# Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
###### Abstract  
This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. In the Sentinel-1 approach, several enhanced radar indices were constructed by Sentinel-1 backscatter coefficient of imagery, and selected the one that was most sensitive to soil water content as the input parameter of a water cloud model. Finally, a water content inversion model for winter wheat crop was established. In the Sentinel-2 approach, the gray relational analysis was used for several optical vegetation indices constructed by Sentinel-2 spectral feature of imagery, and three vegetation indices were selected for multiple linear regression modeling to retrieve the wheat crop water content. 58 ground samples were utilized in modeling and verification. The water content inversion model based on Sentinel-2 optical images exhibited higher verification accuracy (R = 0.632, RMSE = 0.021 and nRMSE = 19.65%) than the inversion model based on Sentinel-1 SAR (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%). This study provides a reference for estimating the water content of wheat crops using data from the Sentinel series of satellites. remote sensing; Sentinel-1; Sentinel-2; winter wheat; crop water content +
Footnote †: journal:  
0000-0002-4000-0002-32000
Traditional optical remote sensing methods are mainly based on vegetation indices and radiation transmission models. Vegetation indices used to invert vegetation water content are based on water sensitive bands. These bands are mainly located in the region of near-infrared (NIR) and short-wave infrared (SWIR) [8]. A combination of NIR and SWIR is necessary to retrieve canopy water content at leaf level [9]. Previous studies have developed many vegetation indices, such as crop water stress index (CWSI) [10], normalized difference water index (NDWI) [11], ratio index [12], etc. These indices are validated on the scale of satellite remote sensing, airborne remote sensing, and ground remote sensing. To reveal the relationship between land surface temperature and canopy water content, thermal and vegetation indices were integrated to invert the canopy water content, and the accuracy was higher than that used only one of them [13]. The combination of thermal images and hyperspectral data can more precisely invert the canopy water content [14]. For radiation transmission models, the PROSPECT model and PROSAIL model can retrieve canopy water content more accurately [15]. The combination of hyperspectral data and PROCEST model has high inversion accuracy of canopy water content [16]. However, when it was applied to large area of canopy water content inversion based on satellite remote sensing data, the accuracy will be low. Synthetic-aperture radar (SAR) signals are strongly-penetrating and are unaffected by bad weather conditions, which makes SAR a useful tool for long-term agricultural monitoring in diverse environments [17; 18]. SAR signals can penetrate crop canopies, potentially overcoming the phenomenon of optical data saturation, which can occur in high density vegetation. For moderately vegetated land surfaces, such as agricultural fields, the accuracy of the soil moisture estimation decreases due to the sensitivity of microwave signals to canopy water content [19; 20]. These features, plus the sensitivity of scattered SAR microwaves to soil and vegetation characteristics, have led many researchers to explore SAR applications in agricultural monitoring. Many scholars carried out studies of soil water content inversion in cropland with vegetation cover. Water cloud model (WCM), as a semi-empirical model, was a classical vegetation contribution model [21].