Modeling of turbidity retrieval of Hulunnaoer based on airborne hyperspectral imagery
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Abstract:
To verify the ability of Headwall hyperspectral imagery for monitoring water quality , three turbidity retrieval models including band-ratio, first-derivative, and partial least-squares models were established with the Headw all airborne hyperspectral imagery and sy nchronous measured turbidities in Hulunnaoer on September 17th, 2018. The three models were adopted to estimate the spatial distributions of turbidity in Hulunnaoer. The results showed that: the three constructed models based on the airborne Headwall hy perspectral data verify that the root mean square error ( RMSE) is less than the verification sample turbidity extreme value differ ence of 5.3 NTU, and the MRE is less than 10% , the three models had good performances in turbidity retrieval and the Headw all hy perspectral imagery could be used for water quality retrieval; the partial least squares model with adeterm ination coefficient R2 ( 01 95) , and a comprehensive error CE( 1.74% ) showed the better performance compared to the band ratio model and the first-order differ ential model, which was the optimal model for turbidity retrieval in Hulunnaoer; the range of turbidity was between 21.2 and 54.4 NTU in the eastern area of Hunlunnaoer on September 17th, 2018, the distributions of turbidity in Hunlunnaoer showed an increasing trend from north to south, the turbidity in the center was relatively low , while the turbidity in the southern area was relatively high due to the exists of algae. This study quantitatively retrieved turbidity, which can provide a reference for remote sensing of water quality based on aerial hyperspect ral remote sensing imageries in the future.
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