## 5 Results and Discussion
### Consolidated Trends
#### Conventional Spectral Bands and VIs  
Combining bands and VIs provide accurate LULC classification and LULCC detection results [19; 71; 83]. Among L8/OLI and S2/MSI bands, Green, Red, and Red-edge are useful because they correlate with chlorophyll and other pigments, NIR bands are useful to determine leaf structure information, while SWIR is sensitive to water content in vegetation and vegetation structure [38; 84; 85]. Of these bands, Red, Green, and NIR bands are the conventional ones for VI calculation [86]. VIs are indicators of photosynthetic activity and vegetative vigor which are used to assess variations in the physiological states and biophysical properties of vegetation, mainly through SITS analysis [86]. Nowadays, several applications comprise the use of VI-based approaches for assessing the heterogeneous and continuous nature of the land cover composition and change processes [87]. We observed that the use of VIs derived from open data, before the large availability of S2/MSI data, often focused on the reflectance of visible and NIR: (1) Normalized Difference Vegetation Index (NDVI) [88], a chlorophyll sensitive index sufficiently stable to compare seasonal changes in vegetation growth [89], (2) Soil-Adjusted Vegetation Index (SAVI) [90] that minimizes soil brightness influences with a correction factor in the NDVI formula [89], (3) Enhanced Vegetation Index (EVI) [89], with corrections for atmospheric noise, which permits less-saturation of high biomass [91], and (4) Normalized Difference Water Index (NDWI) [92], useful to measure vegetation water status [93]. The predominance of these VIs is justified by their functionality and presence of compound bands in many sensors [40; 86]. Variations were formulated to reduce problems. The Green NDVI (GNDVI) [94] introduced more sensitivity to chlorophyll-a content than that of the NDVI, making it useful to detect stressed and senescent vegetation as well as to estimate green crop [95]. Different SAVI-derived VIs were created to mitigate soil background effects in the reflectance signal during the green-up phenological phase, as the Modified SAVI (MSAVI) [96], and the Optimized SAVI (OSAVI) [97]. The EVI-2 [98] was proposed to reduce the influence of soil reflectance on the EVI. For water detection, the NDWI using Green and NIR [99], and the Modified NDWI (MNDWI) [100], were developed. Conventional VIs remain widely applied with L8/OLI and S2/MSI bands. [PERSON] et al. [101] classified pasturelands in Brazil using only L8/OLI NDVI into an RF algorithm, obtaining an OA equal to 87% separating this class. [PERSON] et al. [72] developed a cropland classification for Australia and China, using NDVI and bands of L8/OLI into an RF algorithm, achieving a coefficient of determination of 0.85. [PERSON] et al. [18] integrated NDVI SITS and textural features from S2/MSI to perform LULC classification in cloud prone areas of Thailand, using RF. The OA reached 89% classifying seven classes, highlighting forests and increasing the separability between crops and other vegetation types. [PERSON] et al. [42] integrated L8/OLI and S2/MSI for winter crop mapping and winter wheat assessment at a regional scale, considering NDVI SITS and crop calendar, achieving an OA equal to 90% separating winter crops. [PERSON] et al. [102] attested the relevance of NDVI, GNDVI, and SAVI in a multi-temporal crop type classification in Coalville, United Kingdom using RF and decision tree algorithms, with an OA equal to 91% classifying 16 classes. Mapping eight crop typesin the Yellow River Delta, between 1985 and 2015, using NDVI, SAVI, MNDWI, and textural features from multi-temporal Landsat data and RF classifier, [PERSON] et al. [6] obtained an average OA equal to 89%. [PERSON] et al. [103] used L8/OLI bands and conventional VIs (including NDVI, EVI, and SAVI) to classify six crop types in Hokkaido, Japan, achieving an OA equal to 94.5%. [PERSON] et al. [104] obtained relevant results using EVI SITS to classify summer crops in California using deep learning algorithms.