###### Abstract
Remote sensing technology has been widely studied in the monitoring of crop maturity [23; 24; 25], and most of the studies were based on the following two methods: one of them is to use time-series remote sensing data to monitor the whole growth period of crops and to analyze the changes of characteristic parameters at the end of crop growth to determine the maturity period of crops; another way is to use remote sensing data to quantify physiological and biochemical parameters related to crop maturity characteristics, such as the leaf area index (LAI), leaf chlorophyll content (LCC), etc., and then achieve a crop maturity assessment [26; 27; 28]. At present, there are a lot of studies using remote sensing technology to monitor the growth of winter wheat and predict its yield and quality, but there are only a few studies using remote sensing technology, especially satellite imageries, to monitor the whole grain-filling process of winter wheat [29; 30; 31]. Remote sensing has been reported as a potential tool to monitor winter wheat maturity, but because of the lack of additional studies, results reported thus far do not agree on the most appropriate vegetation indices to use, justifying the need for new studies, especially under different varieties [32]. Therefore, based on the fact that winter wheat maturity monitoring methods are still highly subjective and do not consider the variability of plants in the field, utilizing satellite imageries can help to solve this issue. In view of the demand characteristics of large-scale monitoring in agriculture, the current remote sensing imageries used in crop monitoring were mainly originated via MODIS, NOAA/AVHRR, etc. [33; 34; 35]. In recent years, with the in-depth exploration of the potential of Sentinel series data and Landsat series data, the application of medium- and high-resolution multispectral satellite remote sensing imageries in precision agriculture has been brilliant [36; 37; 31]. However, these imageries have a relatively low spatial resolution and are difficult to apply to the high-precision remote sensing monitoring of winter wheat at the small field level. On the other hand, considering the characteristics of satellite imageries and the influence of weather factors, the revisit cycle of these medium- and high-resolution imageries is several days, which is relatively long, and it is difficult to obtain high-quality data in time. This limits the continuous monitoring of crops, making it difficult for crops to be monitored for specific indicators during critical growing periods. PlanetScope is the only remote sensing satellite system with global high-resolution, high-frequency, full coverage in the world. Currently, more than 100 satellites have been launched. They form a satellite constellation to ensure that imageries with a resolution of 3-5 m are obtained every day in any area. Since 2021, the newly launched SuperDoves have a fifth spectral band (red-edge), which is valuable for plant health assessments including NDRE. Time-series satellite imageries with high-spatial and -temporal resolution can better monitor the growth and health of crops and improve farming efficiency and monitoring accuracy in fields and subfields [38; 39; 40]. The main objectives of this research using high-temporal/spatial-resolution imageries were: (I) to provide evidence of the potential use of satellite imageries in monitoring the variability of the winter wheat grain-filling process and identify potential vegetation indices to monitor the maturity variability in fields and subfields; and (II) to propose a new methodology to monitor winter wheat maturity using vegetation indices to assist winter wheat growers in improving management strategies for their fields. It is hypothesized that vegetation indices derived from the satellite imageries can be used to monitor variability in the winter wheat grain-filling process, thus improving the precision of field management.