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Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing

by 1,2,3, 1,2,3,4,*, 1,2,3 and 1,2,3
State Key Laboratory of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China
College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
Beijing Laboratory of Water Resources Security, Beijing 100048, China
Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing 100048, China
Author to whom correspondence should be addressed.
Academic Editors: Kun Shi and Thomas Meixner
Water 2022, 14(1), 128;
Received: 9 December 2021 / Revised: 28 December 2021 / Accepted: 3 January 2022 / Published: 5 January 2022
The water components affecting turbidity are complex and changeable, and the spectral response mechanism of each water quality parameter is different. Therefore, this study mainly aimed at the turbidity monitoring by unmanned aerial vehicle (UAV) hyperspectral technology, and establishes a set of turbidity retrieval models through the artificial control experiment, and verifies the model’s accuracy through UAV flight and water sample data in the same period. The results of this experiment can also be extended to different inland waters for turbidity retrieval. Retrieval of turbidity values of small inland water bodies can provide support for the study of the degree of water pollution. We collected the images and data of aquaculture ponds and irrigation ditches in Dawa District, Panjin City, Liaoning Province. Twenty-nine standard turbidity solutions with different concentration gradients (concentration from 0 to 360 NTU—the abbreviation of Nephelometric Turbidity Unit, which stands for scattered turbidity.) were established through manual control and we simultaneously collected hyperspectral data from the spectral values of standard solutions. The sensitive band to turbidity was obtained after analyzing the spectral information. We established four kinds of retrieval, including the single band, band ratio, normalized ratio, and the partial least squares (PLS) models. We selected the two models with the highest R2 for accuracy verification. The band ratio model and PLS model had the highest accuracy, and R2 was, respectively, 0.65 and 0.72. The hyperspectral image data obtained by UAV were combined with the PLS model, which had the highest R2 to estimate the spatial distribution of water turbidity. The turbidity of the water areas in the study area was 5–300 NTU, and most of which are 5–80 NTU. It shows that the PLS models can retrieve the turbidity with high accuracy of aquaculture ponds, irrigation canals, and reservoirs in Dawa District of Panjin City, Liaoning Province. The experimental results are consistent with the conclusions of the field investigation. View Full-Text
Keywords: UAV hyperspectral; turbidity; retrieval model; remote sensing; water body UAV hyperspectral; turbidity; retrieval model; remote sensing; water body
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MDPI and ACS Style

Cui, M.; Sun, Y.; Huang, C.; Li, M. Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing. Water 2022, 14, 128.

AMA Style

Cui M, Sun Y, Huang C, Li M. Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing. Water. 2022; 14(1):128.

Chicago/Turabian Style

Cui, Mengying, Yonghua Sun, Chen Huang, and Mengjun Li. 2022. "Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing" Water 14, no. 1: 128.

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