In this paper,a high spatial-temporal resolution sea ice concentration estimation method for the Arctic melting season is proposed,aiming to improve the overestimation of sea ice concentration in seawater by the Glob-al Navigation Satellite System-Reflectometry(GNSS-R).The method utilizes machine learning algorithms to ex-tract feature parameters from the Delay Doppler Maps(DDM)obtained through GNSS-R and combines them with sea surface temperature data to establish a LightGBM model.The inversion results are then subjected to correlation analysis and evaluation against reference sea ice concentration values.The model's performance is compared with the sea ice concentration product from OSI SAF,demonstrating good consistency,with correlation coefficient,mean absolute error,and root mean square error being 0.965,0.061,and 0.090,respectively.This approach enables high-precision estimation of sea ice concentration in the Arctic marginal ice zone.
维普
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