Applying Gaussian Pigment (GPig) Phytoplankton Community Composition Algorithm to OCI data#
Author(s): Anna Windle (NASA, SSAI), Ian Carroll (NASA, UMBC)
Adapted from code developed by: Max Danenhower (Bowdoin College), Ali Chase (University of Washington)
Last updated: July 21, 2026
The following notebooks are prerequisites for this tutorial.
Learn with OCI: Data Access
An Earthdata Login account is required to access data from the NASA Earthdata system, including NASA ocean color data.
Summary#
This notebook applies the inversion algorithm described in Chase et al., 2017 to estimate phytoplankton pigment concentrations from PACE OCI Rrs data. This algorithm, called Gaussian Pigment (GPig), is currently being implemented in OBPG’s OCSSW software. This work was originally developed in MatLab by Ali Chase and subsequently translated to Python by Max Danenhower, Charles Stern, and Ali Chase. This tutorial demonstrates how to apply the Python GPig algorithm to Level-2 (L2) and Level-3 Mapped (L3M) PACE OCI data.
Learning Objectives#
At the end of this notebook you will know:
How to use a packaged Python project
How to run the GPig Algorithm on PACE OCI L2 and L3 data
1. Setup#
The GPig Python code has been packaged to allow it to be easily installed, imported, and reused. While the package is not on PyPI or conda-forge, it can be installed directly from the source repository on GitHub. If you have followed the setup instructions, then GPig is available to import along with the other packages needed for this notebook.
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import earthaccess
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from gpig import L2_utils, L3_utils
Set (and persist to your home directory on the host, if needed) your Earthdata Login credentials.
auth = earthaccess.login()
Assign “global” variables, which could be anything you want to define once and use consistently.
crs = ccrs.PlateCarree()
2. Search and Access Data#
We need the following data to run GPig:
Rrs: PACE OCI L2 or L3M AOP data products
Sea surface salinity: JPL SMAP-SSS V5.0 CAP, 8-day running mean, level 3 mapped, sea surface salinity (SSS) product from the NASA Soil Moisture Active Passive (SMAP) observatory
Sea surface temperature: Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature
We can use earthaccess to find this data.
tspan = ("2025-05-01 20:13", "2025-05-01 20:13")
bbox = (-127, 40, -126, 41)
results = earthaccess.search_data(
short_name=["PACE_OCI_L2_AOP_NRT", "PACE_OCI_L2_AOP"],
temporal=tspan,
bounding_box=bbox,
count=1,
)
l2_paths = earthaccess.open(results)
results = earthaccess.search_data(
short_name="SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5",
temporal=tspan,
count=1,
)
sss_paths = earthaccess.open(results)
results = earthaccess.search_data(
short_name="MUR-JPL-L4-GLOB-v4.1",
temporal=tspan,
count=1,
)
sst_paths = earthaccess.open(results)
Let’s take a look at our L2 granule:
datatree = xr.open_datatree(l2_paths[-1])
rrs = datatree["geophysical_data"]["Rrs"]
for variable in ("longitude", "latitude"):
rrs[variable] = datatree["navigation_data"][variable]
rrs
<xarray.DataArray 'Rrs' (number_of_lines: 1710, pixels_per_line: 1272,
wavelength: 172)> Size: 1GB
[374120640 values with dtype=float32]
Coordinates:
longitude (number_of_lines, pixels_per_line) float32 9MB ...
latitude (number_of_lines, pixels_per_line) float32 9MB ...
* wavelength (wavelength) float32 688B 346.0 348.5 350.9 ... 716.8 719.3
Dimensions without coordinates: number_of_lines, pixels_per_line
Attributes:
long_name: Remote sensing reflectance
units: sr^-1
standard_name: surface_ratio_of_upwelling_radiance_emerging_from_sea_wat...
valid_min: -30000
valid_max: 25000fig, ax = plt.subplots(figsize=(8, 6), subplot_kw={"projection": crs})
da = rrs.sel({"wavelength": 500}, method="nearest")
da.plot(
x="longitude",
y="latitude",
vmin=0,
vmax=0.008,
ax=ax,
cbar_kwargs={"label": r"$\mathrm{Rrs\ [sr^{-1}]}$"},
)
ax.set_extent([-135, -115, 35, 55], crs=crs)
ax.coastlines(resolution="10m")
ax.add_feature(cfeature.BORDERS, linestyle=":")
ax.gridlines(
draw_labels=["left", "bottom"],
linewidth=0.5,
color="gray",
alpha=0.5,
linestyle="--",
)
plt.show()
3. Run GPig on L2 Data#
Now, we can use L2_utils.estimate_inv_pigments to calculate phytoplankton pigment concentrations for this data. Let’s take a look at the docstring for this function:
?L2_utils.estimate_inv_pigments
You can see that this function accepts a bounding box (bbox) as a parameter. The default is None which means it will run the algorithm on every single pixel in the L2 file, which can take a long time. We will supply the bbox parameter with the following coordinates:
48 N, 47 S, -125 E, -126 W.
Let’s first see what this bounding box covers:
bbox = (-126, 47, -125, 48)
lon_min, lat_min, lon_max, lat_max = bbox
rect_lon = [lon_min, lon_max, lon_max, lon_min, lon_min]
rect_lat = [lat_min, lat_min, lat_max, lat_max, lat_min]
ax.plot(rect_lon, rect_lat, color="red", linewidth=2)
fig
Let’s run it. This can take some time depending on bounding box size.
l2_pigments = L2_utils.estimate_inv_pigments(
l2_paths[-1],
sss_paths[-1],
sst_paths[-1],
bbox,
)
<File-like object S3FileSystem, ob-cumulus-prod-public/PACE_OCI.20250501T201332.L2.OC_AOP.V3_2.nc>
-126 47 -125 48
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The inversion provides four phytoplankton pigment concenrations. Let’s plot them:
fig, axs = plt.subplots(2, 2, figsize=(10, 8), sharex=True, sharey=True)
titles = ["Chlorophyll-a", "Chlorophyll-b", "Chlorophyll-c", "PPC"]
for ax, var, title in zip(axs.flat, l2_pigments, titles):
da = l2_pigments[var]
da = np.log10(da.where(da > 0))
im = da.plot(
x="longitude",
y="latitude",
cmap="viridis",
shading="auto",
ax=ax,
cbar_kwargs={"label": r"$\mathrm{%s\ [log_{10}(mg\ m^{-3})]}$" % var},
)
ax.set_title(title)
ax.set_xlim(bbox[0], bbox[2])
ax.set_ylim(bbox[1], bbox[3])
plt.tight_layout()
plt.show()
4. Run GPig on L3M Data#
We can also run GPig on L3M data. Let’s open a PACE OCI L3m (4km), SSS and SST data for June 12, 2024:
tspan = ("2024-06-12", "2024-06-12")
results = earthaccess.search_data(
short_name=["PACE_OCI_L3M_AOP_NRT", "PACE_OCI_L3M_AOP"],
temporal=tspan,
granule_name="*DAY*4km*",
count=1,
)
l3m_paths = earthaccess.open(results)
results = earthaccess.search_data(
short_name="SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5",
temporal=tspan,
count=1,
)
sss_paths = earthaccess.open(results)
results = earthaccess.search_data(
short_name="MUR-JPL-L4-GLOB-v4.1",
temporal=tspan,
count=1,
)
sst_paths = earthaccess.open(results)
Let’s quickly look at L3M Rrs at 500 nm:
l3_dataset = xr.open_dataset(l3m_paths[-1])
l3_dataset
<xarray.Dataset> Size: 26GB
Dimensions: (lat: 4320, lon: 8640, wavelength: 172)
Coordinates:
* lat (lat) float32 17kB 89.98 89.94 89.9 ... -89.9 -89.94 -89.98
* lon (lon) float32 35kB -180.0 -179.9 -179.9 ... 179.9 179.9 180.0
* wavelength (wavelength) float32 688B 346.0 348.5 350.9 ... 716.8 719.3
Data variables:
Rrs (lat, lon, wavelength) float32 26GB ...
nflh (lat, lon) float32 149MB ...
avw (lat, lon) float32 149MB ...
aot_865 (lat, lon) float32 149MB ...
angstrom (lat, lon) float32 149MB ...
Attributes: (12/55)
product_name: PACE_OCI.20240612.L3m.DAY.AOP.V3_2.4km.nc
instrument: OCI
title: OCI Level-3 Standard Mapped Image
project: Ocean Biology Processing Group (NASA/G...
platform: PACE
source: satellite observations from OCI-PACE
... ...
keywords: Earth Science > Oceans > Ocean Optics ...
id: L3/PACE_OCI.20240612.L3m.DAY.AOP.V3_2....
history: /sdps/sdpsoper/Science/OCSSW/V2026.2/b...
processing_version: 3.2
identifier_product_doi_authority: https://dx.doi.org
identifier_product_doi: 10.5067/PACE/OCI/L3M/OC_AOP/3.2fig, ax = plt.subplots(figsize=(10, 6), subplot_kw={"projection": crs})
da = l3_dataset["Rrs"].sel({"wavelength": 500}, method="nearest")
da.plot.imshow(
x="lon",
y="lat",
vmin=0,
vmax=0.008,
ax=ax,
cbar_kwargs={"label": r"$\mathrm{Rrs [sr^{-1}]}$", "fraction": 0.025},
)
ax.coastlines(resolution="10m")
ax.gridlines(
draw_labels=["left", "bottom"],
linewidth=0.5,
color="gray",
alpha=0.5,
linestyle="--",
)
plt.show()
Let’s look at what L3_utils.estimate_inv_pigments requires as input:
?L3_utils.estimate_inv_pigments
We’ll provide the Rrs, SSS, and SST file and input a bounding box corresponding to the coast of Washington, U.S.
Let’s take a look at what data this bounding box covers:
bbox = (-127, 40, -126, 41)
lon_min, lat_min, lon_max, lat_max = bbox
rect_lon = [lon_min, lon_max, lon_max, lon_min, lon_min]
rect_lat = [lat_min, lat_min, lat_max, lat_max, lat_min]
ax.plot(rect_lon, rect_lat, color="red", linewidth=2)
ax.set_extent([-130, -115, 32, 50], crs=crs)
fig
l3_pigments = L3_utils.estimate_inv_pigments(l3m_paths, sss_paths, sst_paths, bbox)
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The inversion provides four phytoplankton pigment concentrations. Let’s plot them:
fig, axs = plt.subplots(2, 2, figsize=(10, 8), subplot_kw={"projection": crs})
titles = ["Chlorophyll-a", "Chlorophyll-b", "Chlorophyll-c", "PPC"]
for ax, var, title in zip(axs.flat, l3_pigments, titles):
da = l3_pigments[var]
da = np.log10(da.where(da > 0))
da.plot.imshow(
cmap="viridis",
ax=ax,
cbar_kwargs={"label": r"$\mathrm{%s\ [log_{10}(mg\ m^{-3})]}$" % var},
)
ax.set_title(title)
ax.coastlines(resolution="10m")
ax.gridlines(
draw_labels=["left", "bottom"],
linewidth=0.5,
color="gray",
alpha=0.5,
linestyle="--",
)
plt.tight_layout()
plt.show()
You have completed the notebook on the GPig Python algorithm!