# Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

^{2}) between the measured and estimated SSM values was 0.89. Bousbih et al. [20] retrieved SSM in the semi-arid region of the Kairouan plain using VV polarization backscattering coefficients recorded by Sentinel-1 and normalized difference vegetation index (NDVI) data from Sentinel-2 images. They compared the SSM values using the water cloud model (WCM) and a neural network (NN) and found that both of the measurements were accurate, with a root mean square error (RMSE) of less than 5%. Wang et al. [21] used Sentinel-1 SAR data to estimate SSM in wheat farmlands based on the WCM and the advanced integral equation model (AIEM). To evaluate the accuracy of the different optical datasets used in SSM retrieval, they compared seven vegetation indices based on Landsat-8, Sentinel-2, and GF-1 data and concluded that the Sentinel-2 data achieved higher accuracy than the data from the other sources.

## 2. Study Area and Dataset

#### 2.1. Study Area

^{2}in 2019.

#### 2.2. Remote Sensing Data

#### 2.2.1. Sentinel-1 Data

#### 2.2.2. Sentinel-2 Data

#### 2.3. In Situ Measurements

#### 2.3.1. SSM Measurements

#### 2.3.2. Vegetation Parameters

^{2}of each wheat field. The fresh weight (${W}_{F}$) was measured after the wheat samples were brought back to the laboratory, and the dry weight (${W}_{D}$) was obtained by drying the wheat in an oven. The specific equation for VWC is as follows:

## 3. Methods

^{2}) and root mean square error (RMSE) were calculated to evaluate the accuracy of the SSM estimates, and the SSM values were mapped throughout the study area. More details on each part of the soil moisture retrieval algorithm are given below.

#### 3.1. Modified WCM

^{2}). The empirical parameters a and b depend on the vegetation type and the incident angle. Bindlish and Barros [30] proposed values for a and b for different land cover types. As the crop in the study area was wheat, winter wheat was selected as the land cover type, with values of 0.0018 and 0.138 for a and b, respectively.

#### 3.2. Building the VWC Model

_{1}(central wavelength = 1.374 μm), SWIR

_{2}(central wavelength = 1.610 μm), and SWIR

_{3}(central wavelength = 2.190 μm). As the spatial resolution of SWIR

_{1}is only 60 m, SWIR

_{2}and SWIR

_{3}were selected to calculate the vegetation indices (NDWI

_{1610}and NDWI

_{2190}) in this study.

_{1610}, NDWI

_{2190,}or NDRI).

#### 3.3. SVR Estimation of SSM

^{2}.

## 4. Results and Discussion

#### 4.1. Estimating VWC from Sentinel-2 Data

^{2}and RMSE between the estimated and measured VWC values based on the 16 validation data points are shown in Table 2.

^{2}and RMSE values of 0.917 and 0.162, respectively. The R

^{2}gradually increased with an increasing number of characteristic parameters, whereas the RMSE gradually decreased. Regarding the characteristic parameters of multiple regression models that included VWC with three vegetation indices (Figure 4g–j), NDVI + NDWI

_{2190}+ NDRI yielded the best result, with R

^{2}and RMSE values of 0.963 and 0.108, respectively. NDVI + NDWI

_{1610}+ NDWI

_{2190}+ NDRI yielded the best result of all combinations. The R

^{2}between the estimated and measured SSM was 0.965, and the correlation between them was statistically significant at the 0.01 level. Compared with the inputs of the three characteristic parameters, the R

^{2}values for model No. k were 0.079, 0.027, 0.057, and 0.002 higher, respectively. RMSE decreased by 0.084, 0.035, 0.065, and 0.006, respectively. Therefore, model No. k was used as the modified VWC estimation model in this study.

#### 4.2. SSM Retrieval Results Using the Modified WCM

^{2}and RMSE values are presented in Table 3. In order to evaluate the accuracy of the modified model, we compared the performances with the original WCM (where VWC is composed of NDVI) and the radar backscatter coefficient, which ignores the influence of vegetation.

^{2}between the estimated and measured SSM was 0.86 for the modified WCM, and the RMSE of the estimated SSM was 2.119 %, while the R

^{2}and RMSE values of SSM were 0.801 and 2.992%, and 0.661 and 3.314% respectively, for the original WCM and radar backscatter coefficient. The correlation between the estimated and measured SSM was statistically significant at the 0.01 level.

^{2}between the estimated and measured SSM was 0.667 for the modified WCM, and the RMSE of the estimated SSM was 3.629%, while the R

^{2}and RMSE values of SSM were 0.586 and 3.994%, and 0.451 and 4.192% respectively, for the original WCM and radar backscatter coefficient. The correlation between the estimated and measured SSM was statistically significant at the 0.05 level.

#### 4.3. Discussion

^{2}of 0.903 and an RMSE of 0.014 cm

^{3}/cm

^{3}. Zhao et al. [40] estimated the SSM for winter wheat fields using Sentinel-1 and Sentinel-2 data. Based on near-infrared, red, and shortwave infrared bands, they proposed a new fusion vegetation index (FVI) to estimate VWC. They used the Maclaurin series to improve the WCM and considered that the single-polarization backscattering coefficients could be replaced by VV/VH. As a result of their retrieval analysis, they obtained an R

^{2}value of 0.7642 and an RMSE of 0.0209 cm

^{3}/cm

^{3}in VV/VH; their R

^{2}and RMSE values were 0.6791 and 0.0249 for VV polarization, and 0.5151 and 0.0289 for VH polarization, respectively. In the present study, vegetation indices including NDVI, NDWI

_{1610}, NDWI

_{2190}, and NDRI were used (NDRI was composed of two red edge bands). To the best of the authors’ knowledge, this study is the first to propose removing the influence of vegetation on SSM estimation by using the red side bands in Sentinel-2 data. Baghdadi et al. [7] estimated the SSM of crop fields and grasslands from Sentinel-1/2 data. They combined the WCM with the integral equation model (IEM) [41] using real data composed of a C-band radar backscattered signal, NDVI, soil moisture, and surface roughness values. Their results indicated that the soil contribution to the total radar backscatter signal was lower in VH polarization than in VV polarization. Zeng et al. [17] studied SSM under different vegetation covers based on Sentinel-1A and SVR techniques and concluded that VV polarization could achieve high retrieval accuracy. Wang et al. [42] combined full polarization Radarsat-2 SAR data and SVR techniques to estimate soil moisture in sparsely vegetated arid areas. They determined that the inversion accuracy of the co-polarization data (VV or HH polarization) was higher than that of the cross-polarization data (VH or HV polarization). Comparing the inversion results reported in this study with those of the previous studies mentioned above, it is possible to conclude that VV polarization is more sensitive to SSM than VH polarization.

## 5. Conclusions

_{1610}, NDWI

_{2190}, and NDRI), was able to effectively remove the influence of the vegetation canopy on the backscattering coefficient of the Sentinel-1SAR data;

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Miralles, D.G.; Teuling, A.J.; Heerwaarden, C.; De Arellano, J.V. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci.
**2014**, 7, 345–349. [Google Scholar] [CrossRef] - Vereecken, H.; Huisman, J.A.; Franssen, H.H.; Brüggemann, N.; Bogena, H.R.; Kollet, S. Soil hydrology: Recent methodological advances, challenges, and perspectives. Water Resour. Res.
**2015**, 51, 2616–2633. [Google Scholar] [CrossRef] - Cho, E.; Choi, M. Regional scale spatio-temporal variability of soil moisture and its relationship with meteorological factors over the Korean peninsula. J. Hydrol.
**2014**, 516, 317–329. [Google Scholar] [CrossRef] - Brocca, L.; Tullo, T.; Melone, F.; Moramarco, T.; Morbidelli, R. Catchment scale soil moisture spatial–temporal variability. J. Hydrol.
**2012**, 422, 63–75. [Google Scholar] [CrossRef] - Jiang, J.; Hu, D.; Li, Y.; Tang, X.; Li, J. Research of soil moisture retrieval model of wheat covered surface based on mimics model. J. Triticeae Crop
**2015**, 35, 707–713. [Google Scholar] - Dong, J.Z.; Wade, C.; Kenneth, J.T.; Michael, H.C. Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation. Remote Sens. Environ.
**2020**, 242, 111756. [Google Scholar] [CrossRef] - Baghdadi, N.; El, H.M.; Zribi, M.; Bousbih, S. Calibration of the water cloud model at C-band for winter crop fields and grasslands. Remote Sens.
**2018**, 9, 969. [Google Scholar] [CrossRef] [Green Version] - Bai, X.; Zhang, L.; He, C.; Zhu, Y. Estimating regional soil moisture distribution based on NDVI and land surface temperature time series data in the upstream of the heihe river watershed, northwest china. Remote Sens.
**2020**, 12, 2508. [Google Scholar] [CrossRef] - Soliman, A.; Heck, R.J.; Brenning, A.; Brown, R.; Miller, S. Remote sensing of soil moisture in vineyards using airborne and ground-based thermal inertia data. Remote Sens.
**2013**, 5, 3729–3748. [Google Scholar] [CrossRef] [Green Version] - Wang, J.; Ding, J.L.; Chen, W.Q.; Yang, A.X. Microwave modeling of soil moisture in oasis regional scale based on sentinel-1 radar images. J. Infrared Millimeter Waves
**2017**, 36, 120–126. [Google Scholar] - Peischl, S.; Walker, J.P.; Ye, N.; Ryu, D.; Kerr, Y. Sensitivity of multi-parameter soil moisture retrievals to incidence angle configuration. Remote Sens. Environ.
**2014**, 143, 64–72. [Google Scholar] [CrossRef] - Pan, M.; Sahoo, A.K.; Wood, E.F. Improving soil moisture retrievals from a physically-based radiative transfer model. Remote Sens. Environ.
**2014**, 140, 130–140. [Google Scholar] [CrossRef] - Zhu, L.; Walker, J.P.; Shen, X. Stochastic ensemble methods for multi-SAR-mission soil moisture retrieval. Remote Sens. Environ.
**2020**, 251, 112099. [Google Scholar] [CrossRef] - Aubert, M.; Baghdadi, N.N.; Zribi, M.; Ose, K. Toward an operational bare soil moisture mapping using terrasar-x data acquired over agricultural areas. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2013**, 6, 900–916. [Google Scholar] [CrossRef] - He, B.; Xing, M.; Bai, X. A synergistic methodology for soil moisture estimation in an alpine prairie using radar and optical satellite data. Remote Sens.
**2014**, 6, 10966–10985. [Google Scholar] [CrossRef] [Green Version] - Zhou, P.; Ding, J.L.; Wang, F.; Guljamal, U.; Lei, L.P. Retrieval methods of soil water content in vegetation covering areas based on multi-source. J. Remote Sens.
**2010**, 14, 959–973. [Google Scholar] - Zeng, X.; Xing, Y.; Wei, S.; Zhang, Y.; Wang, C. Soil water content retrieval based on sentinel-1a and landsat 8 image for bei’an-heihe expressway. J. Eco-Agric.
**2017**, 25, 118–126. [Google Scholar] - Temimi, M.; Leconte, R.; Chaouch, N.; Sukumal, P.; Khnbilvardi, R.; Brissette, F. A combination of remote sensing data and topographic attributes for the spatial and tempeoral monitoring of soil wetness. J. Hydrol.
**2010**, 388, 28–40. [Google Scholar] [CrossRef] - Attarzadeh, R.; Amini, J.; Notarnicola, C.; Greifeneder, F. Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at plot scale. Remote Sens.
**2018**, 10, 1285. [Google Scholar] [CrossRef] [Green Version] - Bousbih, S.; Zribi, M.; El Hajj, M.; Baghdadi, N.; Lilichabaane, Z.; Gao, Q.; Fanise, P. Soil moisture and irrigation mapping in a semi-arid region, based on the synergetic use of Sentinel-1 and Sentinel-2 data. Remote Sens.
**2018**, 10, 1953. [Google Scholar] [CrossRef] [Green Version] - Wang, Q.; Li, J.; Jin, T.; Chang, X.; Li, D. Comparative analysis of Landsat-8, Sentinel-2, and GF-1 data for retrieving soil moisture over wheat farmlands. Remote Sens.
**2020**, 12, 2708–2714. [Google Scholar] - Kim, H.O.; Yeom, J.M. Effect of red-edge and texture features for object-based paddy rice crop classification using rapideye multi-spectral satellite image data. Int. J. Remote Sens.
**2014**, 35, 7046–7068. [Google Scholar] [CrossRef] - Fang, C.; Wang, L.; Hanqiu, X.U. A comparative study of different red edge indices for remote sensing detection of urban grassland health status. J. Geo-Inform. Sci.
**2017**, 19, 1382–1392. [Google Scholar] - Pasolli, L.; Notarnicola, C.; Bruzzone, L. Estimating soil moisture with the support vector regression technique. IEEE Geosci. Remote Sens. Lett.
**2011**, 8, 1080–1084. [Google Scholar] [CrossRef] - Lee, J.S. Speckle filtering of synthetic aperture radar images: A review. Remote Sens. Rev.
**1994**, 8, 313–340. [Google Scholar] [CrossRef] - Attema, E.P.W.; Fawwaz, T. Vegetation modeled as a water cloud. Radio Sci.
**1978**, 13, 357–364. [Google Scholar] [CrossRef] - Bao, Y.; Lin, L.; Wu, S.; Abdalla, K.; Petropoulos, G.P. Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model. Int. J. Appl. Earth Obs. Geoinf.
**2018**, 72, 76–85. [Google Scholar] [CrossRef] - Yadav, V.P.; Prasad, R.; Bala, R.; Vishwakarma, A.K. Estimation of soil moisture through water cloud model using sentinel -1A SAR data. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019. [Google Scholar]
- Yang, G.; Yue, J.; Li, C.; Feng, H.; Lan, Y. Estimation of soil moisture in farmland using improved water cloud model and radarsat-2 data. Trans. Chin. Soc. Agric. Eng.
**2016**, 32, 146–153. [Google Scholar] - Bindlish, R.; Barros, A.P. Parameterization of vegetation backscatter in radar-based, soil moisture estimation. Remote Sens. Environ.
**2001**, 76, 130–137. [Google Scholar] [CrossRef] - Gutman, G.; Ignatov, A. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens.
**1998**, 19, 1533–1543. [Google Scholar] [CrossRef] - Lin, L.; Bao, Y.; Zuo, Q.; Fang, S. Soil moisture retrieval over vegetated areas based on sentinel-1 and fy-3c data. Remote Sens. Technol. Appl.
**2018**, 33, 750–758. [Google Scholar] - Deering, D.W. Rangeland Reflectance Characteristics Measured by Aircraft and Spacecraft Sensors; Texas A&M University: College Station, TX, USA, 1978. [Google Scholar]
- Gao, B. NDWI-a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ.
**1996**, 58, 257–266. [Google Scholar] [CrossRef] - Pasolli, L.; Notarnicola, C.; Bertoldi, G.; Bruzzone, L.; Remelgado, R.; Greifeneder, F. Estimation of soil moisture in mountain areas using svr technique applied to multiscale active radar images at c-band. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2017**, 8, 262–283. [Google Scholar] [CrossRef] - Bertoldi, G.; Chiesa, S.D.; Notarnicola, C.; Pasolli, L.; Niedrist, G.; Tappeiner, U. Estimation of soil moisture patterns in mountain grasslands by means of SAR radarsat2 images and hydrological modeling. J. Hydrol.
**2014**, 516, 245–257. [Google Scholar] [CrossRef] - Wang, J.; Cai, L.; Zhao, X. Multiple-instance learning via an RBF kernel-based extreme learning machine. J. Intell. Syst.
**2016**, 26, 185–195. [Google Scholar] [CrossRef] - Guo, J.; Liu, J.; Ning, J.; Han, W. Construction and validation of soil moisture retrieval model in farmland based on sentinel multi-source data. Trans. CSAE
**2019**, 35, 71–78. [Google Scholar] - Oh, Y. Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces. IEEE Trans. Geosci. Remote Sens.
**2004**, 42, 596–601. [Google Scholar] [CrossRef] - Zhao, J.H.; Zhang, B.; Li, N.; Guo, Z.W. Cooperative inversion of winter wheat covered surface soil moisture based on Sentinel-1/2 remote sensing data. J. Electron. Inform. Technol.
**2021**, 43, 692–699. [Google Scholar] - Fung, A.K.; Li, Z. Backscattering from a randomly rough dielectric surface. IEEE Trans. Geosci. Remote Sens.
**1992**, 30, 356–369. [Google Scholar] [CrossRef] - Wang, Y.; Kong, J.; Yang, L.; Li, J.F.; Zhang, W. Remote sensing inversion of soil moisture in vegetation-sparse arid areas based on SVR. J. Geo-inform. Sci.
**2019**, 21, 1275–1283. [Google Scholar]

**Figure 2.**Remote sensing images of the study area. (

**a**) Sentinel-1 SAR image (VV polarization). (

**b**) Sentinel-2 composite false red green blue (RGB) images (Band 8 = red, Band 4 = green, and Band 3 = blue).

**Figure 5.**Graph of relationship between VV, VH polarization backscattering coefficients. (

**a**) VV polarization. (

**b**) VH polarization.

**Figure 6.**Scatterplot of estimated versus measured SMC (%) values. (

**a**) Radar backscattering coefficient of VV polarization. (

**b**) Radar backscattering coefficient of VH polarization. (

**c**) Soil backscattering coefficient of VV polarization by the original WCM. (

**d**) Soil backscattering coefficient of VH polarization by the original WCM. (

**e**) Soil backscattering coefficient of VV polarization by the modified WCM. (

**f**) Soil backscattering coefficient of VH polarization by the modified WCM.

**Figure 7.**SSM maps of wheat-covered fields in study area. (

**a**) 3 April 2019. (

**b**) 3 May 2019. (

**c**) 23 May 2019.

Phenology | Sentinel-1 | Sentinel-2 | In Situ |
---|---|---|---|

Jointing stage | 4-9-2019 | 4-3-2019 | 4-7-2019 |

Heading stage | 5-3-2019 | 5-3-2019 | 5-3-2019 |

Filling stage | 5-27-2019 | 5-23-2019 | 5-27-2019 |

No. | The Multiple Regression Models | R^{2} | $\mathrm{RMSE}/\mathrm{kg}\xb7{\mathrm{m}}^{-2}$ | Note |
---|---|---|---|---|

a | $\mathrm{y}=0.3{\mathrm{e}}^{2.538{\mathrm{x}}_{1}}-0.019{\mathrm{e}}^{3.949{\mathrm{x}}_{2}}-0.058$ | 0.871 | 0.201 | ${\mathrm{x}}_{1}$ is NDVI, ${\mathrm{x}}_{2}$ is NDWI _{1610},${\mathrm{x}}_{3}$ is NDWI _{2190}, and${\mathrm{x}}_{4}$ is NDRI |

b | $\mathrm{y}=0.384{\mathrm{e}}^{2.538{\mathrm{x}}_{1}}-0.14{\mathrm{e}}^{2.635{\mathrm{x}}_{3}}-0.051$ | 0.876 | 0.197 | |

c | $\mathrm{y}=0.15{\mathrm{e}}^{3.949{\mathrm{x}}_{2}}+0.22{\mathrm{e}}^{2.635{\mathrm{x}}_{3}}+0.04$ | 0.799 | 0.251 | |

d | $\mathrm{y}=0.376{\mathrm{e}}^{3.933{\mathrm{x}}_{4}}-0.064{\mathrm{e}}^{3.949{\mathrm{x}}_{2}}+0.151$ | 0.907 | 0.171 | |

e | $\mathrm{y}=0.391{\mathrm{e}}^{3.933{\mathrm{x}}_{4}}-0.088{\mathrm{e}}^{2.635{\mathrm{x}}_{3}}+0.169$ | 0.908 | 0.17 | |

f | $\mathrm{y}=0.101{\mathrm{e}}^{2.538{\mathrm{x}}_{1}}+0.218{\mathrm{e}}^{3.933{\mathrm{x}}_{4}}+0.019$ | 0.917 | 0.162 | |

g | $\mathrm{y}=0.402{\mathrm{e}}^{2.538{\mathrm{x}}_{1}}-0.26{\mathrm{e}}^{3.949{\mathrm{x}}_{2}}-0.439{\mathrm{e}}^{2.635{\mathrm{x}}_{3}}-0.034$ | 0.886 | 0.189 | |

h | $\mathrm{y}=0.199{\mathrm{e}}^{2.538{\mathrm{x}}_{1}}-0.234{\mathrm{e}}^{3.949{\mathrm{x}}_{2}}+0.306{\mathrm{e}}^{3.933{\mathrm{x}}_{4}}+0.048$ | 0.938 | 0.14 | |

i | $\mathrm{y}=0.019{\mathrm{e}}^{3.949{\mathrm{x}}_{2}}-0.439{\mathrm{e}}^{2.635{\mathrm{x}}_{3}}+0.39{\mathrm{e}}^{3.933{\mathrm{x}}_{4}}+0.171$ | 0.908 | 0.17 | |

j | $\mathrm{y}=0.32{\mathrm{e}}^{2.538{\mathrm{x}}_{1}}-0.433{\mathrm{e}}^{2.635{\mathrm{x}}_{3}}+0.375{\mathrm{e}}^{3.933{\mathrm{x}}_{4}}+0.088$ | 0.961 | 0.11 | |

k | $\mathrm{y}=0.261{\mathrm{e}}^{2.538{\mathrm{x}}_{1}}+0.127{\mathrm{e}}^{3.949{\mathrm{x}}_{2}}$ $-0.604{\mathrm{e}}^{2.635{\mathrm{x}}_{3}}+0.428{\mathrm{e}}^{3.933{\mathrm{x}}_{4}}+0.092$ | 0.965 | 0.105 |

Reference Samples | VV | VH | ||
---|---|---|---|---|

R^{2} | RMSE (%) | R^{2} | RMSE (%) | |

radar backscattering coefficient | 0.661 | 3.314 | 0.451 | 4.192 |

soil backscattering coefficient by original WCM | 0.801 | 2.992 | 0.586 | 3.994 |

soil backscattering coefficient by modified WCM | 0.86 | 2.119 | 0.667 | 3.629 |

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**MDPI and ACS Style**

Li, Y.; Zhang, C.; Heng, W.
Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2. *Water* **2021**, *13*, 1981.
https://doi.org/10.3390/w13141981

**AMA Style**

Li Y, Zhang C, Heng W.
Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2. *Water*. 2021; 13(14):1981.
https://doi.org/10.3390/w13141981

**Chicago/Turabian Style**

Li, Yan, Chengcai Zhang, and Weidong Heng.
2021. "Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2" *Water* 13, no. 14: 1981.
https://doi.org/10.3390/w13141981