Huang Xin-Xi, Ying Han-Ting, Xia Kai, Feng Hai-Lin, Yang Yin-Hui, Du Xiao-Chen
College of Information Engineering, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China.
Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou 311300, China.
Huan Jing Ke Xue. 2020 Aug 8;41(8):3591-3600. doi: 10.13227/j.hjkx.201911141.
Unmanned aerial vehicle (UAV) multispectral remote sensing can be used to monitor multiple water quality parameters, such as suspended solids, turbidity, total phosphorus, and chlorophyll. Establishing a stable and accurate water quality parameter inversion model is a prerequisite for this work. The matching pixel-by-pixel (MPP) algorithm is an inversion algorithm for high resolution features of UAV images; however, it is associated with problems of excessive computation and over-fitting. To overcome these problems, the optimize-MPP (OPT-MPP) algorithm is proposed. In this study, Qingshan Lake in Hangzhou City, Zhejiang Province, was used as the research area. Forty-five samples were collected to construct the OPT-MPP algorithm inversion model for two water quality parameters:the suspended sediments concentration (SS) and turbidity (TU). The results showed that the optimal suspended sediment concentration inversion model had a determination coefficient () of 0.7870 and a comprehensive error of 0.1308. The optimal turbidity inversion model had a of 0.8043 and a comprehensive error of 0.1503. Hence, the inversion of the spatial distribution information for water quality parameters in each experimental area of QingShan Lake was realized by using the optimal models of the two established parameters.
无人机（UAV）多光谱遥感可用于监测多个水质参数，如悬浮固体、浊度、总磷和叶绿素。建立稳定、准确的水质参数反演模型是这项工作的前提。逐像素匹配（MPP）算法是一种用于无人机图像高分辨率特征的反演算法；然而，它存在计算量过大和过拟合的问题。为克服这些问题，提出了优化MPP（OPT-MPP）算法。本研究以浙江省杭州市的青山湖为研究区域。采集了45个样本，构建了用于两个水质参数（悬浮沉积物浓度（SS）和浊度（TU））的OPT-MPP算法反演模型。结果表明，最优悬浮沉积物浓度反演模型的决定系数（）为0.7870，综合误差为0.1308。最优浊度反演模型的决定系数为0.8043，综合误差为0.1503。因此，利用所建立的两个参数的最优模型实现了青山湖各试验区水质参数空间分布信息的反演。
