## 5 Results and Discussion
### Trends to Be Further Explored
#### 5.3.1 Less Frequently Used Spectral Bands and VIs  
Despite the advantages derived from the integrated use of L8/OLI and S2/MSI, large data volume and different characteristics of recent sensors introduce substantial challenges to store, to develop classification approaches, and to improve the reasoning about LULC dynamics [12], suggesting that we should explore innovative perspectives of analysis. An emerging trend, as an alternative, is to expand the exploitation of the electromagnetic spectrum. It is reasonable to suppose that better classification accuracies can be achieved if all bands throughout the SITS are used as features, instead of a greenness-related spectral index formulated from only two bands per image [71] because the full optical spectrum comprises wavelengths which are sensitive to vegetation properties other than greenness [62]. A common practice is the use of bands beyond the visible and NIR as input for LULC classification approaches. However, it was restricted to a few research groups for a long time due to costs associated with image acquisitions, storage and access, and the required specialized skills and software to transform imagery into actionable information [57; 143]. In recent years, the exploring of the electromagnetic spectrum by the broader science community has expanded with the advent of Earth Observation missions characterized by global coverage and medium spatial resolution data, especially Landsat-like (10-30 m) missions, as the L8/OLI and the S2/MSI, which jointly have provided global diffusion of narrow and sensitive spectral bands within this range of spatial resolution [28; 40]. The most highlighted bands in L8/OLI and S2/MSI are the SWIR and Red-edge, and VIs based on them have increased classification accuracies in different landscapes around the world [85; 127; 144]. Most of these VIs were little explored for a long time [40] but turned on only after the S2/MSI mission advent. [PERSON] et al. [145] attested the value of S2/MSI multi-temporal data for crop type classification, analyzing nine crop types in Austria using and RF algorithm, getting an OA equal to 96% in 2016 and 2017. [PERSON] et al. [40] evaluated 82 VIs calculated from S2/MSI data to identify six crop types using RF and SVM, recommending the application of less frequently used VIs with an OA higher than 89%. [PERSON] et al. [146] observed the importance of S2/MSI's SWIR and Red-edge bands to classify vegetation species and crops by using the RF algorithm. [PERSON] et al. [147] verified that combining S2/MSI's SWIR and the first Red-edge (band 5) achieved the highest importance for mapping the African Savannah. [PERSON] et al. [148] identified SWIR and first Red-edge as the most informative S2/MSI bands for vegetation classification in Australia. [PERSON] et al. [149] used multi-temporal S2/MSI data to classify five tree species in Sweden with RF, highlighting Red-edge and SWIR to detect phenological differences, with an OA equal to 88.2%. [PERSON] et al. [150] evaluated the performance of L8/OLI and S2/MSI data to map successional forest stages in the Atlantic rainforest, Brazil, with SVM and RF, and considered that Red-edge and SWIR bands were decisive attributes to differentiate similar phenologies. [PERSON] [151] highlighted the importance of Red-edge and SWIR of S2/MSI to map boreal landscapes. Influenced by plant constituents such as pigments, leaf water, and biochemical components, SWIR bands have been used as a foliar water content indicator [62]; to detect drought stress, and to identify forest disturbances [84]. Nevertheless, the most published VIs based on SWIR have been little applied for LULC classification [152], although, when used, they presented good results. [PERSON] et al. [153] integrated different SWIR VIs, such as Normalized Difference Moisture Index (NDMI) [154], Normalized Burn Ratio (NBR) [155], and NBR-2 [155], with NDVI, EVI, and SAVI in an RF model to monitor pasturelands in the Brazilian Amazon using Landsat time series.