A Parallel-Cascaded Ensemble of Machine Learning Models for Crop Type Classification in Google Earth Engine Using Multi-Temporal Sentinel-1/2 and Landsat-8/9 Remote Sensing Data  
[PERSON]  
1 Department of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; [EMAIL_ADDRESS] (E.A. ); [EMAIL_ADDRESS] (M.J.V.Z. ); [EMAIL_ADDRESS] (A.T.D. )2 Department of Earth Sciences & CERI Research Center, Sapienza University of Rome, Ple Aldo Moro, 5, 00185 Rome, Italy  
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[PERSON]  
1 Department of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; [EMAIL_ADDRESS] (E.A. ); [EMAIL_ADDRESS] (M.J.V.Z. ); [EMAIL_ADDRESS] (A.T.D. )2 Department of Earth Sciences & CERI Research Center, Sapienza University of Rome, Ple Aldo Moro, 5, 00185 Rome, Italy  
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[PERSON]  
1 Department of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; [EMAIL_ADDRESS] (E.A. ); [EMAIL_ADDRESS] (M.J.V.Z. ); [EMAIL_ADDRESS] (A.T.D. )2 Department of Earth Sciences & CERI Research Center, Sapienza University of Rome, Ple Aldo Moro, 5, 00185 Rome, Italy  
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[PERSON]  
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Footnote 15 RS satellite data can be divided into two main categories: Multispectral (MS) and Synthetic Aperture Radar (SAR) data. Many crop type mapping approaches primarily relied on MS data due to its strong ability to capture the spectral properties of crops and track vegetation phenology. Widely used MS RS data sources include the Moderate Resolution Imaging Spectroradiometer (MODIS) [5; 6], Landsat (L) series (particularly L4, 5, 8, and 9) [7; 8], and Sentinel-2 (S2) [9; 10; 11; 12; 13]. On the other hand, some studies focused solely on SAR data for crop type mapping, with Sentinel-1 (S1) being the most commonly utilized one due to its public availability [14]. This is because SAR data offer the advantage of providing data under various weather and lighting conditions [15]. Furthermore, the backscatter SAR signal is sensitive to surface parameters, such as crop humidity, crop biomass structure, soil conditions, and surface roughness [16; 17]. It is worth emphasizing that in the reviewed literature, most papers utilized multi-temporal MS or SAR data to span the entire cropping year. This methodology enables a more comprehensive understanding of phenological changes throughout the growing season, leading to more precise identification of different crop types [9; 18]. Synergistic approaches that combine various sources of MS data demonstrated higher overall accuracies than single source approaches in crop classification [19]. This is because MS satellites have different temporal resolutions, which provide enhanced phenological information and, consequently, lead to improved classification accuracy. The most common combination of MS data involves the synergistic use of S2 and L8/9 due to their similar characteristics [20; 21]. Additionally, including S1 images in combination with MS data in classification models has the potential to enhance crop mapping accuracies [22]. As mentioned, SAR data capture the physical and structural properties of the crops, complementing the spectral information obtained from MS sensors. In this regard, scholars have commonly employed the combination of S1 with either S2 [23] or L8 [24]. However, there is a limited number of studies that have classified crop types using a multi-source combination of multi-temporal S1/2 and L8/9 data, followed by this article. The development of multi-temporal multi-source approaches can pose serious challenges due to the substantial volume of RS data that must be stored and processed [25]. However, in recent years, the emergence of Google Earth Engine (GEE), a cloud-based processing platform, has greatly facilitated RS applications [26; 27]. GEE has been extensively employed in various fields, including water resources management [28; 29], long-term land cover change detection [30], land cover classification [31], insect and disease monitoring [32].