Conference Agenda

In this paper, an improved Doppler radar imaging model (IDopRIM) is proposed. The model is validated against measurements and an empirical model. It is indicated that the IDopRIM effectively addresses the overestimation issue of the original DopRIM model and presents a significant accuracy improvement. For most conditions, the root mean square (RMS) errors of the proposed model are within 0.3 m/s. Furthermore, based on the IDopRIM model, the effect of current-wave interaction on the OSC retrieval under the DCA framework is investigated for a typical ocean dynamic phenomenon, namely, the internal wave. Both numerical simulation data and a real SAR observation image are utilized for analyses. Experimental results suggest that the contribution of current-wave interaction to the sea surface Doppler velocity cannot be ignored for the IW ocean surface with rapidly-varying current. Under certain conditions, the relative errors of the OSC retrieval due to neglecting such a contribution could be larger than 30%.

The ocean waves are the most common and obvious ocean phenomenon. Its parameters retrieval is particularly important for the development of marine resources, the construction of marine engineering and marine transportation. Synthetic aperture radar (SAR) is widely applied to the ocean wave observation and parameters retrieval with obvious advantages of all-day, all-weather. With the rapid development of SAR technology, the resolution of SAR images becomes higher every day. High-resolution SAR brings us more plentiful target details, but sea spikes are prone to appear when observing the ocean. It causes the information of ocean waves to be overwhelmed by sea spikes, which seriously affects the retrieval of the ocean wave parameters from SAR. As a step forward, an ocean wave parameters retrieval from SAR images under sea spike interference is proposed in this paper. Firstly, the VV and HH dual-polarized data is used to remove the nonpolarized components of the SAR echo to suppress the sea spikes. The clear SAR image of ocean waves is obtained that is not disturbed by the sea spikes. Then, the azimuth cutoff, wavelength and propagation direction of ocean waves are estimated in the two-dimensional wavenumber domain. Finally, the three parameters are input into the empirical model. The significant wave height and the mean wave period are retrieval in combination with SAR system parameters. This method is applied to a field X-band SAR data. The significant wave height and the mean wave period of ocean waves are obtained. The results are similar to the European Centre for medium-Range Weather Forecast (ECMWF) data, which verifies the effectiveness of the proposed method.

It is very important to evaluate the performance of ocean wave directional spectrum by satellite remote sensing under high sea conditions. By the statistical comparison method presented in [Ying et al., 2022], under different sea conditions (wind wave mainly/swell mainly) and high sea surface conditions (wind speed of 16 m/s-24 m/s, significant wave height from 6m to 10 m), the ocean wave directional spectra obtained from CFOSAT SWIM with those measured by NDBC buoys. This includes the comparison of the omni-directional wave height spectrum and the directional function at the peak wave number. The comparison results show that under high sea conditions, wave directional spectra provided by the SWIM beams at 8° and 10 ° incidence have a high consistency with those from buoy data, for both the wind wave case and swell case. The wave directional spectra by SWIM 6 ° incidence have good performance for swell case, however, its performance degrades for the wind wave case due to the non-linear surfboard effect in the radar imaging mechanism.

To enhance the precision of nearshore data products obtained from radar altimeters, a novel two-step retracking algorithm for nearshore constructed waveforms has been introduced. Leveraging Empirical Mode Decomposition (EMD), this method extracts trend information along the trailing edge of the waveform. Constructed waveforms are then crafted by connecting the leading edge and trailing edge trend information. The retracking process involves two steps: the first step retracking only a segment of the leading edge, obtaining crucial 4-parameter a priori information. Subsequently, in the second step retracking, retracking encompasses both the leading and trailing edges based on the acquired a priori information. The HY-2B radar altimeter waveform was used for testing, the results show that the nearshore constructed waveform two steps retracking algorithm is better than the Maximum Likelihood Estimation (MLE4) retracking algorithm used in the operational operation of the HY-2B radar altimeter and the Adaptive Leading Edge Subwaveform algorithm (ALES) in significant wave height (SWH) and sea level anomalies (SLA). Compared with the MLE4 algorithm, the standard deviation of the difference in SWH is reduced by 14%, and the standard deviation of the difference in SLA is reduced by about 18%. The two steps retracking algorithm for constructed waveform makes full use of the information of the trailing edge of the waveform, effectively reduces the influence of the peak noise on the leading edge of the waveform, and improves the utilization rate and retracking accuracy of the waveform.

Internal tides are internal waves that occur within the stable density stratification of the ocean at tidal frequencies. Internal tides play a crucial role in various ocean processes, including vertical nutrient transport, underwater sound propagation, ocean structure and ocean circulation. Internal tides are a key dynamic process influencing global climate change. Understanding the spatiotemporal characteristics of internal tides is essential for parameterizing ocean mixing driven by internal tides and improving numerical simulation studies. Satellite altimetry data, with its broad spatial coverage and long temporal range, is well-suited for studying the global spatiotemporal characteristics of internal tides compared to in situ observations and numerical simulations. In this study, we utilized altimetry data from 13 satellites spanning from 1993 to 2021, including T/P, Jason-1/2/3, GFO, ERS-1/2, Sentinel-3A/B, HY-2A/B, CryoSat-2 and SARAL. The global low-mode M2, S2, K1, O1 internal tide parameter information was extracted using the plane wave fitting method, and the spatiotemporal characteristics of global low-mode internal tides were analyzed. By analyzing the global internal tide extraction results, the main active regions of internal tides in the global ocean were summarized, and internal tide propagation characteristics were analyzed for key regions.

Satellite altimeters provide precise measurements of sea surface height, allowing for the detection of dynamic changes in the ocean surface, such as wind, waves, tide, mesoscale eddy, ocean circulation and so on. This enhances our understanding of the physical processes and climate change in the ocean. However, traditional satellite altimeters suffer from sparse and discontinuous along-track sea surface height data, limiting the comprehensive capture and understanding of ocean circulation patterns. By fusing along-track sea surface height data from different satellites, the limitations of individual satellite observations can be overcome, resulting in a more comprehensive representation of sea surface height. In the fusion of multi-source satellite altimetry data, the fixed spatiotemporal scales used in the conventional methods may overlook the local characteristics and dynamics of sea surface undulations. This study utilizes high-resolution ocean reanalysis data and considers spatiotemporal correlations in oceanic physical processes to calculate spatiotemporal correlation scales. These variable spatiotemporal correlation scales are then applied in the fusion of multi-source satellite altimetry data, allowing for dynamic adjustments of scales based on the characteristics of each grid point. This approach better adapts to the dynamic environments and data distributions in different regions. By fusing multi-source satellite altimetry data with spatiotemporal variable scales, overall data consistency is maintained, and the utilization of spatiotemporal relationships between the data is improved. This method also enables the capture of local details and changes in the ocean surface.

Observations from satellite altimeters are an essential part of mapping the marine gravity field and bathymetry. As the majority of the ocean basins are yet to be mapped by sonar, obtaining reliable data of sea surface height and sea surface slopes is key to improving our understanding of the marine gravity field. Improvement in altimeter systems has enabled the marine gravity field to be determined to a few mGal, however with conventional satellite altimetry, improvements are challenging.

The major challenge is the sampling geometry of conventional satellite altimeters, with along-track (majorly north-south) sea surface slopes being much better determined than across-track slopes (east-west). With the KaRIn instrument on the Surface Water and Ocean Topography (SWOT) satellite, swath-altimetry with 2-dimensional observations of the sea surface height is possible. From these observations, the directional sea surface slopes in both along-track and across-track are possible to determine. However, determining the resolution and precision with which the sea surface slope is determined, is of fundamental importance for the improvement of the mapping of the marine gravity field.

With three parallel beams, ICESat-2 is another satellite that can determine the east-west sea surface slope. From observing the difference in sea surface height between beams, we are able to determine the directional sea surface slopes in north-south and east-west components, with very promising results. With data from ICESat-2, we aim to validate the SWOT directional sea surface slopes at cross-overs between SWOT and ICESat-2 and determine the relative initial performance.

One of the most important applications of remote sensing (RS) technologies is about the detection and monitoring of ground changes using remotely-sensed images (e.g., [1–3]). In this framework, the last three decades has shown the development and the subsequent application of several interferometric synthetic aperture radar (InSAR) approaches [4-7] for the continuous monitoring of state of conservation/maintenance of public/private infrastructures. In this work, we clarify the potential of InSAR methodologies, complemented with naïve artificial intelligence (AI) approaches, to automatically discover differential displacement signs over single infrastructures. We made profit from the recent advances in the field, as reported in the following principal publications [8-12], to find out a list of synthetic coherent indices (e.g., conservation criticalities, angular distortion, etc.) whose relevance has been tested in a real context. A set of Sentinel-1 SAR data collected over the highly urbanized area of Shanghai has been used. Preliminary, the ground displacements and the relative movements of buildings/infrastructures have been retrieved by processing the SAR data at the single-look scale [13] implementing conditioned multi-temporal phase unwrapping operations [14]. Overall, more than 12 millions of single measurement points have been recovered, over which the selected coherent synthetic indices have been tested. Experimental results demonstrate the importance of integrated methods, fostered by InSAR and AI, for the fast mapping of hazardous conditions and can further be extended to perform analyses in other contexts, considering the specific characteristics of these new environments.

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Predicting SST Responses to Typhoon Wind Pump using Satellite data and Machine Learning Model

Danling Tang^1,2,*, Hongxing Cui^1,2, Yi Sui^3**, Hongbin Liu^2,1

¹ Guangdong Remote Sensing Center for Marine Ecology and Environment, Southern Marine

Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China. *lingzistdl@126.com

² Department of Ocean Science, Hong Kong University of Science and Technology, Hong Kong, China

³Tsinghua Shenzhen international Graduate School, Shenzhen, 518055, China. **Sui.Yi@sz.tsinghua.edu.cn

Typhoon wind pump has significant impacts on ocean, including affecting the Sea Surface Temperature (SST). An advanced model for the SST modification generated by typhoon wind pump is presented in this study. The model is applied in the northwest Pacific, machine learning techniques such as random forest and extreme gradient boosting (XGBoost). The model is able to predicts the spatial structure and temporal evolution of SST cooling by incorporating 12 predictors related to typhoon characteristics and pre-storm ocean states. The results indicate our model has the advantage in accuracy when compared with conventional numerical models. Key predictors include typhoon intensity, speed, size. Pre-storm ocean conditions are also important, like mixed layer depth and SST, identified through feature importance assessments and SHapely Additive exPlanations (SHAP), bringing attribute-oriented explain ability to the proposed method. This model shows the ability to predict the spatial structure and temporal evolution of SST cooling for different Typhoon intensities, along with its explanatory power regarding the ocean-atmosphere interactions during typhoon. This model is an insightful tool for analyzing the complex responses of oceanic conditions to typhoon wind pump effects.