GNSS-IR multi-satellite dual-frequency combination soil moisture inversion combined with machine learning

doi:10.13474/j.cnki.11-2246.2023.0302

Abstract

Abstract: In order to improve the accuracy of soil moisture inversion and overcome the shortcomings of soil moisture inversion based on single satellite and single frequency point, this paper proposes a soil moisture inversion method based on GNSS-IR multi-satellite dual-frequency combination combined with machine learning. By using the SNR of GNSS satellite L1 and L2 frequency points as the data source, the soil moisture inversion is studied. The BP and RBF neural network algorithms are used to construct the soil moisture prediction model, and compared with the linear regression prediction model. The experimental results show that:①Compared with the single satellite, the soil moisture inversion of multi-satellite combination increases the effective satellite utilization rate and improves the accuracy of soil moisture inversion. ②The correlation coefficient between L1 and L2 dual-frequency mean fusion delay phase observations of multi-satellite combination and soil moisture is 0.956, which is better than the inversion results of L1 and L2 frequency points. ③The prediction accuracy of BP and RBF neural network models is better than that of ULR model.

Key words: multi-satellite, GNSS-IR, mean fusion, soil moisture, neural network

CLC Number:

P237

NIE Shihai, WANG Long, WANG Mengke, LI Peng, LIANG Lei, HUANG Danni, LIU Bin. GNSS-IR multi-satellite dual-frequency combination soil moisture inversion combined with machine learning[J]. Bulletin of Surveying and Mapping, 2023, 0(10): 98-104.

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