## 5 Results and Discussion  
The 193 recent papers from 2015 to 2020 were developed by research groups of 35 countries (Figure 2), according to the affiliation of the principal author. Those with over 10 publications include, respectively, USA ( 43), China ( 24), Germany (20), France (13), and Brazil (12). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fields** & **Definition** & **Categories** \\ \hline Year & Year of publication & Year \\ \hline Source title & Journal & Journal name \\ \hline Institution & Name & Institution Name \\ \hline Area & Mapped area & Country, area of interest \\ \hline Sensor type & Sensor used & S2/MSI, L8/OLI, both \\ \hline Sampling strategy & Sampling adopted & Stratified random sampling, \\  & & simple random sampling, others \\ \hline Site type & Study area & Agricultural lands, Natural vegetation \\ \hline Classification method & Method adopted & RF, SVM, others \\ \hline Accuracy measure & Accuracy index and values & OA, Kappa, F-score, and \% of accuracy \\ \hline \hline \end{tabular} Source: Adapted from [PERSON] et al. [26].  
\end{table} 
Table 2: Schematic process to filter, select, and organize the papers. Figure 1: Schematic process to select the papers from 2015 to 2020 presented in this systematic review. The main sources of the 221 peer-reviewed publications were the journals: Remote Sensing of Environment ( 58 papers), Remote Sensing ( 48 papers), and ISPRS Journal of Photogrammetry and Remote Sensing (16 papers). The top 15 journals are presented in Figure 3.  
We noted the prevalence of SITS, multi-temporal analysis, and two principal classification approaches: (a) Pixel-based, by using SITS for monitoring pixel trajectories along the time, and (b) Object-based, by the geographic object-based image analysis (GEOBIA), evaluating pixel neighborhood relations for segmentation and classification, which are often used in conjunction with robust machine learning algorithms. We observed the growing use of phenological metrics and deep learning methods. The text mining analysis highlighted the occurrence and link strength of each term (Figure 4). Figure 3: Top 15 journals where the selected papers were published. Figure 2: Global distribution of the research groups responsible for the papers selected, highlighting the 15 countries with the greatest number of publications. The network reveals a high connection among relevant and different features, techniques, and methods used as a strategy to address LULC-related purposes. The strength of the connections reinforces the association of 'land use', 'land cover', and 'land use change' with'machine learning' techniques, 'time series analysis', and object-based ('OBIA', 'GEOBIA','segmentation') approaches, as well as'surface reflectance', 'big data', 'data fusion' ('analysis-ready data', 'data cube'),'vegetation indices', and 'data mining' for 'image classification'. By text mining, it was also was possible to detect the evolution of the research terms occurrence throughout the period of analysis (Figure 5). Figure 4: Scientific network mapping of the most used keywords terms in the selected papers. Colors indicate the cluster in which each keyword was related the most. Lines represent co-occurrence link strength among terms. The temporal analysis of the research terms occurrence in the network reveals the consolidation of target covers ('agriculture', 'grassland', 'deforestation') and the connection increase among different features, techniques, and methods for LULC classification (i.e., the link among'vegetation indices', 'time series analysis', 'cloud computing'). The connections reinforce innovative approaches with the association of'red-edge' and 'land surface phenology' with'machine learning' techniques,'satellite image time series' and object-based ('OBIA', 'GEOBIA','segmentation') approaches, and the integrated use of'surface reflectance', 'analysis-ready data', 'data cube','vegetation indices', and metrics related to 'phenology'.