TY - JOUR
T1 - Soil moisture monitoring using GNSS interference signal
T2 - proposing a signal reconstruction method
AU - Han, Mutian
AU - Zhu, Yunlong
AU - Yang, Dongkai
AU - Chang, Qing
AU - Hong, Xuebao
AU - Song, Shuhui
N1 - Publisher Copyright:
© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2020/4/2
Y1 - 2020/4/2
N2 - Soil moisture monitoring using Global Navigation Satellite System (GNSS) interference signal has gained wide interests in recent years. It utilizes the variation pattern of the routinely measured Signal-to-Noise Ratio (SNR) that contains the interference information between the direct and soil reflected signal. Previous studies have shown that the amplitude of the de-trended SNR data is not a good indicator for soil moisture variation. The possible cause found in this study is that the de-trending operation only reduces the additive direct signal component in the SNR data, while the multiplicative direct signal component in the amplitude of SNR data is unaffected. Therefore, a method is proposed to reduce the contamination of the direct signal component on the amplitude through signal reconstruction and then normalization. Experiment data are collected and processed to calculate the normalized amplitude based on the reconstruction method. The results show that the overall correlation coefficient of the normalized amplitude with in-situ soil moisture reaches to 0.6966 under highly rough surface condition, while it is only 0.4314 for the amplitude obtained from the conventional method. A quadratic model is used to retrieve soil moisture from the normalized amplitude, the retrieval error is less than 0.085 cm3 cm−3.
AB - Soil moisture monitoring using Global Navigation Satellite System (GNSS) interference signal has gained wide interests in recent years. It utilizes the variation pattern of the routinely measured Signal-to-Noise Ratio (SNR) that contains the interference information between the direct and soil reflected signal. Previous studies have shown that the amplitude of the de-trended SNR data is not a good indicator for soil moisture variation. The possible cause found in this study is that the de-trending operation only reduces the additive direct signal component in the SNR data, while the multiplicative direct signal component in the amplitude of SNR data is unaffected. Therefore, a method is proposed to reduce the contamination of the direct signal component on the amplitude through signal reconstruction and then normalization. Experiment data are collected and processed to calculate the normalized amplitude based on the reconstruction method. The results show that the overall correlation coefficient of the normalized amplitude with in-situ soil moisture reaches to 0.6966 under highly rough surface condition, while it is only 0.4314 for the amplitude obtained from the conventional method. A quadratic model is used to retrieve soil moisture from the normalized amplitude, the retrieval error is less than 0.085 cm3 cm−3.
UR - https://www.scopus.com/pages/publications/85079327509
U2 - 10.1080/2150704X.2020.1718235
DO - 10.1080/2150704X.2020.1718235
M3 - 文章
AN - SCOPUS:85079327509
SN - 2150-704X
VL - 11
SP - 373
EP - 382
JO - Remote Sensing Letters
JF - Remote Sensing Letters
IS - 4
ER -