## 5 Results and Discussion
### Consolidated Trends
#### 5.1.4 Landsat 8 and Sentinel-2 Data Integration  
In an era marked by improvements in methods for LULC and LULCC analysis, the aggregation of spectral features is a trend to be further explored because integrating multispectral sensors' data improves monitoring effectiveness [105]. The use of multi-source data is a current need, especially to detect crop dynamics [19], and the L8/OLI and S2/MSI are useful not only for characterizing land use across large areas but also for promoting monitoring at crop field level over large areas [33]. Integration techniques have been used to improve the spatial resolution of SITS data sets needed for phenological characterization [28]. The integration between L8/OLI and S2/MSI data is a recent trend [22; 106; 107], and has been applied to develop dense SITS for improving temporal resolution and LULC classification approaches [108; 109]. The open access to L8/OLI and S2/MSI data, the similar wavelengths, and the same geographic coordinate system provide an excellent opportunity to integrate these data to produce SITS with a consistent spatial resolution for continuous global monitoring, enabling the mapping of cloud prone areas and the downscaling of the observed 30 m L8/OLI data to 10 m [21]. The Harmonized Landsat Sentinel-2 (HLS) product, which integrates L8/OLI and S2/MSI data inputs into a unique consistent dataset with higher temporal frequency (2-4 days) and a standardized 30 m spatial resolution, has focused on the necessary radiometric and geometric corrections to generate a seamless surface reflectance product, based on methods for atmospheric correction, cloud/shadow screening, geometric resampling, geographic registration, corrections for bandpass difference, and bidirectional reflectance distribution function (BRDF) effects [22; 106]. However, technical challenges still remain. Pixel-level co-registration between L8/OLI and S2/MSI have been challenging and approaches to register the data and to provide atmospheric corrections, cloud, and cloud shadow masking algorithms are being developed for both sensors [42; 56; 77]. The differing swath width and orbit tracks result in sun- and view-angle variability, causing non-negligible reflectance variations for certain applications [110]. There may be substantial regional variation implicit in such adjustments, which should be considered when applied regionally [111]. Despite this issue, HLS products have been recently used for different applications. On the national-scale across Germany, its quality was attested to classify vegetation classes and to map the frequency of grassland moving events, respectively, showing the value of multi-sensor SITS for characterizing LULCC intensity across large areas [107; 112]. [PERSON] et al. [113] also assessed the potential of HLS to characterize grasslands, observing its high sensitivity at the start of the growing season. [PERSON] et al. [105] monitored land-surface phenology in drylands using a regression tree modeling framework to integrate information of HLS products. They characterized seasonal variations with a substantial agreement between observed and predicted values (\(R^{2}\) 0.98), showing enhanced monitoring capabilities derived from HLS data. Classifying crop types in the USA, [PERSON] et al. [114] attested the ability of HLS products to improve information about crop location and extent within the crop season, achieving accuracies of above 85%. [PERSON] et al. [115] evaluated HLS products to map single- and double-crop in study areas of the USA, South Africa, India, and China, achieving OAs higher than 95% with 15-day harmonized NDVI and EVI SITS. Other projects are advancing in L8/OLI and S2/MSI data integration. [PERSON] [116] developed the FORCE (Framework for Operational Radiometric Correction for Environmental monitoring) for the mass-processing and analysis of Landsat and S2/MSI images, generating highly ARD (hARD)and products with cloud masking, radiometric and topographic or BRDF correction, aiming to solve spatio-temporal inconsistencies caused by cloud cover.