# Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
###### Abstract
It treated vegetation as a continuous and evenly distributed aggregate, then the effects of vegetation have been addressed, and soil water content in vegetation-covered areas has high inversion accuracy. Fusion of SAR and optical data, vegetation index, and WCM were used to invert soil moisture in vegetation-covered areas, the inversion accuracy is higher than that of SAR alone [22]. By the fusion of radiometer brightness temperatures and radar backscatter, it can reach a high soil moisture inversion accuracy, but on a lower spatial resolution [23]. In order to invert soil moisture in crop growing areas, soil moisture index (SMI) was created, and it can better monitor changes in soil water content in the near-surface of agricultural areas [24]. Traditional microwave remote sensing methods for monitoring the water content of vegetation are less. Empirical and semi-empirical models evolved earlier than physical models because they require fewer parameters. The inversion accuracy of vegetation water content based on empirical model is high, but it is limited to the study area [25]. Physical models can retrieve vegetation water content more accurately and used minimum data of sample points. It can be used for any vegetation type and SAR data type [26]. Recently, the study of vegetation water content monitoring based on passive microwave sensors has also been proposed. However, backscatter is greatest sensitivity to leaf moisture, but the trunk moisture is significant at low values of leaf moisture content [27]. This shows that Vegetation Optical Depth (VOD) is sensitive to changes in vegetation water content, as plants respond to water stress [28]. In agriculture and based on active microwave sensors, researchers investigated the effect of diurnal variation in vegetation water content of an agricultural canopy on backscatter for different radar configurations, the results showed the very significant effects that vegetation water content dynamics have on radar backscatter [29]. Most of the above studies were based on passive microwave sensors for vegetation water content monitoring in a large scale. However, there were few studies on quantitative inversion of crop canopy water content based on active microwave sensors in a plot scale. Moreover, the complementary remote sensing data sources for monitoring crop water content have not been discussed. Research on combining optics and SAR for remote sensing of vegetation has also been conducted [30]. However, comparative analyses of optical and SAR-based vegetation water content models are lacking, especially those that use data from the Sentinel-1 satellite. Sentinel-1 is a dual-polarized radar remote sensing satellite with the potential ability to rapidly and continuously monitor crop water content [31]. A model for monitoring winter wheat crop water content that is constructed from dual-polarized Sentinel-1 remote sensing data could enable early detection of water shortages in winter wheat crops, and ultimately provide decision support in agricultural production. Sentinel-2 is an optical satellite carrying a multi-spectral imager (MSI) with 13 bands as its main payload, and it can effectively monitor crop growth [32, 33]. In this study, Sentinel-1 and Sentinel-2 images were used to construct and compare models for monitoring winter wheat crop water content. The combination and complementarity of the two data sources provide a feasible method for monitoring winter wheat crop water content due to the short satellite revisit period, rich data sets, and reliability in bad weather. The model based on Sentinel-1 and Sentinel-2 data enables accurate and rapid estimates of winter wheat crop water content over large land areas. The structure of this paper is as follows. In Section 2 the study area, data sources, and the method used to verify model accuracy were described. In Section 3 the results of model accuracy verification and affecting factors for model estimates were analyzed. In Section 4 the findings were further discussed.