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.

Sea surface salinity (SSS) is a fundamental parameter for understanding ocean phenomena and plays a vital role in studying global climate change and weather prediction models. Following the earlier launch of Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) satellites, the Chinese Ocean Salinity and Soil Moisture Mission (COSM) was successfully launched on November 14, 2024. The launched satellite is equipped with the 2-D L-band Aperture Synthesis Microwave Radiometer (LASMR) and the Microwave Imager Combined Active and Passive (MICAP) components to gather high-precision SSS information. This paper presents the Airborne LASMR (ALASMR), which features a Y-shaped two-dimensional synthetic aperture microwave radiometer and contains 11 antenna units with a unit spacing of 0.82λ. The ground test investigations of the ALASMR have been conducted to evaluate antenna patterns, test the sensitivity of receiving channels, and conduct ocean aviation experiments. To examine the flight observation, uniform salinity is assumed for the sea area obtained from the calibration platform of National Satellite Ocean Application Service (NSOAS) whereas the salinity gradient is taken for the Laizhou Bay area. The ALASMR exhibits enhanced imaging performance, with a spatial resolution of about 0.35 km and a width of about 1.24 km at the flight altitude of 1.2 km. The retrieval of SSS in the sea area of the ocean calibration platform is also demonstrated in this work. This indicates that ALASMR can reliably execute salinity observation of near-shore with high precision and provide a cross-calibration data source for COSM after its launch.

The Chinese Ocean Salinity Mission was officially initiated in 2020. In July 2023, a flight campaign was conducted to assess the system design, data processing methodologies, and performance criteria. This paper discusses the system architecture of the L/C/K-band one-dimensional microwave interferometric radiometers (MIR) tested in the flight campaign and presents the performance evaluations derived from imaging results.

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.

Sea surface temperature (SST) and wind speed (SSWS) are two significant parameters in the coupled ocean-atmosphere system. Nowadays, space-borne microwave radiometers are the main approaches to measuring global SST and SSWS with high coverage and high accuracy. For conventional processing of passive microwave data, radiometer calibration is conducted to convert the radiometer raw output voltage data into the top of atmosphere (TOA) brightness temperatures (TBs). After calibration, sea surface parameters are retrieved from TBs using statistically or physically based retrieval methods. Considering the complex procedures of instrument calibration and retrieval, a novel estimation method based on deep learning is proposed to obtain SST and SSWS directly from radiometer voltages. A comprehensive matchup dataset, including the data measured by the scanning microwave radiometer (SMR) onboard the Chinese HY-2B satellite, the European reanalysis 5 (ERA5) products and the WindSat measurements, is used to develop and test the deep learning models. Validation against ERA5 and WindSat products indicates that the deep learning models perform well. To compare with traditional methods, we also retrieve SST and SSWS from brightness temperature data of SMR using a statistical regression retrieval algorithm. The comparison suggests that the deep learning models provide results close to and sometimes better than the regression algorithm. Furthermore, the feature importance of deep learning models and the dependence of their performances on sea state are analyzed. In this paper, it is demonstrated that the deep learning method is a reliable and feasible tool for SST and SSWS estimation from the radiometer voltages with high accuracy, which can simplify the data processing procedure and improve the processing efficiency.

Satellite SSS products are usually validated by comparing them with collocated in situ observations from Argo, mooring buoys, and ship measurements. However, the remote sensed data are the measurements averaged spatially within the satellite footprint and in situ observations are pointwise measurements. The different spatial resolution of satellite and in situ data causes the problem of representativeness error (RE) that affects the validation results of satellite SSS. Many efforts have been devoted to properly estimating the REs. REs can be resolved naturally by a quadruple collocated dataset and linear algebra method if the source of REs is known beforehand. Therefore, identifying the source of REs is more important than estimating them. In this study, we find that, for a given quadruple dataset, the results in the error estimation from triple collocation subsets show different characteristics. These differences can be used to identify the source of RE. Then, we present a novel methodology of RE identification based on the characteristics of the error estimation. Using the match-up SSS dataset provided by Pi-MEP and numerical experiments, the feasibility of this method is verified. The results show that for the quadruple collocation (QC) of SMOS, SMAP, Argo, and WOA, the RE occurs in SMOS and SMAP which have similar resolutions with a value of 0.10 psu.

Scatterometer wind data are critical for meteorological and oceanographic applications. The differences between scatterometer winds and buoy reference winds largely depend on the type of reference wind used in the fitting of the scatterometer’s geophysical model function (GMF). This study evaluates scatterometer winds using advanced buoy reference winds, specifically stress-equivalent winds (U10S), and equivalent-neutral winds (U10N). We utilized HSCAT-B scatterometer wind products retrieved using both NSCAT-4 and NSCAT-5 GMFs). NSCAT-4 winds were corrected to stress-equivalent winds, and these scatterometer winds were compared with various buoy reference winds, including true buoy winds, stress-equivalent winds, and equivalent-neutral winds. The results show that in extratropical regions, stress-equivalent winds provided a closer match to scatterometer winds, while in tropical regions, equivalent-neutral winds exhibited smaller errors. Scatterometer winds derived from the NSCAT-5 GMF demonstrated superior consistency with buoy reference winds, further reducing wind speed biases compared to NSCAT-4. An extended triple collocation (ETC) analysis was conducted to address the uncertainties arising from differences in spatial resolution between scatterometer and buoy measurements. The findings emphasize the importance of using appropriate wind correction methods for scatterometer wind validation, particularly in regions with significant atmospheric variability.

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.

Radio frequency interferences (RFIs) in the L-band heavily contaminate remote sensing data and bring many challenges to the product quality of synthesis aperture interferometric radiometers. Detecting and localizing RFIs is the urgent need for switching off these RFIs and conducting mitigation algorithms. The localization algorithm based on the brightness temperature (BT) image is limited by the resolution to separate closely spaced RFIs and achieve high localization accuracy in some cases. To achieve super-resolution, the MUSIC method using subspace decomposition technique is suggested. Nevertheless, when the BT image SNR is low, the power of RFI is insufficient to overwhelm the background scene, and the performance of the MUSIC algorithm is unsatisfactory. In this paper, a further improvement is proposed by combining BT image and subspace decomposition. In the BT image domain, background scene cancellation and RFI target enhancement are carried out for enhancing the SNR. In the frequency domain, the MUSIC algorithm is applied utilizing subspace-decomposition to achieve super-resolution localization. Experiments and simulations based on SMOS data validate that the method presented in this paper performs better than both the classical MUSIC algorithm and BT-based localization algorithm in low SNR cases.

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 scanning microwave radiometer (SMR) is an important payload of the Haiyang-2B (HY-2B) satellite. The main reflector of the SMR has its own emission due to the imperfect reflector material and surface roughness; however, the reflector’s emissivity was not measured or estimated during the prelaunch phase. During the post-launch phase, it was discovered that the radiation contribution from the reflector changed with its position and over time. This phenomenon affected the accuracy of the calibration by a few Kelvins. This report analyzes the main reflector’s emissivity based on the observed results when HY-2B pitched over to view the homogeneous and isotropic cold space. The emissivity of the reflector was approximately 3%, but owing to the larger field of view, the emissivity of 6.925 and 10.7 GHz were not well assessed. After the radiation from the reflector has been corrected, the calibration error caused by the reflector’s emissivity is reduced.

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.

Currently, many microwave radiometers have been successfully used in earth-observation and ocean-monitoring systems. In China, the scanning microwave radiometers (SMR) onboard the Haiyang-2A/2B (HY-2A/2B) satellites are multichannel radiometers capable of obtaining oceanic and atmospheric parameters, such as sea surface temperature (SST), sea surface wind speed (WS), water vapor (WV), and cloud liquid water (LW). As a successor to HY-2A, the HY-2B satellite was launched successfully on October 25, 2018. Compared with the HY-2A’s SMR, the HY-2B’s SMR is an improved microwave radiometer. This article is concerned with the instrument design of the HY-2B’s SMR and its improvements. The important improvements of the HY-2B’s SMR in regard to the feedhorns and cold-sky reflector (CSR) are verified, and the early in-orbit performance of the HY-2B’s SMR is evaluated. The results demonstrate that the HY-2B’s SMR exhibits an important improvement on the HY-2A’s SMR in terms of the quality of the brightness temperature (TB) products and calibration accuracy. The results also show that the HY-2B’s SMR has a good performance in terms of the global TB images and the retrieved products with a good agreement with the expectations.

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.

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.

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.

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.

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.

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.

A new soil moisture retrieval algorithm developed in this paper, using C- and K-band microwave radiometer channels of Ocean Salinity Satellite (OSS). In this new algorithm, K-band(23.8GHz) brightness temperature (BT) is used to estimate land surface temperature, and C-band BT in H polarization used to retrieve soil moisture by τ - ω model, in which soil roughness (h) and vegetation parameters (τ) are combined in a single factor for their similar change trends with BT. The validation is done using AMSR-E data of the same channels with Naqu soil moisture monitoring network data in the central Tibetan Plateau, result shows the new algorithm has very good accuracy, at correlation coefficient, bias and RMSE.

The HY-2A launched in August 2011 is the first satellite for ocean dynamic environment measurement. The calibration of HY-2A altimeter is performed using cross-calibration methods with Jason-2 mission. Range absolute calibration activities is ongoing based on transponders. The main parameters in IGDR products are validated by in-suit NDBC buoys and Jason-2 measurements. The calibration of HY-2A scatterometer is implemented using open sea measurements, Amazon rainforest measurements and transponders. Wind vectors products are validated by NDBC buoy observations. The key results are as follows: The accuracy of the sea surface height is about 7.0cm by crossover analysis of HY-2A alone. Dual-crossover analysis with Jason-2 altimeter shows HY-2A altimeter total performances is close to the Jason-2. The calibration coefficients of sigma0 for scatterometer are 1.7dB for VV and HH polarization using ocean calibration technique, the monitoring result of the scatterometer measurement stability by Amazon rainforest shows that the instrument system is stable, the RMS of wind speed and wind direction retrieved by scatterometer are about 1.19m/s and 18.74°.