Use of an optimum interpolation method to construct a high-resolution global ocean surface vector wind dataset from active scatterometers and passive radiometers

This study presents a new 0.25° gridded 6-hourly global ocean surface wind vector dataset from 2000 to 2015 produced by blending satellite wind retrievals from five active scatterometers (QuikSCAT, ASCAT-A, ASCAT-B, OSCAT, and HY-2A), nine passive radiometers (four SSM/I sensors, two SSMIS sensors, TMI, AMSR-E, and AMSR2) and one polarimetric radiometer (WindSat) with reanalysis from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) employing an optimum interpolation method (OIM). The accuracy of this wind product is determined through various comparisons with buoy measurements, NCEP/NCAR reanalysis and the cross-calibrated multi-platform (CCMP) winds. The comparisons indicate that OIM winds agree well with buoys, showing a root-mean-squared difference of 1.32 m s^?1 for wind speed and 24.73° for wind direction over 0–30 m s^?1 wind speed range. And the quality of OIM winds is improved significantly relative to NCEP/NCAR reanalysis and can be comparable with CCMP winds. Furthermore, OIM winds can reveal abundant small-scale features that are not visible in reanalysis data. In addition, the wind speed and direction retrievals of most satellites are proved to play an important role in generating the high-quality product, but the procedure for including HY-2A winds and WindSat wind directions should be further explored.     

Characterizing vegetation indices derived from active and passive sensors

New ‘active’ sensors containing their own light source may provide consistent measures of plant and soil characteristics under varying illumination without calibration to reflectance. In 2006, an active sensor (Crop Circle) and various passive sensors were compared in a wheat (Triticum aestivum L., c.v. Chara) experiment in Horsham, VIC, Australia. The normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) were calculated from plot data with a range of canopy cover, leaf area and biomass. The active sensor NDVI and SAVI data were slightly less effective than corresponding passive sensor data at estimating green cover (r ²?=?0.80?0.90 vs. ～0.95). Passive sensor measurements showed strong non-linearity for estimating dry biomass and green leaf area index (GLAI), whereas SAVI calculated from the active sensor was linear (r ²?=?0.86 and 0.90). Scaling effects were not apparent when point, transect and plot areas were compared at the given level of spatial variation. Sensor height above the target confounded SAVI data probably due to differential irradiance from the light sources and the unbalanced effect of the ‘L’ factor within the algorithm. The active sensor was insensitive to light conditions (r ²?=?0.99 for cloudy vs. clear skies) and had no requirement for optical calibration.     

主被动传感器自适应量测融合算法

针对观测平台和运动对象间的距离参数会对传感器随机测量误差带来影响的问题,提出了一种基于模糊距离阈值的主被动传感器量测融合算法。讨论了根据距离参数选择主被动融合跟踪模式的方法,采用指数函数和模糊处理技术,利用已有信息实时改变主、被动传感器在量测融合过程中所占的权重。仿真结果表明,当传感器和运动对象间的距离对随机测量误差的影响不能忽略时,基于模糊距离阈值的主被动传感器变权重融合算法和传统的固定权重融合算法相比更加稳定,能够充分发挥主、被动传感器间的互补特性。     

An algorithm for the retrieval of sea surface wind fields using X-band TerraSAR-X data

TerraSAR-X (TS-X) is a new, fully polarized X-band synthetic aperture radar (SAR) satellite, which is a successor of the Spaceborne Imaging Radar X-band Synthetic Aperture Radar (SIR-X-SAR) and the SRTM. TS-X has provided high-quality image products over land and oceans for scientific and commercial users since its launch in June 2007. In this article, a new geophysical model function (GMF) is presented to retrieve sea surface wind speeds at a height of 10 m (U ₁₀) based on TS-X data obtained with VV polarization in the ScanSAR, StripMap and Spotlight modes. The X-band GMF was validated by comparing the retrieved wind speeds from the TS-X data with in situ observations, the high-resolution limited area model (HIRLAM) and QuikSCAT scatterometer measurements. The bias and root mean square (RMS) values were 0.03 and 2.33 m s^?1, respectively, when compared with the co-located wind measurements derived from QuikSCAT. To apply the newly developed GMF to the TS-X data obtained in HH polarization, we analysed the C-band SAR polarization models and extended them to the X-band SAR data. The sea surface wind speeds were retrieved using the X-band GMF from pairs of TS-X images obtained in dual-polarization mode (i.e. VV and HH). The retrieved results were also validated by comparing with QuikSCAT measurements and the results of the German Weather Service (DWD) atmospheric model. The obtained RMS was 2.50 m s^?1 when compared with the co-located wind measurements derived from the QuikSCAT, and the absolute error was 2.24 m s^?1 when compared with DWD results.     

Effects of atmospheric water and surface wind on passive microwave retrievals of sea ice concentration: a simulation study

The atmospheric effects on the retrieval of sea ice concentration from passive microwave sensors were examined using simulated data typical for the Arctic summer. The simulation includes atmospheric contributions of cloud liquid water (CLW), water vapour (WV) and surface wind on the microwave signatures. A plane parallel radiative transfer model was used to compute brightness temperatures at SSM/I frequencies over surfaces that contained open water, first‐year (FY) ice, multi‐year (MY) ice and their combinations. Synthetic retrievals were performed using the NASA Team (NT) algorithm for the estimation of sea ice concentrations. Our results show that if the satellite sensor's field of view is filled with only FY ice, the retrieval is hardly affected by the atmospheric conditions because of the high contrast between emission signals from the FY ice surface and the atmosphere. Pure MY ice concentration is generally underestimated because of the low MY ice surface emissivity, which results in the enhancement of emission signals from the atmosphere. In marginal ice areas, the atmospheric and surface effects tend to degrade the accuracy at low sea ice concentrations. FY ice concentration is overestimated and MY ice concentration is underestimated in the presence of atmospheric water and surface wind at low ice concentration. Moreover, strong surface wind appears to be more important than atmospheric water in contributing to the retrieval errors of total ice concentrations over marginal ice zones.     

Development of polarization ratio model for sea surface wind field retrieval from TerraSAR-X HH polarization data

In this article, the polarization ratio (PR) of TerraSAR-X (TS-X) vertical–vertical (VV) and horizontal–horizontal (HH) polarization data acquired over the ocean is investigated. Similar to the PR of C-band synthetic aperture radar (SAR), the PR of X-band SAR data also shows significant dependence on incidence angle. The normalized radar cross-section (NRCS) in VV polarization data is generally larger than that in HH polarization for incidence angles above 23°. Based on the analysis, two PR models proposed for C-band SAR were retuned using TS-X dual-polarization data. A new PR model, called X-PR hereafter, is proposed as well to convert the NRCS of TS-X in HH polarization to that in VV polarization. By using the developed geophysical model functions of XMOD1 and XMOD2 and the tuned PR models, the sea surface field is retrieved from the TS-X data in HH polarization. The comparisons with in situ buoy measurements show that the combination of XMOD2 and X-PR models yields a good retrieval with a root mean square error (RMSE) of 2.03 m s^–1 and scatter index (SI) of 22.4%. A further comparison with a high-resolution analysis wind model in the North Sea is also presented, which shows better agreement with RMSE of 1.76 m s^–1 and SI of 20.3%. We also find that the difference between the fitting of the X-PR model and the PR derived from TS-X dual-polarization data is close to a constant. By adding the constant to the X-PR model, the accuracy of HH polarization sea surface wind speed is further improved with the bias reduced by 0.3 m s^–1. A case acquired at the offshore wind farm in the East China Sea further demonstrates that the improvement tends to be more effective for incidence angles above 40°.     

单矢量水听器对海面目标高分辨定位方法研究

建立单矢量水听器简正波接收模型,引入高分辨算法构造代价函数对目标的距离和角度进行匹配搜索,实现对浅海中水面目标的定位。仿真结果表明：当接收信噪比不低于15 dG时,该方法依靠单矢量水听器就可以对水面目标进行定位。通过海试数据处理结果分析,在3 km范围内,在较高信噪比时,定位误差小于5%。实验数据验证了算法的有效性。     

Seasonal and interannual studies of vortices in sea surface temperature data

An algorithm for calculating feature displacement velocities and for detecting vortices has been applied to 13 years of sea surface temperature data derived from Advanced Very High Resolution Radiometer (AVHRR) data. A unique global event database for seasonal and interannual studies of the spatial distribution of oceanic vortices was created for the years 1986–1998. The results indicate that (1) the number of vortices in each season is fairly constant from year to year in each hemisphere—however, their preferred locations change on seasonal to interannual time-scales; (2) the maximum number of vortices were detected in the summer and in the winter in all oceans and the minimum number were detected in the autumn; and (3) the distribution of the spatial density function shows preferred localizations such as 40°?S, the tropical instability region, marginal seas, western boundary and eastern boundary current regimes.     

Fusion of remotely sensed data for soil moisture estimation using relevance vector and support vector machines

A data assimilation (DA) methodology that uses two state-of-the-art techniques, relevance vector machines (RVMs) and support vector machines (SVMs), is applied to retrieve surface (0–6 cm) soil moisture content (SMC) and SMC at a depth of 30 cm. RVMs and SVMs are known for their robustness, efficiency and sparseness and provide a statistically sound approach to solve inverse problems and thus to build statistical models. Here, we build a statistical model that produces acceptable estimations of SMC by using inexpensive and readily available data. The study area for this research is the Walnut Creek watershed in Ames, south-central Iowa, USA. The data were obtained from Soil Moisture Experiments 2002 (SMEX02) conducted at Ames, Iowa. The DA methodology combines remotely sensed inputs with field measurements, crop physiological characteristics, soil temperature, soil water-holding capacity and meteorological data to build a two-step model to estimate SMC using both techniques, i.e. RVMs and SVMs. First, the RVM is used to build a model that retrieves surface (0–6 cm) SMC. This information serves as a boundary condition for the second step of this model, which estimates SMC at a depth of 30 cm. An exactly similar routine is followed with an SVM for estimation of surface (0–6 cm) SMC and SMC at a depth of 30 cm. The results from the RVM and SVM models are compared and statistics show that RVMs perform better (root mean square error (RMSE)?= 0.014 m³ m^?3) when compared with SVMs (RMSE?= 0.017 m³ m^?3) with a reduced computational complexity and more suitable real-time implementation. Cross-validation techniques are used to optimize the model. Bootstrapping is used to check over/under-fitting and uncertainty in model estimates. Computations show good agreement with the actual SMC measurements with coefficients of determination (R ²) for RVM equal to 0.92 and for SVM equal to 0.88. Statistics indicate a good model generalization capability with indexes of agreement (IoAs) for RVM equal to 0.97 and for SVM equal to 0.96.     

Comparisons of regional satellite sea surface temperature gradients derived from MODIS and AVHRR sensors

Regional sea surface temperature (SST) gradients were examined for a 6-year (2003–2008) period using data from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on two NOAA satellite platforms. Two regions, one in the California Current System and the other in the Gulf Stream, representing an eastern boundary upwelling region and strong western boundary current, respectively, were chosen to investigate the seasonal variability, statistical differences and similarities, and correlations with respect to the two sets of SST gradients. Results indicated higher gradient magnitudes using MODIS SST in relative comparison to those derived from AVHRR that are attributed to instrument and algorithm differences. These observed differences are important for any studies that employ SST gradients, such as fisheries investigations that have traditionally relied on AVHRR SST gradients only.     

Some active and passive microwave signatures of Antarctic sea ice from mid-winter to spring 1991

Antarctic sea ice is often covered by a deep snow layer which acts as an emitter and a scatterer to microwave radiation leading to possible misinterpretations of ice signatures, particularly at high frequencies. The algorithms for ice identification, based on the observations of the Special Sensor Microwave Imager, at 19GHz (vertical and horizontal polarizations) and 37Ghz (vertical polarization), have proven to be inefficient for distinguishing new and old ice over the Antarctic Ocean. At an equivalent resolution and analysed on a weekly basis, complementary information can be obtained from active microwave measurements provided, at 5·3GHz (vertical polarization), by the Active Microwave Instrument, the scatterometer of ERS–1. Based on data obtained from the end of August to the end of November 1991, during the austral winter and spring radar backscatter is analysed as a function of the incidence angle. At low incidence angles, the derivative of the backscatter is closely related to the water concentration as derived from passive radiometry, whereas, at high incidence angles, the backscatter is mainly due to ice, as the water contribution is strongly reduced. During the whole period, stable features are apparent on the images obtained from the backscattering coefficients at 50°. On those images, higher values characterize the marginal ice zone, the polynya areas and the advected ice within the Ross Sea. At high incidence angles, the strong signatures of deformed/ rough ice depart significantly from the information classically extracted from the radiometers, the brightness temperatures as well as the derived products, polarization, spectral gradient ratios and concentration. It is therefore possible to classify the Antarctic ice cover into geographical clusters where the active microwave signatures can be attributed. to a peculiar ice type. Though those clusters are not totally identified, their stability and the coherence of their patterns show that they are related to geophysical structures. Four backscatter curves, simulating distinct behaviours over the Antarctic region, are proposed for sea water, marginal ice, first-year ice of the inner part of the pack and multi-year ice.     

An assessment of veering wind effects on scatterometry from the sea surface

To characterize scatterometer returns from the sea surface near meteorological fronts, we investigated microwave scattering from seas in which long waves are at oblique angles to short waves. We simulate the effects of veering winds on C- and K_u-band scatterometers by using models in which the short waves align with the wind friction velocity u_? , but the long waves are at oblique angles to the u_? direction. The analysis reveals two main effects due to the rotation of the long wave slope probability density distribution. Azimuthally averaged normalized radar cross-section a _o decreases as the oblique angle increases. Additionally, two regimes exist. In the small angle regime, azimuthal scans of normalized radar cross-section σ^o exhibit features similar to the classic double-maxima pattern for non-veering wind conditions, but the axis of σ^o maxima is rotated toward the long-wave axis. In the large angle regime, more than two maxima are apparent in azimuthal scans. Therefore it may be inappropriate to use standard three term Fourier cosine models for some veering wind conditions.     

A comparative assessment of IRS-P4 (MSMR)-derived sea surface temperature and sea surface wind speed over the north Indian Ocean

Indian Remote Sensing Satellite (IRS-P4) multi-frequency scanning microwave radiometer (MSMR) provides geophysical parameters like sea surface temperature (SST), sea surface wind speed (SSWS), integrated water vapour (IWV) and cloud liquid water (CLW). The retrieval procedure of these parameters given by Gohil et al. (2000 Gohil, B.S., Mathur, A.K. and Varma, A.K. Geophysical parameter retrieval over global oceans from IRS-P4 (MSMR). Preprints, Fifth Pacific Ocean Remote Sensing Conference. December5–82000, Goa. pp.207–211. Goa, , India: National Institute of Oceanography.  [Google Scholar], Geophysical parameter retrieval over global oceans from IRS-P4 (MSMR). In Preprints, Fifth Pacific Ocean Remote Sensing Conference, 5–8 December 2000, Goa, India (Goa: National Institute of Oceanography), pp. 207–211) was summarized by Sharma et al. (2002 Sharma, R., Babu, K.N., Mathur, A.K. and Ali, M.M. 2002. Identification of large scale atmospheric and oceanic features from IRS-P4 multifrequency scanning microwave radiometer: preliminary results. Journal of Atmospheric and Oceanic Technology, 19: 1127–1134. [Crossref], [Web of Science ®] , [Google Scholar], Identification of large scale atmospheric and oceanic features from IRS-P4 multifrequency scanning microwave radiometer: preliminary results. Journal of Atmospheric and Oceanic Technology, 19, pp. 1127–1134) and Jena (2007 Jena, B. 2007. Studies on the retrieval, validation and applications of geophysical parameters from IRS-P4 (MSMR) data, Orissa: PhD thesis, Berhampur University.  [Google Scholar], Studies on the retrieval, validation and applications of geophysical parameters from IRS-P4 (MSMR) data. PhD thesis, Berhampur University, Orissa). Demonstration of self-consistency of these parameters has primary scientific importance. This article deals with the validation of MSMR geophysical parameters such as SST and SSWS with in situ observations (buoy data) over the north Indian Ocean during 2000. Result shows that the MSMR-derived SST and SSWS can be utilized for several applications because of their reasonable accuracy and coverage even under cloudy condition.     

Biomass retrieval from high-dimensional active/passive remote sensing data by using artificial neural networks

Retrieval of the biomass parameters from active/passive microwave remote sensing data is performed based on an iterative inversion of the artificial neural network (ANN). The ANN is trained by a set of the measurements of active and passive remote sensing and the ground truth data versus Day of Year during growth. Once the ANN training is complete, the ANN can be used to retrieve the temporal variations of the biomass parameters from another set of observation data. The retrieved biomass include canopy height, canopy water content and dry matter fraction, and the wetness of the underlying land. Two examples for wheat and oat are illustrated. The retrieved biomass parameters agree well with the real data of the ground truth.     

Interpretation of scatterometer ocean surface wind vector EOFs over the Northwestern Pacific

Satellite scatterometer winds over the northwestern Pacific were analyzed with the vector empirical orthogonal function (VEOF) method. The Hilbert-Huang transform (HHT), a newly developed non-linear and non-stationary time series data processing method, was also employed in the analysis. A combination of European Remote Sensing Satellite (ERS) −1/2 scatterometer, NASA Scatterometer (NSCAT) and NASA's Quick Scatterometer (QuikSCAT) winds covering the period from January 1992 to April 2000 and the area of 0-50°N, 100-148°E constitutes the baseline for this study. The results indicate that annual cycles dominate the two leading VEOF modes. The first VEOF shows the East Asian monsoon features and the second represents a spring-autumn oscillation. We removed the annual signal from the data set and calculated the interannual VEOFs. The first interannual VEOF represents the interannual variability existing in the spring-autumn oscillation. The temporal mode is correlated with the Southern Oscillation Index (SOI), but has a half-year lag with respect to the SOI. The spatial mode of the first interannual VEOF reflects the response of the tropical and extratropical winds to ENSO events. The second interannual VEOF is another ENSO related mode, and the temporal VEOF mode is correlated with the SOI with a correlation coefficient of 0.78, revealing the wind variability over mid-latitudes, which is associated with ENSO events. Further analysis indicated that the wind variability over the coast of East Asia represents anomalies of a Hadley cell. The quasi-biennial oscillation (QBO) was found in the temporal mode, indicating and verifying that the QBO in the wind fields is related to ENSO events. The third VEOF shows the interannaul variability in the winter-summer mode and displays the interannual variability of the East Asian monsoon. The three leading interannual VEOFs are statistically meaningful as confirmed by a significance test.     

Electromagnetic wave scattering from the sea surface in the presence of wind wave patterns

The study is concerned with electromagnetic wave (EM) scattering by a random sea surface in the presence of coherent wave patterns. The coherent patterns are understood in a broad sense as the existence of certain dynamical coupling between linear Fourier components of the water wave field. We show that the presence of weakly nonlinear wave patterns can significantly change the EM scattering compared to the case of a completely random wave field. Generalizing the Random Phase Approximation (RPA) we suggest a new paradigm for EM scattering by a random sea surface.

The specific analysis carried out in the paper synthesizes the small perturbation method for EM scattering and a weakly nonlinear approach for wind wave dynamics. By investigating, in detail, two examples of a random sea surface composed of either Stokes waves or horse-shoe (‘crescent-shaped’) patterns the mechanism of the pattern effect on scattering is revealed. Each Fourier harmonic of the scattered EM field is found to be a sum of contributions due to different combinations of wave field harmonics. Among these ‘partial scatterings’ there are phase-dependent ones and, therefore, the intensity of the resulting EM harmonic is sensitive to the phase relations between the wind wave harmonics. The effect can be interpreted as interference of partial scatterings due to the co-existence of several phase-related periodic scattering grids. A straightforward generalization of these results enables us to obtain, for a given wind wave field and an incident EM field, an a priori estimate of whether the effects due to the patterns are significant and the commonly used RPA is inapplicable. When the RPA is inapplicable, we suggest its natural generalization by re-defining the statistical ensemble for water surface. First, EM scattering by an ‘elementary’ constituent pattern should be considered. Each such scattering is affected by the interference because the harmonics comprising the pattern are dynamically linked. Then, ensemble averaging, which takes into account the distribution of the pattern parameters (based on the assumption that the phases between the patterns are random), should be carried out. It is shown that, generally, this interference does not vanish for any statistical ensemble due to dynamical coupling between water wave harmonics. The suggested RPA generalization takes into account weak non-Gaussianity of water wave field m contrast to the traditional RPA which ignores it.     

Cloud climatology over the oceanic regions adjacent to the Indian Subcontinent: inter-comparison between passive and active sensors

Understanding the cloud vertical structure and its variation in space and time is important to reduce the uncertainty in climate forcing. Here, we present the cloud climatology over the oceanic regions (Arabian Sea, Bay of Bengal, and South Indian Ocean) adjacent to the Indian subcontinent using data from the Multiangle Imaging Spectroradiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), GCM-Oriented CALIPSO Cloud Product (GOCCP), and International Satellite Cloud Climatology Project (ISCCP). Fractional cloud cover (f_c) shows stronger seasonal variations over the Arabian Sea (mean annual f_c lies in the range 0.5–0.61) and Bay of Bengal (mean annual f_c lies in the range 0.69–0.75) relative to the South Indian Ocean (mean annual f_c lies in the range 0.64–0.71). Inter-comparison of statistics from passive (MISR, MODIS and ISCCP) and active (GOCCP) sensors reveals the challenges in interpreting satellite data for climate implications. While MISR detects more low clouds because of its stereo technique, MODIS and ISCCP detect more high clouds because of their radiometric techniques. Therefore, a combination of these two techniques in passive sensors may lead to more realistic understanding of the cloud vertical structure. GOCCP (active sensor) can detect multilayer cloud, but accuracy reduces if the high clouds are optically thick. A dominance of low and high clouds throughout the year is observed in these regions, where cumulus and cirrus dominate among low and high clouds, respectively.     

Assessment of an analytical model for sea surface wind speed retrieval from spaceborne SAR

An analytical model based on radar backscatter theory was utilized to retrieve sea surface wind speeds from C-band satellite synthetic aperture radar (SAR) data at either vertical (VV) or horizontal (HH) polarization in transmission and reception. The wind speeds were estimated from several ENVISAT Advanced SAR (ASAR) images in Hong Kong coastal waters and from Radarsat-1 SAR images along the west coast of North America. To evaluate the accuracy of the analytical model, the estimated wind speeds were compared to coincident buoy measurements, as well as winds retrieved by C-band empirical algorithms (CMOD4, CMOD_IRF2 and CMOD5). The comparison shows that the accuracy of the analytical model is comparable to that of the C-band empirical algorithms. The results indicate the capability of the analytical model for sea surface wind speed retrieval from SAR images at both VV and HH polarization.