Deep learning for water quality multivariate assessment in inland water across China

作者：Aamir Ali, Guanhua Zhou, Franz Pablo Antezana Lopez, Chongbin Xu, Guifei Jing, Yumin Tan · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2024 · DOI：10.1016/j.jag.2024.104078 · 被引用次数：28 · 研究领域：Water Quality Monitoring Technologies、Water Quality Monitoring and Analysis、Water Quality and Pollution Assessment

• Demonstration of limited but representative training dataset for efficient modeling. • Robust DNN models for independent and simultaneous retrieval of Chl-a, TSS and SDD. • Better performance of DNN over XGBoost, RF, and SVM. • Applicability of models on heterogeneous lakes. • Challenges of significant water quality degradation trends in Chinese lakes. Remote sensing of optically complex inland waterbodies is challenging due to the complex nonlinear correlation between water quality parameters and water optical properties. However, integration of deep learning techniques and representative datasets offers the potential to address these challenges effectively. This study aims to develop robust deep learning models, utilizing limited but highly representative dataset of in-situ water quality and radiometrically corrected hyperspectral remote sensing reflectance (R rs ) measurements collected from optically diverse lakes of China, for independent and simultaneous retrieval of Chlorophyll-a (Chl-a), Secchi Disk Depth (SDD), and Total Suspended Solids (TSS) using Sentinel-2 analysis ready products. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) provides over 400 such measurements for Chinese lakes, which are simulated to Sentinel-2 R rs with its spectral response function to build a representative dataset. Using this dataset, Multilayer Perceptron (MLP) based Deep Neural Network (DNN) models are developed and compared wi...