## 4 Discussion  
At present, the ability to rapidly monitor crop growth and yield over large scales based on remote sensing data is an area of active research. There are wide research studies and applications for field crop monitoring by remote sensing technology being conducted, such as on wheat, rice, soybean, maize and potato [18; 43; 48; 49]. However, there are few reports describing the wheat grain-filling progress using time-series remote sensing data. The results of this study demonstrate the great potential of using time-series multispectral satellite imageries to accurately monitor the grain-filling process of multiple wheat cultivars. Remote sensing has been widely exploited to predict yield and explain senescence in many crops, with senescence being the key point for identifying crop maturity. The changes in VIs also tend to be obvious when senescence increases [26; 50]. In this study, the time-series Planet imageries covering the whole grain-filling process of winter wheat were used to explore its influence on the simulation of the grain-filling process by constructing VIs. As the best variable, the PSRI can accurately simulate the changing trend of dry and fresh thousand-grain weight in the overall model, and the NDRE has the best performance in multiple single-cultivar models (Table 3). The PSRI can be used to maximize the sensitivity of the ratio of carotenoids (such as \(\alpha\)-carotene and \(\beta\)-carotene) to chlorophyll, and its increase predicts increased canopy stress, the onset of vegetation senescence and the maturation of plant grains [51]. However, this change in VI values may occur for a variety of reasons, affecting the final winter wheat. The uncertain grain-filling habit of wheat makes the determination of plant senescence points challenging. Despite that, the positive correlation between maturity and the PSRI found in this research showed that the VI values increased when winter wheat maturity increased (Figure 5). This presents a potential for the use of remote sensing to assist in the decisions made regarding harvest winter wheat fields. It is obvious that the NDRE based on the red-edge band as an independent variable has a significant role in the wheat grain-filling process model. Similar to the NDVI, the NDRE is a vegetation index constructed based on the normalized ratio of NIR and red-edge bands to measure and analyze vegetation health in multispectral imageries. The red-edge band is the region between 680 and 750 nm and is considered the most significant sign of green vegetation. Because it has the point where the reflectance rises the fastest, this point is also known as the maximum value of the first derivative of the plant spectrum in this wavelength range [52]. As a transition band between the red and NIR bands, the red-edge band marks the boundary between chlorophyll absorption in the red region and scattering in the NIR region due to leaf internal structures [53]. Compared with the \"saturation\" phenomenon that the NDVI is prone to produce at the later stage of growth, the NDRE can reflect more sensitively the chlorophyll content of vegetation, such as after crop canopy closure [54; 55]. In the early stage of crop grain formation, the demand for sugar molecules produced by photosynthesis is usually high, and the demand for sugar molecules is reduced near the harvest period. To a certain extent, chlorophyll reflects the ability of crop photosynthesis activities; therefore, using NDRE information can help us realize the acquisition of crop grain growth to optimize the harvest time based on transformation in photosynthesis. Among the three indicators of the grain-filling process of winter wheat in this study, whether it is the overall model or the single-cultivar model, the simulation effect of the dry thousand-grain weight is the best, followed by the fresh thousand-grain weight, and the simulation effect of water content is the worst.