1. Introduction
Soil moisture content (SMC) is an important parameter in the change process of land surface systems, playing a critical role in agricultural management, surface water cycling, and energy exchange in near-earth space [
1,
2,
3,
4,
5]. SMC is an indispensable basic element for crop growth and development in agricultural production. The SMC can be used as an effective index of farmland health. The content of soil moisture is directly related to the health status of crops. Drought can lead to soil drying and cracking, hinder crop growth, reduce crop yield, and threaten food security. Therefore, access to accurate SMC information on farmland is of great significance for crop yield estimation, drought monitoring, and agricultural production management. Traditionally, SMC monitoring and investigation methods were ground-based point measurements or interpolated lines or grids. The implementation is not only time-consuming and labor-intensive, but also difficult to obtain spatially continuous soil moisture information in time, which has great limitations for its operational use [
6]. Satellite remote sensing has the advantages of being fast, macro, and dynamic, providing an effective means to obtain soil moisture information.
For visible-infrared bands, the SMC estimation is from the relationships between soil spectral reflectance and soil moisture or vegetation traits occur under water stress, such as the single spectral analysis method [
7,
8,
9] and the vegetation index method [
10,
11,
12]. For thermal-infrared bands, the SMC is derived from the parameters related to the soil thermal properties, such as the thermal inertia method [
13,
14,
15] and the temperature index method [
16,
17]. However, the optical/thermal methods are associated with some inherent limitations, such as vegetation interference, night effects, poor temporal resolution, and cloudy conditions [
18]. Comparing with optical/thermal remote sensing, Synthetic Aperture Radar (SAR) has all-weather day-and-night penetration capabilities, and can provide effective and cost-efficient means of monitoring and assessing SMC over vegetation cover conditions [
19,
20,
21]. SAR remote sensing retrieves SMC by establishing relationships between the radar backscattering coefficient and soil moisture, because the amount of water held in the soil is directly proportional to the dielectric constant of soil [
22]. The basis of SMC retrieval is the soil dielectric constant. However, besides the soil dielectric constant, SAR signal is also affected by other factors, such as surface roughness, vegetation cover, and radar system parameters. Therefore, if we only pay attention to the relationship between soil moisture and the SAR backscattering coefficient and ignore the influence of other parameters, we can only obtain a trend of overall change, which is far from meeting the requirements of SMC retrieval. There are still some challenges in retrieving SMC using SAR data over vegetated areas. The observed SAR signals include the combined information of vegetation moisture and SMC because the vegetation canopy contains water [
23,
24]. The vegetation canopy volume scattering, the interaction between soil and vegetation and the observed backscattering are highly nonlinear [
25,
26,
27], which increases the difficulty of SMC retrieval. When using SAR to retrieve SMC under vegetation cover, the SMC is underestimated if vegetation effect is not considered [
28,
29]. Therefore, removing the influence of vegetation is the key in SMC retrieval using SAR.
To remove the vegetation effects, a number of methods has been established by scholars. These methods can be generally classified into two categories: methods based on polarization decomposition and methods based on vegetation scattering models [
30]. Hajnsek et al. [
31] used surface, dihedral, and vegetation scattering to interpret the scattering processes and to retrieve SMC. The potential of using polarimetric decompose for estimating volumetric SMC under agricultural vegetation was proved. Combining polarimetric decomposition techniques with the concept of multi-angularity, Jagdhuber et al. proposed a retrieval technique for SMC under low vegetation cover [
32]. He et al. estimated SMC in agricultural crop fields from fully polarimetric SAR data through the polarimetric decomposition of a SAR coherency matrix. Martino et al. [
33] used a polarimetric two-scale two-component model (PTSTCM) to describe the surface scattering component, and a randomly (uniformly, vertically, or horizontally) oriented thin-dipole model to describe the volume scattering contribution from the vegetation layer that covers the scattering surface. The problem of negative power in SMC retrieval is significantly mitigated. It is noteworthy that polarization decomposition method for soil moisture retrieval requires fully polarization data. Vegetation scattering models describing the scattering processes of microwave signals have been established (such as Michigan microwave canopy scattering (MIMICS) [
34]; Water cloud model (WCM) [
35]; the distorted Born approximation (DBA) [
36]; the Tor Vergata model (TVM) [
37]), which can also be used to remove the influence of vegetation cover on soil moisture retrieval. Among these models, WCM has been widely used for SMC retrieval over vegetated areas because it only requires a few parameters and has high accuracy [
30].
SAR backscattering includes mixed signals of vegetation and soil from agricultural fields, while vegetation contributions vary at different growth stages [
38]. In addition, soil surface roughness also affects radar backscattering [
39]. Over vegetated areas, soil surface roughness cannot be easily measured directly, which has caused some problems for soil moisture retrieval. Therefore, the key issue in the quantitative estimation of SMC is to separate the effect of vegetation and soil roughness. In this paper, a method was developed to retrieve SMC over agriculture fields during the crop growing season. The proposed method combined the optimal roughness method and ratio method. The ratio method was used to separate the scattering contribution of vegetation from total backscattering. The vegetation correction procedure is embedded within a SMC retrieval algorithm, in which the surface scattering component is formulated by the Calibrated Integrated Equation Model (CIEM) [
40]. For the problem that roughness cannot be measured, the optimal roughness was defined by a global search method. Finally, the developed method is applied to estimate SMC over agriculture fields. In addition, the proposed model was compared with the WCM to evaluate its effectiveness and accuracy.
3. Methods
In this study, the scattering model CIEM was used to simulate the backscattering coefficients of soil surface covered by winter wheat. After the vegetation correction, the soil moisture estimation and accuracy verification were carried out using a look-up table approach. The workflow of SMC retrieval is shown in
Figure 3. The specific experiment included the following steps:
(1) The model was constructed based on the observed total coefficients, the vegetation parameters (e.g., RVI, LAI and NDVI), and the bare soil backscattering coefficients simulated by the CIEM model. Then, the undetermined coefficients of the model were fitted using the least-squares method.
(2) For each sampling date, 70% of the data were randomly selected as the training set and the rest as the validation set. Basic statistics (e.g., mean, standard deviation, etc.) of the training and validation sets were calculated and compared, and they were not statically different.
(3) After fitting the undetermined coefficients of the model, the estimated bare soil backscattering could be obtained. Using a look-up table approach, the SMC of the training set was estimated based on the minimum cost function strategy (Equation (2)) and the simulated bare soil backscatter coefficients. Then the RMSE between the measured and estimated SMC was calculated as the estimation accuracy of SMC on the training set.
(4) All the RMS heights (s) within the given roughness parameter interval were traversed and steps (1) and (2) repeated until s reached the maximum value in the given range. The estimation accuracy of SMC on the training set was then compared with different s, and the s with the highest estimation accuracy was taken as the optimal roughness parameter of the study area on the sampling date. The optimal roughness was taken as the effective roughness of the model.
(5) For the validation set, the estimated backscattering coefficients of bare soil were obtained using the constructed model. Then the SMC of the validation set was estimated using the minimum cost function strategy and the backscattering coefficients of bare soil simulated by the CIEM model with the optimal roughness parameter. Finally, the accuracy evaluation was conducted using the measured SMC on the validation set.
(6) The SMC mapping of the study area was conducted using the constructed model.
3.1. Vegetation Correction
3.1.1. Ratio Method
The ratio of bare soil backscattering contribution to the observed backscattering can be affected by vegetation cover and remote sensing sensor configuration [
43]. Therefore, for areas with vegetation cover, when the sensor configuration parameters were fixed, the ratio of the surface backscattering to the total backscattering coefficients was only related to some vegetation description parameters, which can be expressed using the following equation:
where
pp represents the
HH or
VV polarization mode,
is the backscattering coefficient of bare soil, and
represents the total backscattering coefficient.
The ratio function
F on the right side of Equation (3) does not have a fixed expression form. Scholars have used a variety of ratio functions in order to better characterize the backscattering contribution of vegetation in the ratio equation [
3,
43,
44]. A combination of the power exponential function and linear function could better characterize the vegetation scattering [
45].
where
V is the vegetation description parameter,
a,
b and
c are the undetermined coefficients of the ratio function.
For the selection of vegetation description parameters in the ratio function, the current research has not reached a consistent conclusion. Some studies believed that the vegetation water content (VWC) had a strong attenuation effect on the scattered radiation of the microwave signal in the vegetation layer, so VWC was suitable for vegetation parameter description. There is a strong correlation between radar vegetation index (RVI) and VWC [
46]. The RVI can be obtained directly from SAR images without being affected by weather conditions. Therefore, RVI was selected as the vegetation parameter to parameterize the ratio function in this study. The definition of RVI is shown as follows:
where
,
and
are the backscattering coefficient of different polarization modes. In addition to RVI, leaves have been considered to be able to control the scattering and attenuation of microwave signals in the canopy to a certain extent [
39], especially for herbaceous vegetation. Therefore, LAI was used to describe the scattering characteristics of vegetation. NDVI can well represent vegetation greenness information, reflecting a growth state of vegetation, and does not depend on field measurements which can be obtained from optical remote sensing data. Therefore, NDVI was also used to parameterize the ratio function. These three vegetation parameters were compared with for the SMC estimation performance. LAI was derived from field measurements, and NDVI was extracted from Sentinel-2. Since the data of LAI and NDVI did not cover all sampling dates, only the SMC of the sampling dates with data was estimated when using these two parameters.
3.1.2. WCM
The WCM is a semi-empirical backscattering model proposed by Attema and Ulaby [
35]. The WCM divides the observed total backscattering into the three parts: the vegetation canopy scattering component, the multiple scattering component between vegetation and the soil surface, and the surface scattering component attenuated by the vegetation, which can be expressed as follows:
where
is the observed total backscattering coefficient,
is the scattering component from the vegetation canopy,
is the multiple scattering component formed between vegetation and soil, and
is the surface scattering component attenuated by vegetation layer.
represents the two-way attenuation through the vegetation canopy.
A and
B are the undetermined coefficients of the model, which are generally obtained by least-squared fitting according to the measured data.
and
represent the direct scattering and the attenuation properties of vegetation, respectively. In this study,
and
were parameterized by the three vegetation parameters (RVI, LAI and NDVI) that were used to construct the ratio function. Among the scattering components in the WCM model,
accounts for a very small proportion compared to the remaining two components, which can usually be ignored.
Using the McLaughlin series to expand the parameter
, the WCM can be further simplified [
47].
Ignoring the multiple scattering components between soil and vegetation, and retaining the first two terms of the McLaughlin series expansion, the WCM can be expressed as follows:
Taking
as the dependent variable and
as the independent variable, let
a = 2
AB and
b = −2
B/
cos, Equation (8) can be simplified to the following expression:
where
V represents the vegetation description parameter. According to the simplified WCM model (Equation (9)), the functional relationship between the backscattering coefficient of bare soil and the total backscattering coefficient can be established after obtaining the undetermined coefficients.
3.2. Surface Backscatter Modeling
3.2.1. CIEM Model
The integrated equation model (IEM) is a highly applicable bare soil scattering model [
48], which constructs the functional relationship between co-polarized backscattering coefficients and surface roughness parameters (e.g., RMS height (
s) and correlation length (
L)), incident angle and wavelength, and soil dielectric constant. The field measurement of the surface RMS height (
s) and correlation length (
L) are greatly affected by human factors. Baghdadi et al. [
40] constructed the functional expression between
s and
L based on a large number of roughness data from field surveys and C-band SAR data combined with simulated value of IEM model, and proposed a calibrated integrated equation model (CIEM), which reduced the dependence on
s or
L.
The empirical relationship between correlation length and RMS height in the CIEM model establish for C-band SAR can be expressed as follows:
where
L is the surface correlation length,
s is RMS height,
pp is the
HH or
VV polarization mode, and
a,
b,
c and
d are the undetermined coefficients. According to the different polarization modes, the values are as follows:
3.2.2. Optimal Roughness Parameters
The correlation length (
L) in the CIEM is expressed as an empirical function of the RMS height (
s), reducing the number of unknown roughness parameters. Therefore, only the
s was used as the roughness parameter in this study. The roughness parameter is an important parameter in microwave scattering model of bare soil. However, the measurement process of roughness parameters is complex and easily affected by human factors and the measuring instruments used, especially in densely vegetated areas [
49]. In this study, the optimal roughness parameter based on the global search method [
50] was used to parameterize the bare soil scattering model. The idea of this method is to assume that the roughness parameters in the study area have constant values during a certain period of time. The solution process of the optimal roughness parameters adopts the following method: given a roughness parameter range, for this study, the range of the RMS height was set to 1 mm–30 mm and the step size was 1 mm. Then the bare soil scattering model was constructed using the roughness parameter within the given range to estimate SMC. The SMC estimation accuracy with different roughness conditions was obtained, and finally the RMS height corresponding to the highest estimation accuracy was taken as the optimal roughness parameter of the study area. For the estimation of the overall SMC in the study area, the optimal roughness parameter was essentially used as an empirical parameter of the bare soil scattering model to improve the estimation performance of the overall SMC, and there would be deviations from the real roughness parameters at different locations in the study area. Therefore, it could not represent the real roughness conditions of all sampling points.
3.3. Accuracy Assessment
The accuracies of different models were compared and evaluated using statistical indicators, including the coefficient of determination (R
2, Equation (12)) and the root-mean-square error (RMSE, Equation (13)).
where
n is the number of sample points;
and
represent the measured SMC value and estimated SMC value of the i-th sample site respectively; and
is the mean value of the measured SMC values.
5. Conclusions
In this study, based on RADARSAT-2 SAR data and the measured SMC information, we investigated the potential of a simple method combined with three different vegetation parameters to estimate SMC under vegetation cover. In addition, the WCM was also used to retrieve SMC for comparison. Based on the study, the following conclusions can be drawn:
- (1)
As an empirical coefficient in the scattering model, the optimal roughness parameter could effectively improve the estimation accuracy of SMC.
- (2)
A reference incident angle could be used as an effective adjustment parameter to improve the estimation accuracy of SMC.
- (3)
Comparing with LAI and RVI, NDVI is more suitable as the vegetation description parameter of the model.
- (4)
In general, the estimation performance of the ratio method is better than that of WCM.
Despite the encouraging results of this study, there are certain limitations that need to be addressed in future studies. The measured data in this study only covered part of the wheat growing period. In the future, it will be necessary to evaluate the effectiveness of the proposed method over longer wheat growth cycles. In addition, the same method should be tested on other crop types to further verify its effectiveness.