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Irrigation is important for agricultural production and is often decisive for this, especially in arid and semi-arid areas, where precipitation is insufficient. In Brazil, irrigated agriculture is responsible for 46% of withdrawals from water bodies and 67% of the consumption of the total volume of water collected, representing the highest consumptive use in the country. Remote sensing technologies have great potential for developing methods for monitoring irrigated areas. However, mapping irrigated areas is still a challenge, due to the complexity and diversity of irrigation methods and crops, especially in a country with continental dimensions like Brazil. Remote sensing techniques for mapping irrigated areas in Brazil have been applied mainly in areas with center pivot irrigation in the Cerrado, and with paddy rice in the south of Brazil. But few or no applications, involving mapping of crops irrigated by other irrigation methods, mainly in the semi-arid, have been carried out. The objective of this work was to investigate a method for classifying irrigated agriculture in a semiarid region of Brazil, based on the use of Sentinel 2 imagery and random forest algorithm. We proposed a novel and robust methodology showing with preliminary results that it´s possible to identify irrigated agriculture in this region with a class-f1-score of 74% for complementary irrigation and 95% for center-pivots.

The Brazilian Savanna is the second largest biogeographical region in Brazil and present different vegetation types, consisting mostly of tropical savannas, grasslands, and forests. The forest types have different tree cover and floristic composition, which is associated to leaf deciduousness. Considering the importance of Cerrado to biodiversity conservation and the maintaining of environmental services, the development of methods to map the different forest types in Cerrado is important for conservation programmes, subsidize restauration plains, and to allow estimations of carbon sink and stock. Mapping heterogeneous tropical areas, such as the Brazilian Savanna, is very complex due to the natural factors and peculiarities of the vegetation types, and it's still particularly challenging to separate between different forest formations. In this study we tested machine learning approaches based on the use of dense image time series, in order to evaluate the separability Dry Tropical Forests and Savanna woodlands. We considered the Brazilian State of Tocantins as the study area, which is located in the Northern region of the country. RF classification of Landsat dense time series showed an overall accuracy of 0.85005, while the LSTM approach presented an overall accuracy of 0.88601, with the highest f1-score for the savanna woodlands class, suggesting the capability of the recurrent neural networks on handling complex long-term dependencies such as the EVI dense time series data. This study showed the potential for the development of a semi-automatic method for discriminating the different types of forest formations in the Brazilian Savanna, based on remote sensing.