## 5 Results and Discussion
### Trends to Be Further Explored
#### 5.3.2 Phenological Metrics  
Phenology-based LULC classification approaches are useful for mapping different crop types [184] and can be important for ensuring food security and agronomic management of crops. This situation permeated the development of methods to extract phenological metrics (phenometrics) (i.e., start, peak, end, and length of a season, maximum and quartile values of spectral indices and spectral bands) to describe critical moments of phenological cycles through timing and value of specific temporal points of interest [63; 104; 116]. Phenometrics can be defined as the temporal characteristics that indicate crop seasonality, and have the potential to represent crop growth [185]. [PERSON] et al. [28] summarized different methods for extracting phenometrics, pointing out that the selection of the most appropriate method should consider the biogeographical characteristics of the study area, potential noise sources in the data, and the shape of VI time series. The current practices for large area multi-temporal LULC classification consist of deriving metrics from the time series and then classify the metrics bands [1; 186]. Most of these efforts to extract phenological information from vegetation were conducted using MODIS data due to its high temporal resolution and applicability to obtain robust time series information [12; 123]. However, MODIS images at 250 m or 500 m spatial resolution have problems with mixed pixels [66]. Phenology-based LULC classification using Landsat-like data is growing after the S2/MSI launch but remains challenging. We noted difficulties to extract phenometrics from L8/OLI and S2/MSI data, especially in regions marked by climatic conditions that cause signal interferences, modifying time series patterns. L8/OLI data are less explored due to the temporal resolution of 16 days, and S2/MSI data are mostly used for detecting plant pigments, discriminating different vegetation classes, mapping crop types, and estimating the yield of crops, and even within the croplands, phenometrics are not always calculated [187]. Integrating L8/OLI and S2/MSI data would significantly improve the temporal resolution of observations [41] and could provide an effective solution to the low availability and low spatial resolution; however, it is still common to integrate MODIS data to improve crop phenological cycle identification [66]. [PERSON] [116] presented how the FORCE framework approach has innovative ways for deriving phenometrics from dense time series. The framework is capable of deriving this information from MODIS, L8, and S2 data, and can be used as baseline products to support scientific to operational applications for environmental and agricultural monitoring purposes. To complement data products already derived from time series MODIS images, [PERSON] et al. [66] extracted phenological information to calculate cropping intensity, presenting that the expansion of high-quality observations from the integration of L8/OLI and S2/MSI data improved the results. [PERSON] et al. [47] recognized the importance of phenometrics to maximize time series information and minimize noise before large area multi-temporal LULC classification, as these metrics are robust to deal with missing data and phenological variations, citing the implementation of data fusion approaches based upon blending of MODIS and Landsat data to produce synthetic gap-free composites. [PERSON] et al. [123] combined the MODIS MCD12Q1 500 m land cover product and phenometrics extracted from consistent and pre-processed Landsat time series to classify 16 LULC classes over North America. They obtained OAs ranging between 93.13% and 95.44% in RF approaches. Using Landsat EVI and co-registered accumulated growing degree-days (AGDD) MODIS time series as input for LULC classification, [PERSON] et al. [188] adopted a model-fitting approach to describe phenological characteristics, such as the timing of the growing season, EVI peak, and timing of the EVI peak of eight LULC types in South Dakota (USA).