无人机高光谱数据具有波段信息丰富、时空分辨率高等特性，常被应用于水质参数的反演当中。以辽河湿地为研究对象，基于实地水质参数数据与无人机高光谱数据，分别构建了叶绿素a(Chl-a)和总氮(TN )多种光谱指数。最终，基于相关性关系确定最佳Chl-a和TN光谱指数，进而建立一次线性模型、指数模型和偏最小二乘(PLS)模型。结果表明：Chl-a最优模型(归一化指数-指数模型)的精度为R²=0.90 ,RMSE=0.003 5 mg/L, TN最优模型(差值指数-PLS模型)的精度为R²=0.66 ,RMSE=0.24 mg/L。结合同时期同区域的无人机高光谱影像反演得到研究区水域的Chl-a和TN的浓度分布图，验证了无人机高光谱数据应用于水质参数反演的可行性。

Hyperspectral data from unmanned aerial vehicles(UAVs), known for their rich band information and high spatio-temporal resolution,is often used in the inversion of water quality parameters. Taking water quality parameters of Liao River wetland as the main object, based on the field water quality parameter data and UAV hyperspectral data, multiple spectral indices for chlorophyll-a(Chl-a) and total nitrogen( TN) were constructed respectively. Furthermore, the optimal band combination was determined by the highest correlation coefficient. Based on the optimal index of Chl-a and TN, the linear model, exponential model and partial least squares(PLS) model were established. The results show that the accuracy of optimal Chl-a model(normalized spectral indices-exponential model) is R²=0.90, RMSE=0.003 5 mg/L. The accuracy of optimal TN model(difference spectral indices-PLS model) is R²=0.66, RMSE=0.24 mg/L. The concentration distribution map of Chl-a and TN in the study area was obtained by UAV hyperspectral images in the same period and region, which could verify the feasibility of applying UAV hyperspectral data to water quality parameter inversion.