I. N. Mordvintsev

Estimating multiyear sea-ice concentration using passive microwave data and MLP neural networks

Three ice-type classification methods utilizing SSM/I passive microwave data were compared. Each applied a multilayer perceptron (MLP) neural network (NN) with OKEAN (radar and passive microwave) sea-ice learning data, a different learning algorithm based on, respectively, error back propagation and simulated annealing (M1), dynamic learning and polynomial basis functions (M2), and dynamic learning with two-step optimization (M3). M2 and M3 methods used the Kalman filtering technique. Our studies demonstrated, that for sea-ice inversions the modified MLP NN with M1 algorithm was more efficient because M2 and M3 algorithms caused overfitting. Multiyear (MY) sea-ice concentration maps were generated from SSM/I Tbs (19 GHz V, 19 GHz H and 37 GHz V channels) using modified MLP NN with M1 algorithm, OKEAN and ERS learning data. These maps were compared with respective MY sea-ice concentration maps developed using NASA Team algorithm (NTA). Our studies demonstrated the superiority of the NN method compared to the NTA.

Comparative analysis of multisensor satellite monitoring of Arctic sea-ice

This report represents comparative analysis of nearly coincident Russian OKEAN-01 polar orbiting satellite data, Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) imagery. OKEAN-01 ice concentration algorithms utilize active and passive microwave measurements and a linear mixture model for measured values of the brightness temperature and the radar backscatter. SSM/I and AVHRR ice concentrations were computed with NASA Team algorithm and visible and thermal-infrared wavelength AVHRR data, accordingly.