Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China.
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, 116024, China.
Environ Res. 2024 Jan 1;240(Pt 1):117430. doi: 10.1016/j.envres.2023.117430. Epub 2023 Oct 20.
Chlorophyll-a (Chla) in inland waters is one of the most significant optical parameters of aquatic ecosystem assessment, and long-term and daily Chla concentration monitoring has the potential to facilitate in early warning of algal blooms. MOD09 products have multiple observation advantages (higher temporal, spatial resolution and signal-to-noise ratio), and play an extremely important role in the remote sensing of water color. For developing a high accuracy machine learning model of remotely estimating Chla concentration in inland waters based on MOD09 products, this study proposed an assumption that the accuracy of Chla concentration retrieval will be improved after classifying water bodies into three groups by suspended particulate matter (SPM) concentration. A total of 10 commonly used machine learning models were compared and evaluated in this study, including random forest regressor (RFR), deep neural networks (DNN), extreme gradient boosting (XGBoost), and convolutional neural network (CNN). Altogether, 41 basic bands and 820 band ratios between the 41 bands were filtered by measuring their correlation with Ln(Chla) and several bands brought into different machine learning models. Results demonstrated that the construction of Chla concentration remote estimation model based on SPM classification could significantly improve the correlation between Ln(Chla) and 41 basic spectral band combinations, the correlation between Ln(Chla) and 820 band ratios, and the model verification R from 0.41 to 0.83. Furthermore, B3, B20, and B32 were finally selected based on correlation with SPM to classify SPM and the classification accuracy could reach 0.9. Finally, we concluded that RFR model performed best via comparing the R, RMSE, and MAPE. By comparing the relative contribution of input bands in different groups, B3 contributed most to three groups. The model constructed in this study has promising prospects for promotion and application in other inland waters, and could provide systematic research reference for subsequent research.
内陆水中的叶绿素-a（Chla）是水生生态系统评估的最重要光学参数之一，长期和日常 Chla 浓度监测有助于对藻类水华进行预警。MOD09 产品具有多种观测优势（更高的时间、空间分辨率和信噪比），在水色遥感中起着极其重要的作用。为了基于 MOD09 产品开发一种高精度的内陆水叶绿素-a 浓度的机器学习模型，本研究提出了一个假设，即通过悬浮颗粒物（SPM）浓度将水体分为三组后，Chla 浓度反演的准确性将会提高。本研究共比较和评估了 10 种常用的机器学习模型，包括随机森林回归器（RFR）、深度神经网络（DNN）、极端梯度提升（XGBoost）和卷积神经网络（CNN）。总共筛选了 41 个基本波段和 41 个波段之间的 820 个波段比，通过测量它们与 Ln（Chla）的相关性以及将几个波段引入不同的机器学习模型来进行筛选。结果表明，基于 SPM 分类构建 Chla 浓度遥感估算模型可以显著提高 Ln（Chla）与 41 个基本光谱波段组合之间的相关性、Ln（Chla）与 820 个波段比之间的相关性以及模型验证 R 值从 0.41 到 0.83。此外，最终基于与 SPM 的相关性选择了 B3、B20 和 B32 来对 SPM 进行分类，分类精度可达 0.9。最后，我们通过比较 R、RMSE 和 MAPE 得出 RFR 模型表现最佳。通过比较不同组中输入波段的相对贡献，B3 对三组的贡献最大。本研究构建的模型在其他内陆水域的推广和应用前景广阔，可为后续研究提供系统的研究参考。
