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Ensuring the representativeness of water samples is a critical component of surface water monitoring, particularly during routine assessments and emergency situations in areas with limited accessibility. Traditional sampling methods, such as manual collection from shorelines or boats, often face logistical, safety, and operational constraints. In contrast, drone-based sampling technique have emerged as viable alternatives, offering increased flexibility, reduced field risks, and access to otherwise unreachable locations. This study evaluates the comparability between drone-assisted surface water sampling and conventional methods, with particular emphasis on parameters of the Water Quality Index (WQI) adopted by CETESB (Environmental Company of the State of São Paulo) for aquatic environmental quality assessments. Sampling campaigns were conducted at six sites across São Paulo State, Brazil, using both manual collection and an unmanned aerial vehicle (DJI Matrice 350, China) equipped with the SPEEDiP V2 water sampling system (Soarability, China). Paired samples were collected and analyzed for WQI parameters namely Escherichia coli, pH, biochemical oxygen demand, nitrogen series, total phosphorus, water temperature, dissolved oxygen, turbidity, and total solids. A total of 72 method–site–parameter combinations were statistically evaluated using t-tests or Mann–Whitney U tests. Over 90% of the comparisons showed no statistically significant differences between drone-based and manual sampling methods (p-value < 0,05), indicating strong analytical agreement. Isolated discrepancies were attributed to procedural factors such as limited sample volume or measurement instability, rather than systematic bias. Field operations confirmed the practicality of drone-based sampling, demonstrating its suitability as a complementary tool for surface water monitoring. While not intended to fully replace conventional strategies, drone-assisted sampling offers a safe and reliable alternative, particularly in locations where traditional access is limited or poses operational challenges. Its integration into routine environmental monitoring programs can enhance flexibility and coverage without compromising data quality. Broader adoption will require the development and dissemination of standardized protocols, as well as targeted training, to ensure methodological consistency, data comparability, and compliance with regulatory frameworks.