## 6 Conclusions  
L8/OLI and S2/MSI data have a vast recent application that demonstrates their potential to overcome the challenges concerning the identification of LULC classes in heterogeneous ecological gradients from forests to agricultural areas. The principal gap becomes a representative sample dataset. Classification models tend to achieve higher accuracies when using representative samples, while the use of RF, SVM, and neural network algorithms have allowed detecting LULC classes, with no significant difference among them, besides the fact that deep learning methods are also growing. Moreover, spectral band combinations, exploring the electromagnetic spectrum, as highlighted by the use of SWIR and Red-edge bands, increase the classification performance of a wide range of LULC classes as well as the retrieval of biophysical parameters. Less frequently applied VIs have been useful to distinguish more vegetation classes from a multi-temporal and multi-sensor analysis perspective [222], especially in areas with a high diversity of classes with similar spectral characteristics. However, its important to revisit previous studies to understand how they were used and to avoid confusions before choosing a VI correspondent with the research objectives. Despite our focus in L8/OLI bands, we noted less application of thermal bands of the L8/Thermal InfraRed Sensor (TIRS) for LULC classification and LULCC detection. This scarceness of thermal bands application also was detected and discussed by [PERSON] et al. [50] in a meta-review article about remote sensing for agricultural applications. The authors explain that this occurs since the signal is much more variable in time according to the plant stand microclimate, and the calibration of the TIR sensor in field conditions is also more complex and delicate as compared to the solar spectral domain. The application of thermal bands is more useful using UAV and in-field cameras, as pointed out by [PERSON] et al. [223]. The results presented in this systematic review can guide the scientific community to the use of L8/OLI and S2/MSI data for LULC and LULCC applications. In addition, the prognosis on the continuity of Landsat and Sentinel series, expanding the use of the electromagnetic spectrum and incorporating SAR data, is promising and will ensure the availability of data necessary for the remote sensing community interested in LULC and LULCC applications, creating a robust and dense virtual constellation to overcome the limitations of traditional approaches, and enabling more detailed and consistent knowledge of the landscape. The following are available online at [[http://www.mdpi.com/2072-4292/12/18/3062/s1](http://www.mdpi.com/2072-4292/12/18/3062/s1)]([http://www.mdpi.com/2072-4292/12/18/3062/s1](http://www.mdpi.com/2072-4292/12/18/3062/s1)), Table S1: Conventional vegetation indices cited in the systematic review, Table S2: Less frequent vegetation indices cited in the systematic review as input for LULC mapping, Table S3: Less frequent vegetation indices that can be further explored as input for LULC mapping. According to the CRediT roles: M.E.D.C. : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing--original draft preparation. M.C.A.P. : Conceptualization, Formal analysis, Validation, Writing--review and editing. I.D.S. : Conceptualization, Formal analysis, Funding acquisition, Methodology, Supervision, Validation, Writing--review and editing. All authors have read and agreed to the published version of the manuscript. This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brasil (CAPES)-Finance Code 001, Process Number 88887.351470/2019-00 (M.E.D.C. ), and the Environmental Monitoring of Brazilian Biomes project (Brazil Data Cube), funded by the Amazon Fund through the financial collaboration of the Brazilian Development Bank (BNDES) and the Foundation for Science, Technology, and Space Applications (FUNCATE), Process Number 17.2.0536.1 (M.C.A.P.).