On Accurate Detection of Oceanic Features from Satellite IR Data Using ICSED Method

ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the performances of these methods in detecting oceanic features for both noise free and noise contaminated AVHRR (Advanced Very High Resolution Radiometer) IR image with Kuroshio. Also, practical experiments in detecting the eddy of Kuroshio with these methods are carried out for comparison. Results show that the ICSED algorithm has more advantages than other methods in detecting mesoscale features of ocean. Finally, the effectiveness of window size of ICSED method to oceanic features detection is quantitatively discussed.

Separation of ocean features from multifrequency full-polarization SAR imagery

Different ocean features usually appear in synthetic aperture radar (SAR) images simultaneously. This makes the image complicated and hard to understand. Because of lower signal-to-noise rate , it is much more difficult to separate different ocean features than to separate different land features. A completely novel method is presented to separate ocean features from multifrequency polarimetric SAR imagery. AIRSAR data from Jet Propulsion Laboratory (JPL), National Aeronautics and Space Administration (NASA) are used in the case studies and good results are achieved.

Delineation of Mesoscale Features of Ocean on Satellite IR Image

An ICSED (Improved Cluster Shade Edge-Detection) algorithm and a series of post-processing technique are discussed for automatic delineation of mesoscale structure of the ocean on digital IR images. The popular derivative-based edge operators are shown to be too sensitive to edge fine-structure and to weak gradients. The new edge-detection algorithm is ICSED (Improved Cluster Shade Edge-detection) method and it is found to be an excel lent edge detector that exhibits the characteristic of fine-structure rejection while retaining edge sharpness. This char acteristic is highly desirable for analyzing oceanographic satellite images. A sorting technique for separating clouds or land well from ocean at both day and night is described in order to obtain high quality mesoscale features on the IR image This procedure is evaluated on an AVHRR (Advanced Very High Resolution Radiometer) image with Kuroshio. Results and analyses show that the mesoscale features can be well identified by using ICSED algorithm.

A performance evaluation of remotely sensed sea surface salinity products in combination with other surface measurements in reconstructing three-dimensional salinity fields

Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles（S） using multilinear regressions. The performance is evaluated using a total root mean square（RMS） error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.