Modeling of Suspended Particulate Matter Concentration in an Extremely Turbid River Based on Multispectral Remote Sensing from an Unmanned Aerial Vehicle (UAV)

作者：Yinghui Zhai, Pu Zhong, Hongtao Duan, Dan Zhang, Xin Chen, Xingjian Guo · 发表于：Remote Sensing · 年份：2023 · DOI：10.3390/rs15225398 · 被引用次数：9 · 研究领域：Remote Sensing and LiDAR Applications、Soil erosion and sediment transport、Land Use and Ecosystem Services

Following consecutive years of governance efforts, there has been a substantial reduction in sediment transport in the Yellow River, resulting in significant changes in its water–sediment dynamics. This necessitates precise monitoring of sediment-bearing tributary inflows, a crucial requirement for effective governance strategies on the Loess Plateau’s current developmental stage. While satellite remote sensing technology has been widely used to estimate suspended particulate matter concentration (CSPM) in open water bodies like oceans and lakes, its application in narrow rivers presents challenges related to hybrid pixel and proximity effects. As a result, the effectiveness and competence of satellite remote sensing in monitoring CSPM in such confined river environments are reduced. This study attempted to use unmanned aerial vehicle (UAV) remote sensing with multispectral technology to invert CSPM in the Wuding River, a sediment-bearing Yellow River tributary. A novel CSPM concentration inversion model was introduced for highly turbid river settings. The results showed that the accuracy of the new band ratio model in this study is significantly improved compared with the existing models. The validation dataset had a coefficient of determination (R2) of 0.83, a root mean square error (RMSE) of 3.73 g/L, and a mean absolute percentage error (MAPE) of 44.95% (MAPE is 40.68% at 1–20 g/L, and 12.37% at >20 g/L). On this basis, the UAV also monitored the impacts of heavy rainf...