Multi-star linear regression retrieval model for monitoring soil moisture using GPS-IR

doi:10.11947/j.AGCS.2020.20190095

Abstract

Abstract: Global positioning system interferometric reflectometry (GPS-IR) is a new remote sensing technique that can be used to estimate near-surface soil moisture from signal-to-noise ratio (SNR) data recorded by a measurement receiver. Considering that there are few studies on the inversion of soil moisture by multi-satellite combination, a multi-star linear regression soil moisture inversion model is proposed. The experiment shows that: ①The multi-satellite combination inversion mode can more comprehensively reflect the soil moisture information within the effective monitoring range near the station, and effectively improve the phenomenon that the inversion process is prone to abnormal jump when using single satellite inversion. At the same time, it improves the accuracy of soil moisture inversion during sudden rainfall periods. ②When the number of combined satellites reaches 6 or more, the correlation coefficient between the inversion result and the soil moisture reference value is greater than 0.9, which is at least 20.8% higher than that of a single satellite.

Key words: GPS-interferometric reflectometry, soil moisture, signal to noise ratio, multi-satellite combination, retrieval accuracy

CLC Number:

P228

LIANG Yueji, REN Chao, HUANG Yibang, PAN Yalong, ZHANG Zhigang. Multi-star linear regression retrieval model for monitoring soil moisture using GPS-IR[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(7): 833-842.

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