## 3 Background  
Systematic reviews were recently conducted concerning techniques for LULC and LULCC analysis. [PERSON] et al. [44] discussed the need for the provision of spatially explicit cropland datasets at a global scale and reviewed the strengths and weaknesses of the various approaches used to  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Spectral Band**} & **L8/OLI Central** & **S2/MSI Central** \\  & **Wavelengths** & **Wavelengths (2A, 2B)** \\ \hline Coastal/Aerosol & 442.9 nm (30 m) & 442.7 nm (60 m), 442.2 nm (60 m) \\ \hline Blue & 482 nm (30 m) & 492.4 nm (10 m), 492.1 nm (10 m) \\ \hline Green & 561.4 nm (30 m) & 559.8 nm (10 m), 559 nm (10 m) \\ \hline Red & 654.6 nm (30 m) & 664.6 nm (10 m), 664.9 nm (10 m) \\ \hline \multirow{3}{*}{Red-edge} & & 704.1 nm (20 m), 703.8 nm (20 m) \\  & - & 740.5 nm (20 m), 739.1 nm (20 m) \\  & & 782.8 nm (20 m), 779.7 nm (20 m) \\ \hline NIR & 864.7 nm (30 m) & 832.8 nm (10 m), 832.9 nm (20 m) \\  & 864.7 nm (20 m), 864.0 nm (20 m) \\ \hline SWIR & 1608.9 nm (30 m) & 1613.7 nm (20 m), 1610.4 nm (20 m) \\  & 2200.7 nm (30 m) & 2202.4 nm (20 m), 2185.7 nm (20 m) \\ \hline Panchromatic & 589.5 nm (15 m) & - \\ \hline Cirrus & 1373.4 nm (30 m) & 1373.5 nm (60 m), 1376.9 nm (60 m) \\ \hline Water vapor & - & 945.1 nm (60 m), 943.2 nm (60 m) \\ \hline \hline \end{tabular} Source: ESA [31] and NASA [32].  
\end{table}
Table 1: Characteristics of L8/OLI and S2/MSI sensors.  
develop such data. [PERSON] and [PERSON] [45] discussed the most used techniques for LULC classification at a regional or global level. [PERSON] et al. [1] presented issues and opportunities associated with the use of SITS of large-area products for LULC characterization. [PERSON] et al. [11] provided guidance on the performance of different supervised pixel-based image classification processes, involving algorithms and input data. [PERSON] et al. [46] analyzed machine learning implementation in LULC classification models, focusing on algorithms, training data requirements, user-defined parameters, and computational costs. [PERSON] et al. [47] revisited the status of LULC classification with EO data to explain the new era of LULC analysis (Land Cover 2.0) enabled through free and open access data, analysis-ready data (ARD), and high-performance computing. [PERSON] et al. [48] analyzed the different EO platforms and datasets, spatial-spectral-temporal characteristics of satellite data, and approaches applied in LULC classification and change detection, providing recommendations to generate accurate maps. [PERSON] et al. [49] discussed the challenges from the uptake of big EO data, describing the challenges to generate consistent and systematic global information, and pointing out to the importance of web-based workflows. [PERSON] et al. [8] presented current and potential uses of EO data to support sustainable and social policies in the agricultural and food security contexts, highlighting activities of the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Initiative. [PERSON] et al. [50] presented an overview of the recent techniques for providing operational services for agricultural applications. [PERSON] et al. [51] presented guidance in the use of big EO data for accurate spatiotemporal event detection. [PERSON] et al. [28] summarized emerging methods based on SITS analysis for phenology detection, detailing applications to detect and estimate general and species-specific phenological stages. [PERSON] et al. [52] evaluated the inclusion of S2/MSI data for LULC monitoring with traditional classification methods. Despite these reviews, the knowledge of using L8/OLI and S2/MSI, showing trends, gaps, and future possibilities in the face of modern concepts, methods, and technologies, has not yet been summarized.