The directional spectrum characterizes wave energy distribution in wavenumbers and directions. It contributes significantly to understanding rough sea conditions and electromagnetic scattering mechanisms. In this research, SWIM measurements are compared with the angular spreading function of Elfouhaily spectrum, a widely applied wind-wave spectrum. The comparison reveals the model's characteristic of wind-spectrum describing with limited consideration of swell components in ocean surface roughness. The experiments reveal distinctive appearances at specific angles, demonstrating a correlation with wind speed. Conclusions indicate the necessity to refine the model structure to formulate a novel model for the ocean surface description based on the SWIM observations.

Rain affects on the Ku-band wind scatteroemters and are generally labelled in the Quality Control (QC) procedures applying metrics quantifying the QC indicators such as MLE, representing normalized Euclidian distance summaries from the scatterometer observed Normalized Radar Cross-Sections (NRCS) in a Wind Vector Cell and the wind-NRCS mapping model determined surface, and Jossindicating the heterogeneity. Yet the complex rainy scenes are not analyzed specifically. In this research, the wave energy spectra from buoy measurements are collocated with scatterometer observations. Then the rain effects in the wind scatterometers are addressed in terms of energy distribution with references from the energy governing equation and rain affected spectra. Wind, wave and rain induced features in spectra are obtained applying the rain screening ability from MLE and Joss. The possibility for rain contaminated wind correction in a parameterized spectrum is discussed for the combined application of wind and wave measurements that are obtained from CFOSAT and its follow-on missions.

In polar regions, sea ice introduces deviations of scatterometer observations from the empirical wind model, i.e., the Geophysical Model Functions (GMF) that map them to the ocean surface wind fields and sea ice GMFs have been developed to aid in the Bayesian estimation of sea ice presence to discriminate between water and ice. In this research, collocations of C- and Ku-band scatterometer products from ASCAT-A, ASCAT-B and OSCAT-2 are registered with SIC products from AMSR-2 in the Northern hemisphere, and with rain rates from the global precipitation mission (GPM). Then the ice screening ability in the wind QC indicators MLE and Jossare investigated for sea ice identification as an extension to their QC ability of rain in the tropical regions. Finally, simultaneous wind and SIC estimation is discussed.

C-band scatterometer winds have been adjusted for extreme conditions and in this research extension to Ku-band scatterometers is investigated. With rain rates from the Global Precipitation Measurement mission collocated to the Ku-band scatterometer observations to identify and exclude rain contamination of winds, calibration of the Ku-band observations can be done. Using high-wind cases extracted from collocated C- and Ku-band observations, we develop a calibration model and extend the Ku-band winds to 35 m/s. Validation is obtained from the set not included in the model derivation, indicating a speed error less than 10% for wind speed larger than 30m/s. The modified speed is consistent with the Step Frequency Microwave Radiometer measurements, when collocated with another Ku-band scatterometer. A comparison for the Tropical Cyclone Manyi in 2018 shows the adjustedd wind fits better with the best-track information provided by the Chinese Meteorological Administration, while more details are revealed. Results can be improved after obtaining more collocations with the dual-frequency scatterometer "WindRad" onboard the FY-3E satellite. A method for wind direction enhancement in extreme conditions is also discussed.

The significant wind radii are vital parameters representing tropical cyclones (TC) structure and determining the extent of the destructive winds of TCs. While the lack of in situ observations in the far sea leads to the scarcity of TC wind radii data is gradually resolved with. s the development of high spatial-temporal resolution remote sensing detection technology. Then the significant wind radii of TCs from them are well researched and providing good options for operational applications. However, IR images characterize generally the cloud-top scenes for TCs. On the other hand, scatterometers together with radiometers operating at microwave frequencies advantaging at offering good information for ocean surface wind fields of TCs, introducing good references for IR winds during their passing in the Sun-Synchronous Orbits. In this research, first, significant wind radii are obtained from the wind fields obtained from combined observations of the scatterometer (HSCAT) and radiometer (HRAD) on-board the HY-2A satellite in the method proposed in [1], then radius values are fitted and comparisons are made with those from IR images obtained in method described in [2], following with a case study of the TC Pabuk in 2013. Results indicate consistency between the significant wind radii from both methods, while more samples are to be check for a much definite conclusion. The analysis of differences in observation and retrieving are also to be conducted in further research, with calibration made to the IR radii. When this research conducted forms important first step for providing reliable wind radii from remote sensing methods.

In tropical regions, Ku-band scatterometer observations are affected by heavy rain, and affected data are labeled in the quality control (QC) step during wind product generation. This is achieved by setting thresholds for QC indicators. Among the applied indicators, it has been shown that$J_{\mathrm {oss}}$can be beneficially applied for reducing the false alarm rate (FAR) for Ku-band observations for data sets collocated to C-band observations. In this letter, the FAR reduction method based on$J_{\mathrm {oss}}$is further generalized to be tested without collocated C-band observations, which extension is subsequently applied to the CSCAT wind products. Results prove the effectiveness of the FAR reduction method without C-band collocations for Ku-band scatterometers. Verification with rain rates from the Global Precipitation Mission (GPM) proves the capability of the recruited data set to benefit nowcasting applications. Moreover, the results of this research indicate the consistency of products in rainy conditions for the new rotating-fan-beam CSCAT and the existing rotating-pencil-beam Ku-band scatterometers. Further research would be to analyze detailed rain effects in different rain development stages in a wind vector cell (WVC) for tropical regions.

Decades of ocean surface vector wind (OSVW) data acquired from space-based radar scatterometry have been providing short and long-term researches and applications information about ocean surfaces. The main objective of the project, stands and metrics of ocean surface vector wind by space-borne microwave remote sensing, of W orking Group on Calibration and Validation of the Committee on Earth Observation Satellites (CEOS WGCV), is to develop the standard and guideline for the requirement, procedure, processing and assessment for the space borne radar scatterometer measurement calibration, wind retrieval approaches, wind data validation and assessment for OSVW, which will be used to assure the consistency of the data quality of these satellites and instruments are the prerequisite for related scientific researches and applications. This synthesizes calibration, standardized practices of retrieval approaches for ocean surface winds, development of guidelines/standards of validation of ocean surface winds, and identifying and organizing collocation related data. This presentation will provide an overview of the proj ect and the recent progresses.

With the advent of the golden era of scatterometry, with seven scatterometers currently operating in orbit and a few others to be launched in the near future, a wide variety of scientific and operational applications will certainly benefit from consolidated wind retrieval procedures. In particular, an important component of the scatterometer wind processing is the quality control (QC) procedure. Over the last two decades, several QC indicators have been developed for C-band and Ku-band scatterometers, and used in the operational generation of sea surface wind products. Such indicators mostly aim at identifying and filtering retrieved wind quality degradation due to high wind variability and/or rain contamination effects. As such, the different QC indicators may be applied for different oceanographic and meteorological applications. The methods will be presented at the conference to motivate a discussion on their application-dependent use and come up with a consolidated view from the different user communities.

The quality control (QC) indicator$J_{\text{oss}}$proposed in 2020 represents the spatial wind speed difference between locally inverted wind speed and neighbor wind speeds, which is associated to rain for tropical regions.$J_{\text{oss}}$is effective in rain screening and hence false alarm rate (FAR) reduction. The research is here extended to higher wind speeds and missed detection rate (MDR) in QC acceptance sets, besides, performance of$J_{\text{oss}}$at the edge and nadir parts of the swath are examined. This allows a more complete evaluation of the$J_{\text{oss}}$rain screening features. On-going research is directed towards extension to non-tropical regions where the MDR, as well as FAR, involves different rain features, while the sea ice complicates the QC at higher latitudes.

Uncertainties in wind inversion from scatterometer observations are contributed by system and geophysical noise. In practice, both can be quantified by the indicators applied in the quality control (QC) procedures during wind processing. In this research, the underlying principles of three reported indicators, MLE, SE and Joss, are discussed for CSCAT. In the observation scenes of this Ku-band scatterometer, one of the major reasons for geophysical noise are rain clouds, which are analyzed specifically with respect to those indicators. Finally, examples for super typhoon Lekima, followed by Krosa in 2019, are discussed. We confirm that the MLE and Jossindicators are relatively independent from each other, and show different features in rain screening. The combined application of them would result in a better result of rain labelling. Another conclusion derived from this research is that SE and Jossare similar indicators of spatial heterogeneity in scatterometer wind fields, but that the wind speed depression measured by Joss is a more unique indicator of rain than SE. This research contributes to improving the quality of wind retrieval from scatterometers.

An airborne validation experiment using a pencil-beam Doppler scatterometer was carried out in south of Yangjiang River, Guangdong Province, China. Its purpose is to validate the feasibility of spaceborne Doppler scatterometers and simultaneous observations of ocean wind and currents. This study presents the pencil-beam airborne Doppler scatterometer system, and its data processing algorithm. To investigate the accuracy of measured ocean wind and currents in the airborne campaign, we made a comparison with other datasets. The results show that 1) The speed error of wind is 1.2m/s, and wind direction error is around 13.19°. 2) The current speed error is better than 0.15 m/s, the current speed is better than 15 degree.

Space-borne Doppler scatterometer is a new and promising sensor for wide-swath, high-accuracy, and simultaneous measurements of ocean surface current and wind vector. Doppler shift of ocean surface backscattering is determined by the relative motion between the sensor and ocean surface, it includes contributions from ocean surface current, wind-wave, and satellite motion. Accurate Doppler shift due to ocean surface current is estimated by removing the wind-wave and satellite motion effects. Satellite motion contribution can be accurately estimated from the satellite attitude motion of spacecraft. The main challenge for ocean surface current retrieval is uncertainty/error in the estimation of wind-wave induced Doppler shift. In this study, effects of wind estimation errors on surface current retrieval are evaluated through the Doppler scatterometer system simulations. Simulation results show that (1) surface current retrieval is affected more by estimation error in wind direction than in wind speed; and (2) wind estimation errors have larger effect on retrieval accuracy of ocean surface current direction than that of surface current speed.

Space-borne scatterometer observations are well-known ocean surface wind remote sensing instruments, they usually work at C- or Ku-band. Particularly in tropical regions, their observations are affected by rain. Rain-affected data are labeled in a quality control (QC) step during wind product generation. This is achieved by setting threshold of QC indicators. Among the applied QC indicators, a new one namely Joss proposed by Xu & Stoffelen in 2019, is characterized by a higher probability of detection (POD) of rain events. Joss particularly reduces the false alarm rate (FAR) for Ku-band observations during the rain screening as tested with reference to C-band observations., However, it is not possible in tropical regions for the Ku-band rotating fan-beam scatterometer onboard the Chinese-French Oceanographic SATellite (CFOSAT), to obtain C-band collocations with time differences less than 30 min. In our research, first, based on the results of the existing FAR reduction method, collocated C-and Ku- band observations are further investigated. Then the method is generalized, without the assistance of collocated C-band observations. Finally, application to wind product FAR reduction of CSCAT is presented. Results prove the effectiveness of the theory of this paper. Rain references are provided by products from the Global Precipitation Mission (GPM). Further research would be analysis on detailed rain effects to Ku-band scatterometers in different rain processes in tropical and sub-tropical regions to improve the established method.

Scatterometers provide consistent observations of ocean surface wind with reliable quality. In tropical regions, Quality Control (QC) rejects observations effected by rain clouds to guarantee the quality of wind products obtained with references to Geophysical Model Functions (GMFs). GMFs map scatterometer observed normalized radar cross-sections (NRCS) to wind fields without considering rain effects. The rejected groups have information of both rains and winds, and can be utilized for quantitative modelling of rain effects. Wind scatterometers usually operate at C-or Ku-band, they are affected differently by rain clouds due to differences in sensing wavelengths. Collocated observations with both frequencies would enable quantitative evaluation of rain effects. This study exploits these collocated C- and Ku-band measurements and seeks to ultimately develop a method for improving surface wind retrieval using a rain-cloud correction factor, with the preliminary model outline proposed. A vector radiative transfer based layer scattering model for rain clouds above ocean surface that accounts for scattering and attenuation effects of rain column and sea surface roughness will be used in quantitative analysis of rain cloud effects on surface wind retrieval at C- and Ku-band. Rain drop size distribution (DSD) and surface modifications from rains under different wind speed are considered in the model. For rain-free condition, a modified IEM surface scattering model is utilized in the quantitative analysis. Model validation and evaluation of the proposed correction factor are conducted using collocated data obtained from existing C- and Ku-band scatterometer observations, with time lag less than 5 minutes and spatial difference less than 25 km, and references to rain rates from the Meteosat Second Generation (MSG) Satellite.

Spaceborne scatterometers for ocean surface winds usually operate in Ku- or C-band. Rather strict quality control (QC) procedures are included in the Ku-band wind retrieval chain for labeling rain-contaminated observations. Existing QC factors represent the deviation of measurements from the wind geophysical model function (GMF) modeled measurement surface. Other QC indicators flag outliers by examining neighborhood consistency. In this article, spatial heterogeneity of rain is further exploited by a new indicator for Ku-band QC, namely, JOSS, the speed component of the observation cost function, JO, of the selected solution (JOS) in the 2-D variational ambiguity removal (2-DVAR) step of the wind retrieval. First, the characteristics of 2-DVAR speeds in rainy condition are analyzed, and then, the ability of JOSS in quality labeling is proposed and verified by applying it to the Ku-band scatterometer on-board ScatSat. Its effectiveness for rain screening is confirmed with collocated references from the C-band scatterometer on-board the MetOp-B satellite, which are much less affected by rain. With reference to collocated rain rates from the Global Precipitation Mission (GPM), the more direct relations to rain and wind speed errors of the newly proposed QC indicator JOSS than existing QC indicators, including JOS, are illustrated by the analysis of its correlation with rain rates. In a novel approach, JOSS is applied to accept (unflag) more than 75% of the data rejected by the widely applied maximum likelihood estimation (MLE) thresholds (i.e., correct false alarms) in the tropics. The promising results open a new opportunity for improving QC of rain in the Ku-band wind scatterometry benefitting scatterometer applications.

The sea surface winds from the CFOSAT scatterometer (CFOSCAT) are retrieved using the maximum likelihood estimator, and the inversion residual is used to sort the good-quality winds from the poor-quality ones. A two-dimensional variational analysis ambiguity removal (2DVAR) scheme is then applied over the CFOSCAT swath such that a unique wind field is selected from the available local scatterometer wind vector ambiguities. The preliminary results of CFOSCAT Level 2 (L2) processing show that the retrieved wind speed is overestimated under low-wind conditions (w15 m/s). Moreover, the inversion residual for the sweet swath (where there are more than 10 views) is generally higher than that for the nadir/outer swath. These imply that observations with different geometries (views) at the same WVC are inconsistent with respect to the geophysical model function, and thus a comprehensive calibration is highly demanded. A more detailed assessment of the CFOSCAT wind quality will be carried out after calibration and validation campaign.

CFOSCAT is the first rotating fan-beam scatterometer in space. It is on board the China France Oceanography Satellite (CFOSAT) launched on 29thOctober, 2018. It can provide wind field products in the unit of Wind Vector Cells. One of the advantages for this novel antenna geometry is being able to obtain more observations of the scene, though for edge and nadir WVCs, sampling issues as well as larger noise level hinder wind retrievals. This is first analyzed in this paper and a new procedure is proposed, tailored for those observations of CFOSCAT. In the proposed method, wind speeds are retrieved from Neural Networks trained for specific edge and nadir WVCs, and based on the obtained speed, a modified principle component analysis (PCA) is applied for CFOSCAT wind direction retrieval. The new process chain is verified by simulated data, and further research is soon planned modifying it for real data after they are released.

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.

The Ku-band rotating fan-beam scatterometer (RFSCAT) onboard the China-France Oceanography Satellite (CFOSAT) is dedicated to the measurement of sea surface wind vectors. The flight model of RFSCAT has been assembled and tested, and the data processing algorithms have been developed. This paper provides an overview of the design and characteristics of RFSCAT instrument, and then a prelaunch analysis is carried out to estimate the scatterometer backscatter and wind accuracy. The overall study includes two parts: first, a forward model is developed to simulate the ocean backscatter signals under certain wind conditions; second, a wind inversion processor is used to retrieve sea surface wind vectors from the outputs of the forward model. Simulations show that the backscatter accuracy and the retrieved wind quality of RFSCAT are quite promising and meet the scientific requirements of the mission.

Wind fields over ocean surface can be effectively retrieved by scatterometer measurements with GMFs (geophysical model functions) relating them and observing geometry with wind speeds. However, existed GMFs are not suitable for rainy conditions which require extra information for rain induced factors in observations that vary with rain conditions. The combined observations of scatterometer and radiometer are suitable for the problem. In this paper, this was realized by the method based on SVM (support vector machine) with kernel function established in this paper considering the form of existed GMFs. Experiments had been conducted over HY-2A scatterometer (HSCAT) and radiometer (HRAD) who give observations at almost the same time and operation frequencies suitable for this problem. The training progress was achieved by taken Windsat wind fields as true values. The method had been verified by comparison made between the wind fields retrieved with that of Buoy data. Discussion were made for further researches exploring the underlying physical progress of the SVM established.

The scatterometer (SCAT) onboard China France Oceanography Satellite (CFOSAT) will be the first scanning scatterometer with rotating fan-beams. Wind retrieving methods for existed scatterometers, which are with rotating pencil beams or fixed fan beams, are needed to be modified considering efficiency in information extraction from the numerous observations acquired in this working mode. This was achieved in the research of this paper by applying Principle Component Analysis (PCA) method. Firstly, vectors for applying PCA are composed in two different ways for analysis of the effectiveness of information extraction for the SCAT data and for wind retrieving respectively. Then the description of simulated SCAT data considering observing geometry and SCAT working mode was given. Then experiments of PCA based wind retrieving method was carried out with conclusion that it was effective in information extraction for preparing data sets for wind retrieving. Finally, further research has been discussed.

In typhoon conditions, air-sea interactions are fierce, the wind speeds are quite high and usually tangled with complex rains. Under such circumstances, the combined observations of scatterometer and radiometer would be able to provide good information although traditional wind filed retrieving methods for them, i.e. MLE based on GMF (Geographical Modelling Function) for scatterometer and RTM(Radiative Transfer Method) which are originated from low or median wind conditions will not be applicable. In this paper, a neural network was employed to combine the observations of the two sensors onboard HY-2A satellite: radiometer (HRAD) and scatterometer (HSCAT) to achieve wind speed retrieving under typhoon conditions. The network was trained by GRAPS (global/regional assimilation and prediction system) data used as true values of wind speeds in typhoons. The established network was verified by data excluded from training data set. Results show that the wind speeds obtained from the network are better than the products of HY-2A compared to GRAPS data. The research of this paper has provided a clue of retrieving typhoon wind speeds for HY-2A products and for the data processing of coming HY satellite series, though further work on wind direction obtain, data comparison between different kinds of assimilation and perdition systems and the test of this method on other scatterometer and radiometer data are to be done in the near future.

The wind fields retrieved by scatterometers from GMF (Geographical Modelling Function) would be invalidated under rainy conditions, due to scattering and attenuation effects of rain drops. The scatterometer (HSCAT) and the scanning microwave radiometer (RM) onboard HY-2A can give observations over sea surface at the same time. In this paper, firstly, data of HSCAT and RM onboard HY-2A observing rainy ocean surface were selected as experiment data, with corresponding buoy wind fields. Secondly, an artificial neural network was established for wind fields retrieval under rain conditions, which was tested by other data sets.

The Rotating fan-beam scatterometer (RFSCAT) onboard the Chinese-French Oceanography Satellite (CFOSAT) is a novel kind of scatterometer whose performance had been preliminarily verified by airborne campaign. In this paper the whole data processing chain that can achieve wind field derivation from original data set transmitted from the scatterometer had been proposed with definition of corresponding data levels that would be applied for RFSCAT in the CFOSAT mission. The pre-processing part in the processing chain was validated for it was derived from the same system verified by the airborne campaign. While for the post-processing prime algorithms, validity was concluded from processing some of the data gained in that experiment as well. Further researches needed had been explored as well.

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.

The RFSCAT(Rotating Fan-beam SCATterometer) is one of the two payloads of CFOSAT, the China-France Oceanography Satellite. It is a radar scatterometer designed to measure the electromagnetic back-scatter from wind roughened ocean surface. The operating frequency of the scatterometer is 13.256GHz (Ku-band) and has a swath about 1,000 kilometers. In this paper, an end-to-end data simulation system of CFOSAT RFSCAT is developed for processing algorithm validation and performance analysis. Using the simulation system, the slice and WVC geometries are determined and the measurement performance is analyzed. Based on the simulated data, the wind is retrieved and the performance of wind retrieval is estimated. Meanwhile, according to the simulation and wind retrieval of CFOSAT RFSCAT, some considerations of the actual data processing are introduced.

The scatterometer onboard the China-France Oceanography Satellite (CFOSAT) will be the first rotating fan-beam scatterometer (RFSCAT) ever flown in space for global ocean vector wind measurement. The status and progress of the scatterometer will be presented, including the mission schedule, payload design and development, validation experiments. The internal calibration strategy, Doppler compensation method and onboard signal processing arithmetic are also reported.

Dual Frequency Polarized Scatterometer (DFPSCAT) is one of the three payloads onboard the satellite of Water Cycle Observation Mission (WCOM). DFPSCAT is an X/Ku band rotating pencil beam scatterometer with 2-5 km resolution and 1000km swath for mapping of snow water equivalent (SWE) and freeze-thaw process [1]. DFPSCAT achieves fine resolution by linear frequency modulation pulse compression along the elevation direction, and by unfocused synthetic aperture processing (a technique where the Doppler effect is exploited to synthesize a longer aperture to achieve an improved resolution), as well as super-resolution reconstruction by oversampling in the direction of the azimuth.

Ocean surface current is a very important parameter of ocean dynamic environment, which has been connected to global climate change, marine environment forecasting, marine navigation, engineering security and so on. Doppler scatterometer is a new type of scatterometer that can be used to measure ocean surface current as well as the ocean wind vector in space. The Doppler Scatterometer is based on a real aperture radar, which can achieve a very wide swath. It can provide the ocean surface current and wind vector information in a certain resolution and achieve global coverage quickly, which is very important for the marine environment forecasting and climate changes research.

In order to satisfy a high resolution for the measurement of snow water equivalent, we use the system of using the dual frequency(X-band 9.6GHz and Ku-band 17GHz ) and full polarization. This paper discusses the various system options(scanning pencil-beam, high PRF, high SNR) and tradeoffs considered for improving the azimuth resolution of scatterometer are required.

The RFSCAT(Rotating Fan-beam SCATterometer) is one of the two payloads of CFOSAT, the China-France Oceanography Satellite. It is a radar scatterometer designed to measure the electromagnetic back-scatter from wind roughened ocean surface. The operating frequency of the scatterometer is 13.256GHz (Ku-band) and has a swath about 1,000 kilometers. In this paper, based on the characteristics of echo signal of RFSCAT, the on-board processing of RFSCAT signal is introduced. A Doppler pre-compensation LUT and A slice division LUT are developed, and the signal processing algorithms are validated and the wind retrieval performances of RFSCAT are analyzed based on the well-developed data simulation system of CFOSAT RFSCAT.

For the operational spaceborne scatterometers, there is no effective way to eliminate the influence of the rain. Echoes contaminated by the rain are discarded directly. In this paper, a special designed spaceborne scatterometer and its decoupling methods of echoes from sea surface and rain are introduced. The main steps of decoupling methods are as follows. First, the scatterometer system parameters are slightly adjusted to acquire both the rain and sea surface echoes simultaneously. Next, the data of the scatterometer are copied and processed separately, one copy for rain retrieving and the other copy for sea surface backscatter retrieving. Finally, the rain attenuations are compensate to modify the bottom rain rate and the sea surface backscatter energy.

Polarimetric backscatter measurements has potential benefits to improve wind retrieval performance [1]. It simultaneously measures co-polarized backscatter coefficient (σhh, σvv) and the correlation coefficient of the co- and cross-polarized component (σvhhh, σhvvv) of radar returns. A pre-processing design of spaceborne polarimetric scatterometer will be introduce in this paper considering to improve the resolution by range filtering [2]. A corresponding formulation for assessing the variance of the measurements due to fading and the thermal noise is presented considering the factors affecting the measurement accuracy.

Scatterometers are key instruments for remotely sensing of ocean surface winds. The Ku-band airborne full polarized scatterometer (AFPSCAT) of the Airborne Remote Sensing System (ARSS) was developed mainly for this application by the Key Laboratory of Microwave Remote Sensing in National Space Science Center of Chinese Academy of Sciences. Its rotating fan-beam antenna is unique among existed fully polarized scatterometers for remote sensing, which is a special type of scatterometer capable of collecting the co-polarized and cross-polarized backscattering as well as providing complex correlation of the scattering matrix. An initial test of this instrument and its potential for measuring wind field was done by flying with a plane over the Yellow Sea area in China. In this paper, the instrument was depicted, and then the experiment was described as well as data processing progress. Finally results were analyzed and conclusions were drawn that the instrument worked well for wind retrieving and further work needed to be done before its forward applications.

Airborne experiments were carried out to validate the spaceborne scatterometer. Airborne scatterometer use all the instruments of the spaceborne scatterometer to validate the system design and reliability, except the power amplifier and the antenna. The software of the scatterometer is redesigned due to the observation geometry of airborne platform. The wind retrieval results are compared with the HY-2A scatterometer and the real time wind vector measured on the ship at the center of testing region.

Microwave remote sensing (MRS) has been widely employed for biomass estimation. However, the retrieval of biomass related parameters for fractional areas with both trees and bare ground areas remains a problem unsolved. The Freeman-Durden decomposition (FD) was developed for applications with vegetation and could make efficient use of both amplitude and phase information provided by polarized MRS data. Based on FD, the authors devised a methodology to solve the problematic caused by discontinuous distribution of vegetation. Effectiveness of the proposed method was tested by experiments on RADARSAT-2 quad-pol images and corresponding in situ data.