Data and code for POD-based multi-wavelength fluorescence estimation of chlorophyll a in optically complex waters

This archive contains the data and Python code associated with a POD-based multi-wavelength fluorescence framework for estimating chlorophyll a (Chl a) in optically complex waters. The materials support development and evaluation of a multi-wavelength physical baseline and residual-correction model using Phytoplankton Optical Detector (POD) excitation-channel responses.The archive includes laboratory monoculture and turbidity-standard datasets, routine field training samples, independent bloom and mixed-sample test datasets, model comparison tables, HPLC diagnostic pigment data, YSI-POD comparison data, sampling-station coordinates, representative FlowCam images, model-training and prediction scripts, and figure-generation scripts. The residual-correction workflow combines laboratory Chl a and turbidity-standard samples with field samples to train a high-order spectral-feature SE-ResNet model, and independent test data are provided for evaluating model performance.A README file describes the full data inventory, software requirements, model workflow, and figure-generation workflow.