# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 1 Introduction
However, deep learning methods are still limited by issues such as insufficient generalization performance and a lack of labeled samples. Additionally, due to the unique imaging mechanism of SAR, interpreting SAR images presents significant challenges. Most target detection and image classification tasks based on SAR data focus on strong and stable backscatter objects such as ships, aircraft, and ports [30; 31]. However, publicly available datasets for SAR-based change detection are scarce, and accurately labeled sample data are even more limited [32]. At present, change detection in SAR imagery primarily targets large-scale applications, such as flood monitoring [33; 34]. As a result, current SAR change detection methods often rely on unsupervised techniques and pseudo-labeling approaches [35; 36]. While these studies have made progress, they often rely on optical imagery as a single data source, which limits the ability to comprehensively reflect surface change characteristics. The integration of optical imagery and SAR data has become an important direction for multi-source data analysis due to their complementary information [37]. Optical images provide rich spatial details, while SAR data have certain penetration capabilities, allowing it to reveal structural features beneath the surface [38; 39], making their combination an effective way to improve the accuracy and comprehensiveness of change detection. In response to the issues mentioned above, this study focuses on two main problems: (1) the large uncertainties in existing cropland information products that prevent their direct use as reference masks for cropland change monitoring; (2) there is a relative lack of research on the collaborative use of multi-source data for cultivated land non-agricultural monitoring. To address these issues, this study takes Shanwei City, Guangdong Province, as the experimental area and conducts research on typical non-agriculturalization monitoring of cropland (including cropland changes to constructed buildings, ponds, and pushed-fill soils) based on the data and computational power provided by the GEE cloud platform in combination with optical imagery and SAR data. This approach aims to make full use of the advantages of multi-source data to achieve more comprehensive and accurate extraction of cropland change information, thereby providing technical support for non-agriculturalization monitoring. Finally, this study will explore the spatial and temporal distribution characteristics of non-agriculturalization patches in Shanwei City to provide a scientific basis and decision-making reference for cropland protection in the region. The Supplementary Materials explain the experimental procedures for this study, including two key files: CVA and cropland. The main contributions of this research are as follows:  
(1) Proposing a Solution to Address the Inconsistencies and Uncertainties in Existing Cropland Products: To address the inconsistencies and uncertainties arising from differences in data sources, methodologies, and class definitions in existing cropland information products, this study proposes an innovative approach. By integrating multiple LULC products, the method updates cropland information and enhances the reliability of cropland mapping, providing a robust reference for monitoring cropland changes. (2) Monitoring Cropland Conversion Using Multi-Source Data Collaboration: While most existing studies rely on single data sources, this research combines optical imagery and SAR data, leveraging their complementary strengths to capture detailed spatial and structural information. This approach provides more accurate change detection results and highlights the potential of SAR data in monitoring cropland conversion. The proposed method demonstrates the advantages of SAR data in detecting buildings, ponds, and reclaimed land, achieving higher monitoring accuracy compared to single-source methods.