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Algae Dataset

Every data hub, dataset, sensor, variable, paper and limitation in one tree — and for each: where it comes from, how far back it goes, how often it captures, why it belongs in a cholera study, and why it does not work everywhere.

Highlight site All three Chakaria HDSS Matlab HDSS Dhaka City Choosing a site greys out everything that does not apply there.

ALGORITHMIC limitations

CRITICAL Sen2Cor is land-optimised and fails over dark turbid water algorithmic

GEE's Sentinel-2 L2A uses Sen2Cor, which was designed for land surfaces. Over dark eutrophic water it frequently returns NEGATIVE reflectance, which propagates into any index built from it.

What to do: For anything you intend to publish as a concentration, download L1C from the Copernicus Data Space and run ACOLITE Dark Spectrum Fitting - dataset AL18. Alternatively calibrate locally against in-situ chlorophyll, or use the Bangladeshi coefficients in Sarker et al. 2019.

HIGH Suspended sediment biases every inland chlorophyll retrieval algorithmic

The Meghna carries an enormous sediment load and Dhaka's water bodies are turbid year-round. High TSS inflates red and NIR reflectance, which contaminates NDCI and FAI. This is measurement error, not collinearity - the chlorophyll series is partly a sediment series.

What to do: Always extract turbidity alongside chlorophyll and carry it as a covariate whether or not it is a predictor of interest. It is in the pipeline output as the turbidity column.

HIGH OC3M overestimates chlorophyll in turbid case-2 water algorithmic

The standard band-ratio algorithm assumes optically deep case-1 water. Inside the Matamuhuri estuary and near the coast, suspended sediment and CDOM inflate the retrieval.

What to do: Valid offshore of Chakaria, not inside the estuary. Use the coastal box for the offshore signal and Sentinel-2 for the estuary itself.