| Cite this article: | 蔡建楠,刘海龙,姜波,等.基于GA-PLS算法的河网水体化学需氧量质量浓度高光谱反演[J].灌溉排水学报,2020,():-. |
| CAI Jiannan,LIU Hailong,JIANG BO,et al.基于GA-PLS算法的河网水体化学需氧量质量浓度高光谱反演[J].灌溉排水学报,2020,():-. |
|
| |
|
|
| This article has been：Browse 1823Times Download 0Times |
|
|
|
| DOI： |
|
| Hyperspectral Retrieval of Chemical Oxygen Demand for River Network Waters Based on GA-PLS Algorithm |
|
CAI Jiannan1, LIU Hailong2, JIANG BO2, HE Tianhui3, CHEN Wenjie4, FENG Zhiwei4, LI Zhuolin4, XING Qianguo2
|
|
1. Zhongshan Environmental Monitoring Station 2. Zhongshan Ecology and Environmental Agency;2.Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences;3.Zhongshan Environmental Montoring Station;4.Zhongshan Ecology and Environmental Agency
|
| Abstract: |
| 【Objective】Hyperspectral remote sensing has been proven to be an effective tool for water quality monitoring. However, there are also defects such as data redundancy and susceptibility to environmental noise, which affect the accuracy and stability of water quality retrieval by hyperspectral technique. The Genetic Algorithm-Partial Least Squares (GA-PLS) algorithm with the function of selecting sensitive spectral variables can overcome the above disadvantages. In the field of water quality remote sensing monitoring, GA-PLS was mainly used for the retrieval studies of optically active parameters such as transparency, chlorophyll-a, suspended matter and turbidity in lakes and reservoirs, but there were few reports on hyperspectral retrieval for comprehensive indicators of river water quality. The purpose of this paper is to establish a hyperspectral retrieval model for chemical oxygen demand (COD) concentration in the river network of the Pearl River Estuary and validate the improvement of GA-PLS algorithm on the modeling effect.【Method】Hyperspectral data and COD concentration data of 146 samples from the water body of the Pearl River Estuary were collected, and the characteristic bands of hyperspectral reflectance data were screened by GA-PLS algorithm to retrieve COD concentration. Differences in model retrieval effects with different band combinations as input variables were compared.【Result】The COD concentration hyperspectral retrieval model based on GA-PLS algorithm is better than the full-spectrum PLS model. The minimum RMSEP of the validation set is 4.887 mg·L-1, which is 11.4% lower than the full-spectrum PLS model. With 74 filtered bands (accounting for 2.9% of full bands) as input variables, the model can still maintain good stability and retrieval accuracy. Some of the characteristic bands obtained by the GA-PLS algorithm have clear physical meaning, indicating that the screening results are reasonable.【Conclusion】The GA-PLS algorithm can be used for characteristic bands selection of the hyperspectral data to optimize the dimension and further simplify the model. Within the main distribution range of the COD concentration of samples, the GA-PLS algorithm model has good accuracy for water quality retrieval and category classification accuracy. |
| Key words: hyperspectra; genetic algorithms; partial least squares; chemical oxygen demand; river network waters |
|
|
|
|
|
|
