Retrieving GNSS-IR soil moisture based on deep belief network

doi:10.13474/j.cnki.11-2246.2020.0292

Abstract

Abstract: Retrieving soil moisture based on reflected signals acquired by geodetic GNSS receivers is a research hotspot in the field of GNSS. In order to overcome the shortcomings of conventional linear regression and BP neural network algorithms, this paper proposes a GNSS-IR soil moisture retrieval method based on deep belief network. The results show that the coefficient of determination, the average absolute error and the root mean square error of soil moisture based on this method are 0.909 8, 0.017 and 0.021. Compared with the linear regression and BP neural network algorithm, they are more consistent with the measured data, and can effectively improve the accuracy of soil moisture inversion, which proves the validity and reliability of the method.

Key words: GNSS-IR, soil moisture, signal-to-noise ratio, deep belief network, retrieving

CLC Number:

P228

CHEN Kun, SHEN Fei, CAO Xinyun, ZHU Yifan. Retrieving GNSS-IR soil moisture based on deep belief network[J]. Bulletin of Surveying and Mapping, 2020, 0(9): 100-105.

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