文献类型： 外文期刊

作者： Zhang, Donghui ¹ ; Zhang, Lifu ¹ ; Sun, Xuejian ¹ ; Gao, Yu ² ; Lan, Ziyue ² ; Wang, Yining ² ; Zhai, Haoran ² ; Li, Jingru ² ; Wang, Wei ² ; Chen, Maming ² ; Li, Xusheng ⁵ ; Hou, Liang ⁶ ; Li, Hongliang ⁷ ;

作者机构： 1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China

2.Tianjin Progoo Informat Technol Co Ltd, Progoo Res Inst, Tianjin 300380, Peoples R China

3.Shihezi Univ, Key Lab Oasis Ecoagr, Xinjiang Prod & Construct Corps, Shihezi 832003, Peoples R China

4.Chengdu Univ Technol, Sch Earth Sci, Chengdu 610059, Peoples R China

5.Beijing Res Inst Uranium Geol, Natl Key Lab Remote Sensing Informat & Imagery An, Beijing 100029, Peoples R China

6.Hebei Acad Agr & Forestry Sci, Inst Agr Informat & Econ, Shijiazhuang 050051, Hebei, Peoples R China

7.Tianjin Inst Metrol Supervis & Testing, Tianjin 300192, Peoples R China

关键词： hyperspectral imager; UAV remote sensing; water quality monitoring; space-ground data; buoy spectrometer; water eutrophication; absorption characteristics

期刊名称：REMOTE SENSING （ 影响因子：5.349； 五年影响因子：5.786 ）

ISSN：

年卷期： 2022 年 14 卷 15 期

页码：

收录情况： SCI

摘要： The effective integration of aerial remote sensing data and ground multi-source data has always been one of the difficulties of quantitative remote sensing. A new monitoring mode is designed, which installs the hyperspectral imager on the UAV and places a buoy spectrometer on the river. Water samples are collected simultaneously to obtain in situ assay data of total phosphorus, total nitrogen, COD, turbidity, and chlorophyll during data collection. The cross-correlogram spectral matching (CCSM) algorithm is used to match the data of the buoy spectrometer with the UAV spectral data to significantly reduce the UAV data noise. An absorption characteristics recognition algorithm (ACR) is designed to realize a new method for comparing UAV data with laboratory data. This method takes into account the spectral characteristics and the correlation characteristics of test data synchronously. It is concluded that the most accurate water quality parameters can be calculated by using the regression method under five scales after the regression tests of the multiple linear regression method (MLR), support vector machine method (SVM), and neural network (NN) method. This new working mode of integrating spectral imager data with point spectrometer data will become a trend in water quality monitoring.