Article
A GNSS-IR Method for Retrieving Soil Moisture Content from
Integrated Multi-Satellite Data That Accounts for the Impact of
Vegetation Moisture Content
Jichao Lv
1
, Rui Zhang
1,2,
* , Jinsheng Tu
3
, Mingjie Liao
1
, Jiatai Pang
1
, Bin Yu
1
, Kui Li
1
, Wei Xiang
1
,
Yin Fu
1
and Guoxiang Liu
1
Citation: Lv, J.; Zhang, R.; Tu, J.; Liao,
M.; Pang, J.; Yu, B.; Li, K.; Xiang, W.;
Fu, Y.; Liu, G. A GNSS-IR Method for
Retrieving Soil Moisture Content
from Integrated Multi-Satellite Data
That Accounts for the Impact of
Vegetation Moisture Content. Remote
Sens. 2021, 13, 2442. https://doi.org/
10.3390/rs13132442
Academic Editor: Damian Wierzbicki
Received: 25 May 2021
Accepted: 21 June 2021
Published: 22 June 2021
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1
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University,
Chengdu 611756, China; lvjichao@my.swjtu.edu.cn (J.L.); lmj8407@my.swjtu.edu.cn (M.L.);
Pjt_Shawn@my.swjtu.edu.cn (J.P.); ybdouble@my.swjtu.edu.cn (B.Y.); likui@my.swjtu.edu.cn (K.L.);
xiangwei@my.swjtu.edu.cn (W.X.); rsyinfu@my.swjtu.edu.cn (Y.F.); rsgxliu@swjtu.cn (G.L.)
2
State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed
Railway Safety, Southwest Jiaotong University, Chengdu 611756, China
3
College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239099, China;
tujinsheng@chzu.edu.cn
* Correspondence: zhangrui@swjtu.edu.cn
Abstract:
There are two problems with using global navigation satellite system-interferometric
reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the
difference between the reflection regions, and the difficulty in circumventing the impact of seasonal
vegetation growth on reflected microwave signals. This study presents a multivariate adaptive
regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact
of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI)
calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI)
to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-
satellite data is established based on the phase shift. To examine its reliability, the MARS model
was compared with a multiple linear regression (MLR) model, a backpropagation neural network
(BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with
time-series observation data collected at a typical station. The MARS model proposed in this study
effectively retrieved the SMC, with a correlation coefficient (R
2
) of 0.916 and a root-mean-square
error (RMSE) of 0.021 cm
3
/cm
3
. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%,
and 16.6% increases in R
2
and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC
retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the
feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the
MARS model in retrieving the SMC.
Keywords:
GNSS-IR; signal-to-noise ratio; soil moisture content retrieval; vegetation moisture
content; MARS
1. Introduction
The soil moisture content (SMC) is an important index for terrestrial hydrologic cir-
culation and research in fields such as agriculture, meteorology, and hydrology. Accurate
real-time SMC is an important reference for agricultural irrigation, meteorological forecast-
ing, and water resource recycling [
1
,
2
]. Global navigation satellite system-interferometric
reflectometry (GNSS-IR) is a new microwave sensing technique that primarily takes ad-
vantage of the interference effect that is generated by direct and surface-reflected GNSS
signals at the receiver, to retrieve surface parameters based on the characteristics of the
interference signal. This technique is mainly employed to retrieve the SMC, snow depths,
and vegetation parameters [3,4].
Remote Sens. 2021, 13, 2442. https://doi.org/10.3390/rs13132442 https://www.mdpi.com/journal/remotesensing
