## 5 Results and Discussion
### Trends to Be Further Explored
#### 5.3.5 Incorporating Radar Data  
Another type of integration to be more explored is the integration of optical and Synthetic Aperture Radar (SAR) data. SAR data are useful for extracting crop planting areas and identifying crop types [131]. This technology improves the acquisition of data in decisive monitoring periods since it explores surface characteristics and is independent of solar illumination and cloud cover [114], factors that may limit the optical data acquisition. Despite this limitation, SAR data was restricted to the scientific community for a long period because of their limited availability caused by the scarcity of good digital elevation models (DEM), as well as the more complex data structures required concerning optical data [114; 131]. Due to the launch of the ESA's Sentinel-1 mission that comprises a constellation of two polar-orbiting satellites to acquire imagery regardless of the weather, and the availability of a dual polarization (VV+VH) C band (5.36 GHz) SAR data product, the use of SAR data has been diffused to mitigate the cloud interference [3] and classification limitations in heterogeneous landscapes [109]. Due to cloud cover, the optical data can present discontinuity in key growth stages of crops [158]. For crop types with similar phenological cycles, this integration can improve reliable discrimination [85]. As SAR can reflect vegetation structure, and optical imagery captures crop multi-spectral information, the synergetic use of both types of data can be complementary to each other [93], integrating SAR sensor with all-weather capability with spectral information and short revisit periods provided by the optical sensor [166]. The principal SAR features observed were Haralick textures, polarizations, backscattering coefficients, and polarimetric decompositions. [PERSON] et al. [131] used S1, S2/MSI, and L8/OLI multi-temporal data and SVM, RF, and Artificial Neural Network algorithms to identify six crop types obtaining a high OA (0.93), especially with RF. [PERSON] et al. [166] integrated S1 and S2/MSI data for LULC classification in the Magdalena region, Colombia, producing a LULC map with an OA equal to 88.75% when mapping six classes using the SVM algorithm. [PERSON] et al. [20] used multi-temporal optical and SAR data and applied the multi-layer perceptron classifier for crop mapping in Ukraine, achieving an OA over 88% for 11 classes. [PERSON] et al. [3] integrated S1 and S2/MSI data to classify 13 LULC classes in the Chennai Basin, India, a cloud-prone monsoon region with small-scale agriculture and multiple cropping patterns, in the 2015/16 harvest period, with an OA equal to 91.53%. [PERSON] et al. [211] attested that the integration between L8/OLI, S1, and S2/MSI elevated the classifications' accuracy in Myanmar in 2017 and 2018 up to 91%. Improvements in this sense were also observed by [PERSON] et al. [178] when mapping 30 LULC classes over Wales. [PERSON] et al. [138]used S1 and S2/MSI time series to monitor forest and agriculture mosaics over two heterogeneous landscapes: a temperate mountainous landscape in the Cantabrian Range (Spain) and a tropical forested landscape in Paragominas (Brazil). The mean Kappa increased from 0.59-0.83 to 0.55-0.85 when integrating both data. In addition, the method enables defining key periods that discriminate LULC classes. The analysis showed the importance of VV and VH polarization and SWIR bands' data for the classification. [PERSON] et al. [175] also obtained positive effects optimizing the temporal date images to map sunflower fields. Mapping wetland classes, [PERSON] and [PERSON] [212] attested that Red-edge bands have significant influence over intensive vegetated classes, while radar bands have more influence over partially decayed vegetated ones. [PERSON] et al. [183] evaluated the integrated use of S1 and S2/MSI data with the SVM classifier for mapping wetlands in the National Park of Koronia and Volvi Lakes, in Greece.