## 5 Results and Discussion
### Trends to Be Further Explored
#### 5.3.2 Phenological Metrics
Fitted parameter coefficients and phenometrics at pixel-level were submitted to an RF classifier to characterize LULC for the study area. They achieved OAs over 75%, showing the potential and limitations of integrating phenometrics to improve land surface characterization and LULC classifications. [PERSON] et al. [12] developed the multi-sensor land surface phenology (MS-LSP) algorithm to detect seasonal variations in vegetation phenology and to calculate phenometrics in HLS EVI time series for all of North America. They compared them with metrics extracted from the PhenoCam network and the MODIS MCD12Q2 product, showing a correlation equal to 90% with PhenoCam data for 50% green-up dates for deciduous broadleaf forests. [PERSON] et al. [189] used MODIS and HLS NDVI image composites to extract phenometrics and then map invasive annual grass species in the western USA, in a data mining framework. They achieved high correlations to different phenological stages, improving the mapping in comparison to models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. Different approaches were developed only with Landsat-like data. [PERSON] et al. [177] demonstrated the efficacy of using phenometrics extracted from multi-temporal S2/MSI data for updating rice crop maps and monitoring cropping practices over a large and heterogeneous region, by detecting key phenological dates, as transplanting and heading dates. In a deep learning-based multi-temporal crop classification in California (USA), [PERSON] et al. [104] presented the importance of phenometrics for improving classification accuracy. They explored neural network classifiers, XGBoost, RF and SVM, and metrics derived from Landsat 7 Thematic Mapper (L7/TM) and L8/OLI time series, achieving OAs above 82%. [PERSON] et al. [109] explored phenometrics derived from dense Landsat time series to improve the classification of 7 Cerrado physiogonies, in Brazil. Their phenology-based physiogonomy map achieved OAs of 63% with 10-fold cross-validation and 96% with a validation scheme to accommodate for spectral-temporal class similarities along the vegetation gradient. Using phenometrics derived from Landsat time series, [PERSON] et al. [156] improved the separability among six classes of cropland, pasture, and natural savanna vegetation in the Brazilian Cerrado by a spectral-temporal approach. They achieved an OA equal to 93%. [PERSON] et al. [190] applied several phenometrics of summer and winter growing seasons derived from Landsat data to identify crop and no-crop areas in Queensland, Australia, at field-scale. The OAs were above 90% and these metrics were among the most important variables in the classification. [PERSON] et al. [107] discussed the potential of phenometrics to classify crop types with higher thematic detail from multiple crop seasons over large study areas. This was attested by [PERSON] et al. [68], after extracting different phenometrics as attributes from dense Landsat time series for classifying croplands in different Brazilian regions with accuracies higher than 90%. [PERSON] and [PERSON] [191] extracted phenometrics of the green-up and senescence phenological stages in L8/OLI time series to identify optimal image acquisition dates to map five fruit-tree crop types in Chile. They obtained an OA equal to 94%. [PERSON] et al. [192] highlighted the potential of phenometrics derived from S2/MSI and L8/OLI NDVI smoothed time series to identify 12 LULC classes (10 crop types) over irrigated areas in Morocco using RF classification. The classificationsbased on S2/MSI and L8/OLI data presented OA of 93% and 90%, respectively, showing potential for detecting phenological stages of different cropping systems over semi-arid regions. [PERSON] et al. [193] integrated L8/OLI and HJ-1/Charge-Coupled Device (HJ-1/CCD) data in an approach to mask non-vegetation areas and then extract key phenometrics of four crop types from long-term NDVI time series profiles in Jingzhou, China.