## 3 Methods
### Land Use and Land Cover Classification and Associated Changes
#### 3.2.2 Land Use and Land Cover Classification Method  
The LULC classification workflow can be divided into several parts: (i) Pre-processing, (ii) Feature Extraction, (iii) Training, (iv) Classification and (v) Post-processing. These steps are subsequently described in more detail below:  
1. Pre-processing  
The pre-processing includes the cloud masking of all EO input data and a reprojection into the same geographic grid and projection UTM 38N. For Landsat data, the cloud masking is performed by using the provided quality layer (BQA) which provides information in clouds, cloud shadow, cirrus, snow and ice and cloud confidence. For Sentinel-2 the MAJA cloud masks were used to mask out saturated or defected pixels, dark areas, snow, thin cirrus clouds as well as clouds with high and medium probability and their shadows. 2. Feature Extraction  
The feature extraction first includes the derivation of meaningful indexes that will help distinguish vegetated and non-vegetated classes. For this reason, for each available image, the Normalized Difference Vegetation Index (NDVI) as well as the Normalized Difference Water Index (NDWI) was calculated. After this step of feature engineering, the data were temporarily aggregated: For each spectral band and year, three percentiles (p) were derived: p20, p50 (median) and p80. This temporal aggregation is used to reduce the amount of data for the model and therefore decrease training and processing time as well as for the purpose of creating a less complex model (with less input features) with the capabilities of better generalization. 3. Training  
The training of every supervised classification task requires reference data. These are already to some extent described in Section 2.2. However, two key datasets that were essential to the LULC classification are described here in more detail. This on one hand includes the Global Crop Extent Layer which was produced by [PERSON] et al. (2010) [16] and serves as the only available input dataset on agricultural activities in the area. Secondly, the Open Street map dataset which offers a very high accuracy and level of completion in the area of interest and serves as training data for most thematic classes: Settlements, Roads, Water bodies and rivers. The other classes (Bare Soil and other vegetation) were sampled manually with the help of very high-resolution data as well times-series information from Sentinel-2 imagery. 4. Classification  
The classification itself was performed with the highly popular tree based Random Forest classifier [17], the training parameters (although often neglected in case of random forest) were tuned via cross validation and overall accuracy as a target metric. The final model with 500 trees yielded an out-of-bag (OOB) error of 14.6% for the classification of the most recent year 2019 based on Sentinel-2 data. 5. Post-Processing  
The subsequent post-processing included an overlay and insertion of thematic information from Open Street Map and Service 2 with the aim of enhancing the accuracy of the product. Overall, it was decided to take over the following thematic classes from Open Street Map: Settlements, roads, water bodies and rivers. This was done for the reason that a human-based manual very detailed delineation of these object was judged to be superior to the artificial intelligence-based classification based on high resolution imagery. The Forest class was entirely taken over from Service 2. Furthermore, the MMU was applied to the classification, however it has to be stressed that for Settlements and Primary Roads the MMU was lowered to 0.02 ha. This step was necessary to preserve the small-scale scattered structure of these anthropogenic objects in the area. A larger MMU would yield an underestimation of these areas. Please note that the above elaborated workflow only describes the classification task for the baseline epoch 2019.