## 1 Introduction  
In recent years, with the development of imaging spectrometry, using remote sensing data to detect the chemical characteristics of vegetation has become an important topic in the study of global change. Vegetation water content (VWC) has been recognized as a key variable for assessing crop physiological status, due to its close association with plant transpiration, photosynthesis, vegetation stress, and biomass productivity [1]. The water deficit directly affects the physiological and biochemical processes and morphological structures of plants, thus affecting growth. Knowledge of vegetation moisture can guide accurate irrigation, forecast yield, evaluate natural droughts, and predict forest fires and other natural disasters [2; 3]. Therefore, the estimation of high-precision and long time-series VWC products, especially during key phenological stages, is important for vegetation research. The conventional field-based methods for VWC measurement are destructive and labor-intensive, especially in large areas with great within-field variabilities in soil infiltration characteristics or microtopography [4]. As an alternative, remote sensing techniques, with which it is easier to acquire long time-series VWC spatial information over a wide range nondestructively, can overcome the above shortcomings [5]. There is a long history of using remote sensing data to estimate vegetation water information. Commonly used remote sensing technologies include optical and microwave remote sensing. The former refers to the remote sensing technology that detects the target surface objects by using the reflection characteristics of the visible light band. The latter refers to the remote sensing technology of microwave electromagnetic wave with a wavelength of 1-1000 mm and can be divided into active remote sensing and passive remote sensing according to its working principles. For optical remote sensing, some empirical methods exploit the obvious correlation between biophysical parameters, such as the Normalized Difference Vegetation Index (NDVI) [6], land surface temperature (LST) [7], and other variables, to assess VWC. Moreover, a reduction in VWC will cause variations in spectral reflectance. Red, the near-infrared (NIR), and the short-wave infrared (SWIR) bands are sensitive to vegetation water stress and are used to compose various water indices to indicate VWC [8]. Common indices include the normalized difference water index (NDWI) [9], the normalized difference infrared index (NDII) [10], the simple ratio water index (SRWI) [11], and the global vegetation water moisture index (GVMI) [12]. Meanwhile, microwave remote sensing also has been used to estimate VWC, since the dielectric constant of water and dry vegetation differs significantly, and thus the amount of water stored in vegetation directly affects how microwave radiation interacts with vegetation canopies. For active microwave remote sensing, studies have shown that the scattering coefficients and the polarization of signals are sensitive to VWC [13]. [PERSON] et al. [14] and [PERSON] et al. [15] suggested that retrieving VWC using the L-band radar vegetation index (RVI) and HV radar backscattering was feasible. For passive microwave remote sensing, researchers have also illustrated the feasibility of detecting VWC based on brightness temperature, owing to its effect on the emissivity of the canopy [16; 17]. Although the spatial resolution of VWC obtained by optical remote sensing inversion is usually high, the optical images are vulnerable to cloud and fog, resulting in missing information. In comparison, owing to the long wavelength of microwave signals, they usually have strong penetrative abilities and are not affected by cloud cover. However, microwave signals can not only penetrate clouds, but also the thickest vegetation canopies, and, therefore, the measured vegetation information from microwave signals is affected by the roughness of the ground, soil moisture, and other factors [18].