data is performed using machine learning methods, namely Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Convolutional Neural Networks (CNNs). This process incorporates an improved hydrodynamic modulation transfer function (MTF), calibrated with more than 8000 Sentinel-1 (S-1) Ground Range Detected (GRD) images from 2021–2024, to account for shear currents induced by oceanic eddies. This study primarily aims to the differences between algorithm for one-dimension wave spectrum retrieval from S-1 SAR image as passing oceanic eddy and examine the characteristics of sea surface waves change observed by SAR passing through oceanic eddies. SAR retrievals are compared with significant wave heights (SWHs) from Haiyang-2 (HY-2) altimeter and wave spectrum from the Surface Wave Investigation and Monitoring (SWIM) and National Data Buoy Center (NDBC) buoys. The XGBoost model is established as superior for retrieving one-dimensional wave spectra and SWH, recording the lowest RMSE (0.33 m vs. SWIM, 0.30 m vs. HY-2, 0.68 m vs. Buoy) and highest correlation in validation. Applied to a North Atlantic case study, it effectively derives sea states from S-1 image. This high-resolution analysis reveals that oceanic eddies significantly amplify wave energy, resulting in elevated SWH inside their cores. While model accuracy declines in high kinetic energy eddies, the SAR-derived retrievals maintain reliability across eddy-affected regions.

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.

In our study, machine learning is applied for wind speed retrieval in tropical cyclones (TCs) utilizing the wave spectrum measured by Surface Wave Investigation and Monitoring (SWIM) onboard the Chinese–French Oceanography SATellite (CFOSAT). These measured waves with a spatial resolution of 18 km are collocated with wind products of 0.25° spatial resolution derived from a Soil Moisture Active Passive (SMAP) microwave radiometer in the western Pacific Ocean from 2019–2021. Through our abundant dataset, we find that wind speeds up to 45 m/s are linearly correlated with significant wave height (SWH) with a 0.8 correlation (COR) and cross-zero mean wave period (MWP) with a 0.56 COR. Based on this finding, a machine learning method, denoted as Adaptive Boosting (AdaBoost), is applied to relate wind speed with two parameters (i.e., SWH and MWP). The wind speeds retrieved from SWIM-measured wave spectra are compared with the wind products obtained from SMAP radiometers in the China Seas during the TC season of 2021: we obtain a 2.78 m/s root mean square error (RMSE), a 0.85 COR, and a 0.21 scatter index (SI). These results are better than those obtained using parametric formulas among the wind-wave triplets, i.e., an RMSE > 4 m/s of wind speed, a COR0.25. We conclude that cyclonic winds and waves can be synchronously measured by SWIM without any prior information.

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.

Heavy rain is associated with strong winds and extreme waves in a tropical cyclone (TC). In this paper, a practical algorithm for rain rate retrieval in TCs is proposed through 24 dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) Sentinel-1 (S-1) synthetic aperture radar (SAR) images acquired in interferometric-wide (IW) swath mode, in which 13 images are collocated with the observations from stepped-frequency microwave radiometers (SFMRs). TC winds are directly obtained from VH-polarized images utilizing the geophysical model function (GMF) S-1 IW mode wind speed retrieval model after noise removal (S1IW.NR). The normalized radar cross section (NRCS) at VV-polarization channel is simulated using GMF CMOD5N and VH-polarized SAR wind. It is found that the difference between the simulated NRCSs and measurements from SAR is linearly related to the rain rate and oscillates with the incidence angle. Following this finding, an empirical algorithm for SAR rain rate retrieval is developed, denoted as CRAIN2_S1, which considers the influence of the radius of the maximum wind speed. The proposed algorithm is applied to 11 images in the dataset, and the validation of the rain rate (up to 35 mm/hr) against the products from global precipitation measurements (GPMs) has a root mean square error of 1.74 mm/hr, a correlation coefficient of 0.92 and a scatter index of 0.29. Collectively, it is concluded that the algorithm CRAIN2_S1 can be practically applied for dual-polarized SAR rain rate retrieval without any external information.

The first rotating fan beam scatterometer onboard China-France Oceanography Satellite (CFOSAT) was successfully launched on October 29, 2018. CFOSAT SCATterometer (CSCAT) is dedicated to the monitoring of sea surface wind vectors but also provides valuable data for the applications over land and Polar Regions. This article provides an overview of the relevant procedures of CSCAT data processing, including onboard signal processing and operational ground processing. Then a post-launch analysis is carried out to evaluate the first results of CSCAT L1 and L2 products. It shows that the CSCAT instrument is generally stable in terms of noise measurements and internal calibration, unless there is any important change in the system configuration. Specifically, the CSCAT backscatter (σθ) precision and wind quality are studied using a set of collocated ancillary data. The σθprecision degrades as wind speed decreases, and it is relatively low at high incidence angles (e.g., θ >46°). In particular, backscatter estimation of the horizontally polarized beam should be further improved by correcting the noise subtraction factor. The retrieved CSCAT winds are in good agreement with the European Centre for Medium Range Weather Forecasts (ECMWF) winds, the Advanced Scatterometer (ASCAT) winds, as well as the buoy winds. However, due to unresolved calibration and interbeam consistency problems, the wind quality degrades remarkably for the out-swath and the nadir-region wind vector cells, implying that the σθcalibration should be improved in the future updates.

On August 16, 2011, the HY-2 satellite was successfully launched by a CZ-4B rocket from the Taiyuan site in Shanxi, China. HY-2 satellite, which is an ocean dynamic environmental satellite on-orbit operation in the world, has four payloads of microwave sensing. It can synchronous measure the ocean dynamic environmental information, such as wind field, significant wave height, sea surface height and sea surface temperature.