###### Abstract  
Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10-30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publications, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios. operational land imager; multiSpectral instrument; vegetation indices; text mining 2020 IEEE/RSJ Web Conference 2020  
Remote sensing satellite data are a valuable source to produce up-to-date LULC classifications [3], and big Earth Observation (EO) data sets are constantly applied for getting information about LULC and LULCC [9]. The use of Satellite Image Time Series (SITS), for example, allows a better reasoning of the landscape and intrinsic processes such as deforestation, greenhouse gas emission, and agricultural expansion [10]. However, LULC classification is a complex process influenced by many factors [11]. Over the last two decades, the main efforts in terms of algorithms, products, and applications have been especially related to moderate spatial resolution instruments, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (250 \(\times\) 250 m), because the data derived from this spatial degree have become more available and easier to process [12]. Phenology metrics, for example, which are relevant features to understand phenological cycles and to detect subtle dissimilarities among similar classes, have been routinely generated from moderate resolution data [13]. The efforts using MODIS have achieved relevant results [14; 15; 16]. However, for many applications, information is required at finer spatial resolutions [12]. As cited by [PERSON] et al. [17], medium-resolution (Landsat-like, 10-30 m) is more adequate to detect most human-nature interactions, and the low to moderate resolution sensors are not appropriate for high-precision LULC classification research at regional scales [18]. For detailed information on plant phenology, it is important to derive this data from medium-resolution sensors, such as Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) [4]. This factor has expanded the handling and analysis of big Earth spatio-temporal datasets of multi-source data with a Landsat-like resolution [19], and the increase in spatial resolution, free access policies, and systematic global coverage have created a new era to improve LULC and LULCC applications [20; 21].