# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 1 Introduction  
Food security is a critical national priority, with cropland serving as a fundamental resource for food production. Ensuring national food security depends on maintaining sufficient cropland availability. China, characterized by high population density and limited cropland resources, faces significant challenges in this regard. According to the third national land survey, China's total cropland area has decreased by 113 million mu over the past decade [1]. The rapid pace of industrialization and urbanization has led to the illegal occupation of cropland in some regions, resulting in the \"non-agriculturalization\" and \"non-grainization\" of these lands [2, 3, 4]. In recent years, cropland protection and food security have faced significant challenges due to factors such as the COVID-19 pandemic,extreme weather events, and global instability [5]. Monitoring changes in cropland is therefore crucial for safeguarding food security. Cropland change detection typically involves two main components: acquiring cropland distribution information and extracting cropland change information [6]. By first determining cropland distribution, boundary constraints can be established for change detection, enabling the removal of pseudo-changes outside the cropland range. In recent years, the abundance of remote sensing data, the advent of cloud computing platforms like Google Earth Engine (GEE), and advancements in deep learning models have led to the development of numerous land-use and land cover (LULC) maps with broad coverage and high resolution [7; 8; 9; 10; 11]. Since 2021, researchers have generated three 10 m resolution LULC maps using open-source Sentinel data, cloud computing, and machine learning/deep learning techniques [8; 9]. Additionally, for national-scale studies, the DAMO Academy has produced a 10 m resolution LULC map for China (AIEC) using a deep learning model [10]. [PERSON] et al. further advanced this field by producing China's first 1-m-resolution land cover map, SinoLC-1, using a super-resolution deep learning model [11]. However, due to the different data sources and production methods used by these products, the accuracy of various land classes in different regions can vary, leading to significant inconsistencies and uncertainties [12; 13; 14]. Despite these challenges, in cropland mapping tasks, obtaining high-quality samples from existing products shows better prospects than collecting samples through visual interpretation [15]. Therefore, integrating multiple existing LULC products offers a promising approach for accurately determining large-scale cropland distribution. Currently, many studies focus on change detection using remote sensing images, with traditional methods mainly applied to low- and medium-resolution imagery. Common approaches include the image algebra method, image transformation method, and post-classification comparison [16]. The image algebra method calculates algebraic differences between bands to measure changes, with change vector analysis (CVA) being the most widely used technique [17]. The image transformation method extracts features from raw data for change detection using methods such as Principal Component Analysis (PCA) or Independent Component Analysis (ICA) [18]. The post-classification comparison method involves independently classifying images and comparing them to obtain specific information on change types. With the development of artificial intelligence, deep learning methods, particularly convolutional neural networks (CNNs) and transformer networks, have significantly advanced change detection research, achieving improved results on high-resolution optical change detection datasets [19; 20; 21; 22; 23; 24; 25; 26]. Moreover, some researchers have applied deep learning techniques to cropland non-agriculturalization and non-graimization monitoring [27; 28], establishing systematic approaches for cropland change detection [29].