Satellite remote sensing plays a fundamental role in observing oceanic processes by providing large-scale, long-term, and continuous measurements. With the increasing availability of multisource satellite data, challenges such as data gaps, complex environmental conditions, and the limitations of conventional retrieval methods have become more evident. In recent years, artificial intelligence (AI) has emerged as a practical and effective approach to address these issues. This article reviews the development of AI techniques in satellite ocean remote sensing, focusing on three main application areas: parameter retrieval, data reconstruction, and image-based ocean phenomenon detection. For geophysical variable retrieval, AI models such as convolutional neural networks (CNNs) and Transformer architectures have improved the accuracy of ocean waves, sea surface, salinity, wind, and ocean color estimates, especially under extreme or noisy conditions. In the field of data reconstruction, AI methods enable the completion of missing data in both surface and subsurface ocean layers, offering finer spatial–temporal resolution and better consistency than traditional interpolation approaches. For image interpretation, deep learning (DL) models have been applied to detect and segment dynamic ocean features such as mesoscale eddies, internal waves, sea ice, and tropical cyclones (TCs), achieving high efficiency and precision. This article also highlights the integration of AI with physical knowledge, the use of multisource fusion, and the trend toward near real-time (NRT) applications. These developments indicate that AI will play an increasingly important role in future satellite-based ocean observation and environmental monitoring.

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.

The Chinese Ocean Salinity Mission (COSM) is dedicated to the global sea surface salinity (SSS) using the interferometric aperture synthesis radiometry. The airborne COSM was conducted during July to August 2023 at Qixia of Shandong Province. The introduction of airborne COSM experiment and the pre-simulation results based on the design of airborne instruments, which are the scaled-down model of spaceborne payloads, are presented. The preliminary sea surface salinity (SSS) retrieval results show that, the best accuracy of SSS is achieved when considering all the available measurements from two payloads together in a combined retrieval. The results from the airborne COSM experiment verify the interferometric synthetic aperture payloads performance and will also help to improve the data processing technique and the key error correction methods in the future in-orbit operational phase.

This article employs the modulated correlation function (MCF) model to describe the ocean surface in radar backscattering. Surface correlation functions derived from wind wave spectra: Apel, Elfouhaily, Kudryavtsev, and Hwang models are examined. The spectral properties of the above spectra are also analyzed, including the height, slope, and saturation spectra. The results suggest that the MCF model is applicable in depicting spatial correlation of ocean surface. At the same time, the spectrum of MCF may not be proper in describing the energy cascade of ocean waves. The comparisons of backscatter are made among the MCF model and wind wave spectra, with regard to dependences of radar frequency at L-, C-, X-, and Ku-bands, wind vector, and incidence angle. The results indicate that the MCF model yields the overall minimum errors in radar backscatter against geophysical model functions (GMFs). We validate the model with the airborne and spaceborne radar measurements and show that the MCF model gives good agreement at the C-band but only moderate at the Ku-band. Meanwhile, the backscatters calculated based on different wind wave spectra can significantly deviate from each other, related to the magnitudes of the short-wave spectrum. Besides, the breaking wave effect on radar backscatter is also discussed.

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.

The incomplete eye structure during the generation and weakening stages of tropical cyclones (TCs) makes it difficult to accurately locate low-intensity TCs in satellite infrared (IR) images. Here, we develop a physics-enhanced deep convolutional neural network (CNN) to determine centers of tropical depressions (TDs) and tropical storms (TSs) with maximum sustained wind speed (MSW) below 63 kt. This is accomplished by integrating consecutive IR images from the Himawari-8 geostationary satellite and historical information of TCs including center position, MSW, and the minimum pressure. Multi-channel images of 196 TCs over the Northwest Pacific from 2015 to 2021 are randomly divided into a 3:1:1 ratio for model training, validation, and testing. Sensitivity experiments are designed to investigate the influence of different inputs on model performance. The best results are achieved by combining 18 hours of images at three IR channels and historical TC information at 3-h intervals as model inputs. The mean distance between the model identified center and that recorded in the Best Track dataset for TD and TS levels are 20.1 km and 19.1 km, respectively. This indicates an accuracy improvement of 63.0% and 54.6%, respectively, over the model which only considers images at the current moment. Compared with some other state-of-the-art models, the center positions of TDs and TSs determined by our model agree better with the Best Track records. The CNN model also performs quite well in determining the center of stronger TCs, with an average error of 14.1 km, indicating it is robust for all-level TCs.

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.

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.

Microwave radiometers are widely used in Earth observation and ocean monitoring for their strong penetration ability. Nevertheless, their utilization is somewhat constrained by their intrinsic low-resolution capability, particularly in complex applications such as coastal zone monitoring and analysis of typhoons. To overcome this limitation, resolution enhancements are necessary. Here, we present a novel resolution enhancement technique, the alternating proximal gradient (APG) method, applied to Haiyang-2B (HY-2B) Scanning Microwave Radiometer (SMR) data. This method is based on the decorrelation approach and incorporates total variation constraint. The APG method, along with the Backus-Gilbert (BG) method, are both applied to the 6.925GHz data of HY-2B SMR. Both simulated and real HY-2B SMR data from two representative regions, including land-sea transition zones and cyclonic storms, are analyzed. Results show that both the BG method and the APG method can significantly enhance details obscured in the original SMR data. Furthermore, the enhanced brightness temperatures are compared to the AMSR-2 25 km product, revealing that the APG method provides more consistent results and contains more reliable information than the BG method in terms of resolution enhancement.

This study develops a new sea surface temperature (SST) retrieval algorithm based on statistical regression that utilizes WindSat satellite data and in situ SST quality monitor (iQuam) buoy data. The proposed algorithm introduces new rules to remove abnormal brightness temperatures (TB) due to land, sea ice, radio frequency interference (RFI), and sun glint. Besides, SST data from 2019 to 2022 are generated from the HY-2B satellite’s scanning microwave radiometer (SMR) using a two-step retrieval method. Our study reveals that wind speed (WS), cloud liquid water (LW), and wind direction have significant crosstalk effects on SST retrieval, which increases exponentially when the cloud LW exceeds 0.1 mm. The crosstalk effect can be significantly reduced by incorporating WS fitting in the SST retrieval algorithm. By fitting iQuam SST data, the multiple localizing sea surface WS and latitude algorithms are developed, significantly reducing the impact of crosstalk parameters on SST retrieval and improving the SST retrieval accuracy. Also, the algorithm performed well in correcting the nonlinear response of TB and SST. The results reveal that the retrieval algorithm eliminates bias across different WSs, water vapor (WV), and cloud LW ranges and performs consistently on ascending and descending orbits. The overall bias of the SMR four-year SST product is$-0.004\,\,^{\circ }\text{C}$, with a standard deviation (STD) of 0.588 °C, posing a substantial improvement over the existing product with a bias of$-0.022\,\,^{\circ }\text{C}$and an STD of 0.803 °C. Moreover, the four-year fluctuation of STD is very small, indicating excellent stability of the retrieval algorithm.

The Chinese Ocean Salinity Satellite is dedicated to global sea surface salinity (SSS) mapping with two payloads onboard, which are the interferometric microwave radiometer (IMR) and the microwave imager combined active and passive (MICAP), both using the interferometric aperture synthesis radiometry. One of the payloads is an L-band interferometric radiometer system with a Y-shaped antenna array. The other one, MICAP, is a 1-D passive/active combined system, where the L-, C-, and K-band interferometric radiometers’ antenna arrays are arranged in a line, and the active device is an L-band scatterometer. Based on the payload configurations, a series of simulations is applied to analyze the payloads performance, including the brightness temperature (TB) characteristic, the SSS accuracy, and the effects of Sun and land contamination. The TB simulation results show that the IMR possesses a finer spatial resolution compared to the MICAP L-band radiometer, whereas the latter achieves a better TB radiometric resolution. With the assistance of the C- and K-band radiometers and the L-band scatterometer, the combined retrieval accuracy improves compared with the SSS retrieved using only L-band TB and auxiliary parameters. The best accuracy of SSS is achieved when considering all the available measurements from two payloads together in a combined retrieval. Finally, the results related to Sun and land contamination that endanger the interferometric radiometer measurements are presented and discussed. The performance simulation in this article has been used as a reference for the design phase of two payloads and will be updated with the development of payload manufacture.

Long-term and stable brightness temperature (TB) measurements observed by microwave radiometers are of great significance in studying the variation in geophysical parameters and their trend analysis. A modified vicarious cold reference (MVCR) method for the scanning microwave radiometer (SMR) TBs onboard the Haiyang-2B (HY-2B) satellite is developed and used to estimate the stability of the TBs of L2A-TB and L2A-TC, which corresponds to TBs before and after intercalibration, respectively. Data from November 1, 2018, to October 31, 2021, are used for the stability assessment. The results show that there are several channels in L2A-TB with annual drifts of TBs greater than 0.1 K per year (K/year), while the TBs of L2A-TC after intercalibration are quite stable, with annual drifts that are all less than 0.1 K/year. In conclusion, the SMR TBs after intercalibration achieve better stability. The robustness of the MVCR method is demonstrated using simulated SMR TBs. After removing the annual harmonics, the coldest TB sequence is more stable, and a more realistic annual drift can be obtained.

The China France Oceanography Satellite (CFOSAT) was launched on October 29, 2018, and is equipped with two sensors: a space-borne rotating fan beam scatterometer (CSCAT) and a space-borne surface wave investigation and monitoring (SWIM) radar. The CSCAT is dedicated to monitoring sea surface winds (SSWs), and the SWIM radar is designed to measure surface ocean waves. In this study, CSCAT SSW products during global tropical cyclones (TCs) were validated using wind data from multiple sources, including European Center for Medium-Range Weather Forecasts (ECMWFs) reanalysis (ERA5), cross-calibrated multi-platform (CCMP), soil moisture active passive (SMAP), WindSat, HY-2B, advanced scatterometer (ASCAT), and buoy wind. Wave parameters (significant wave height (SWH), dominant direction, and wavelength) were validated using wave data from ERA5, Jason3, and the HY-2B altimeter. The results show the following: 1) errors of the CFOSAT wind speed (WS) and SWH increase with the enhancement of TC intensity; 2) the WS accuracy of wind vector cells (WVCs) near the nadir is better than that at the nadir and the edge of the swath; 3) the wind direction (WD) correlation between the CSCAT and ERA5, ASCAT, and WindSat is more obvious at high WSs (>20 m/s) than at lower WSs (< 6 m/s); 4) the accuracy of the SWIM off-nadir SWH product is lower than that of the nadir SWH; and 5) a triple collocation comparison among the CFOSAT SSW, ERA5 SSW, and WindSat SSW indicates that the CFOSAT SSW provides the best performance.

In this study, an Attention U-net network was proposed to reconstruct the subsurface temperature (ST) field with high temporal and spatial resolution in the South China Sea (SCS) from sea surface parameters observed by satellites. In addition to sea surface temperature, sea level anomaly and sea surface wind field, the wind stress curl, which influences three-dimensional structure of temperature through the induced Ekman pumping and transport, was also input into the model. The 5-day average vertical temperature profiles with spatial resolution of 0.5° from Simple Ocean Data Assimilation (SODA) reanalysis were used for training and evaluating the network. The results show that the Attention U-net model performs quite well in ST reconstruction in the upper 100 m layers of the SCS. The additional input of wind stress curl helps to improve the model accuracy. The average root mean square error (RMSE)/bias of ST decreases from 1.08°C/-0.21°C to 1.01°C/-0.05°C. Particularly, the RMSE near the thermocline is reduced significantly by up to 10.9%. The estimation error of the Attention U-net model is much smaller than that of some linear and tree models in the SCS, especially in shallow waters and regions with complex dynamic processes. The case study also shows that our model is capable of capturing the evolution of mesoscale processes in the SCS. The combination of satellite observations with high-precision ST reconstruction model will help us comprehensively understand the fine structure and variation of temperature and circulation in the marginal seas and open oceans.

The Chinese Ocean Salinity Satellite is part of the Chinese ocean dynamic satellite series, and is dedicated to global SSS observation with two payloads onboard. These two payloads, named the Interferometric Microwave Radiometer (IMR) and the Microwave Imager Combined Active and Passive (MICAP), both adopt interferometric aperture synthesis technology. IMR is a two-dimensional L-band interferometric radiometer system with a Y -shaped antenna array. MICAP is a one-dimensional passive/active combined system, where the L-, C-, and K-band interferometric radiometers' antenna arrays are arranged in a line, and the active device is an L-band scatterometer. Based on the payload configurations, an end-to-end simulation shows that, with the assistance of the C- and K-band radiometers and the L- band scatterometer, the combined retrieval accuracy improves compared with the SSS retrieved using only L-band TB and auxiliary parameters. The best accuracy of SSS is achieved when considering all the available measurements from two payloads together in a combined retrieval.

Haiyang-2B (HY-2B) is the second marine dynamic environment satellite of China. It carries several payloads, which includes a scanning microwave radiometer (SMR). Sea surface temperature (SST) is one of the main products of SMR. This study compares SST derived from SMR to in-situ SSTs in all of 2020. The collocations of HY-2B SRM and insitu SSTs were generated with the spatial window of 25km and the temporal window of 30 min, and the matchup data set includes 2696012 points from all over the global sea surface areas. Results show that SMR SST has a mean bias of 0.068°C (SMR minus buoy) and root-mean-square error (RMSE) of 0.851 °C in the global ocean area. By analyzing the global bias distribution map, we can conclude that the larger SST bias points mainly concentrated along the coast, sea ice, and high wind speeds areas where deteriorated the global overall accuracy performance. To prove it, three different areas were selected and analyzed separately. The results show that the RMSE in open ocean over mid and low latitudes is 0.66°C, while RMSEs in the nearshore zone and high wind speeds areas are 0.8°C and 1.1 °C, respectively.

Haiyang-2B (HY-2B) is the second marine dynamic environment satellite of China. Sea surface temperature (SST) products from the scanning microwave radiometer (SMR) onboard HY-2B satellite are evaluated against in situ measurements. Approximately, ten months of data are used for the initial evaluation, from January 15, 2019 to November 15, 2019. The temporal and spatial windows for collocation are 30 min and 25 km, respectively, which produce 450 416 matchup pairs between HY-2B/SMR and in situ SSTs. The statistical comparison of the entire data set shows that the mean bias is -0.13 °C (SMR minus buoy), and the corresponding root-mean-square error (RMSE) is 1.06 °C. Time series of collocations for the SST difference shows that a good agreement is found between HY-2B/SMR and in situ SSTs after June 15, revealing a mean bias and an RMSE of only 0.09 °C and 0.72 °C, respectively. A three-way error analysis is conducted between the SMR, Global Precipitation Measurement Microwave Imager (GMI), and in situ SSTs. Individual standard deviations are found to be 0.41 °C for the GMI SST, 0.15 °C for the in situ SST, and 1.03 °C for the SMR SST. The results indicate that the HY2B/SMR SST products need to be improved during the period from January 15, 2019 to June 15, 2019.

China's ocean dynamic monitoring satellite Haiyang-2B (HY-2B) has been in orbit for more than one year. Payload named scanning microwave radiometer (SMR) with five frequency channels onboard the HY-2B is an instrument mainly used to monitor and study ocean environment and to obtain ocean dynamic parameters like sea surface wind, temperature, etc. One issue is that high wind speeds in typhoons cannot be monitored by brightness temperature (TB) data at high frequency channels due to heavy rain, while the low frequency channel TB are capable of high wind speed retrieval but are limited to the low spatial resolution. This paper intends to enhance SMR's spatial resolution in low frequency channels by applying the interpolation method developed by Backus and Gilbert. Preliminary results indicate that TB data after spatial resolution enhancement show more details of typhoon structures and will provide more information for the high wind speed retrieval.

Chinese ocean salinity satellite is a mission designed for global ocean salinity monitoring. Two main payloads equipped onboard the satellite are the Interferometric Microwave Radiometer (IMR) and the Microwave Imager Combined Active and Passive (MICAP) which both have L-band radiometer and adopt aperture synthesis technology. A simulation is designed according to the payloads' configurations and consists of three parts: the brightness temperature generator, the radiometer system and the parameters retrieval. Preliminary results, which contain IMR and MICAP's brightness temperature resolution, spatial resolution, and parameter retrieval accuracy, are presented.

The daily-composited 9 km sea surface temperature (SST) data from the 10-band Chinese Ocean Color and Temperature Scanner (COCTS) onboard the Haiyang-1C (hy -1C) satellite are evaluated for global area against daily 9 km SST product from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite and in situ data. Around four months of the data are used for the validation from Jun. 18, 2019 to Jan. 9, 2020. The in situ SST data are averaged at a spatial resolution of 9 km which is similar to that of the COCTS and MODIS SST data. For daytime, the bias, standard deviation (STD) and robust standard deviation (RSD) of the SST difference between COCTS and buoy data are -0.31 °C, 1.09°C and 0.66°C. And the bias, standard deviation and RSD of the SST difference between COCTS and MODIS data are -0.14°C, 0.95°C and 0.60°. The error sources are discussed. The results indicate that studies should focus on error sources to improve the HY-1C/COCTS SST products accuracy.

China's Haiyang-2B satellite (HY-2B) has been in orbit for over a year, since it was launched on October 25th, 2018. HY-2B is equipped with both active and passive microwave remote sensors. Payload named scanning microwave radiometer (SMR) with five frequency channels in HY-2B is an instrument mainly used to monitor and investigate ocean environment and obtain ocean dynamic parameters like sea surface temperature, surface wind speed, atmospheric water vapor and cloud liquid water, etc. This paper introduce a method to optimize the masks of land and sea ice based on HY-2B SMR in order to improve the inversion accuracy of sea surface temperature. The idea of the method is subdividing the 3dB footprint into more sophisticated points, and every point is geolocated and marked as Land, Sea water or Sea ice. Finally, the subdivided mask is used for SST inversion and compared with the WINDSAT SST inversion results of the Coriolis satellite. The chosen of the method's parameters will compromise between numbers available for inversion and inversion accuracy.

In this work, the cyclostationary empirical orthogonal function analysis and the empirical mode decomposition (EMD) method is used to compute the low frequency parts (T >~5 years) in tide gauge, satellite altimetry and reconstructed sea level data. High spatio-temporal correlations are observed between tide gauge measurements North of Cape Hatteras (NCH) and other datasets in the subpolar and tropical regions in the North Atlantic Ocean. The altimeter and tide gauge data show significant different spatial pattern of correlation over north with that South of CH. In the last two decades, the weakening of Atlantic Meridional Overturning Circulation (AMOC) might be related to the phase reversal of the correlations in the NCH as well as the strengthening of positive correlations in the tropical regions. Moreover, higher correlations observed near the tide gauges on the coasts of the NCH are presented by comparing the correlations in the time span of 2003-2012 with that in 1993-2002. Both the North Atlantic Oscillation, Atlantic Multidecadal Oscillation and ocean heat content variations, which could affect AMOC and Gulf Stream variations, are linked to the variations of the correlations.

To analyze the wind power and develop tools for the offshore wind farm siting in the coastal region of Guangdong with water depth 30~50m, wind data from satellites, the CCMP analysis, Lidars, wind towers, buoys, a high-resolution numerical model are used to calculate wind resources and wind energy reserves. Overall, the northeastern wind with speed above 8m/s is prevailing in Autumn and Winter and the southern wind with low speed is prevailing in Spring and Summer. The eastern part of the off-shore region of Guangdong province is better for wind farm siting than the western region.

As the Hai-Yang 2B (HY-2B) satellite was launched on 25 October 2018, the brightness temperature measured by the scanning microwave radiometer (SMR) on the HY-2B Satellite are analyzed including the commissioning phase. In order to evaluate the brightness temperature situation, comparisons between SMR and GMI brightness temperature from 6 to 37GHz over the ocean are preliminary implementation, and the results are encouraging.

To analyze the wind power and develop tools for the off-shore wind farm siting in the coastal region of Guangdong with water depth 30~50m, wind data from satellites, the CCMP analysis, Lidars, wind towers, buoys, a high-resolution numerical model are used to calculate wind resources and wind energy reserves. Overall, the northeastern wind with speed above 8m/s is prevailing in Autumn and Winter and the southern wind with low speed is prevailing in Spring and Summer. The eastern part of the off-shore region of Guangdong province is better for wind farm siting than the western region.

The algorithm of inversing the SST from the SMR onboard on HY-2B satellite is an empirical regression method based on the physical model to calculate the empirical relationship between the brightness temperature and the different physical parameters of the ocean and atmosphere. In order to test the preliminary result of SST retrieved from HY-2B satellite SMR, the SST is validated according to the IQUAM in-situ SST and the space-borne WindSat SST. The performance of the instrument is still being estimated and SST retrieval algorithm is still improving.

Imaging ALTimeter (IALT) is a new type of radar altimeter system, which observes the earth from 2° to 7° incident angles. In comparison to the conventional altimeters such as HY-2A altimeter, Jason-1/2, TOPEX/Poseidon, which observe the ocean at nadir, the swath of IALT is much wider and its spatial resolution is much higher. The IALT on board Tiangong-2 space station is launched on 15th September, 2016 at Jiuquan Satellite Launch Center. The in-orbit assessment of IALT is done until 30th April, 2017. In this paper, the ocean surface wind speed inversion method based on IALT is established. The neural network algorithm is used for ocean surface wind speed retrieval, and the spatial resolution of retrieved wind speed is 25km. The wind speed inversion accuracy is evaluated by comparing with the ECMWF reanalysis wind speed, buoy wind speed, and boat measurement wind speed. The results show that the Root-Mean-Square (RMS) of retrieved wind speed is 1.85m/s, and the Bias of retrieved wind speed is about -0.21m/s. The wind speed inversion accuracy satisfies performance requirement.

Ocean surface wind field is a very important marine dynamic parameter in marine environment forecasting and climatological studies. Spaceborne scatterometer is one of the most efficient remote sensors than can provide global ocean surface wind measurement. Polarimetric scatterometer (PolScat) simultaneously measures co-polarized and cross-polarized backscattering coefficient and the correlation coefficient of the co- and cross-polarized component of radar echoes which can significantly improve the performance of the sea surface wind field measurements. In this paper, we derive the error model of correlation scattering coefficient from radar echo signals. The effect of antenna polaxis deflection on correlation scattering coefficient error is analyzed. Moreover, an “end-to-end” system simulation model of PolScat is established. A modified Maximum Likelihood Estimation (MLE) is used for ocean surface wind field inversion. Both the co-polarized backscattering coefficient and correlation scattering coefficient are included in the objective function of MLE. The simulation results show that PolScat can effectively reduce the probability of ambiguous solutions. The wind speed and wind direction inversion accuracy of PolScat is better than 1m/s and 15° respectively.

The wind vector inversion from space-borne microwave radiometer is mainly done on the ground, and need to transfer large amount of data block to the ground when crossing ground stations, which delays the real-time inversion and usage of satellite data. We propose a rapid wind vector retrieval algorithm for the onboard processing of microwave polarimetric radiometer brightness temperature (TB). The rapid wind vector retrieval algorithm is validated using WindSat polarimetric TB. With this rapid onboard inversion algorithm, only the inversion result will be transfer down to ground stations in order to reduce onboard storage space and amount of downlink data transmission, and to improve real-time inversion.

The Ocean Salinity (OS) Satellite mission of the State Oceanic Administration of China dedicates to an “all-weather” estimate of high-quality global SSS and to reduce geophysical errors due to surface roughness and sea surface temperature from space. The payload of this mission has the capability of L/C/K multi-frequency passive and L-band active measurement, and can implement the simultaneously remote sensing of SSS, SST, WS and atmospheric parameters. The OS Satellite data processing prototype software has been developed to simulate the instrument and to generate Level 0 to Level 2 data. Based on the prototype software, the end to end study of the OS Satellite mission is performed and the errors of simultaneous retrieval of multiparameters are analyzed and noises level and stability requirement of instruments are estimated.

The Microwave Imager Combined Active/Passive (MICAP) is a suit of active/passive instrument package, which has been proposed for demonstrating the capability of remote sensing the sea surface salinity (SSS), sea surface temperature (SST) and wind speed (WS). In this paper, a nonlinear optimization algorithm for simultaneous retrieval of the above parameters is described. The sensitivity of active/passive microwave observations to SSS is analyzed using the nonlinear optimization algorithm. The results show that the root mean square (RMS) error on the retrieved SSS estimated by using the nonlinear optimization algorithm is enough to meet the requirement of Ocean Salinity Satellite. This is submitted for the special session of “New Developments of Chinese Oceanographic and Meteorological Satellites”.

Sea surface salinity (SSS) has a profounding influence on the the exchanges of matter and energy at the air-ocean interface. It is also a driving force for ocean circulations. The capability of accurate measurement of SSS with high spatial and temporal resolution shall be a desirable boost to the global climate models. The scientific missions Soil Moisture and Ocean Salinity (SMOS) [1] and Aquarius [2] are specific to this end.

Water Cycle Observation Mission (WCOM) is an earth science mission proposed and focused on the research of water cycle under global change. With its three dedicated designed main payloads, WCOM can achieve synchronized observation on a group of global water cycle key parameters, including soil moisture, ocean salinity, snow water equivalent, soil freeze-thaw, atmospheric water vapor, precipitation and other associated parameters. One of the three WCOM main payloads names IMI (Interferometric Microwave Imager), which is a newly designed L/S/C tri-frequency radiometer aiming to provide advanced measurement capability on soil moisture and ocean salinity. In this paper, the instrument concept and preliminary system design of WCOM/IMI will be introduced. Recent progresses on the filed experiments of an L-band demonstrator will also be introduced.

Polarimetric Microwave Imager (PMI) is planning as one of the main payload on WCOM satellite with the purposes of collecting the radiation from the Earth surface to retrieve the geophysical parameters of the land, ocean and atmosphere. PMI is passive microwave radiometer with frequencies of 6.8, 10.7, 18.7, 23.8, 37 .0 and 89GHz. The radiometers at 10.7, 18.7 and 37.0 GHz are full-polarized to measure four Stokes of the surface, whether from the ocean or the land. The other frequencies are traditional radiometer with vertical and horizontal channels. Since the third and the fourth Stokes parameter are sensitive to the orientation feature of the surface, they can be used to retrieve the wind vector, rain, snow cover, forest distribution and polar icecaps. Furthermore, with the higher resolutions at 37.0 and 89.0 GHz, the freeze-thaw on the land and rain on the ocean can be more accurately evaluated. In the paper, some primacy consideration and design on PMI are given for the application fulfilling the requirements of WCOM.

The MICAP(microwave imager combined active and passive), which has been selected to be a candidate payload for future Chinese ocean salinity mission, contains an L-band digital beam forming(DBF) scatterometer for the purpose of elimination of ocean surface roughness and wind retrieving. In this paper, data pre-processing flow for this scatterometer to obtain data ready for sea surface salinity and ocean surface wind retrieval was presented after corresponding data level definitions in this flow had been introduced. The processing flow was verified by processing data resulted from a data set simulated in a simple progress. Future work to perfect the pre-processing progress was also discussed.

A 1-D interferometric system at 1.4GHz, 6.9GHz, 18.7 GHz and 23.8GHz combined with a scatterometer at 1.26GHz, called microwave imager combined active/passive (MICAP), has been proposed to retrieve sea surface salinity (SSS) and to reduce geophysical errors due to surface roughness and sea surface temperature (SST). The MICAP will be a candidate payload onboard the Ocean Salinity Satellite of China. The sensitivity of active/passive microwave observations to SSS, SST and wind is analyzed and the stability requirement of the instruments is estimated, with the objective of designing an optimized satellite instrument, dedicated to an “all-weather” estimate of the SSS with high accuracy from space.

Microwave remote sensing provides an opportunity to retrieve wind speed (WS) inside tropical cyclones (TCs) due to the high atmospheric transmissivity through clouds and under rain conditions. A WS retrieval algorithm for WS above 20m/s in TCs using brightness temperature at 6.8- and 10.7-GHz has been developed and a new set of parameters has been optimized from 6.9GHz and 10.7GHz TB and the HWind analysis matches. This algorithm is estimated to have an encouraging degree of accuracy for retrieving WS in TCs. Then the retrieved wind speeds of TCs are used to study wave heights in TCs in the South China Sea.

Sea surface salinity (SSS) plays an important role in global water cycle. In recent years, satellite based remote sensing has proven to be a promising approach for global SSS observation. A new payload concept, named MICAP (microwave imager combined active and passive), has been introduced in this paper. MICAP is a suit of active/passive instrument package, which includes L/C/K band one-dimensional MIR (microwave interferometric radiometer) and L-band DBF (digital beamforming) scatterometer, sharing a parabolic cylinder reflector. MICAP has been selected to be a candidate payload for future Chinese ocean salinity mission. In this paper, the MICAP instrument concept, specification and preliminary system design will be introduced.

In this paper, we give a method of calibrating Tbon HY-2 satellite by cross comparing them with those from WindSat satellite. The results show that the re-calibrated Tbwere obviously closer to the simulated Tbby using ECWMF data. We will further investigate the accuracies with newly launched microwave radiometer AMSR2.

Sea Surface Salinity (SSS) measurements from the Soil Moisture and Ocean Salinity (SMOS) mission during 3 years (June 2010-May 2013) provide an unprecedented opportunity to observe the salinity structure of Tropical Instability Waves (TIWs) from space. The variations of SMOS SSS signals follow Tropical Atmosphere Ocean (TAO) SSS signals with high correlation coefficients and close peak amplitudes at 5 locations where strong TIW signals are observed in TAO SSS. The east-west contrast in peak amplitudes of SMOS SSS signals is stronger than OSTIA SST signals. Band of negative correlations between SSS and SST signals appears just north of the equator west of 100°W and around 8°N west of 110°W for the 33-day signals.

The sea surface salinity (SSS) retrieved from the Soil Moisture and Ocean Salinity (SMOS) mission are systematically lower than ARGO SSS in rainy regions. These freshenings increase (in absolute value) with increasing SSM/I rain rates (−0.2psu/mm/hr) closely collocated in time with SMOS SSS. They are attributed to a stratification of the salinity in the first meters of the ocean surface following a rain event.

The variability observed on SMOS (Soil Moisture and Ocean Salinity) SSS (sea surface salinity) recorded in 2010 and 2011 is partly attributable to geophysical variations but also to imperfections in various corrections (e.g. sun aliases, sea surface scattering of the galactic signal). We perform a retrieval of bistatic coefficients from SMOS Tbs, suggesting more peaked coefficients than the ones currently used for simulating the galactic contribution.

Two years after the launch of SMOS (Soil Moisture and Ocean Salinity) in November 2009, the level 1C brightness temperatures processed with the ESA SMOS L1 operational prototype v504, the up-to-date ESA reprocessing version, were released. Systematic biases of several Kelvins, which depend on the location of the measurement in the field of view, are still observed between averaged TB measurements and simulations. Biases in the field of view derived from comparisons between measurements and simulations (the so-called Ocean Target Transformations, OTTs) during 38 periods in phase with the NIR calibration events from June 2010 to December 2012 are analyzed.

We validate Soil Moisture and Ocean Salinity (SMOS) sea surface salinity (SSS) retrieved during August 2010 from the European Space Agency SMOS processing. Biases appear close to land and ice and between ascending and descending orbits; they are linked to image reconstruction issues and instrument calibration and remain under study. We validate the SMOS SSS in conditions where these biases appear to be small. We compare SMOS and ARGO SSS over four regions far from land and ice using only ascending orbits. Four modelings of the impact of the wind on the sea surface emissivity have been tested. Results suggest that the L-band brightness temperature is not linearly related to the wind speed at high winds as expected in the presence of emissive foam, but that the foam effect is less than previously modeled. Given the large noise on individual SMOS measurements, a precision suitable for oceanographic studies can only be achieved after averaging SMOS SSS. Over selected regions and after mean bias removal, the precision on SSS retrieved from ascending orbits and averaged over 100 km$ \times$100 km and 10 days is between 0.3 and 0.5 pss far from land and sea ice borders. These results have been obtained with forward models not fitted to satellite L-band measurements, and image reconstruction and instrument calibration are expected to improve. Hence, we anticipate that deducing, from SMOS measurements, SSS maps at 200 km$\times$200 km, 10 days resolution with an accuracy of 0.2 pss at a global scale is not out of reach.

The Soil Moisture and Ocean Salinity (SMOS) satellite, launched in November 2009, carries the first interferometric radiometer at L-band (1.4 GHz) in orbit. Over the open ocean and for moderate wind speeds (WSs), the SMOS brightness temperatures (TB) are at first order consistent with simulated TB of theoretical prelaunch models implemented in the European Space Agency Level 2 Ocean Salinity processor. However, we found large discrepancies between measurements and model simulations when WS is above 12$\hbox{ms}^{-1}$. A new set of parameters for a sea wave spectrum and a foam coverage model that can be used for simulating L-band radiometer data over a large range of WS is proposed based on the deduced wind-induced components from the SMOS data. The quality of the SMOS retrieved sea surface salinity (SSS) with the new emissivity model is estimated by comparing it with the World Ocean Atlas 2005 climatological SSS and the Array for Real-Time Geostrophic Oceanography (ARGO) SSS.

The Soil Moisture and Ocean Salinity (SMOS) mission carries the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument. It is the first time that an interferometric radiometer is in orbit. The objective of this paper is to assess the quality of the brightness temperatures (TBs) derived from this novel instrument, as processed with the SMOS operational chain at the end of the SMOS commissioning phase. Extensive comparisons have been conducted between reconstructed TBs derived from MIRAS measurements (MIRAS TB) and TBs simulated using the default radiative transfer model implemented in the European Space Agency SMOS ocean salinity processor and the European Centre for Medium-Range Weather Forecast forcings. At first order, the North-South variability of MIRAS TB due to geophysical variations of temperature, salinity, and wind speed over the ocean is consistent with the simulated L-band signal, and the standard deviation of the MIRAS TB minus the model simulations is close to the theoretical radiometric resolution. On the other hand, biases of several Kelvins, that depend on the location in the field of view, are observed between averaged MIRAS TB and simulations. After these biases are removed, the North-South gradient of sea surface salinity is well sensed by MIRAS except at high wind speed.

SMOS (Soil Moisture and Ocean Salinity) satellite, launched in November 2009, carries the first interferometric radiometer at L band (1.4GHz) in orbit. Over the open ocean and for moderate wind speeds, SMOS brightness temperatures (TB) are at first order consistent with simulated TB of theoretical pre-launch models implemented in the ESA Level 2 Ocean Salinity processor. However, we found large discrepancies between measurements and model simulations when wind speed is above 12 ms−1. A new set of parameters for a sea wave spectrum and a foam coverage model that can be used for simulating L-band radiometer data over a large range of wind speed is proposed based on the deduced wind induced component from SMOS data. The quality of SMOS retrieved SSS with the new emissivity model is estimated by comparing it with WOA05 climatological SSS.

SMOS (Soil Moisture and Ocean Salinity), launched in November 2, 2009 is the first satellite mission addressing the salinity measurement from space through the use of MIRAS (Microwave Imaging Radiometer with Aperture Synthesis), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at L-band. This paper presents a summary of the sea surface salinity retrieval approach implemented in SMOS, as well as first results obtained after completing the mission commissioning phase in May 2010. A large number of papers have been published about salinity remote sensing and its implementation in the SMOS mission. An extensive list of references is provided here, many authored by the SMOS ocean salinity team, with emphasis on the different physical processes that have been considered in the SMOS salinity retrieval algorithm.

Results of ocean surface wind speed retrieval from C-band ENVISAT ASAR images using a physics wind model are shown. The physics model is based on the radar backscatter theory in which the radar cross section is calculated considering the contribution of both Bragg scattering or resonance and specular reflection from the sea surface. The wind speeds retrieved from VV- or HH-polarized ASAR images using the physics model were compared to both buoy measurements in Hong Kong coastal waters and that retrieved using the empirical C-band algorithms (CMOD4, CMOD5, CMOD_IRR2). The results show the feasibility of the physics model for ocean surface wind speed retrieval from both VV and HH-polarized ASAR images at moderate wind condition.

This paper presents a detailed analysis of a simplified Two-Scale Model for ocean surface polarimetric microwave emission, and investigates the extent to which varying ocean surface length scales contribute to brightness temperature zeroth and second azimuth harmonics. The Two-Scale Model can be expressed as a weighting function M0,2multiply ocean surface curvature spectrum C0,2. This implies a simple way to investigate the effect of curvature spectrum on ocean emission. It is found that ocean waves with wavelengths both comparable to and much greater than the electromagnetic wavelength can contribute to these harmonics, depending on the value of the ocean surface spectrum in these length scales. An ocean wave spectrum was derived from polarimetric microwave radiometer data according to constrained linear least-squares method.