A Chlorophyll Concentration Inversion Method Based on OWTs and 1D CNN-Transformer Feature Extraction

The inversion of chlorophyll-a (Chla) across water bodies is challenged by the diversity of optical water types (OWTs) and by the nonlinear superposition of optical signals from optically active constituents. To address these challenges, this study introduces an inversion method based on water classification that integrates dual-stream feature extraction (via a one-dimensional convolutional neural network (1D CNN)-Transformer) and multimodel evaluation, termed OWT-CCINET. We first applied k-means clustering to the reflectance spectra, dividing the samples into four optical water types (OWTs): eutrophic, turbid, clear, and moderately turbid. For each group, feature extraction was adjusted using either sequential or parallel branches. Several feature combinations were then tested with different regression models, and the most suitable model for each OWT was determined based on a comparative evaluation. Compared with conventional models such as the OCx and CI hybrid algorithms, the model with targeted feature extraction and model selection based on water classification significantly improved the accuracy and applicability for Chla inversion across various OWTs. By applying OWT-CCINET, the natural log (ln)-transformed Chla concentrations were estimated, and the model achieved outstanding results, with a mean absolute error (MAE) of 0.410, a root mean square error (RMSE) of 0.549, and a coefficient of determination (R²) of 0.899. After minimally harmonizing MODIS and MERIS to SeaWiFS-equivalent bands, OWT-CCINET maintained stable cross-sensor performance, supporting long-term, multi-mission applications. This study provides novel methodologies and theoretical support for the development of cross-water type models, offering significant value for practical applications such as marine environmental monitoring.