Análise do desenvolvimento e uso de água da cana-de-açúcar por meio de sensoliamento remoto suborbital

Abstract

This paper explores the cultivation of sugarcane (Saccharum officinarum) in Brazil, the world's largest producer. However, the average yield (73.7 tons/ha) is below that of other countries, and the state of Paraíba has an even lower productivity (53.5 tons/ha in 2021). Factors such as irregular distribution of fertilizer and irrigation contribute to production variability. Given the reality and the importance of this sector, producers are increasingly focused on technologies that optimize production and minimize costs, such as the use of Remotely Piloted Aircraft (RPAs), or drones, for remote sensing in precision agriculture. Monitoring via remote sensing is crucial for predicting harvests and planning the production chain efficiently. This paper aims to analyze, through the NDVI and NDRE indices, the development, productivity, and water use of sugarcane in three different irrigation regimes. The methodology involved monitoring crop development, relating vegetation indices (VIs) to environmental variables such as photosynthetically active radiation (PAR) and evapotranspiration, and determining specific crop coefficients for each phenological stage of the plant. The study area was a property of the Japungu Plant, located in Rio Tinto, Paraíba, during a sugarcane production cycle, which has supplementary drip irrigation. Evapotranspiration and Kc values were determined with micrometeorological data. IVs were generated from images from a Phantom 4 Pro Multispectral. It was observed that NDVI and NDRE can be used to estimate the crop coefficient (Kc) of sugarcane, with a good fit of the data (R² = 0.62), proving to be an effective tool to optimize irrigation and resource management in the field. Vegetation indices were effective to evaluate sugarcane development. One of the blades used (80 mm) was superior in productivity (tons of sugarcane/ha), however, there was no statistical difference between the irrigation plots. The use of meteorological data and vegetative indexes is essential for efficient crop management.