Simulation of Soil Roughness Impact in GNSS-R Soil Moisture Retrieval

LIANG Yong; YANG Lei; WU Qiulan; HONG Xuebao; HAN Moutian; YANG Dongkai

doi:10.13203/j.whugis20160557

Volume 43 Issue 10

Oct. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(10): 1546-1552. > DOI: 10.13203/j.whugis20160557

LIANG Yong, YANG Lei, WU Qiulan, HONG Xuebao, HAN Moutian, YANG Dongkai. Simulation of Soil Roughness Impact in GNSS-R Soil Moisture Retrieval[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1546-1552. DOI: 10.13203/j.whugis20160557

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Simulation of Soil Roughness Impact in GNSS-R Soil Moisture Retrieval

1.
School of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, China
2.
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
3.
Beihang BeiDou Application Research Institute of Jinhua, Jinhua 321000, China

Funds:

The National Key Research and Development Plan in 13th Five-Year 2016YFC0803104

the National High-tech Research and Development Program(863 Program) of China 2013AA102301

Open Project of National Engineering Research Center for Information Technology in Agriculture KF2015W003

Grant of Beihang University BeiDou Technology Transformation and Industrialization BARI1709

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Author Bio:
LIANG Yong, PhD, professor, specializes in digital agriculture and remote sensing. E-mail:yongl@sdau.edu.cn
Corresponding author:
YANG Lei, PhD, lecturer. E-mail: yanglei_sdau@163.com
Received Date: May 18, 2017
Published Date: October 04, 2018

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Abstract

Abstract

This paper presents two dual antenna GNSS-R(global navigation satellite system reflectometry) soil moisture retrieval models with soil roughness compensation-an analytic model and an artificial neural network (ANN) model. Then a simulator for GNSS-R soil moisture retrieval is built in consideration of GPS L1 C/A code modulation. After that the impact of soil roughness is elaborated. The simulation results show that the roughness compensation is necessary for the analytic model when the RMSH(root mean square high) is larger than 0.010 m. The roughness compensation works well for small roughness, but there are some limitations for large roughness. Under the situation where RMSH is greater than 0.025 m, the accuracy of ANN model is 36.83%-72.36% higher than the analytic model without roughness compensation, and the accuracy of ANN model is 42.86%-54.40% higher than the analytic model with roughness compensation. The ANN model achieves similar accuracy regardless of roughness compensation, and the accuracy of ANN model without compensation is still 35.83%-53.48% higher than the analytic model with compensation.
- GNSS-R,
- dual antenna,
- soil moisture,
- soil roughness,
- artificial neural network

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References (21)

References

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