## 2 Materials and Methods
### Remote Sensing Data Processing  
The research employed Sentinel-2 satellite images, accessed from the Google Earth Engine (GEE) collection ee.ImageCollection (\"COPERNICUS/S2_SR_HARMONIZED\"), provided by the European Union, ESA, and the Copernicus program. To ensure high-quality imagery suitable for land cover analysis, a cloud and shadow masking approach was applied. Specifically, only pixels with a cloudiness or shadow probability of 10% or lower were retained, effectively filtering out much of the interference from clouds or cloud shadows that can hinder accurate land cover classification. Additionally, only scenes with a total cloud cover not exceeding 30% were selected from the designated observation period. This selection criterion helped maximize the clarity and usability of the data for accurate mapping and classification. Following this pre-processing, the selected images were grouped into three distinct seasonal composites, representing spring, summer, and autumn phenological phases of vegetation growth [33]. These seasonal composites were then combined into a multi-layered image stack, enabling more comprehensive data analysis across different periods within the growing season (Figure 3). A Sentinel-2 image for a single season consists of 12 bands: B1, B2, B3, etc. (excluding the B10--Cirrus band). Three seasons were analyzed: spring, summer, and autumn, resulting in a total of 36 bands in the stack (3 \(\times\) 12).  
The classification was performed in Google Earth Engine using the imported training dataset and the Sentinel-2 stack for 2023. The code is publicly accessible, allowing anyone to perform classification for any time period ([[https://code.eartheengine.google.com/526e3](https://code.eartheengine.google.com/526e3)]([https://code.eartheengine.google.com/526e3](https://code.eartheengine.google.com/526e3)) bb5 ff3 da58 af2a06 ff5e9725b32, accessed on 25 December 2024).  
The built-in classifiers available in GEE, Random Forest (RF), Support Vector Machine (SVM), and CART, were applied with default settings. The Random Forest (RF) algorithm [34], based on random decision trees, provides high accuracy for large datasets. The following parameters were adapted for the classification: number of trees = 10; maximum number of variables used for splitting = (12)\({}^{0.5}\), approximately 3 or 4. The Support Vector Machine (SVM) classifier [35], based on a decision boundary function, performs well with  
Figure 4: LUCAS 2018 validation; codes and colors are specified in Table 2.  
complex data. The classification used the following parameters: kernel type = radial basis function (RBF); error tolerance = 0.001. The CART (Classification and Regression Trees) algorithm [36] employs a single decision tree. In our study, the maximum tree depth was not limited, and the minimum number of data points in a leaf node was set to 1. The matching of model parameters and their validation were performed on the training set. The final accuracy evaluation was performed on a test set that the models had never seen. We utilized the confusion matrix, generated in GEE from the test dataset and exported as a text file, to compute all accuracy metrics using a Python script.