## 5 Results and Discussion
### Consolidated Trends
#### 5.1.1 SITS and Pixel-Based Approaches  
Dense SITS derived from big EO medium-resolution data have been used to improve LULC classifications, since they permit the detection of phenological variation over specific periods, such as growing seasons [5; 61; 62]. Being suitable to deal with the crop dynamics [63] and the heterogeneity of natural areas [64], the SITS analysis can potentialize the use of VI information as input in LULC classification methods, improving the separability of classes [5]. In addition, considering LULCC as a dynamic process, without guarantee of spatial-coherence along the time, SITS are used to detect specific changes by analyzing pixel reflectance trajectories [9; 65]. The effectiveness of SITS analysis needs to address the gap of training sample data, which can be overcome by using reference data from the past, and low and irregular temporal availability of satellite images caused by a high presence of clouds, and the variability of pseudo periodic phenomena (e.g., vegetation cycles) [10]. Since more medium-resolution datasets have become available, the above-mentioned data problem can be addressed [35]. Several papers have pointed out the importance of the pixel-based approach, using L8/OLI and S2/MSI data as input in classification models to provide accurate LULC maps. [PERSON] et al. [66] integrated L8/OLI and S2/MSI SITS using the GEE platform for mapping crop types  
Figure 6: Global distribution of the study areas considered for applications in the selected papers, highlighting the 15 countries with the greatest number of publications.  
at a 30-m spatial resolution over seven regions of China in the 2016-2017 harvest season, achieving an OA of 93%. In addition, integrating L8/OLI and S2/MSI on GEE, [PERSON] et al. [67] developed an approach for cropland mapping in Africa, in 2015-2016, using an object-based segmentation and selecting training samples to create pixel-based classifications. Using RF and SVM algorithms, the OA was equal to 94% mapping croplands. [PERSON] et al. [30] explored integrated L8/OLI and S2/MSI SITS for crop classification in the Duero river basin, Spain, for the 2017 spring and summer seasons, achieving an OA equal to 87% and 92% when classifying 15 crop types individually and grouped, respectively.  
Other approaches have emerged. Combining S2/MSI pixel- and object-based SITS as input in RF and Time-Weighted Dynamic Time Warping (TWDTW) algorithms, [PERSON] and [PERSON] [65] classified seven crop types in study areas in Romania, Italy, and in the USA. The pixel-based RF obtained an OA between 87% and 97%, and the object-based TWDTW obtained an OA between 78% and 96%. Using phenological metrics derived from dense Landsat EVI SITS in a pixel-based approach, [PERSON] et al. [68] classified croplands in the Cerrado biome, Brazil, between 2013 and 2017, using RF and a hierarchical classification model from land cover to 16 crop rotation classes, getting accuracies higher than 90%. [PERSON] et al. [69] proposed a patch-based Convolutional Neural Network (CNN) for L8/OLI data considering the spatial relation between a pixel and its neighborhood, and patch-based samples from multidimensional data. The method outperformed pixel-based neural network, pixel-based CNN, and patch-based neural network OAs by 24.36%, 24.23%, and 11.52%, respectively, considering eight LULC classes. Using SITS for monitoring phenological stages, [PERSON] et al. [70] mapped winter wheat areas up to six weeks before harvest in Lebanon, in 2016 and 2017. The OA was 87.0% in 2017, distinguishing wheat from similar winter cereal crops. Other papers were developed by using pixel-based approaches [18; 71; 72], remarking their potential for monitoring heterogeneous landscapes.