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Doppler velocity can be derived by calculating Doppler shift anomalies between predicted and estimated Doppler centroids. The predicted Doppler centroid is calculated based on a geometric model of satellite assuming that the target is not moving. The estimated Doppler centroid can be directly extracted from the raw SAR signal data by applying the average cross-correlation coefficient method. It is known that wind-generated ocean waves can significantly contribute to Doppler velocity due to the correlation between orbital motions of the waves and (tilt and hydrodynamic) modulated radar cross sections, in addition to what sea surface current contributes. In this study, the characteristics of Doppler velocities under hurricane conditions were investigated using RADARSAT-1 ScanSAR raw data. Five different hurricanes (Hurricane Dean, Hurricane Ivan, Hurricane Kyle, Hurricane Lili, and Typhoon Xangsane) and sequential acquisitions of two hurricanes (Hurricane Kyle and Hurricane Lili) were selected to study the contribution of wind-induced waves to Doppler velocities and compared with in situ measurements of drifting buoys. The results show that hurricane-generated seas and associated winds and waves appear to be different from ordinary sea state. This leads to lower estimates of Doppler velocities than expected and much closer to sea surface current velocities. [ABSTRACT FROM PUBLISHER]

Support vector machines (SVMs) have been applied to land cover classification, and a number of studies have demonstrated their ability to increase classification accuracy. The high correlation between the data set and SVM training model parameters indicates the high performance of the classification model. To improve the correlation, research has focused on the integration of SVMs and other algorithms for data set selection and SVM training model parameter estimation. This letter proposes a novel method, based on a particle filter (PF), of estimating SVM training model parameters according to an observation system. By treating the SVM training function as the observation system of the PF, the new method automatically updates the SVM training model parameters to values that are more appropriate for the data set and can provide a better classification model than can the original model, wherein the parameters are set by trial and error. Various experiments were conducted using Radarsat-2 synthetic aperture radar data from the 2011 Thailand flood. The proposed method provides superior performance and a more accurate analysis compared with the standard SVM. [ABSTRACT FROM PUBLISHER]

An analysis of the sensitivity of synthetic aperture radar (SAR) backscatter (σo) to crop and soil conditions was conducted using 57 RADARSAT-2 C-band quad-polarized SAR images acquired from April to September 2009 for large fields of wheat, barley, oat, corn, onion, and alfalfa in Barrax, Spain. Preliminary results showed that the cross-polarized σHVo was particularly useful for monitoring both crop and soil conditions and was the least sensitive to differences in beam incidence angle. The greatest separability of barley, corn, and onion occurred in spring after the barley had been harvested or in the narrow time window associated with grain crop heading when corn and onion were still immature. The time series of σo offered reliable information about crop growth stage, such as jointing and heading in grain crops and leaf growth and reproduction in corn and onion. There was a positive correlation between σo and the Normalized Difference Vegetation Index for onion and corn but not for all crops, and the impact of view direction and incidence angle on the time series was minimal compared to the signal response to crop and soil conditions. Related to planning for future C-band SAR missions, we found that quad-polarization with image acquisition frequency from 3-6 days was best suited for distinguishing crop types and for monitoring crop phenology, single- or dual-polarization with an acquisition frequency of 3-6 days was sufficient for mapping crop green biomass, and single- or dual-polarization with daily image acquisition was necessary to capture rapid changes in soil moisture condition. [ABSTRACT FROM AUTHOR]

Mapping and monitoring changes in the distribution of cropland provide information that aids sustainable approaches to agriculture and supports early warning of threats to global and regional food security. This paper tested the capability of Phased Array type L-band Synthetic Aperture Radar (SAR) (PALSAR) multipolarization and polarimetric data for crop classification. L-band results were compared with those achieved with a C-band SAR data set (ASAR and RADARSAT-1), an integrated C- and L-band data set, and a multitemporal optical data set. Using all L-band linear polarizations, corn, soybeans, cereals, and hay-pasture were classified to an overall accuracy of 70%. A more temporally rich C-band data set provided an accuracy of 80%. Larger biomass crops were well classified using the PALSAR data. C-band data were needed to accurately classify low biomass crops. With a multifrequency data set, an overall accuracy of 88.7% was reached, and many individual crops were classified to accuracies better than 90%. These results were competitive with the overall accuracy achieved using three Landsat images (88.0%). L-band parameters derived from three decomposition approaches (Cloude-Pottier, Freeman-Durden, and Krogager) produced superior crop classification accuracies relative to those achieved using the linear polarizations. Using the Krogager de- composition parameters from all three PALSAR acquisitions, an overall accuracy of 77.2% was achieved. The results reported in this paper emphasize the value of polarimetric, as well as multi- frequency SAR, data for crop classification. With such a diverse capability, a SAR-only approach to crop classification becomes increasingly viable. [ABSTRACT FROM AUTHOR]

In this letter, a new wind-vector algorithm is presented that uses radar backscatter sigma0 measurements at two adjacent subscenes of RADARSAT-1 synthetic aperture radar (SAR) images, with each subscene having slightly different geometry. Resultant wind vectors are validated using in situ buoy measurements and compared with wind vectors determined from a hybrid wind-retrieval model using wind directions determined by spectral analysis of wind-induced image streaks and observed by colocated QuikSCAT measurements. The hybrid wind-retrieval model consists of CMOD-IFR2 [applicable to C-band vertical-vertical (VV) polarization] and a C-band copolarization ratio according to Kirchhoff scattering. The new algorithm displays improved skill in wind-vector estimation for RADARSAT-1 SAR data when compared to conventional wind-retrieval methodology. In addition, unlike conventional methods, the present method is applicable to RADARSAT-1 images both with and without visible streaks. However, this method requires ancillary data such as buoy measurements to resolve the ambiguity in retrieved wind direction [ABSTRACT FROM PUBLISHER]

Ice concentration retrieved from spaceborne passive-microwave observations is a prime input to operational sea-ice-monitoring programs, numerical weather prediction models, and global climate models. Atmospheric Environment Service (AES)-York and the Enhanced National Aeronautics and Space Administration Team (NT2) are two algorithms that calculate ice concentration from Special Sensor Microwave/Imager observations. This paper furnishes a comparison between ice concentrations (total, thin, and thick types) output from NT2 and AES-York algorithms against the corresponding estimates from the operational analysis of Radarsat images in the Canadian Ice Service (CIS). A new data fusion technique, which incorporates the actual sensor's footprint, was developed to facilitate this study. Results have shown that the NT2 and AES-York algorithms underestimate total ice concentration by 18.35% and 9.66% concentration counts on average, with 16.8% and 15.35% standard deviation, respectively. However, the retrieved concentrations of thin and thick ice are in much more discrepancy with the operational CIS estimates when either one of these two types dominates the viewing area. This is more likely to occur when the total ice concentration approaches 100 %. If thin and thick ice types coexist in comparable concentrations, the algorithms' estimates agree with CIS's estimates. In terms of ice concentration retrieval, thin ice is more problematic than thick ice. The concept of using a single tie point to represent a thin ice surface is not realistic and provides the largest error source for retrieval accuracy. While AES-York provides total ice concentration in slightly more agreement with CIS's estimates, NT2 provides better agreement in retrieving thin and thick ice concentrations. [ABSTRACT FROM AUTHOR]

A multisensor data fusion algorithm based on a multilayer neural network is presented for sea ice classification in the winter period. The algorithm uses European Remote Sensing (ERS), RADARSAT synthetic aperture radar (SAR), and low-resolution television camera images and image texture features. Based on a set of in situ observations made at the Kara Sea, a neural network is trained, and its structure is optimized using a pruning method. The performance of the algorithm with different combinations of input features (sensors) is assessed and compared with the performance of a linear discriminant analysis (LDA)-based algorithm. We show that for both algorithms a substantial improvement can be gained by fusion of the three different types of data (91.2% for the neural network) as compared with single-source ERS (66.0%) and RADARSAT (70.7%) SAR image classification. Incorporation of texture increases classification accuracy. This positive effect of texture becomes weaker with increasing number of sensors (from 8.4 to 6.4 percent points for the use of two and three sensors, respectively). In view of the short training time and smaller number of adjustable parameters, this result suggests that semiparametric classification methods can be considered as a good alternative to the neural networks and traditional parametric statistical classifiers applied for the sea ice classification. [ABSTRACT FROM AUTHOR]