Abstract

Microwave scatterometers are capable of acquiring land surface backscattering coefficients day and night under all-weather conditions, offering advantages for snow cover monitoring. However, the relatively low spatial resolution of traditional scatterometer data limits their application in the fine-scale monitoring of snow cover distribution. To improve the spatial representation of snow cover and mitigate mixed-pixel effects in complex spring snowmelt scenarios, this study proposes an adaptive bilateral filtering scatterometer image reconstruction (SIR-ABF) algorithm based on the rotating fan-beam scanning characteristics of the FengYun-3E Wind Radar (FY-3E WindRAD). The Ku-band data of FY-3E WindRAD were reconstructed from the original 10 km resolution to an enhanced resolution of 3.125 km. Furthermore, by integrating the reconstructed scatterometer backscatter with multi-source auxiliary data, an optimal feature subset was determined through a feature selection strategy that considers both feature-label correlation and inter-feature multicollinearity. Finally, the best feature subset was combined with four machine learning (ML) models for snow cover classification. The results indicate that the Support Vector Machine (SVM) achieved the best performance, yielding an Overall Accuracy (OA), Macro-F1, and Kappa coefficient (Kc) of 91.64%, 86.47%, and 0.730, respectively. Compared with the snow cover classification results derived from the original 10 km Ku-band data, the 3.125 km reconstructed data provided more detailed spatial information and better characterized fragmented snow patches and snow transition boundaries. Further comparison with existing snow cover products demonstrated the spatial consistency and continuity of the proposed classification results, highlighting the potential of high-resolution scatterometer data for fine-scale snow cover monitoring during the spring snowmelt period in Northeast China.

IPC Classification

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