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 situ spectra 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 the R ² exceeding 0.90, and the root mean square error (RMSE) of corresponding validation ( N = 35) are 4.40 and 5.23 μ g/L, respectively. Similarly, for independent dataset of Res ( N = 45), GA–ANN and TBM methods show robust performance: the R ² values are 0.87 and 0.80, respectively; and the corresponding RMSE values are 7.79 and 7.73 μ g/L, respectively. For CW dataset ( N = 62), the R ² values of two methods are 0.81 and 0.62, respectively; and the corresponding RMSE values are 0.79 and 1.32 μ g/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-synchronous in situ data ( N = 36) indicated that the GA–ANN model outperforms TBM with higher R ² value (0.91 vs. 0.26) and smaller RMSE (4.41 vs. 13.85 μ g/L) and mean absolute errors (3.40 vs. 11.87 μ g/L) values. Although TBM has obvious overestimation of Chl-a concentration when applied to remote sensing image, we still thought that both GA–ANN and TBM are useful methods for Chl-a estimation in case-II waters, and GA–ANN performs marginally better with less deviation to measured Chl-a for multispectral remote sensing data. The ratio of TSS to Chl-a, experimental measurements, abundance of sampling points, and Chl-a concentration range are several important factors affecting the accuracy and robustness of GA–ANN and TBM methods.

中文翻译：

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沿海地区二类海域叶绿素A的遥感估算：三带模型与遗传算法-人工神经网络模型的比较

叶绿素-a（Chl-a）是浮游植物生物量和富营养化的重要指标，对水的组成和光学特性敏感。开发了一种遗传算法和人工神经网络（GA-ANN）的集成机器学习方法来检索Chl-a的浓度。原位利用来自两个水库（​​Res）和沿海水域（CW）的107个样本的光谱和同时水质参数，对GA-ANN和三波段模型（TBM）进行校准，以比较Chl-a估计值。GA–ANN和TBM方法在Res和CW的联合数据集（WGD）中都表现良好，[R ²超过0.90，且相应验证的均方根误差（RMSE） ñ = 35）是4.40和5.23 μ g / L。同样，对于Res的独立数据集（ ñ = 45），GA–ANN和TBM方法显示出强大的性能： [R ^2个值分别是0.87和0.80；并且相应的RMSE值分别为7.79和7.73μ g / L。对于CW数据集（ ñ = 62）， [R 两种方法的^2个值分别为0.81和0.62；并且相应的RMSE值分别为0.79和1.32μ g / L。当在2019年10月20日应用GA-ANN和TBM模型从两个Res中的校正后的Sentinel 2 MSI反射率数据中检索Chl-a浓度时，使用准同步的MSI派生的Chl-a浓度的验证结果原位 数据 （ ñ = 36）表示GA–ANN模型的TBM表现更高 [R ^2个值（0.91对0.26）和更小的RMSE（4.41对13.85）μ g / L）和平均绝对误差（3.40对11.87） μ g / L）值。尽管将TBM应用于遥感图像时明显地高估了Chl-a的浓度，但我们仍然认为GA-ANN和TBM都是在案例II水域中对Chl-a估计的有用方法，而GA-ANN的性能略好于多光谱遥感数据的实测Chl-a偏差。TSS与Chl-a的比例，实验测量，采样点的丰度以及Chl-a浓度范围是影响GA-ANN和TBM方法的准确性和鲁棒性的几个重要因素。