## 3 Proposed Framework
### Dataset Preprocessing and Preparation  
As mentioned earlier, three sources of satellite data were utilized in this study: S1, S2, and L8/9. S2 and L8/9 images belong to the optical type of satellite data, which face limitations due to cloud coverage, which hinders the full observation of the study site. Consequently, any images with a cloud coverage of more than 10% were excluded. Additionally, clouds and cloud shadows were eliminated from each scene by utilizing the pixel quality attributes which are available alongside each S2 or L8/9 image ('QA60' band for S2 and 'QA_PIXEL' for L8/9). However, the removal of cloudy pixels resulted in data gaps within some images across the study site. Furthermore, it is important to mention that the study area was covered with multiple image scenes in some satellite missions like S1 and L8/9, as depicted in Figure 1b. Consequently, this study used a monthly median compositing approach to generate the input satellite images for the ML models. This approach has been proved to be effective in similar studies [57]. The monthly  
Figure 2: (a) Distribution of collected GT data in the study site; (b) Some in situ images acquired from different target classes during the field surveys; (c) NDVI behavior of randomly selected samples from each target class derived from monthly medians of L8/9.  
composites ensure the gap-free images of the study site and also aid in reducing the possible sensor-related noises in optical datasets (S2 and L8/9) and speckle noise in S1 SAR data.  
Note that multi-temporal satellite imageries were mainly utilized in the literature for crop type classification. This is because multi-temporal data take into account crop growth patterns and phenological information [37]. Therefore, images from '1 November 2021' to '30 December 2022' were selected (based on the aforementioned conditions). This period covers the entire 2022 cropping year of the study site, which is the year of GT data collection. As a result, 14 monthly median composites were generated for each data collection (S1, S2, and L8/9). Considering the varying spatial resolutions among the satellite data sources, all the median composites were resampled using bilinear technique to a spatial resolution of 30 m, based on the lowest spatial resolution provided by L8/9 [58].  
As mentioned in the introduction, the paper proposes a Pa structure at the data level. This means that each prepared multi-temporal composite of S1, S2, and L8/9 data was separately inputted into the ML models. However, for the optical datasets (S2 and L8/9), in addition to the spectral bands, Spectral Indices (SIs) were also utilized to enhance the classification accuracy. This is because previous studies have demonstrated that SIs can improve the identification of complex crop type classes [59; 60], as they are designed to highlight specific objects of interest in optical data [61]. In this vein, five of the most commonly used SIs in the literature were employed: Normalized Difference Vegetation Index (NDVI) [62], Normalized Difference Water Index (NDWI) [63], Normalized Difference Built-up Index (NDBI) [64], Soil Adjusted Vegetation Index (SAVI) [65], and Enhanced Vegetation Index (EVI) [66]. All of these SIs were extracted from both the monthly composites of S2 and L8/9 data. The mathematical formulas for each index are provided in Table 1. It should be noted that no additional features were extracted from the S1 bands (VV and VH). This is because numerous studies have demonstrated that these specific bands contain sufficient information for land cover mapping, making additional feature extraction from them un  
Figure 3: Outline of the proposed framework (The developed JavaScript code of the proposed ensemble structure of this paper in GEE and a part of GT samples can be found at Supplementary Material).  
necessary [67]. In summary, five Feature Collections (FCs) were generated as inputs to the ML models, which are presented in Table 3.