IRRIGATED AGRICULTURE MAPPING IN A SEMI-ARID REGION IN BRAZIL BASED ON THE USE OF SENTINEL-2 DATA AND RANDOM FOREST ALGORITHM
Keywords: irrigated agriculture, phenological metrics, machine learning, time series, earth observation
Abstract. 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.