Rotating Fan-beam Scatterometer Research Articles

Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer

Scatterometers are dedicated to monitoring sea surface wind vectors, but they also provide valuable data for polar applications. As a new type of scatterometer, the rotating fan beam scatterometer delivers a higher diversity of incidence angles and more azimuth sampling. The paper takes the first rotating fan beam scatterometer, the China France Oceanography Satellite scatterometer (CSCAT), as an example to explore the effectiveness of this new type of scatterometer in polar sea ice detection. In this paper, a Bayesian method with consideration of geometric characteristics of CSCAT is developed for sea ice detection. The implementation of this method includes the definition of CSCAT backscatter space, an estimation of the sea ice Physical Model Function (GMF), a calculation of the sea ice backscatter distance to the sea ice GMF, a probability distribution function (PDF) estimation of the square distance to the GMF (sea ice GMF and wind GMF), and a calculation of the sea ice Bayesian posterior probability. This algorithm was used to generate a daily CSCAT polar sea ice mask during the CSCAT mission period (2019–2022) (by setting a 55% threshold on the Bayesian posterior probability). The sea ice masks were validated against passive microwaves by quantitatively comparing the sea ice edges and extents. The validation suggests that the CSCAT sea ice edge and extent show good agreement with the sea ice concentration distribution (i.e., sea ice concentration ≥ 15%) of the Advanced Microwave Scanning Radiometer 2 (AMSR2). The average Euclidean distance of the sea ice edges was basically less than 12.5 km, and the deviation of the sea ice extents was less than 0.3 × 106 km2.

Classification of Arctic Sea Ice Type in CFOSAT Scatterometer Measurements Using a Random Forest Classifier

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD.

Intercomparison of Arctic Sea Ice Backscatter and Ice Type Classification Using Ku-Band and C-Band Scatterometers

As a result of global warming, multiyear ice (MYI) is being replaced by first-year ice (FYI) in the Arctic. Microwave scatterometers in the Ku-band and C-band can provide daily observations of sea ice type. However, their comparative capabilities in mapping ice type have not been thoroughly evaluated. We present a systematic intercomparison of the backscatter signature in VV polarization ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$ </tex-math></inline-formula> ) and the sea ice classification from three scatterometer systems using the same ice classification approach. The systems are the Ku-band quick scatterometer (QSCAT) and the newly launched Chinese rotating fan-beam scatterometer (RFSCAT) and the C-band advanced scatterometer (ASCAT). Three freezing seasons are used, i.e., 2007/08 and 2008/09 for the QSCAT/ASCAT comparison and 2019/20 for the RFSCAT/ASCAT comparison. With reference to ASCAT, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$ </tex-math></inline-formula> bias between QSCAT and RFSCAT results from their different incidence angles. A continuous declining trend of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$ </tex-math></inline-formula> from MYI and FYI is observed during winter, with a greater difference between MYI and FYI in the Ku-band. The MYI and FYI extent derived from QSCAT/RFSCAT is highly consistent with that derived from ASCAT, with a difference less than 7% and 3% for MYI and FYI, respectively. The overall accuracy (OA) is around 77% and 80% for the RFSCAT results and ASCAT results, respectively, compared with Sentinel-1 SAR images. The classification results show high consistency (81%–89%) with ice charts from the Canadian Ice Service. The incorporation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {Tb}}_{36\mathrm {h}}$ </tex-math></inline-formula> from AMSR-E/AMSR2 improves the OA of the classification when using ASCAT or RFSCAT by 7%–11%.

Preliminary Estimate of CFOSAT Satellite Products in Tropical Cyclones

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.

Sea Ice Monitoring with CFOSAT Scatterometer Measurements Using Random Forest Classifier

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). This paper performs sea ice monitoring with the CSCAT backscatter measurements in polar areas. The CSCAT measurements have the characteristics of diverse incidence and azimuth angles and separation between open water and sea ice. Hence, five microwave feature parameters, which show different sensitivity to ice or water, are defined and derived from the CSCAT measurements firstly. Then the random forest classifier is selected for sea ice monitoring because of its high overall accuracy of 99.66% and 93.31% in the Arctic and Antarctic, respectively. The difference of features ranked by importance in different seasons and regions shows that the combination of these parameters is effective in discriminating sea ice from water under various conditions. The performance of the algorithm is validated against the sea ice edge data from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) on a global scale in a period from 1 January 2019 to 10 May 2021. The mean sea ice area differences between CSCAT and OSI SAF product in the Arctic and Antarctic are 0.2673 million km2 and −0.4446 million km2, respectively, and the sea ice area relative errors of CSCAT are less than 10% except for summer season in both poles. However, the overall sea ice area derived from CSCAT is lower than the OSI SAF sea ice area in summer. This may be because the CSCAT is trained by radiometer sea ice concentration data while the radiometer measurement of sea ice is significantly affected by melting in the summer season. In conclusion, this research verifies the capability of CSCAT in monitoring polar sea ice using a machine learning-aided random forest classifier. This presented work can give guidance to sea ice monitoring with radar backscatter measurements from other spaceborne scatterometers, particular for the recently launched FY-3E scatterometer (called WindRad).

CFOSAT Rotating Fan‐Beam Scatterometer Backscatter Measurement Processing

AbstractThe China‐France Oceanography Satellite (CFOSAT) was successfully launched on October 29, 2018, which carries two innovative payloads, that is, the surface waves investigation and monitoring (SWIM) instrument and the rotating fan‐beam scatterometer (RFSCAT). CFOSAT scatterometer (CSCAT) is dedicated to the monitoring of sea surface wind vectors and climate studies, but also for the applications over land and polar regions. CSCAT employs a “rotating fan‐beam” design rather than “pencil‐beam” approach and “fixed fan‐beam” approach adopted by the scatterometers in orbit before. In this paper, the data processing approach of CSCAT is described. The geolocation reference mapping model is employed in CSCAT slice geolocation. In noise processing, CSCAT adopts the method of weighted least squares polynomial surface fitting to conduct noise correction factor estimation adaptively. The attitude cube algorithm used for X integral factor computation is developed to obtain precise backscatter coefficients based on the radar equation. Because of the interdependency of the CSCAT data processing and the hardware configuration and onboard signal processing of the instrument, the instrument module configuration and signal processing design are also analyzed and presented comprehensively. Especially, the influence and compensation of Doppler frequency are introduced. The backscatter coefficient accuracy is estimated on the Amazon forest, and the results show that the backscatter coefficient accuracy of CSCAT on 25 × 25 km grids is less than 0.5 dB except that the near end and far end of the beam are slightly higher. The statistical results show the wind retrieval results meet the CFOSAT mission requirements.

Evaluation of Chinese Scatterometer Ocean Surface Wind Data: Preliminary Analysis

AbstractThree scatterometers have been launched in China including Haiyang‐2A scatterometer (HY‐2A/SCAT), Haiyang‐2B scatterometer (HY‐2B/SCAT), and China French Oceanography Satellite scatterometer (CFOSCAT). Both of HY‐2A/SCAT and HY‐2B/SCAT are rotating pencil‐beam scatterometers while CFOSCAT is the first rotating fan‐beam scatterometer (RFSCAT) in the world. In this study, we focus on the evaluation of Chinese scatterometer operational wind products from January 2019 to April 2019. Scatterometer wind products are collocated with buoy measurements. Error varieties under different atmospheric stratification conditions are also taken into consideration. The average biases and root mean square errors (RMSEs) between buoy and scatterometer data are basically satisfied with the scatterometer mission requirements (2 m/s and 20°). HY‐2B/SCAT wind products have the best data quality. However, HY‐2A/SCAT wind products have the largest deviations compared with buoy measurements and the wind direction RMSE was about 24.19°. The biases change a lot between different atmospheric stratification conditions. HY‐2A/SCAT winds have abnormal large deviations when wind speeds are higher than 10 m/s while HY‐2B/SCAT and CFOSCAT winds are stable under neutral conditions. In brief, the wind data quality of HY‐2B/SCAT is the best and then is CFOSCAT while HY‐2A/SCAT winds have the largest deviations and are unstable under different atmospheric stratification conditions. Atmospheric stratification impacts should be considered in wind retrieval and marine application.

An Improved Method of Noise Subtraction for the CFOSAT Scatterometer

The microwave scatterometer onboard the China-France Oceanography Satellite (CFOSAT Scatterometer, CSCAT) is the first rotating fan-beam scatterometer (RFSCAT) in operation. It is used to obtain the ocean surface wind field by measuring the backscattering coefficient over a large observation swath. To estimate the backscattering coefficient, the scatterometer needs to measure the scattered signal accurately. The process of removing noise from the echo signal (scattered signal plus noise) is called noise subtraction. Noise subtraction has a great influence on the measurement of the backscattering coefficient and subsequent wind field retrieval. Therefore, accurate noise subtraction is necessary and important. In this article, an improved noise subtraction method for CSCAT is proposed. According to the distribution characteristics of backscattering coefficients, noise correction factors are introduced to improve the noise subtraction of CSCAT. The ocean surface wind fields are obtained using the improved backscattering coefficient data, and the deviation of wind speed and direction are calculated by comparison with the European Centre for Medium Range Weather Forecasts wind fields. The results show that the improved noise subtraction method can lead to improvement of the quality of wind field effectively. In addition, based on the improved method of noise subtraction, a method of estimating the noise correction factor is proposed at a large incidence angle, furtherly improving the noise subtraction method. The results show that the proposed method is effective.

Estimation and Correction of Geolocation Errors of the CFOSAT Scatterometer Using Coastline Backscatter Coefficients

The scatterometer onboard the China-France Oceanography Satellite (CFOSAT) is the first rotating fan beam scatterometer (CSCAT) in operation. It is used to measure the ocean surface wind fields over a large observation swath. Geolocation is an important part of the preprocessing of scatterometer data. The accuracy of geolocation has a large impact on the accuracy of wind field retrieval. It is necessary to estimate and analyze the geolocation processing of CSCAT. The challenge is understanding how to estimate the geolocation errors of CSCAT. In this study, the coastline inflection method is used to estimate the geolocation errors of CSCAT based on the distribution characteristics of the backscatter coefficients at both the ocean and the land. The coastline inflection points are obtained using the gradient changes of backscatter coefficients near the coastline. The locations of these points are in line with the trend of the high-precision coastline data. The geolocation errors are estimated by comparing these points with the high-precision coastline. The results show that the current geolocation errors of CSCAT are about 7.31 km, which meets the requirements of 25 × 25 km wind field retrieval. In addition, because the geolocation of scatterometer data is affected by terrain, the geolocation errors of CSCAT are corrected based on an elevation correction algorithm using high-precision digital elevation model data. The results verify the effectiveness of the algorithm.

First Results From the Rotating Fan Beam Scatterometer Onboard CFOSAT

The first rotating fan beam scatterometer onboard China-France Oceanography Satellite (CFOSAT) was successfully launched on October 29, 2018. CFOSAT SCATterometer (CSCAT) is dedicated to the monitoring of sea surface wind vectors but also provides valuable data for the applications over land and Polar Regions. This article provides an overview of the relevant procedures of CSCAT data processing, including onboard signal processing and operational ground processing. Then a post-launch analysis is carried out to evaluate the first results of CSCAT L1 and L2 products. It shows that the CSCAT instrument is generally stable in terms of noise measurements and internal calibration, unless there is any important change in the system configuration. Specifically, the CSCAT backscatter (σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sup> ) precision and wind quality are studied using a set of collocated ancillary data. The σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sup> precision degrades as wind speed decreases, and it is relatively low at high incidence angles (e.g., θ >46°). In particular, backscatter estimation of the horizontally polarized beam should be further improved by correcting the noise subtraction factor. The retrieved CSCAT winds are in good agreement with the European Centre for Medium Range Weather Forecasts (ECMWF) winds, the Advanced Scatterometer (ASCAT) winds, as well as the buoy winds. However, due to unresolved calibration and interbeam consistency problems, the wind quality degrades remarkably for the out-swath and the nadir-region wind vector cells, implying that the σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sup> calibration should be improved in the future updates.

A Perspective on the Performance of the CFOSAT Rotating Fan-Beam Scatterometer

The China-France Oceanography Satellite (CFOSAT) to be launched in October 2018 will carry two innovative payloads, i.e., the surface wave investigation and monitoring instrument and the rotating fan-beam scatterometer [CFOSAT scatterometer (CFOSCAT)]. Both instruments, operated in Ku-band microwave frequency, are dedicated to the measurement of sea surface wave spectra and wind vectors, respectively. This paper provides an overview of the system definition and characteristics of the CFOSCAT instrument. A prelaunch analysis is carried out to estimate the scatterometer backscatter and wind quality based on the developed CFOSCAT simulator prototype. The overall simulation includes two parts: first, a forward model is developed to simulate the ocean backscatter signals, accounting for both instrument and geophysical noise. Second, a wind inversion processor is used to retrieve wind vectors from the outputs of the forward model. The benefits and challenges of the novel observing geometries are addressed in terms of the CFOSCAT wind retrieval. The simulations show that the backscatter accuracy and the retrieved wind quality of CFOSCAT are quite promising and meet the CFOSAT mission requirements.

An advanced wind vector retrieval algorithm for the rotating fan-beam scatterometer

The rotating fan-beam scatterometer (RFSCAT) is a new type of satellite scatterometer that is proposed approximately 10 a ago. However, similar to other rotating scatterometers, relatively larger wind retrieval errors occur in the nadir and outer regions compared with the middle regions of the swath. For the RFSCAT with the given parameters, a wind direction retrieval accuracy decreases by approximately 9 in the outer regions compared with the middle region. To address this problem, an advanced wind vector retrieval algorithm for the RFSCAT is presented. The new algorithm features an adaptive extension of the range of wind direction for each wind vector cell position across the whole swath according to the distribution histogram of a retrieved wind direction bias. One hundred orbits of Level 2A data are simulated to validate and evaluate the new algorithm. Retrieval experiments demonstrate that the new advanced algorithm can effectively improve the wind direction retrieval accuracy in the nadir and outer regions of the RFSCAT swath. Approximately 1.6 and 9 improvements in the wind direction retrieval are achieved for the wind vector cells located at the nadir and the edge point of the swath, respectively.

A Preliminary Study of the Calibration for the Rotating Fan-Beam Scatterometer on CFOSAT

The first rotating fan-beam scatterometer (RFSCAT) will be launched onboard the Chinese-French Oceanic Satellite (CFOSAT) in 2018. It provides a set of radar cross-section (σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> ) measurements at different azimuth/incidence angles over a wind vector cell (WVC), in order to determine the near-surface wind field using the backscatter model, i.e., the so-called geophysical model function (GMF). The accuracy of the retrieved wind vector is a sensitive function of the radiometric accuracy of the σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> measurements. Therefore, in-flight calibration, including the loop-back (internal) calibration and the external calibration performed with natural extended-area targets, is studied in this paper. Several homogeneous areas over land are first analyzed to check the stability and azimuthal dependence of the σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> over these areas. A new calibration mask of the homogeneous land areas is generated and will be used by RFSCAT calibration. Then a simple method of external calibration is proposed to eliminate the azimuthal-dependent σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> errors induced by the insertion loss of the rotating joint, which can be applied to both the rotating pencil-beam scatterometers and the coming RFSCAT. The “observed” σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> of RFSCAT is simulated using the SeasatA scatterometer (SASS) measurements and the “perturbed” azimuthal-dependent σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> errors. The latter is then tracked by the proposed external calibration. The results show that the accuracy of gain corrections is up to 0.2 dB, ensuring consistency between different azimuthal measurements.

Calibration and Estimation of Attitude Errors for a Rotating Fan-Beam Scatterometer Using Calibration Ground Stations

The rotating fan-beam scatterometer (RFSCAT) onboard Chinese-French Oceanic SATellite (CFOSAT) due to launch in 2018 is a new type of radar scatterometer system for ocean surface wind vector measurement. It can give observations with more azimuth and incidence angles for a single wind vector cell (WVC) than other available scatterometers. This has been proved effective in bettering the retrieved wind quality by the simulation approach. However, its innovative observing geometry is challenging for the coming in-orbit external calibration. In this paper, CFOSAT attitude errors are estimated, and its antenna gain pattern is monitored and verified based on the external calibration strategy of a Ku-band scatterometer employing calibration ground stations (CGSs). The effects of satellite attitude errors on the measurements are also analyzed, together with simulation results for the external calibration. It is shown that a gain pattern with accuracy of 0.08 dB and attitude errors within 0.025° are achieved.

Calibration of the Ku-band Rotating Fan-beam Scatterometer Using Land Extended-area Targets

旋转扫描扇形波束散射计(Rotating Fan-beam SCATterometer, RFSCAT)是一种新体制的海洋风场测量雷达散射计。RFSCAT对同一观测面元能够提供更多的方位角和入射角观测组合，改善海面风矢量场的反演精度。为了达到设计的风场反演精度，系统要求定标精度为0.5 dB。该文基于中法海洋卫星(Chinese French Oceanography SATellite, CFOSAT)雷达散射计的系统参数，考虑了在轨测量的主要误差源，分析了地面扩展目标在轨外定标的特点，给出了可行的RFSCAT在轨外定标方法，并利用仿真数据对该方法进行验证。 利用QuikSCAT散射计的L2A数据和图像重构(SIR)数据，针对地球表面归一化雷达后向散射系数(0)稳定的区域，给出了定标地图，为RFSCAT在轨定标提供参考。

Design and optimization of a Ku-band rotating, range-gated fanbeam scatterometer

A Ku-band rotating, fanbeam scatterometer (RFSCAT) will be used to measure the sea surface wind field from a polar-orbiting small satellite with a rotating, range-gated fanbeam radar scatterometer. The measurement principle and instrument are introduced in this article. An end-to-end simulation model is adopted to analyse and optimize the performances of the system, taking into account the radar observation conditions. Then two kinds of spatially coherent wind fields, namely a parallel wind field and a circular wind field, as well as an incoherently random wind are simulated to assess the system performances. The results show that with the chosen configurations, the performance for wind vector retrieval of the discussed scatterometer is roughly comparable to the pencil-beam radar scatterometers (e.g. SeaWinds on QuikSCAT), and exceeds it at some wind regimes over part of its swath. Particularly, it usually has a better performance for wind direction retrieval, though with a narrower swath.