Lab4a - Working with NASA PACE data in HyperCoast#
This notebook demonstrates how to visualize and analyze Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) data interactively with HyperCoast.
Environment setup#
Uncomment and run the following cell to install the required packages.
%pip install "hypercoast[extra]"
# %pip install -U localtileserver
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Note: you may need to restart the kernel to use updated packages.
Import libraries.
import earthaccess
import hypercoast
import pandas as pd
c:\Users\C00553090\AppData\Local\miniconda3\envs\hypercoast\lib\site-packages\pandas\core\computation\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.7.3' currently installed).
from pandas.core.computation.check import NUMEXPR_INSTALLED
Search for PACE data#
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, run the following cell and enter your NASA Earthdata login credentials.
earthaccess.login(persist=True)
Search data programmatically#
To search for PACE data programmatically, specify the bounding box and time range of interest. Set count=-1 to return all results or set count=10 to return the first 10 results.
results, gdf = hypercoast.search_pace(
bounding_box=(-90, 25, -88, 28),
temporal=("2024-10-10", "2024-10-10"),
short_name="PACE_OCI_L2_AOP_NRT",
count=10,
return_gdf=True,
)
Plot the footprints of the returned datasets on a map.
gdf.explore()
Download the first dataset from the search results. Note that the download may take some time.
hypercoast.download_pace(results[:1], out_dir="data")
Search data interactively#
To search for PACE data interactively, pan and zoom to the area of interest. Specify the time range of interest from the search dialog, then click on the Search button.
m = hypercoast.Map(center=[30.0262, -90.1345], zoom=8)
m.search_pace(default_dataset="PACE_OCI_L2_AOP_NRT")
m
By default, the search_pace method searches for the PACE_OCI_L2_AOP_NRT dataset, but you can specify the dataset name by setting the default_dataset parameter, such as PACE_OCI_L2_BGC_NRT. For more information about the available datasets, see the PACE Data Products page.
Uncomment the following cell to display the GeoDataFrame of the search results.
# m._NASA_DATA_GDF.head()
Similarly, you can download the first dataset from the search results by uncommenting the following cell.
# hypercoast.download_pace(results[:1], out_dir="data")
Read PACE data#
filepath = "PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc"
plot = hypercoast.view_pace_pixel_locations(filepath, step=20)
Load the dataset as a xarray.Dataset object.
dataset = hypercoast.read_pace(filepath)
# dataset
Visualize PACE AOP data#
Visualize selected bands of the dataset.
hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2)
Add custom projection and administrative boundaries to the map. The default projection is PlateCarree. You can specify a custom projection by setting the crs parameter. For more information about the available projections, see the cartopy projection page.
hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2, crs="default")
Plot spectral signatures#
Plot the spectral signature of a pixel using the extract_pace function. Set return_plot=True to return the plot object.
latitude = 29.9307
longitude = -87.9106
hypercoast.extract_pace(dataset, latitude, longitude, return_plot=True)
Interactive visualization#
Single-band visualization#
Visualize a selected band of the dataset interactively use the add_pace method and speccify the wavelengths parameter.
m = hypercoast.Map()
m.add_basemap("Hybrid")
wavelengths = [450]
m.add_pace(dataset, wavelengths, colormap="jet", vmin=0, vmax=0.02, layer_name="PACE")
m.add_colormap(cmap="jet", vmin=0, vmax=0.02, label="Reflectance")
m.add("spectral")
m.set_center(-80.7382, 26.5295, zoom=6)
m
Multi-band visualization#
Select three spectral bands to visualize as an RGB image.
m = hypercoast.Map()
m.add_basemap("Hybrid")
wavelengths = [450, 550, 650]
m.add_pace(
dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name="PACE"
)
m.add("spectral")
m.set_center(-80.7382, 26.5295, zoom=6)
m
PACE BGC data#
PACE has a variety of data products, including biogeochemical properties. For more information about the available datasets, see the PACE Data Products page.
The PACE Biogeochemical (BGC) data products include chlorophyll-a concentration, particulate organic carbon, and particulate inorganic carbon.
Download PACE BGC data#
Let’s download a sample PACE BGC dataset for the demonstration.
results, gdf = hypercoast.search_nasa_data(
short_name="PACE_OCI_L2_BGC_NRT",
bbox=(-90.5642, 29.9749, -89.7143, 30.42),
temporal=("2024-10-10", "2024-10-10"),
count=1,
return_gdf=True,
)
hypercoast.download_nasa_data(results, out_dir="data")
Load the downloaded dataset as an xarray.Dataset:
filepath = "data/PACE_OCI.20241010T183444.L2.OC_BGC.V2_0.NRT.nc"
dataset = hypercoast.read_pace_bgc(filepath)
Let’s inspect the data variables contained in the dataset:
dataset.variables
We can see that the dataset contains the following variables:
Visualize PACE BGC data#
Since the datasets are not gridded, we need to transform them into gridded data to visualize them. We can use the grid_pace_bgc function to transform the dataset into a gridded format.
First, transform the chlor_a variable into a gridded format:
chlor_a = hypercoast.grid_pace_bgc(dataset, variable="chlor_a", method="linear")
Plot the gridded Chlorophyll Concentration data:
chlor_a.plot(vmin=0, vmax=20, cmap="jet", size=6)
Plot the gridded Phytoplankton Carbon data:
carbon_phyto = hypercoast.grid_pace_bgc(
dataset, variable="carbon_phyto", method="linear"
)
carbon_phyto.plot(vmin=0, vmax=120, cmap="jet", size=6)
Plot the gridded Particulate Organic Carbon data:
poc = hypercoast.grid_pace_bgc(dataset, variable="poc", method="linear")
poc.plot(vmin=0, vmax=1000, cmap="jet")
Plot the gridded BGC data on an interactive map.
m = hypercoast.Map()
m.add_basemap("Hybrid")
m.add_raster(chlor_a, layer_name="Chlorophyll-a", colormap="jet", vmin=0, vmax=20)
m.add_raster(
carbon_phyto, layer_name="Phytoplankton Carbon", colormap="plasma", vmin=0, vmax=120
)
m.add_raster(
poc, layer_name="Particulate Organic Carbon", colormap="coolwarm", vmin=0, vmax=1000
)
m.add_layer_manager()
m.add_colormap(cmap="jet", vmin=0, vmax=20, label="Chlorophyll-a (mg/m3)")
m.add_colormap(cmap="plasma", vmin=0, vmax=120, label="Phytoplankton Carbon (mg/m3)")
m.add_colormap(
cmap="coolwarm", vmin=0, vmax=1000, label="Particulate Organic Carbon (mg/m3)"
)
m.set_center(-80.7382, 26.5295, zoom=6)
m
