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When and where do harmful algal blooms occur in my coastal waters?

oceanbiologywater-qualitypublic-health Datasets: 5 20–60 min
▶ Find the data for your area

Draw a rectangle to pick your area of interest, then see what NASA data covers it (live, here in your browser) or download a ready-to-run notebook with your AOI pre-filled. The notebook runs in any Python environment — it needs a free Earthdata Login to fetch the data.

Current AOI: -71, 41 → -67, 45 (Gulf of Maine)
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When and where do harmful algal blooms occur in my coastal waters?

What you can answer

  • Chlorophyll-a anomaly maps flagging unusually high productivity (potential bloom)
  • Phytoplankton functional type discrimination (PACE hyperspectral) — distinguish cyanobacteria from diatoms
  • Bloom seasonality — 22-year MODIS Aqua climatology for your bay/coast
  • Spatial extent + persistence of an active bloom
  • Trend over the satellite era — has bloom frequency or intensity changed?

What you can NOT answer with these alone

  • Toxin concentrations (microcystin, brevetoxin) — requires water sampling
  • Health risk without combining with shellfish-bed and beach-use data
  • Sub-surface bloom layers — satellites see only the top optical depth (~5-30 m depending on water clarity)
  • Definitive species identification — PACE hyperspectral suggests type; species-level needs lab

Code template

import earthaccess
import xarray as xr
import numpy as np

earthaccess.login(strategy="netrc")

# Gulf of Maine harmful algal bloom monitoring
aoi = (-71, 41, -67, 45)

# 1. PACE OCI hyperspectral chlorophyll (the new tool)
pace = earthaccess.search_data(short_name="PACE_OCI_L2_OC_NRT",
                               bounding_box=aoi,
                               temporal=("2024-04-01", "2025-12-31"))
# Extract chl-a, plus phytoplankton functional type if available
# Threshold: Chl > 10 mg/m³ → potential bloom

# 2. MODIS Aqua chlorophyll for long-term climatology
modis = earthaccess.search_data(short_name="MODISA_L3m_CHL",
                                bounding_box=aoi,
                                temporal=("2002-07-01", "2025-12-31"))
# Compute monthly climatology (mean per pixel per month-of-year)
# Compute current-year anomaly vs climatology

# 3. PACE absorption-by-CDOM (aCDOM)
#    High aCDOM = freshwater inputs / runoff (often drives blooms)
pace_cdom = earthaccess.search_data(short_name="PACE_OCI_L2_BGC",
                                    bounding_box=aoi, temporal=("2024-04-01", "2025-12-31"))

# 4. Plot:
#    - Current bloom intensity map
#    - Bloom-area time series 2002+
#    - Phytoplankton-type pie chart per bloom event

Expected output

  • Chlorophyll-a anomaly map flagging current bloom-affected pixels
  • Time-series: total bloom area (km²) over 22 years showing potential trend
  • PFT composition: % cyanobacteria vs diatoms vs other (PACE)
  • aCDOM context: when is runoff driving the bloom?

Caveats

  • Coastal Case-II waters are hard for satellite ocean-color retrievals (CDOM, sediment, shallow bottom). PACE handles these better than MODIS but algorithms are still maturing.
  • Cloud cover destroys retrievals. Aim for 8-day or monthly composites in cloudy regions.
  • Chl > threshold ≠ bloom — some bays are naturally productive. Use the ANOMALY (current minus climatology), not absolute chl.
  • Toxin concentration is NOT proportional to chl — some species produce intense toxins at low cell counts
  • MODIS reprocessing R2022.0 vs R2018.0 — use R2022.0 by default; reprocess any older data

Cross-DAAC composition

OB.DAAC only — both PACE and MODIS Aqua ocean color, single DAAC.

Sources

⚲ How a scientist answers this
Parameters
Near-surface chlorophyll-a concentration (mg/m3) from PACE OCI L2/L3 (hyperspectral, 2024+) and MODIS Aqua (2002+, for the long climatology), plus PACE phytoplankton-functional-type and CDOM absorption products to separate cyanobacteria from diatoms and flag runoff. A bloom is flagged where chlorophyll exceeds a high percentile of the local climatology (or a regional threshold such as >~10 mg/m3 in coastal waters).
Method
Build a per-pixel climatology of chlorophyll for each calendar period from the MODIS-Aqua record, then express the current scene as a standardized anomaly (z-score) or percent-of-normal; map contiguous high-anomaly pixels as bloom extent, track day-to-day persistence, and use PACE functional-type/CDOM bands to discriminate the dominant phytoplankton group.
Validation
Cross-check PACE against the overlapping MODIS/VIIRS record and confirm blooms against in-situ samples or shellfish-monitoring programs, since satellite chlorophyll is uncertain in turbid, CDOM-rich, or shallow coastal water and cannot measure toxin levels or sub-surface layers.
In plain EnglishMeasure how green the surface water is from space, compare it to the normal seasonal greenness for that spot, and flag unusually high values as a possible bloom — confirming the actual species and toxicity needs water samples.

Make it yours → Change the coastal bounding box, the season/months, and the chlorophyll anomaly threshold, and swap MODIS for PACE to gain phytoplankton-type detail.

⌨ Run the core method · no login

The detection / counting above a threshold at the heart of this question — runnable on synthetic data, right here. The full earthaccess code template further down does it on real NASA data (needs an Earthdata login).

editable · runs in your browser