Image cross‐correlation techniques, such as particle image velocimetry (PIV), can estimate water surface velocity (v _surf) of streams. However, discharge estimation requires water depth and the depth‐averaged vertical velocity (U _m ). The variability of the ratio U _m /v _surf introduces large errors in discharge estimates. We demonstrate a method to estimate v _surf from Unmanned Aerial Systems (UASs) with PIV technique. This method does not require any ground control point (GCP): the conversion of velocities from pixels per frame into length per time is performed by informing a camera pinhole model; the range from the pinhole to the water surface is measured by the drone‐borne radar. For approximately uniform flow, U _m is a function of the Gauckler‐Manning‐Strickler coefficient (K _s ) and v _surf. We implement an approach that can be used to jointly estimate K _s and discharge by informing a system of two unknowns (K _s and discharge) and two nonlinear equations: i) Manning's equation and ii) mean‐section method for computing discharge from U _m . This approach relies on bathymetry, acquired in situ a priori, and on UAS‐borne v _surf and water surface slope measurements. Our joint (discharge and K _s ) estimation approach is an alternative to the widely used approach that relies on estimating U _m as 0.85·v _surf. It was extensively investigated in 27 case studies, in different streams with different hydraulic conditions. Discharge estimated with the joint estimation approach showed a mean absolute error of 19.1% compared to in situ discharge measurements. K _s estimates showed a mean absolute error of 3 m^1/3/s compared to in situ measurements.

中文翻译：

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用Drone-Borne方法联合估算自然流的流量和曼宁粗糙度

图像互相关技术（例如粒子图像测速法（PIV））可以估算河流的水表面速度（v _surf）。但是，流量估算需要水深和垂直平均深度（U _m）。比率U _m / v _surf的可变性在流量估算中引入了较大的误差。我们展示了一种估算v_冲浪的方法 是采用PIV技术的无人机系统（UAS）提供的。这种方法不需要任何地面控制点（GCP）：通过通知相机针孔模型，可以将速度从每帧像素转换为每时间长度。从针孔到水面的范围是由无人机雷达测量的。对于近似均匀的流量，U _m是高克勒-曼宁-斯特里克勒系数（K _s）和v _surf的函数。我们通过通知两个未知数（K _s）的系统，实施一种可共同估算K _s和排放_的方法 和放电）和两个非线性方程：i）曼宁方程和ii）计算U _m的放电的均值截面方法。这种方法依赖于先验获得的测深法，以及UAS进行的v_冲浪和水面坡度测量。我们的联合（放电和K _s）估计方法是一种广泛使用的方法的替代方法，后者依赖于将U _m估计为0.85· v _surf。在27个案例研究中，在不同水力条件下的不同流中对它进行了广泛的研究。与现场排放测量相比，采用联合估算方法估算的排放显示出平均绝对误差为19.1％。ķ 与现场测量相比， _s的估计值显示平均绝对误差为3 m ^1/3 / s。