ASSESSING NITROGEN CONTENT IN FLOODED RICE PLANTATIONS USING TERRESTRIAL AND DRONE-BASED REFLECTANCE SENSORS

Abstract

Nitrogen is essential for agricultural crops, especially rice. Monitoring its demand is crucial. Remote sensing is effective and faster than field sampling, making fertilizer management easier. Thus, in this research, agronomic parameters related to N status in flooded rice cultivation were estimated using a multispectral sensor on board an unmanned aerial vehicle (UAV) and through an optical reflectance sensor (Crop Circle) in the field. The agronomic parameters evaluated were total above-ground biomass (AGB), leaf nitrogen content (LNC), leaf area index (Lai) and productivity. To evaluate the performance in the estimation of these parameters, using a UAV and a proximal terrestrial sensor, they were compared with traditional samples in the experimental plots, using simple and multiple linear regression. The data obtained through the field sensor showed a coefficient of determination (R²) of 0,89 and 0,85 for LNC and LAI, respectively. However, the linear models showed variations according to the phenological stages in which the parameters are estimated, suggesting a strong covariance effect. Thus, the multiple linear regression method was applied to estimate parameters related to nitrogen status in the plant throughout the rice cycle. All parameters (AGB, LAI and LNC), with the exception of productivity, obtained the best performance during the reproductive phenological stage, although effective results were also obtained when analyzing the data of all stages together. To estimate productivity, the grain filling period showed the best performance. It can be said that the analysis method employing a UAV is a promising alternative, as it eliminates the cloud cover of satellites and offers great cartographic accuracy, while also allowing a historical record for a more extensive area, with shorter operating time in relation to optical field sensors and traditional monitoring methods.