## 5 Conclusions  
In this study, we constructed an NDVI time series dataset based on the fusion of GF-6 and Landsat 8 images using STARFM. We combined this dataset with the phenological changes in the major crops in the study area and extracted data related to permanently abandoned farmland for the period of 2021-2022 based on the threshold segmentation of the annual NDVI change and overlay analysis. We verified the accuracy of the extraction results using visual interpretation. Based on the results of the abandoned farmland data extraction, the following conclusions were drawn regarding the features of abandoned farmland in different elevation ranges, slope ranges, distances from residential areas, and road network densities:  
1. By fusing GF-6 and Landsat 8 images using STARFM to construct a dataset for time series analysis, the accuracy of the results can be effectively improved compared with the use of GF-6 data alone. This study is based on the difference in the NDVI valuesat different stages of the crop growth cycle, which is more effective for distinguishing between abandoned land and cultivated land. The accuracy of the results is higher than that obtained using SVM. The method is feasible, and the results are valid and reliable;
2. On the basis of the extraction results, the features of abandoned farmland in the study area were analyzed. The results show that most of the farmland in Minachi County is located in areas of terrain suitable for farming and that abandoned cultivated land is not as affected by elevation and slope as it is by the density of the road network and proximity to residential areas. Abandonment rates tend to be higher in areas with a high road density and close to residential areas. These results may help in the management of cropland abandonment and the regulation of farmland. Due to the data collection and fusion methods used, this study has some limitations:  
1. Although the GF-6 data are of high resolution, they have fewer bands than other sensor-type data. Consequently, these data can only be analyzed by calculating several VIs for analysis, and only the NDVI was used in this study, which is a limitation for abandoned farmland data extraction;
2. When high-resolution NDVI fusion is based on STARFM, the fused value of regions with abrupt NDVI changes is larger than the real value. The fusion accuracy decreases with the increase in the difference between the base date and the prediction date, which has an impact on the accuracy of the extraction results for abandoned farmland. Moreover, it is difficult to generalize the results to regions where cloudy and rainy weather persist for a long period of time.  
Conceptualization, J.D.; methodology, Y.G.; software, Y.G.; validation, Y.G.; formal analysis, Y.G.; investigation, W.L.; resources, W.L.; data curation, Y.G., X.C. and L.L.; writing--original draft preparation, Y.G.; writing--review and editing, Y.G., X.C. and L.L.; visualization, Y.G.; supervision, J.D.; project administration, J.D.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number: 42172330), the 2018 Henan Land and Resources Research Project (grant number: [2018]98-17), the 2021 Henan Natural Resources Research Project (grant number: [2021]157-12), and the High-Resolution Satellite Project of the State Administration of Science, Technology and Industry for National Defense of PRC (grant number: 80-Y50G19-9001-22/23).  
Not applicable.  
The original contributions presented in the study are include in the article material, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.