## 5 Results and Discussion
### Trends to Be Further Explored
#### 5.3.5 Incorporating Radar Data
The OA was equal to 94.82% dividing pasture types into three classes; single-species, two-species composition and multi-species composition, attesting the suitability of S1 and S2/MSI data for improving LULC classification in humid areas. For mangrove areas, less frequent VIs have presented interesting advances in classification workflows [213; 214; 215]. [PERSON] et al. [216] demonstrated that integrating S1 and S2/MSI features improved the classification of pasturelands in Australia. The OA using K-Nearest Neighbours (KNN) classifier was 0.89, while 0.96 and 0.93 when using RF and SVM, respectively, mapping six classes. [PERSON] et al. [217] mapped six crop types in Japan using images of the 2016 growing season with an OA of 96.8% applying a kernel-based extreme learning machine (ELM) classification model, attesting the relevance of VV polarization and Red band. Improvements in classification performance were also noted by [PERSON] et al. [85] evaluating the synergistic use of S1 and S2/MSI time series for oasis crop type discrimination in China. [PERSON] et al. [182] proposed an object-based method for paddy rice mapping using S1 and S2/MSI time series deriving phenological parameters. Integrating S2/MSI NDVI, S1 backscattering time series, and phenology data, the OA was 95% for rice and four other classes, providing suitability for rice mapping in cloudy and rainy areas. Different methodologies integrated L8/OLI, S1, and S2/MSI data into RF and SVM approaches to map rice in heterogeneous and cloud prone areas in China. The results indicated that the integration of VH polarization and spectral indices can produce accurate rice distribution maps in these environments [218; 219]. [PERSON] et al. [141] presented a deep learning architecture to integrate S1 and S2/MSI time series, namely TWINNS (TWIn Neural Networks for Sentinel data), useful to discover spatial and temporal dependencies. [PERSON] et al. [66] developed an algorithm to integrate L7/TM, L8/OLI, and S2/MSI time series data to map cropping intensity at large scales across seven study areas in China. Based on field-scale sample data, the annual cropping intensity maps for the study areas had accuracy rates of 89-99% and Kappa of 0.76-0.91 mapping three classes: single, double, and triple-cropping. The OA of the annual cropping intensity maps was 93%, with a Kappa of 0.84. [PERSON] et al. [220] applied co-registered multi-temporal S1 data and L8/OLI-derived NDVI data to produce 10 m spatial resolution maps of paddy rice fields across the Sanjiang plain, China, with limited ground data. The accuracies were 95.2% (OA) and 96.7% distinguishing paddy rice fields from non-rice fields, vegetated areas, and built areas. Despite these results, the effective integration between these properties is not simple [141]. The joint use of data cube architecture [136] is a promising strategy for overcoming the big volume of remote sensing data generated by processing integrated sources. Other alternatives are cloud computing services, such as AWS and GEE, already consolidated for analysis of large terrestrial surface orbital data sets. [PERSON] et al. [50] explained that the open data policy for remote sensing facilitated the exploitation of dense data from different sources. Such emerging technology also allows for a change in thinking towards crop type mapping over large areas, which can leverage new techniques like adjusting the spatial resolution requirements locally [221].