## 2 Materials and Methods
### Data Collection and Processing
#### 2.2.2 Sample Data  
In this study, the training data were gathered from diverse sources, namely: (1) GlobeLand30 [25] ([[http://www.globallandcover.com](http://www.globallandcover.com)]([http://www.globallandcover.com](http://www.globallandcover.com)) (accessed on 17 May 2023)), offering global land cover data at a 30 m spatial resolution for the years 2000, 2010, and 2020. We specifically chose the 2020 version, developed by the National Geomatics Center of China. (2) GLC_FCS 30 [26] ([[https://doi.org/10.5281/zenodo.3551994](https://doi.org/10.5281/zenodo.3551994)]([https://doi.org/10.5281/zenodo.3551994](https://doi.org/10.5281/zenodo.3551994)) (accessed on 26 May 2023)), a global land cover classification product with a 30 m resolution, released by the Aerospace Information Research Institute of the Chinese Academy of Sciences. This dataset is updated every five years from 1985 to 2020, and we selected data from 2015 and 2020. (3) [PERSON] et al. [27] recently devised an independent method for pan-European land cover mapping using Landsat data at a 30 m resolution for 2015 ([[https://doi.pangoaea.de/10.1594/PANGAEA.896282](https://doi.pangoaea.de/10.1594/PANGAEA.896282)]([https://doi.pangoaea.de/10.1594/PANGAEA.896282](https://doi.pangoaea.de/10.1594/PANGAEA.896282)) (accessed on 28 May 2023)). (4) The ECL10 product [28], crafted using multi-source Earth observation data, particularly focusing on the integration of Sentinel-1 and Sentinel-2 data, to generate a land cover map for 2020 ([[https://doi.org/10.5281/zenodo.4407051](https://doi.org/10.5281/zenodo.4407051)]([https://doi.org/10.5281/zenodo.4407051](https://doi.org/10.5281/zenodo.4407051)) (accessed on 19 May 2023)). The land cover categories considered in this study encompass Cropland, Forest, Grass, Strub, Wetland, Water, Impervious, with the data obtained from the intersection of the different datasets. The selected categories are all encompassed in the previously mentioned products, and their prevalence is notably high. A system transformation has been devised based on the distinctions among various products [27; 29]. After obtaining various land use classification products, this study initially resampled the spatial resolution of each product to 10 m using interpolation. The interpolation method utilized was nearest neighbor interpolation, a straightforward technique that assigns the value of the target point to that of the nearest known point. Subsequently, the classification attributes of each product were transformed into uniform categories for this study through category merging and attribute editing. The modified products were intersected to create a composite raster dataset. Training point data were sampled from this dataset. Due to the scarcity of categories such as shrubland and bare land, the sample sizes were adjusted uniformly to the maximum available. Training points were randomly selected from two experimental areas, South and East, then divided into training and validation datasets in a 7:3 ratio. The distribution of training and validation data, along with detailed sample sizes, is depicted in Figure 2 and Table 2. The numbers in parentheses indicate percentages. To input optimized feature variables for deep feature extraction into the convolutional neural network, a label dataset for deep learning was established. A 1-pixel * 1-pixel area centered on each selected sample point was designated as the label for deep learning.