TITLE:
GNSS-R Soil Salinity Inversion Method Based on Multimodal Low-Rank Fusion Algorithm Using Tianmu-1 Satellite Data
AUTHORS:
Dongmei Song, Xianjun Wang, Mei Yong, Summiya Erdenesukh, Yuhai Bao, Bin Wang
KEYWORDS:
Tianmu-1, GNSS-R, Soil Salinity, Transformer
JOURNAL NAME:
Journal of Computer and Communications,
Vol.13 No.6,
June
30,
2025
ABSTRACT: Soil salinization is a critical global issue that constrains agricultural productivity and the sustainable development of ecological environments. Efficient and accurate monitoring of soil salinity is, therefore, of great significance. As an emerging passive remote sensing technology, Global Navigation Satellite System Reflectometry (GNSS-R) offers advantages such as all-weather, all-day capability and high spatiotemporal resolution, demonstrating broad application potential in soil parameter monitoring. Meanwhile, deep learning methods, with their powerful feature representation and modeling capabilities, have become essential tools in remote sensing inversion tasks. In this study, the Yellow River Delta is selected as the research area, and a multimodal low-rank fusion network model, MLF-Net, is proposed for high-precision soil salinity inversion based on GNSS-R data from the Tianmu-1 satellite. The results show that MLF-Net achieves a correlation coefficient of 0.9137 and a root mean square error (RMSE) of 0.8050 g/kg, outperforming comparison methods including Transformer, XGBoost, and artificial neural networks (ANN), in terms of both accuracy and stability. This study validates the feasibility and advantages of combining GNSS-R technology with deep learning approaches for soil salinity inversion, providing technical support and practical reference for saline-alkali land monitoring and precision agriculture.