# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 6 Conclusions  
In this study, based on the data and arithmetic support provided by the GEE cloud computing platform, we propose a method for rapidly and accurately obtaining cropland non-agriculturalization information. The approach involves mapping cropland extents using multi-source external cropland products and employing these extents as spatial boundaries for subsequent monitoring. By integrating optical and SAR data, the method effectively monitors non-agriculturalization changes and has been successfully applied to track nearly five years of cropland changes in Shanwei City. The conclusions are as follows:  
(1) Feasibility of Rapidly Updating Cropland Maps Using Existing LULC Products: The cropland maps generated in this study achieved an overall accuracy of 90.8%, meeting  
Figure 15: Legend for pseudo-changes due to crop growth. the requirements for serving as a reliable reference for cropland boundaries. Compared to three other LULC maps across different terrains, the results of this study better align with the actual distribution of cropland in Shanwei City. (2) Effectiveness of Multi-Source Data for Monitoring Cropland Non-Agriculturalization: The accuracy of non-agriculturalization monitoring using optical imagery alone was 50.95%. In contrast, integrating SAR and optical imagery significantly improved accuracy, with the experimental accuracy increasing by 22.7%, resulting in an overall accuracy of 73.65%. This approach successfully identified three typical types of cropland non-agriculturalization changes: cropland converted to constructed buildings, cropland converted to ponds, and cropland converted to pushed-fill soils. The additional information provided by SAR data effectively enhanced detection accuracy. (3) Spatial Distribution and Trends in Cropland Non-Agriculturalization: Over the past five years, cropland non-agriculturalization in the Shanwei region has been primarily concentrated in areas with flat terrain, including Haifeng County, Lufeng City, and Chengqu. Among these, Haifeng County exhibited the highest number of change patches, totaling 359, followed by Lufeng City with 278, Chengqu with 121, and Luhe County with 7. Furthermore, the overall number of cropland non-agriculturalization patches in Shanwei City has shown an increasing trend followed by a decrease. However, considering the persistence of cropland changes, enhanced supervision and regulation of cropland use remain necessary. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15031474/s1](https://www.mdpi.com/article/10.3390/app15031474/s1)]([https://www.mdpi.com/article/10.3390/app15031474/s1](https://www.mdpi.com/article/10.3390/app15031474/s1)). The supplementary materials explain the experimental procedures for this study, including two key files: CVA and cropland. The cropland file focuses on experiments related to cropland extraction, while the CVA file details the process for extracting non-agricultural information, specifically land-use changes. Together, these documents provide a comprehensive overview of the methods used in the research. Conceptualization, Q.S. and L.Y. ; methodology, Q.S. and L.Y. ; software, R.G. ; validation, R.G. and J.H. ; formal analysis, R.G. and L.Y. ; resources, Q.S. and J.H. ; writing--original draft preparation, Q.S. and L.Y. ; writing--review and editing, all authors. ; supervision, Q.S. and J.H. ; funding acquisition, Q.S. and J.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Science and Technology Innovation Program of Fujian Province (No. 2021Y3001), and the Science and Technology Innovation Program of Hunan Province (No. 2023 SK2012). Not applicable.