Application of hyperspectral analysis of chlorophyll a concentration inversion in Nansi Lake
| Autor: | Xinyue Yang, Pudong Liu, Pingjie Fu, Yu Cui, Yuxuan Zhang, Fei Meng |
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| Rok vydání: | 2021 |
| Předmět: |
Ecology
Mean squared error Applied Mathematics Ecological Modeling Noise reduction Linear model Hyperspectral imaging Inversion (meteorology) Soil science Hilbert–Huang transform Computer Science Applications Computational Theory and Mathematics Modeling and Simulation Partial least squares regression Environmental science Water quality Ecology Evolution Behavior and Systematics |
| Zdroj: | Ecological Informatics. 64:101360 |
| ISSN: | 1574-9541 |
| Popis: | Chlorophyll-a (Chl-a) is an important water quality safety evaluation index, and accurate Chl-a concentration monitoring is important for the development of aquaculture, aquatic ecosystem balance, and drinking water safety. Rapid and accurate Chl-a concentration determination in water using hyperspectral remote sensing is an important subject in water ecological environment monitoring. In this study, the spectral reflectance and Chl-a concentration of Nansi Lake were measured, and the time-frequency method of empirical mode decomposition (EMD) analysis was used for the noise reduction and reconstruction of the first-order differential of the spectrum to extract sensitive spectral features. The eXtreme Gradient Boosting (XGBoost) machine learning algorithm was used to establish a Chl-a concentration estimation model, and the best parameters and model combinations for the inversion of the Chl-a concentration in the water column of Nansi Lake were determined. The results show that the combined three-band algorithm combination parameters obtained from the EMD noise-reduced reconstruction of spectral first-order differential (OFODSR-D) data fit the measured Chl-a concentrations better than the original spectral (OSR) and OFODSR data, with a maximum correlation coefficient of 0.8588. Second, the models based on OFODSR-D achieved more satisfactory prediction results, with XGBoost having the highest estimation accuracy (R2 of 0.9024 and root-mean-square error (RMSE) of 1.1312 μg·L−1 for the inverse model), followed by the partial least squares regression (PLSR) model and the linear model (R2 of 0.8474 and 0.8326, and RMSE of 13.3031 and 7.6987 μg·L−1, respectively). This study innovatively introduces the EMD method to the spectral processing of water bodies, obtains optimal parameters for the inversion of the Chl-a concentration, and achieves better results. This study provides a new approach to obtaining optimal inversion parameters for Chl-a monitoring in inland lake water bodies using remote-sensing methods. |
| Databáze: | OpenAIRE |
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