# Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

^{0}is considered as a function of SAR configuration and soil parameters. Roughness parameters are the root mean square of height (h

_{rms}), which is the vertical standard deviation of the roughness, and the correlation length (L) represents the horizontal roughness scale. To improve the IEM accuracy, [17,18,19,20] have proposed an IEM’s semi-empirical calibration (CIEM) model by reforming L with the calibrated parameters, called Lopt. Lopt depends on SAR configuration and h

_{rms}[21]. So far, three different function shapes, namely, linear, exponential, and power, have been utilized to compute Lopt from the SAR parameters and h

_{rms}. The Gaussian function shape provides the best accuracy on simulated σ

^{0}from the modified IEM. The IEM modified by [17,18,19,20,21] (CIEM) was evaluated by using a large dataset at X- and C-bands. Their results have shown that the CIEM allows the soil moisture (M

_{v}) estimation with approximately 3 vol.% in the X-band and approximately 6 vol.% in the C-band [3,7,22,23,24,25,26,27]. Recently, Baghdadi et al. (2016) [21] have developed a new model based on Dubois model formulation (MDB) for M

_{v}estimation over bare soils. This model has not been evaluated so far.

^{0}) which is considered as backscattering coefficient, presents the amplitude of the signal returned from target to SAR antenna that is influenced by the soil surface characteristics which is linked to the soil dielectric constant (soil moisture) and soil surface roughness [28]. Retrieval of soil surface parameters from SAR data normally can be realized using the backscattering model that presents the relation between the target parameters (soil moisture and roughness) and the SAR sensor configurations such as incidence angle, polarization, and frequency [29]. Therefore, it can be definite that the models are engaged to simulate the relation between SAR sigma naught (σ

^{0}) value and sensor-surface parameters. In practice, the estimation of soil surface can be realized by inverting the model while the sensor parameters are known and soil moisture parameter is unknown. However, the main problem is that the radar signal is dependent on both surface roughness and soil moisture parameters. In this regards, we have two unknowns. The classical solution is to use two or more equations, which are multi-configuration SAR data solution [10]. In this approach, we need at least two images with different sensor parameters. The NNs as a machine learning algorithm has the ability to retrieve soil moisture based on the model training using only one image (configuration). In addition, such variables estimation is a nonlinear process and normally multifaceted and complex.

_{v}maps. Sahebi et al. (2004) have used the NNs inversion technique to assess soil surface parameters of bare soils from Radarsat-1 SAR data (HH polarization). In this study, the NNs system is trained by two datasets [32]. The first dataset is created using the IEM and the GOM (geometric optics model) [34] and the second one is composed of SAR images and in situ measurements (real dataset). For each dataset, two configurations are considered for NNs training. The first configuration uses backscattering and incidence angles as two inputs. The second configuration uses two different incidence angles as well as the corresponding backscattering coefficients as four inputs. As a result, it was shown that the second configuration provided a better result, considering the trained NNs by the real dataset. Additionally, Baghdadi et al. (2012) [35] have utilized NNs to estimate the soil surface parameters over bare soils. For this case, a synthetic dataset was generated using the CIEM for a wide range of soil conditions (roughness between 0.3 and 3.6 cm and soil moisture between 5 and 45 vol.%). Then, the NNs system was trained by the synthetic dataset and later validated using real SAR data and in situ measurements. In addition, a priori information on the target parameters was considered to improve the accuracy of NNs estimation. The accuracy of the estimation was 7 vol.% for soil moisture and 0.5 cm for soil roughness.

_{v}

_{)}estimation over the study area in Karaj, Iran. A real dataset has been collected to perform the evaluation. This dataset is divided into two parts, the first part is used to validate the simulation of σ

^{0}for both models, and the second part is used to estimate the accuracy of M

_{v}by inversion of CIEM and MDB. To estimate M

_{v}from the model, we generated a synthetic dataset of SAR in the C-band (VV and VH polarizations) for a wide range of soil parameters. Then, the neural networks are trained by the synthetic dataset (inputs include one polarization and incidence angle and outputs comprise soil moisture parameters). Afterward, the trained neural networks are used to estimate M

_{v}based on the real SAR observations obtained from Sentinel-1 images. Finally, the estimated M

_{v}are compared with the in situ measurements.

## 2. Study Area and Dataset

#### 2.1. Study Area

^{2}(Figure 1). This area is mostly covered with agricultural fields with diverse cultural types on a relatively flat relief plateau. The properties of soil texture are generally classified as silty clay loam (8% sand, 30% clay, and 62% silt). Additionally, the area experiences a dry climate in summer and a semi-humid to humid one in fall and winter. The diverse climate conditions in this area lead to a different amount of rain and various soil moisture throughout a year. The diverse climate allows cultivating various products. Thus, different levels of roughness have appeared in the study area for which the direction and depth of plow depend on the specific products. As illustrated in Figure 1, some specific areas include bare soils. To be more familiar with the weather of this area, Table 1 presents some weather data for the acquisition data dates.

#### 2.2. Satellite Data

#### 2.3. Field Data

_{v}in vol.%) was measured in 58 different bare soil land parcels, the areas of which ranged from 2500 to 9000 m

^{2}. The measurement has been realized with a Thetaprobe sensor based on the time domain reflectometry (TDR) concept. These measurements were achieved for the top of soil (0–5 cm depth), corresponding to the length of the Thetaprobe needles. Using the equation presented in the Thetaprobe soil moisture User Manual (Delta Devices Ltd., 1996), the direct outputs (DC voltage in mV) were converted to soil water content (m

^{3}m

^{−3}and vol.%). The TDR is well calibrated based on the gravimetric method. The number of measurements is adjusted based on the size of each area and soil texture, where at least 30 measurements are conducted for each land parcel. The mean of all measurements was later used for further analysis. The roughness (h

_{rms}) is also measured using a home-made profilometer needle with a length of 2 m and a resolution of 1 cm. (Figure 2). The measurements are parallel and perpendicular to the field furrow. On an average, three parallel and three perpendicular measurements are made for each land parcel. However, the exact number of measurements for each land parcel is based on the roughness non-uniformity of each area. Finally, the roughness value of each area is computed using Equation (1) and the images are processed by ENVI and Webplotdigitizer software packages.

_{i}is the surface elevation at point i in cm, and z is the average surface elevation in cm.

## 3. Methodology

^{0}) of each field was compared with the simulated sigma naught (σ

^{0}) calculated from each model for both VV and VH polarization. In the second step, soil moisture of each field was estimated from the Sentinel-1 images and then the accuracy was assessed, and the estimated values were compared with the ground measurement. For this step, the NNs were used. The explanation of the phases is discussed in the following subsections.

#### 3.1. Satellite Data Preprocessing

^{0}using radiometric calibration. Then, the calibrated Sentinel 1 data are georeferenced using the terrain correction algorithm.

^{0}are extracted for each field sample. For this, at first, the selected fields were detected on image based on their geographic coordinate. Then the boarder of each field was determined and the average of σ

^{0}for internal pixels was calculated.

#### 3.2. Evaluation of CIEM and MDB

_{rms}and M

_{v}are obtained from the images and the ground truth measurements are used as known parameters to simulate the σ

^{0}in VV and VH polarizations. Then, the simulated σ

^{0}are compared to the same value from Sentinel-1 SAR observation and the accuracy of the simulated values is computed using the RMSE (root mean square error) and the correlation coefficient (R

^{2}).

#### 3.3. Generating Simulated Datasets Using CIEM and MDB

_{rms}, θ, radar wavelength, and polarization (see Equations (2) and (3) for the C-band). Subsequently, the IEM model is changed to a calibrated IEM by replacing L with Lopt. In this study, the calibrated IEM model is used to generate an initial simulated data for the HV and VV polarizations.

_{rms}are expressed in cm. The formulations for Lopt (Equations (2) and (3)) were obtained with a Gaussian correlation function.

_{rms}and θ, respectively. Each set of data in the wide data, including h

_{rms}, ε

_{r}, and θ as the inputs, and σ

^{0}are generated by the CIEM. A similar approach, is considered for MDB, where instead of applying CIEM the wide data set based on the MDB model is generated.

#### 3.4. Inverse Modeling of Soil Moisture Using an NNs

^{0}and θ values and the output vector contains the soil moisture values. It is worth noting that the feed forward NNs with TRAINLM as the training function is used in this study. In a neural network, a hidden layer is considered as a layer in between the input and output layers. In this layer, neurons are applied as weighted inputs and an activation function is used to create an output or a series of outputs. The NNs used in this study included 20 neurons in the hidden layer.

## 4. Results and Discussion

^{0}) from images are compared with the same parameters simulated from the models. In the second part, the feasibility of the neural networks (NNs) for estimation of the soil surface moisture, from Sentinel-1 images is investigated. During the field campaigns, 58 field parcels were visited to analyze a total of 142 measurements. For evaluation of the models, all data were employed. Moreover, for soil moisture estimation based on NNs, almost 70% of the measurements, namely 100 measurements, were randomly selected. Then, they were used as training data and the remaining 30% of the measurements, namely 42 measurements, were applied to test the accuracy of the model.

#### 4.1. Comparison between the Images Extracted σ^{0} and the Model-Estimated σ^{0}

#### Evaluation of the Models

^{0}values extracted from Sentinel-1 data and those estimated using the CIEM and MDB models in both channels of polarizations, namely VV and HV. Both models have a better performance in the VV polarization compared to the HV polarization. Moreover, for VV polarization, the performance of CIEM is better than that of MDB (RMSE of 0.78 for CIEM and 1.45 for MDB). The MDB overestimates by 2.1 dB the SAR data for low soil surface moisture (SSM) values (SSM < 10 vol.%). This was already observed in the Figure 4 and Figure 5 of the study of Baghdadi et al. [36]. The RMSE of the σ

^{0}estimation in VV is 0.78 dB which is lower than that of VH (RMSE = 2.97 dB). However, for the MDB model, the obtained RMSEs are 1.45 and 1.96 dB for VV and VH polarizations, respectively. Thus, the CIEM model outperformed the modified Dubois model in σ

^{0}estimation.

_{rms}of 1.2 cm. Results showed that the relationship between SAR backscattering and SSM is logarithmic for CIEM and linear for MDB. Thus, the difference between CIEM and MDB simulations are observed mainly for SSM which is lower than 10 vol.%. Indeed, for SSM lower than 10 vol.% the MDB simulations well overestimates the CIEM simulations (by about 2 dB for SSM of 5 vol.%). This means that the CIEM simulates well the SAR backscattering corresponding to low SSM whereas the MDB probably overestimates the SAR backscattering for these same SSM-values. For VH polarization, similar observation is obtained as in the case of VV for SSM lower than 10 vol.%. However, for SSM higher than 25 vol.%, the MDB simulations at VH well underestimates the CIEM ones (by about 2.7 dB for SSM of 20 vol.%). Figure 4d clearly confirms that MDB overestimates the radar signal in VH for low SSM values and underestimates the radar signal for high SSM values.

#### 4.2. Estimation of Soil Surface Parameters Using Neural Networks

^{2}resulted from the models are presented in Table 3. The RMSE for the CIEM model is 3.0 vol.% in the VV polarization. The same accuracy was obtained for the MDB model with an RMSE of 3.3 vol.%. As explained previously, 70% of the field measurements were applied for training data and the remaining 30% were used for the accuracy estimation.

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**(

**a**) A roughness device made in the laboratory; (

**b**) an example of its processing (digitizing and calculation) by Webplotdigitizer and ENVI software.

**Figure 3.**The flowchart of the proposed method (θ: incidence angle, σ

^{0}: backscattering coefficient, NNs: neural networks).

**Figure 4.**The relationship between the σ

^{0}values extracted from synthetic aperture radar (SAR) images and the σ

^{0}values estimated using the calibrated integral equation model (IEM) and modified Dubois models in VV and VH polarizations. (

**a**) CIEM in VV; (

**b**) CIEM in VH; (

**c**) modified Dubois model (MDB) in VV; and (

**d**) MDB in VH.

**Figure 5.**The relationship between the measured moisture (vol.%) vs. the estimated moisture (vol.%) using the neural networks. (

**a**) CIEM with VV; (

**b**) CIEM with VH; (

**c**) MDB with VV; (

**d**) MDB with VH.

Date (dd/mm/yyyy) | Solar Radiation (kWh/m^{2}) | Temperature [min–max] (°C) | Daily Precipitation (mm) | Monthly Precipitation (mm) | Air Humidity (%) | Wind Speed m/s [min–max] | Visibility (Km) |
---|---|---|---|---|---|---|---|

21/09/2017 | 5.8 | [15.1–31.4] | 0 | 0 | 14 | [5–7] | >10 |

19/01/2018 | 4.7 | [4.0–13.5] | 0 | 24 | 32 | [8–22] | 9 |

08/03/2018 | 4.8 | [7.1–16.9] | 0 | 18 | 34 | [6–10] | 9 |

26/04/2018 | 5.0 | [12.6–23.2] | 0 | 12 | 23 | [7–16] | >10 |

**Table 2.**The field measurements and satellite images used in this study (Asc: Ascending, Des: Descending).

Date (dd/mm/yyyy) | Orbit | Incidence Angle θ (°) Over the Study Area [near–far] | # Field Samples | Moisture (%) [min-mean-max] | Soil Roughness (cm) [min–max] |
---|---|---|---|---|---|

21/09/2017 | Asc | [37–38] | 14 | [2.19–10.83–17.7] | [0.64–2.77] |

19/01/2018 | Asc | [37–38] | 14 | [3.22–8.87–13.68] | [1.54–3.08] |

08/03/2018 | Asc | [37–39] | 15 | [14.62–20.79–26.12] | [0.64–3.43] |

26/04/2018 | Des | [37–39] | 15 | [15.45–23.71–30.65] | [0.64–2.54] |

**Table 3.**The statistical parameters used to compare the measured field and estimated moisture (%) using the neural networks based on MDB and CIEM models with VV and VH polarizations.

Moisture (vol.%) (21-9-2017) | Moisture (vol.%) (19-1-2018) | Moisture (vol.%) (8-3-2018) | Moisture (vol.%) (26-4-2018) | Moisture (vol.%) Full Series | |||
---|---|---|---|---|---|---|---|

CIEM | RMSE | VV | 1.6 | 2.7 | 3.7 | 3.5 | 3.0 |

VH | 6.1 | 4.9 | 6.0 | 6.4 | 5.9 | ||

MDB | RMSE | VV | 2.3 | 4.0 | 4.0 | 2.3 | 3.3 |

VH | 8.6 | 8. 6 | 8.9 | 9.2 | 8.8 |

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**MDPI and ACS Style**

Mirsoleimani, H.R.; Sahebi, M.R.; Baghdadi, N.; El Hajj, M.
Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks. *Sensors* **2019**, *19*, 3209.
https://doi.org/10.3390/s19143209

**AMA Style**

Mirsoleimani HR, Sahebi MR, Baghdadi N, El Hajj M.
Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks. *Sensors*. 2019; 19(14):3209.
https://doi.org/10.3390/s19143209

**Chicago/Turabian Style**

Mirsoleimani, Hamid Reza, Mahmod Reza Sahebi, Nicolas Baghdadi, and Mohammad El Hajj.
2019. "Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks" *Sensors* 19, no. 14: 3209.
https://doi.org/10.3390/s19143209