## 5 Results and Discussion
### Gaps and Challenges
#### Challenges to Deal with Big Data and Landsat-Sentinel's Data Integration  
The term 'big Earth data' emerged to describe massive EO datasets that confront analysts and their traditional workflows with a range of challenges [49]. The use of L8/OLI and S2/MSI for providing large-scale LULC classification requires a big dataset of EO data, with the processing of a significant number of orbital images, in which a complex computational infrastructure is needed to store, manage, and process the data [136]. The needs of processing capacity, file storage, and work time represent difficulties when working with long SITS and large study areas [36]. Understanding this and stimulating science users to 'bring their application to the data' prompt, many initiatives to build high-performance computing systems that host large satellite data collections to enable large volume processing [47], as the Australian Geoscience Data Cube [137], the Swiss Data Cube [57], and the Brazil Data Cube ([[http://brazildatacube.org/pt/pagina-inicial-2/](http://brazildatacube.org/pt/pagina-inicial-2/)]([http://brazildatacube.org/pt/pagina-inicial-2/](http://brazildatacube.org/pt/pagina-inicial-2/))). The Committee on Earth Observation Satellites (CEOS) also suggests portable open-access data cube architectures to facilitate the use of EO data and to share knowledge [47]. Alternatively, optimal dates based on crop calendars can reduce the data volume for mapping LULC classes with marked seasonality [138]. However, this depends on the data availability and may fail when images of key crop developmental stages of the crops are not available [34, 139].  
For the first time, the applied science community has had access to systematic and near-daily medium-resolution optical data [114]. Using data provided by different sensors can improve the temporal resolution of SITS, fill data gaps, and improve the quality of the Earth's surface monitoring [140]. However, how to effectively integrate the information provided by different sensors remains an unsolved problem in the science of remote sensing [141], due to different spectral or spatial sensor characteristics, acquisition geometries or illumination conditions, or atmospheric state [142], leading to inconsistencies in products derived from multi-sensor approaches [140]. This issue requires the development of new ways to extract landscape information from big EO data sets [136]. Researchers have been engaged in finding ways to use S2/MSI and Landsat data together. One promising source for driving the next-generation of products is the L8/OLI and S2/MSI harmonization [114], and some groups lead this task [7]. The NASA Multi-source Land Imaging (MuSLI) program has supported land science products in diverse areas, including burned area mapping, forest phenology studies, and fractional water characterization [114]. Similar work is underway in Europe through ESA, Copernicus services, and national monitoring activities complemented by the NASA/USGS Landsat Calibration Team and the ESA S2 Validation Team [118]. Often, the harmonization approach has five steps: (1) grid imagery to a common pixel resolution,projection, and spatial extent (tile); (2) atmospheric correction; (3) cloud mask; (4) adjustments to represent the response from a common spectral bandpass and nadir BRDF-normalization keying off a single, global and constant BRDF shape [110; 142]; (5) performance of geographic co-registration to warp and co-register data [116; 140; 22]. In recent experiments, harmonized data are being re-projected and data cubed [116; 140]. However, a variety of technical challenges remain without a consensus approach, concerning co-registration, atmospheric correction, cloud, and shadow masking [56], sun- and view-angle variability, reflectance differences calibration [22; 123], and the processing baselines and surface reflectance approach for both datasets.