Mesoscale eddies play an important role in the transportation of substance and energy throughout the oceans. Several extraction / identification algorithms have been proposed by scholars during the last few decades. Algorithms based on remotely sensed data are generally acknowledged,but defects are still inevitable when extracting eddies to date. To improve extraction performance,a new Universal Kriging Algorithm based on altimetric remotely sensed sea level anomaly（ SLA） datasets has been proposed to identify mesoscale eddies. SLA fields are transformed to general amplitude fields to ensure rapid and effective implementation. Eddy attributes such as polarity,radius,area,and amplitude can also be acquired using this method.Variograms are generated to determine the window width and lag distance which are used to compute variance fields. Such field data are virtually planar variance grids with each pixel number indicating the variance between one central pixel and pixels at specific lag distances and directions from it on SLAs. Variance fields are defined as"general amplitude fields",assuming that all pixels are potential eddy cores consisting of both true and false ones. The Universal Kriging interpolation is utilized to eliminate false signals and data noises of variance fields,acquire pure signal fields of general amplitudes and discriminate useful signals from noises derived from the variance calculation. Variances of true cores are equivalent to real amplitudes,whereas those of false cores differ from the real amplitudes. However,the features of some of the false cores are connected with eddy boundaries,which are sufficient to determine the characteristic isolines for identifying eddies. The deduced isolines are generated on the general amplitude fields to extract eddy boundaries and attributes from background sea surfaces. The values of these isolines are determined using specific equations of true and general amplitudes of eddy boundaries on the basis of amplitude statistics. They are separated into three latitudinal zones,namely,0° N—30° N,30° N—45 ° N,and 45° N—60° N.Northern Pacific was set as the study area. Eddies were extracted followed by quantitative precision tests using four AVISO SLA datasets in April,2012（ 4th,11 th,18th,and 25th）. A total of 841 eddies were identified,including 450 cyclones and 391 anticyclones. Three multi-core eddies were also captured,with one lasting for at least 15 days（ from 4th to 18th）. Compared with other remote-sensing oriented methods with complex criteria,the Universal Kriging Algorithm achieved a successful detection rate of approximately 90%（ 88. 00%,89. 18%,88. 04%,and 87. 92% with a maximum of 89. 18%） and an excess detection rate of less than 20%（ 11. 50%,14. 95%,18. 66%,and 16. 91% with a minimum of 11. 50%）. Results were acceptable allowing for the spatial resolution of SLAs. Error thresholds were less than 0. 25 degree.The Universal Kriging Algorithm has three noteworthy advantages. First,this algorithm is time saving. In particular,isolines are directly generated on the general amplitude fields,thereby simplifying identification procedures. The method significantly accelerates the extraction with a magnitude of 10 s in the core algorithm routines. Second,the algorithm is stable. Variance calculations along with Universal Kriging’s elimination of noises are used to extract eddies at a relatively constant accuracy based on deduced characteristic isolines on general amplitude fields. Third,the algorithm is self-adaptive. That is,it is applicable to the real-time identification of mesoscale eddies throughout oceans and seas only depending on a relatively small quantity of essential data. Further plans of our research include revealing latent spatial information in marine data fields（ especially in remotely sensed datasets） and exploring the application of the methodology in other ocean-element fields,such as the sea surface temperature,to improve the flexibility of the Universal Kriging Algorithm.

northern Pacific Ocean;mesoscale eddies;sea level anomaly;Universal Kriging Algorithm;variogram;variance field;general amplitude