This study evaluated the efficiency of combining Large-Scale Particle Image Velocimetry (LSPIV) with Chiu's entropy method to estimate river discharge in the Brazilian semi-arid region. Measurements were conducted at six cross-sections of the Acara & uacute; River basin, which covers 10% of the state of Cear & aacute;, using Acoustic Doppler Velocimetry (ADV) as the reference method. LSPIV was applied using smartphone-recorded videos, processed with the RIVeR, PIVlab, and PTVlab software. In the second approach, LSPIV was combined with the entropy method, considering the maximum surface velocity and calculating the entropy coefficient (phi) for each section. Central sections showed lower error values, while camera positioning directly influenced the results. The phi coefficient proved to be unstable, indicating the need for further calibration. Compared to ADV, Method II (LSPIV + Entropy) presented deviations ranging from 2.97% to-33.33%, with a mean absolute error of 20.7%. Method III (LSPIV + Entropy + Elliptical Distribution) showed deviations of up to-45.34%, with a mean absolute error of 23%. Despite the variability, the combination of LSPIV and entropy proved to be a promising, low-cost, and non-intrusive alternative for discharge monitoring in small to medium-sized rivers located in semi-arid regions.