Reconstruction of ocean subsurface temperature anomalies (STA) is of great significance for the study of complex and variable ocean phenomena. Nowadays, STA reconstruction using deep learning is an important method, but the current reconstruction method still has the problem of incomplete feature extraction for multi-scale ocean dynamical processes. To address this problem, this study proposes a Transformer Network based on K-means clustering, referred to here as KMT, to reconstruct the STA in the Western Pacific using Argo measurements and multi-source satellite remote sensing data, and compares it with the Transformer Network inversion. The results show that the reconstruction accuracy of KMT is significantly better than that of Transformer Network, especially in the upper 200 m ocean where the temperature change situation is complicated.The average RMSE of the STA reconstruction accuracy of Transformer Network is 0.91 °C, and the average MAE is 0.73 °C , while the average RMSE of the STA reconstruction accuracy of KMT reaches 0.74 °C , and the average MAE reaches 0.57 °C. This study provides a more efficient and accurate STA reconstruction method for the West Pacific region, which can help to better understand and predict complex dynamical processes at different depths in the ocean.

Ocean subsurface thermal structure prediction is an area of active research field because of its scientific importance attach to ocean dynamic, air-sea interaction, and climate change, but currently, most of the ocean temperature predictions are oriented to the sea surface temperature (SST) due to the lack of observed profile data inside the ocean, especially in some marginal sea areas, such as the South Yellow Sea Cold Water Mass (SYSCWM). In fact, the prediction for ocean subsurface thermal structure is more important than SST in some ocean fields. In this letter, a dynamic coupling vertical multifeature difference time series prediction model based on bi-long short-term memory (DVMFD-Bi-LSTM) is proposed for the subsurface thermal structure prediction in the SYSCWM. Bi-LSTM with the “bi-directional” structure enables information association in temporal dimension. The dynamic coupling vertical mechanism is used to realize the spatial correlation between two adjacent layers of the subsurface ocean, and the difference algorithm is introduced to ensure the accuracy and robustness of the new method. Besides, we construct multifeature datasets to improve data scale and quality and rely on a multistep prediction strategy for multiday prediction. For a more comprehensive evaluation, multiple groups of experiments are set up for comparison, and the RMSE of the new model is reduced to 0.517, and R2 is increased to 0.937, which verifies the good performance of it in both temporal and spatial dimensions.

The azimuth cutoff of interferometric radar altimeter (IRA) image, which is acquired at small incidence angles, is mainly determined by the vertical component of the orbital velocity of ocean waves and is almost independent of the wave direction. Using this property, an inversion method for wind waves and wind speed has been proposed based on the azimuth cutoff of IRA image in combination with the Elfouhaily wind wave spectrum, which can effectively solve the problem of small-scale wind wave parameters loss caused by velocity bunching. In the present work, the wind speed and the significant height of wind waves (SH$_{\mathrm {ww}}$) have been retrieved from five pairs of airborne IRA images acquired in offshore areas. The results show that the differences between SHww retrieved from the five pairs of IRA images used in this article by the new method and the reference SHww are acceptable in ocean wave inversion. However, if the wind fetch is small and the wind direction is inconsistent with wave propagation direction, there is a significant difference between the retrieved wind speed and the reference wind speed when using the new method to retrieve wind speed. Moreover, the results also show that for the sea area with infinite wind fetch, the inversion accuracy of wind speed and SHww determined by the accuracy of the retrieved radar radial significant orbital velocity of wind waves (SV$_{\mathrm {ww}}$). However, for the sea area with finite wind fetch, the inversion accuracy would also be affected by wind fetch.

Wide swath near-nadir interferometric altimetry is a newly developed technology for sea surface height (SSH) measurement. However, the absence of actual measurement data makes this novel SSH mapping technique difficult to verify and apply for wide swath interferometric altimeters. To verify the designed performance of the scheduled wide swath single-pass interferometric altimeter in the "Guanlan Mission", an airborne campaign was carried out off the coast of Rizhao, China on November 16, 2020. An airborne dual-frequency interferometric radar altimeter system (ADIRAS) with a single-pass mode was utilized for SSH measurement as the first flight. Two pioneering and fundamental works have been conducted: an intensive altimetry error analysis according to the ADIRAS parameter settings along the incident direction, an effective SSH reconstruction approach based on a multichannel likelihood (ML) function, and detailed validation procedures through airborne campaigns illustrated in this study. The results indicated that the difference between the wave-induced sea surface elevation (WSSE) variances derived by the ML approach and GNSS buoy was 2 cm2, which was smaller than the results of the single band on Ku (11 cm2) and Ka (6 cm2). Moreover, the estimated Significant Waves Height (SWH) bias of joint bands was 10 cm, which was also superior to that of Ku (39 cm) and Ka (24 cm). Both simulated data and real airborne dual-frequency InSAR data were employed in this study for cross-validation of the proposed method. This approach represents an effective technique for SSH reconstruction of future spaceborne/airborne interferometric altimeters.

Interferometric radar altimeter (IRA) is a new ocean remote sensing sensor. It can be used to retrieve sea surface height (SSH) by means of cross-track interferometry. Compared with the traditional cross-track interferometric synthetic aperture radar (XT-InSAR), IRA works at very small incidence angles for higher altimetry sensitivity. In this case, multiple discontinuous surface scatterers at sea surface would be cut into a same range pixel which leads to severe layover. This layover induced by ocean waves will reduce the correlation between the master-slave images acquired by IRA and increase random interferometric phase noise. At present, how to quantitatively analyze the impact of the ocean wave layover on the decorrelation of IRA images is still a problem that needs in-depth discussion. In this letter, theoretical analysis of the effect of ocean waves on the decorrelation of IRA images has been carried out when the ocean waves layover is considered. And the theoretical results are also compared with the airborne IRA data. It is found that the layover of ocean waves has significant influence on the decorrelation between the master-slave IRA images, especially at very low incidence angles.

The interferometric radar altimeter (IRA) is one of the main payloads of the “Guanlan” ocean science satellite proposed by the National Laboratory for Marine Science and Technology of China. To evaluate the effectiveness and accuracy of the IRA in retrieving the ocean dynamic parameters, such as sea surface height (SSH), ocean wave spectrum, and wind speed, two airborne IRA experiments were carried out at Qingdao Xiaomaidao (XMD) sea area on March 31, 2019, and Rizhao sea area on November 16, 2020. In the present work, wave-induced sea surface elevation (SSE) and its spectrum have been retrieved based on the interferograms acquired by the airborne IRA. To suppress the random phase noise, a mean filtering algorithm has been used in the multilook process of calculating the complex IRA images. The results show that the size of the filter window has a significant effect on the retrieved SSE. If the size of the filter window along THE range direction is too large, the flat earth would cause the spectral density of the retrieved ocean wave to be higher. In addition, the comparisons of the retrieved spectra with the buoy measurements demonstrate that the swell can be well-retrieved by IRA images at low sea-state conditions with significant wave height (SWH) less than 0.7 m. However, for wind wave, because of the effect of the velocity bunching along the azimuth direction, the wind wave spectrum can be extracted only when it propagates approximately along the ground-range direction of the IRA images.

The variational inversion for synthetic aperture radar (SAR) wind retrieval can take errors of all sources involved into account, but the complexity of ascertaining errors of wind vectors is high and the iteration process is very time-consuming. In this paper, we modify the decomposition of wind vectors into speed and direction, and adopt a damped Newton method (DNVAR) to solve the cost function, which is based on inexact line search condition. Experimental results show that DNVAR can effectively reduce background wind vector errors, and the average number of iterations for DNVAR descends greatly. For practical applications, when the background wind speed is higher than 10 m/s, the accuracy of DNVAR is higher than direct SAR wind retrieval (DIRECT), otherwise, DIRECT performs better.

The existing SAR image quality assessment method could not be effectively used for assessing the performance of object detection. Thus, it is difficult to select SAR images and corresponding detection algorithms for SAR object detection. By analyzing the relationship between object detection results and basic image quality indicators, this paper studies the image quality assessment for image object detection. Based on the concept of “application suitability”, basic quality indicators including radiometric resolution, spatial resolution, PSLR and ISLR are integrated into a single indicator called Detection Index, which is able to comprehensively evaluate the degree to which SAR image is suitable for object detection tasks. Experimental results on aircraft detection with single scene show the effectiveness of the proposed model for SAR image capability evaluation in object detection applications.