Visualizing PACE OCI L1 data products with HyperCoast¶

This notebook demonstrates how to visualize Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) OCI L1 data products. Part of the notebook is adapted from the NASA OB.DAAC tutorial - File Structure at Three Processing Levels for the Ocean Color Instrument (OCI). Credits to the NASA OB.DAAC team for the tutorial.

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# %pip install "hypercoast[extra]"

# %pip install "hypercoast[extra]"

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import hypercoast
import xarray as xr

import hypercoast import xarray as xr

To download and access the data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, you can run the following cell to search and download PACE OCI L1 data products.

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hypercoast.nasa_earth_login()

hypercoast.nasa_earth_login()

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hypercoast.nasa_earth_login()

short_name = "PACE_OCI_L1B_SCI"
results, gdf = hypercoast.search_nasa_data(
    short_name=short_name,
    bbox=(-90.5642, 29.9749, -89.7143, 30.42),
    temporal=("2024-06-15", "2024-06-16"),
    return_gdf=True,
)

hypercoast.nasa_earth_login() short_name = "PACE_OCI_L1B_SCI" results, gdf = hypercoast.search_nasa_data( short_name=short_name, bbox=(-90.5642, 29.9749, -89.7143, 30.42), temporal=("2024-06-15", "2024-06-16"), return_gdf=True, )

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gdf.explore()

gdf.explore()

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hypercoast.download_nasa_data(results[0], out_dir="data")

hypercoast.download_nasa_data(results[0], out_dir="data")

Alternatively, use the following code block to download a sample dataset from here.

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url = "https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L1B.nc"
filepath = "data/PACE_OCI.20240615T182549.L1B.nc"
hypercoast.download_file(url, filepath)

url = "https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L1B.nc" filepath = "data/PACE_OCI.20240615T182549.L1B.nc" hypercoast.download_file(url, filepath)

Let's check the top-level groups in the sample dataset.

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hypercoast.netcdf_groups(filepath)

hypercoast.netcdf_groups(filepath)

The top-level groups in the sample dataset are:

['sensor_band_parameters',
 'scan_line_attributes',
 'geolocation_data',
 'navigation_data',
 'observation_data']

Let's open the observation_data group, which contains the core science variables.

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dataset = xr.open_dataset(filepath, group="observation_data")
print(list(dataset.variables))

dataset = xr.open_dataset(filepath, group="observation_data") print(list(dataset.variables))

The data variables include:

['rhot_blue', 'qual_blue', 'rhot_red', 'qual_red', 'rhot_SWIR', 'qual_SWIR']

The dimensions of the rhot_blue variable are ("blue_bands", "number_of_scans", "ccd_pixels"), and it has shape (119, 1710, 1272). The sizes attribute of a variable gives us that information as a dictionary.

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dataset["rhot_blue"].sizes

dataset["rhot_blue"].sizes

The dimensions of the rhot_red variable are ("red_bands", "number_of_scans", "ccd_pixels"), and it has shape (163, 1710, 1272)

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dataset["rhot_red"].sizes

dataset["rhot_red"].sizes

The dimensions of the rhot_SWIR variable are ("SWIR_bands", "number_of_scans", "SWIR_pixels"), and it has shape (9, 1710, 1272)

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dataset["rhot_SWIR"].sizes

dataset["rhot_SWIR"].sizes

Let's plot the reflectance at position 100 in the blue_bands dimension.

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plot = dataset["rhot_blue"].sel({"blue_bands": 100}).plot()

plot = dataset["rhot_blue"].sel({"blue_bands": 100}).plot()