###### Abstract  
Remote sensing has been used as an important means of monitoring crop growth, especially for the monitoring of the formation of crop yield in the middle and late growth period. The information acquisition on the yield formation period of winter wheat is of great significance for winter wheat growth monitoring, yield estimation and scientific management. Hence, the main goal of this study was to verify the possibility of monitoring the grain-filling process of winter wheat and its in-field variability using an alternative non-destructive method based on orbital remote sensing. High-resolution satellite imageries (3 m) were obtained from the PlanetScope platform for three commercial winter wheat fields in Jiangsu Province, China during the reproductive stage of the winter wheat (185-215/193-223/194-224 days after sowing (DAS)). Based on the quantitative analysis of vegetation indices (VIs) obtained from high-resolution satellite imageries and three indicators of the winter wheat grain-filling process, linear, polynomial and logistic growth models were used to establish the relationship between VIs and the three indicators. The research showed a high Pearson correlation (\(p<0.001\)) between winter wheat maturity and most VIs. In the overall model, the remote sensing inversion of the dry thousand-grain weight has the highest accuracy and its R\({}^{2}\) reaches more than 0.8, which is followed by fresh thousand-grain weight and water content, the accuracies of which are also considerable. The results indicated a great potential to use high-resolution satellite imageries to monitor winter wheat maturity variability in fields and subfields. In addition, the proposed method contributes to monitoring the dynamic spatio-temporality of the grain-filling progression, allowing for more accurate management strategies in regard to winter wheat. type: _agronomy_  
202212451. 2451.  
to maturity, it is necessary to continuously and repeatedly select representative wheat ear samples in the field for destructive sampling, then carry out further manual threshing to measure the fresh weight and drying to measure the dry weight of the grain, and finally, perform further statistical analysis. This process is not only time consuming and labor intensive, but also requires destructive sampling during multiple observations, resulting in limited sample data. In addition, the data obtained are often in the form of points, which are different from the actual situation in the field; thus, it is impossible to monitor the wheat grain-filling process in a visual, accurate and dynamic way. To improve the accuracy of monitoring the grain-filling progress, several researchers have been looking for alternative methods, mainly by using digital image analysis [14; 15]. This technique reduces human errors due to an individual's subjectivity in judging wheat maturity, but still presents other challenges. Digital image evaluation is limited by its monitoring range and spectral band information. The standardized acquisition of photos is also very important as, for instance, the amount of light when the photos are taken can seriously affect the results [16; 17]. Classifying the wheat maturity using an image method solves the subjectivity of human viewing, but can add other sources of error in the classification process and does not represent the variability within a field. An alternative method consists of using remote sensing to map wheat maturity in a field and creating zonal management strategies that can account for maturity variability. Due to the macroscopic, objective, timely and economical characteristics of remote sensing technology, along with the development of information technology and the modernization requirements of precision agriculture and smart agriculture, spatial information technology represented by remote sensing technology has been widely used in the field of agriculture [18; 19; 20; 21].