# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 2 Study Area and Data
### Data Sources and Preprocessing  
The data used in this study were all provided by the Google Earth Engine ([[https://earthengine.google.org/](https://earthengine.google.org/)]([https://earthengine.google.org/](https://earthengine.google.org/)), accessed on 10 October 2024) platform (Table 1), which has already performed basic preprocessing and offers a convenient interface for data access. This study utilizes three open-source global 10 m resolution land-use/land cover (LULC) map collections. Due to varying data availability, two products, Google Dynamic World (DW) [9] and ESRI 10m Annual Land Cover (ESRI) [8], were obtained for the period of 2018-2020, while the ESA_WorldCover10m (ESA) [7] product for 2020 was also included, yielding a total of seven maps as the LULC data source for this research. The optical imagery used in this study was obtained from Sentinel-2, while the SAR data were sourced from Sentinel-1. For optical imagery, monthly composites were generated \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Resolution** & **Temporal** & **Link** \\ \hline Sentinel-1 & 10 m & 2019–2024 & [[https://developers.google.cn/earth-engine/datasets/catalog/COPERNICUS_S1_GRD](https://developers.google.cn/earth-engine/datasets/catalog/COPERNICUS_S1_GRD)]([https://developers.google.cn/earth-engine/datasets/catalog/COPERNICUS_S1_GRD](https://developers.google.cn/earth-engine/datasets/catalog/COPERNICUS_S1_GRD)) (accessed on 10 October 2024) \\ Sentinel-2 & 10 m & 2019–2024 & [[https://developers.google.cn/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED](https://developers.google.cn/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED)]([https://developers.google.cn/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED](https://developers.google.cn/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED)) (accessed on 10 October 2024) \\ ESA & 10 m & 2020 & //developers.google.cn/earth-engine/datasets/catalog/ESA, WorldCereal_2021_MARKERS_v100 (accessed on 10 October 2024) \\ DW & 10 m & 2018–2020 & [[https://developers.google.cn/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1](https://developers.google.cn/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1)]([https://developers.google.cn/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1](https://developers.google.cn/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1)) (accessed on 10 October 2024) \\ ESRI & 10 m & 2018–2020 & [[https://gee-community-catalog.org/projects/esrife2020/](https://gee-community-catalog.org/projects/esrife2020/)]([https://gee-community-catalog.org/projects/esrife2020/](https://gee-community-catalog.org/projects/esrife2020/)) (accessed on 10 October 2024) \\ SRTM & 30 m & & [[https://developers.google.cn/earth-engine/datasets/catalog/USGS_SRTMGL_003](https://developers.google.cn/earth-engine/datasets/catalog/USGS_SRTMGL_003)]([https://developers.google.cn/earth-engine/datasets/catalog/USGS_SRTMGL_003](https://developers.google.cn/earth-engine/datasets/catalog/USGS_SRTMGL_003)) (accessed on 10 October 2024) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data source. Figure 1: Overview map of the study area: **(a)** geographic location of the study area; **(b)** distribution of cultivated land in the study area (the total land area is 211.558 square kilometers, and the cropland area is 2571.25 hectares); **(c)** elevation information of the study area. by the median synthesis of images collected in Shanwei each January from 2019 to 2024. This method efficiently produces cloud-free composites, minimizing the effects of clouds and shadows [40]. Corresponding SAR data for the same periods were also acquired. Additionally, monthly time series of SAR data from 2019 were collected to aid in cropland extraction. To enhance the contrast of the images, we applied the percentage truncation method for image stretching. Specifically, we used the pixel values corresponding to the 2 nd and 98 th percentiles of the image histogram as the minimum and maximum gray values, respectively.