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.
- +DATA HUBSwhere the data physically comes from
- −DATASET TYPESgrouped by what kind of water they see
- +1. Ocean colour (hyperspectral)1 datasets
- +1. Ocean colour (merged)3 datasets
- +1. Ocean colour (single sensor)5 datasets
- +2. Inland/coastal high-res6 datasets
- +3. Lake / inland operational2 datasets
- −4. Model / reanalysis1 datasets
- +5. Discovery / portal1 datasets
- +6. Derived / uncalibrated2 datasets
- +7. In-situ validation5 datasets
- +SITE-WISEwhat actually applies at each site
- +DURATION & CAPTURE CADENCEdaily · weekly · 10-day · 16-day · monthly
- +VARIABLESwhat is computed from the bands
- +LIMITATIONSwhy not — typed and severity-ranked
- +PAPERSthe evidence, DOI-linked
Overview
Study-level limitations (6)
CRITICAL No in-situ chlorophyll anywhere in the project validation
Without paired field measurements, NDCI, FAI and the phycocyanin proxy are ordinal indices, not concentrations. Reviewers will treat every inland number as uncalibrated. This is the single largest weakness in the study and it is fixable comparatively cheaply.
CRITICAL Outcome data is behind icddr,b ethical approval access
Cholera case series for all three sites require Research Review Committee and Ethical Review Committee approval plus a data-sharing agreement. Free for collaborators, but it takes weeks to months. Every satellite task can run in parallel · none of them can finish without it.
HIGH Monsoon cloud destroys June-September optical coverage atmospheric
Every optical sensor here - Sentinel-2, Sentinel-3, Landsat, MODIS, ocean colour - is blinded by cloud. June to September is the wettest period in Bangladesh and also the period of highest cholera transmission, so the data is thinnest exactly when it matters most. Worse, cloudiness is not random with respect to blooms: cloudy weeks have different bloom dynamics, which makes the missingness informative rather than ignorable.
HIGH The 2015 sensor discontinuity temporal
Sentinel-2 begins mid-2015 and Sentinel-3 late 2016. Before that you have Landsat at 16 days and MODIS at 250 m. Blending sensors without correction creates a step change in 2015 that a model will read as a trend - a completely artefactual one.
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.
MEDIUM Multiple testing across the lag grid coverage
34 variables x 14 lag steps x 3 sites is 1,428 cross-correlations. At p<0.05 roughly 70 will clear significance by chance alone, and the strongest of those will look publishable.