Remote Sensing Estimation of Chlorophyll-A in Case-II Waters of Coastal Areas: Three-Band Model Versus Genetic Algorithm–Artificial Neural Networks Model

作者：Jinyue Chen, Shuisen Chen, Rao Fu, Chongyang Wang, Dan Li, Yongshi Peng, Li Wang, Hao Jiang, Qiong Zheng · 发表于：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 年份：2021 · DOI：10.1109/jstars.2021.3066697 · 被引用次数：68 · 研究领域：Marine and coastal ecosystems、Water Quality Monitoring and Analysis、Water Quality Monitoring Technologies

Chlorophyll-a (Chl-a), an important indicator of phytoplankton biomass and eutrophication, is sensitive to water constitutes and optical characteristics. An integrated machine learning method of genetic algorithm and artificial neural networks (GA–ANN) was developed to retrieve the concentration of Chl-a.In situspectra and simultaneous water quality parameters of 107 samples from two reservoirs (Res) and coastal waters (CW) were used to calibrate GA–ANN and three-band models (TBM) for comparison of Chl-a estimation. Both GA–ANN and TBM methods perform well for the joint dataset (WGD) of Res and CW with theR2exceeding 0.90, and the root mean square error (RMSE) of corresponding validation (N= 35) are 4.40 and 5.23μg/L, respectively. Similarly, for independent dataset of Res (N= 45), GA–ANN and TBM methods show robust performance: theR2values are 0.87 and 0.80, respectively; and the corresponding RMSE values are 7.79 and 7.73μg/L, respectively. For CW dataset (N= 62), theR2values of two methods are 0.81 and 0.62, respectively; and the corresponding RMSE values are 0.79 and 1.32μg/L, respectively. When the GA–ANN and TBM models were applied to retrieve Chl-a concentration from the calibrated Sentinel 2 MSI reflectance data in two Res on October 20, 2019, however, the validated results of MSI-derived Chl-a concentrations using quasi-synchronousin situdata (N= 36) indicated that the GA–ANN model outperforms TBM with higherR2value (0.91 vs. 0.26) and smaller RMSE (4.41 vs. 13.85μg/...