Work

Dual-Branch Machine Learning framework with decoupled architectures for nonlinear interference mitigation in GNSS-R snow depth estimation

Year: 2026

Type: article

Abstract: Snow depth estimation using Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a critical tool for monitoring global water environment dynamics. However, traditional linear GNSS-R snow depth estimation methods are often constrained by residual nonlinear errors induced by environmental factors and complex multipath effects. While recent studies have explored machine learning (ML) techniques like Support Vector Machines (SVM) and Random Forests (RF), their direct snow depth retrieval approaches are still susceptible to residual nonlinear errors during snow-free periods. To address these limitations, we proposes the Dual-Branch ML framework with decoupled architectures for nonlinear interference mitigation in GNSS-R-derived snow depth estimation. The Multi-Layer Perceptron (MLP), SVM, RF are employed for efficient snow state detection, and the 1D Convolutional Neural Network (CNN), Support Vector Regression (SVR), RF then leverage the extracted features (frequency, amplitude, phase, and previous day’s snow depth) to perform the precise regression task for snow depth estimation, respectively. Experimental results demonstrate significant improvements: the proposed method achieves an average root mean square error (RMSE) of 5.41 cm for the P350 station and 1.89 cm for the AB33 station, with correlation coefficients of 0.995 and 0.999, respectively. This approach not only effectively accounts for nonlinearities in GNSS-R snow depth estimation but also significantly enhances estimation accuracy, offering a robust and promising solution for global snow depth retrieval. • Decoupled ML framework separates snow detection from depth estimation, reducing errors. • ML automates quality control, eliminating manual procedures and enhancing robustness. • Three dual-branch frameworks designed; snow detection cuts errors in snow-free periods. • Framework performs well in both deep and moderate snow, showing strong generalization.

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2026.105102

Authors: Zhihao Jiang, Liang Li, Xiuyun Shi, Weitianhua Ma, He Wang

Institution: Harbin Engineering University

Language: English

Topic: GNSS positioning and interference

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,550