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.

In [1]:

# %pip install "hypercoast[extra]"

# %pip install "hypercoast[extra]"

Import libraries.

In [2]:

import earthaccess
import hypercoast
import pandas as pd

import earthaccess import hypercoast import pandas as pd

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.

In [3]:

earthaccess.login(persist=True)

earthaccess.login(persist=True)

Out[3]:

<earthaccess.auth.Auth at 0x7f2f7861ed20>

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.

In [4]:

results, gdf = hypercoast.search_pace(
    bounding_box=(-83, 25, -81, 28),
    temporal=("2024-07-30", "2024-08-15"),
    short_name="PACE_OCI_L2_AOP_NRT",
    count=10,
    return_gdf=True,
)

results, gdf = hypercoast.search_pace( bounding_box=(-83, 25, -81, 28), temporal=("2024-07-30", "2024-08-15"), short_name="PACE_OCI_L2_AOP_NRT", count=10, return_gdf=True, )

Plot the footprints of the returned datasets on a map.

In [5]:

gdf.explore()

gdf.explore()

Out[5]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Download the first dataset from the search results. Note that the download may take some time.

In [6]:

hypercoast.download_pace(results[:1], out_dir="data")

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.

In [7]:

m = hypercoast.Map(center=[30.0262, -90.1345], zoom=8)
m.search_pace(default_dataset="PACE_OCI_L2_AOP_NRT")
m

m = hypercoast.Map(center=[30.0262, -90.1345], zoom=8) m.search_pace(default_dataset="PACE_OCI_L2_AOP_NRT") m

Out[7]:

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.

In [8]:

# m._NASA_DATA_GDF.head()

# m._NASA_DATA_GDF.head()

Similarly, you can download the first dataset from the search results by uncommenting the following cell.

In [9]:

# hypercoast.download_pace(results[:1], out_dir="data")

# hypercoast.download_pace(results[:1], out_dir="data")

Read PACE data¶

Let's download a sample PACE Apparent Optical Properties (AOP) dataset for the demonstration.

In [10]:

results = hypercoast.search_pace(
    bounding_box=(-83, 25, -81, 28),
    temporal=("2024-07-30", "2024-08-15"),
    short_name="PACE_OCI_L2_AOP_NRT",
    count=1,
)

results = hypercoast.search_pace( bounding_box=(-83, 25, -81, 28), temporal=("2024-07-30", "2024-08-15"), short_name="PACE_OCI_L2_AOP_NRT", count=1, )

In [11]:

hypercoast.download_pace(results[:1], out_dir="data")

hypercoast.download_pace(results[:1], out_dir="data")

Let's make a scatter plot of the pixel locations so we can see the irregular spacing.

In [12]:

filepath = "data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc"
plot = hypercoast.view_pace_pixel_locations(filepath, step=20)

filepath = "data/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.

In [13]:

dataset = hypercoast.read_pace(filepath)
# dataset

dataset = hypercoast.read_pace(filepath) # dataset

Visualize PACE AOP data¶

Visualize selected bands of the dataset.

In [14]:

hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2)

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.

In [15]:

hypercoast.viz_pace(dataset, wavelengths=[500, 510, 520, 530], ncols=2, crs="default")

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.

In [16]:

latitude = 29.9307
longitude = -87.9106
hypercoast.extract_pace(dataset, latitude, longitude, return_plot=True)

latitude = 29.9307 longitude = -87.9106 hypercoast.extract_pace(dataset, latitude, longitude, return_plot=True)

Out[16]:

[<matplotlib.lines.Line2D at 0x7f2f18f4ce30>]

To return the extracted values as an xarray DataArray, set return_plot=False.

In [17]:

array = hypercoast.extract_pace(dataset, latitude, longitude, return_plot=False)
# array

array = hypercoast.extract_pace(dataset, latitude, longitude, return_plot=False) # array

To plot the spectral signatures of multiple pixels, you can specify the pixel locations as a list of tuples. All pixels within the specified latitude and longitude range will be extracted.

In [18]:

latitude = (29.49, 29.50)
longitude = (-88.10, -88.00)
hypercoast.filter_pace(dataset, latitude, longitude, return_plot=True)

latitude = (29.49, 29.50) longitude = (-88.10, -88.00) hypercoast.filter_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.

In [19]:

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

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

Out[19]:

Click on the map to display the spectral signature of the selected pixel.

Convert the spectral data of the selected pixels to a DataFrame.

In [20]:

df = m.spectral_to_df()
df.head()

df = m.spectral_to_df() df.head()

Out[20]:

Convert the spectral data of the selected pixels to a GeoDataFrame.

In [21]:

gdf = m.spectral_to_gdf()
gdf.head()

gdf = m.spectral_to_gdf() gdf.head()

Out[21]:

Convert the spectral data of the selected pixels to a CSV file.

In [22]:

m.spectral_to_csv("data/spectral.csv")

m.spectral_to_csv("data/spectral.csv")

Multi-band visualization¶

Select three spectral bands to visualize as an RGB image.

In [23]:

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

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

Out[23]:

Change band combination¶

Click on the gear icon on the toolbar to change the band combination.

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.

In [24]:

results, gdf = hypercoast.search_nasa_data(
    short_name="PACE_OCI_L2_BGC_NRT",
    bbox=(-90.5642, 29.9749, -89.7143, 30.42),
    temporal=("2024-07-30", "2024-08-15"),
    count=1,
    return_gdf=True,
)
hypercoast.download_nasa_data(results, out_dir="data")

results, gdf = hypercoast.search_nasa_data( short_name="PACE_OCI_L2_BGC_NRT", bbox=(-90.5642, 29.9749, -89.7143, 30.42), temporal=("2024-07-30", "2024-08-15"), count=1, return_gdf=True, ) hypercoast.download_nasa_data(results, out_dir="data")

Load the downloaded dataset as an xarray.Dataset:

In [25]:

filepath = "data/PACE_OCI.20240730T181157.L2.OC_BGC.V2_0.NRT.nc"
dataset = hypercoast.read_pace_bgc(filepath)

filepath = "data/PACE_OCI.20240730T181157.L2.OC_BGC.V2_0.NRT.nc" dataset = hypercoast.read_pace_bgc(filepath)

Let's inspect the data variables contained in the dataset:

In [26]:

dataset.variables

dataset.variables

Out[26]:

Frozen({'chlor_a': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:      Chlorophyll Concentration, OCI Algorithm
    units:          mg m^-3
    standard_name:  mass_concentration_of_chlorophyll_in_sea_water
    valid_min:      0.001
    valid_max:      100.0
    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg..., 'carbon_phyto': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:  Phytoplankton Carbon
    units:      mg m^-3
    valid_min:  0.0
    valid_max:  1000.0
    reference:  Graff, J.R., Westberry, T.K., Milligan, A.J., Brown, M.B., Da..., 'poc': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:  Particulate Organic Carbon, D. Stramski, 2022 (hybrid version)
    units:      mg m^-3
    valid_min:  -32000
    valid_max:  -22000
    reference:  Stramski, D., et al. "Ocean color algorithms to estimate the ..., 'chlor_a_unc': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:      Uncertainty in chlorophyll a concentration
    units:          mg m^-3
    standard_name:  chlorophyll_concentration_in_sea_water standard_error
    valid_min:      0.001
    valid_max:      100.0, 'carbon_phyto_unc': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:  Phytoplankton Carbon standard uncertainty
    units:      mg m^-3
    valid_min:  0.0
    valid_max:  1000.0
    reference:  Graff, J.R., Westberry, T.K., Milligan, A.J., Brown, M.B., Da..., 'l2_flags': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=int32]
Attributes:
    long_name:      Level-2 Processing Flags
    valid_min:      -2147483648
    valid_max:      2147483647
    flag_masks:     [          1           2           4           8         ...
    flag_meanings:  ATMFAIL LAND PRODWARN HIGLINT HILT HISATZEN COASTZ SPARE ..., 'longitude': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:      Longitude
    units:          degrees_east
    standard_name:  longitude
    valid_min:      -180.0
    valid_max:      180.0, 'latitude': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:      Latitude
    units:          degrees_north
    standard_name:  latitude
    valid_min:      -90.0
    valid_max:      90.0})

We can see that the dataset contains the following variables:

• Chlorophyll Concentration
• Phytoplankton Carbon
• Particulate Organic Carbon

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:

In [27]:

chlor_a = hypercoast.grid_pace_bgc(dataset, variable="chlor_a", method="linear")

chlor_a = hypercoast.grid_pace_bgc(dataset, variable="chlor_a", method="linear")

Plot the gridded Chlorophyll Concentration data:

In [28]:

chlor_a.plot(vmin=0, vmax=20, cmap="jet", size=6)

chlor_a.plot(vmin=0, vmax=20, cmap="jet", size=6)

Out[28]:

<matplotlib.collections.QuadMesh at 0x7f2f0ab35430>

Plot the gridded Phytoplankton Carbon data:

In [29]:

carbon_phyto = hypercoast.grid_pace_bgc(
    dataset, variable="carbon_phyto", method="linear"
)
carbon_phyto.plot(vmin=0, vmax=120, cmap="jet", size=6)

carbon_phyto = hypercoast.grid_pace_bgc( dataset, variable="carbon_phyto", method="linear" ) carbon_phyto.plot(vmin=0, vmax=120, cmap="jet", size=6)

Out[29]:

<matplotlib.collections.QuadMesh at 0x7f2f0a9d28d0>

Plot the gridded Particulate Organic Carbon data:

In [30]:

poc = hypercoast.grid_pace_bgc(dataset, variable="poc", method="linear")
poc.plot(vmin=0, vmax=1000, cmap="jet")

poc = hypercoast.grid_pace_bgc(dataset, variable="poc", method="linear") poc.plot(vmin=0, vmax=1000, cmap="jet")

Out[30]:

<matplotlib.collections.QuadMesh at 0x7f2f0a8b0590>

Plot the gridded BGC data on an interactive map.

In [31]:

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

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

Out[31]:

PACE Chlorophyll Level 3 data¶

PACE Level 3 data products are gridded data products that are derived from Level 2 data. Once of the most common Level 3 data products is the Chlorophyll-Carotenoid Index (CCI) dataset.

Let's download some daily PACE Chlorophyll Level 3 data for the demonstration.

In [32]:

temporal = ("2024-07-30", "2024-08-15")
results = hypercoast.search_pace_chla(temporal=temporal)
hypercoast.download_nasa_data(results, "chla")

temporal = ("2024-07-30", "2024-08-15") results = hypercoast.search_pace_chla(temporal=temporal) hypercoast.download_nasa_data(results, "chla")

The downloaded datasets can be found in the chla directory, which contains 17 daily files of CCI data in the netCDF format. The date range of the data is from 2024-07-30 to 2024-08-15.

In [33]:

files = "chla/*nc"

files = "chla/*nc"

Load all the data files in the chla directory as an xarray DataArray

In [34]:

array = hypercoast.read_pace_chla(files)
# array

array = hypercoast.read_pace_chla(files) # array

Select a date and visualize the chlorophyll-a concentration data with Matplotlib.

In [35]:

hypercoast.viz_pace_chla(array, date="2024-07-30", cmap="jet", size=6)

hypercoast.viz_pace_chla(array, date="2024-07-30", cmap="jet", size=6)

Out[35]:

<matplotlib.collections.QuadMesh at 0x7f2f0a768b00>

If the date is not specified, the data are averaged over the entire time range.

In [36]:

hypercoast.viz_pace_chla(array, cmap="jet", size=6)

hypercoast.viz_pace_chla(array, cmap="jet", size=6)

Out[36]:

<matplotlib.collections.QuadMesh at 0x7f2f0a4b9ee0>

To visualize the data interactively, we can select either a single date or aggregate the data over a time range.

First, let's select a single date from the data array:

In [37]:

single_array = array.sel(date="2024-07-30")
# single_array

single_array = array.sel(date="2024-07-30") # single_array

Convert the data array to an image that can be displayed on an interactive map.

In [38]:

single_image = hypercoast.pace_chla_to_image(single_array)

single_image = hypercoast.pace_chla_to_image(single_array)

Create an interactive map and display the image on the map.

In [39]:

m = hypercoast.Map(center=[40, -100], zoom=4)
m.add_basemap("Hybrid")
m.add_raster(
    single_image,
    cmap="jet",
    vmin=-1,
    vmax=2,
    layer_name="Chlorophyll a",
    zoom_to_layer=False,
)
label = "Chlorophyll Concentration [lg(lg(mg m^-3))]"
m.add_colormap(cmap="jet", vmin=-1, vmax=2, label=label)
m

m = hypercoast.Map(center=[40, -100], zoom=4) m.add_basemap("Hybrid") m.add_raster( single_image, cmap="jet", vmin=-1, vmax=2, layer_name="Chlorophyll a", zoom_to_layer=False, ) label = "Chlorophyll Concentration [lg(lg(mg m^-3))]" m.add_colormap(cmap="jet", vmin=-1, vmax=2, label=label) m

Out[39]:

The daily image does not have a global coverage. To visualize the data globally, we can aggregate the data over a time range.

In [40]:

mean_array = array.mean(dim="date")

mean_array = array.mean(dim="date")

Convert the aggregated data array to an image that can be displayed on an interactive map.

In [41]:

image = hypercoast.pace_chla_to_image(mean_array)

image = hypercoast.pace_chla_to_image(mean_array)

Create an interactive map and display the image on the map.

In [42]:

m = hypercoast.Map(center=[40, -100], zoom=4)
m.add_basemap("Hybrid")
m.add_raster(
    image, cmap="jet", vmin=-1, vmax=2, layer_name="Chlorophyll a", zoom_to_layer=False
)
label = "Chlorophyll Concentration [lg(lg(mg m^-3))]"
m.add_colormap(cmap="jet", vmin=-1, vmax=2, label=label)
m

m = hypercoast.Map(center=[40, -100], zoom=4) m.add_basemap("Hybrid") m.add_raster( image, cmap="jet", vmin=-1, vmax=2, layer_name="Chlorophyll a", zoom_to_layer=False ) label = "Chlorophyll Concentration [lg(lg(mg m^-3))]" m.add_colormap(cmap="jet", vmin=-1, vmax=2, label=label) m

Out[42]:

Hypoxia Cruise data¶

The Hypoxia Cruise collected water quality data in the Gulf of Mexico from July 21 to August 2, 2024. In this section, we will visualize the cruise sampling locations.

First, let's download an Excel file containing the cruise sampling locations.

In [43]:

url = "https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Data_Sheet.xlsx"
xls_path = "data/Hypoxia_Data_Sheet.xlsx"
hypercoast.download_file(url, xls_path, overwrite=True)

url = "https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Data_Sheet.xlsx" xls_path = "data/Hypoxia_Data_Sheet.xlsx" hypercoast.download_file(url, xls_path, overwrite=True)

Out[43]:

'/home/runner/work/HyperCoast/HyperCoast/docs/workshops/data/Hypoxia_Data_Sheet.xlsx'

In [44]:

df = pd.read_excel(xls_path)
df.head()

df = pd.read_excel(xls_path) df.head()

Out[44]:

	Station	Station.1	Time	Date	Lon	Lat	Depth (m)	Secchi (m)	Salinity	Water Temp	...	Absorption	CDOM	LISST	Nano	Surface \npH	Sufface \nO2	Bottom \nO2	FL-ECO	FL-CDOM	Notes
0	River stations	NaN	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	R1	St1	09:39:00	2024-07-21	-89.45114	28.89887	NaN	2	28.71	30.74161	...	125ml	yes	yes	no	NaN	NaN	NaN	NaN	NaN	River Mouth
2	R2	St2	09:47:00	2024-07-21	-89.45306	28.90001	NaN	2	25.91325	30.91475	...	150ml	yes	yes	yes	NaN	NaN	NaN	NaN	NaN	River plume, seaside
3	R3	St3	09:59:00	2024-07-21	-89.43833	28.89487	NaN	0.75	24.44862	30.59565	...	100ml	yes	yes	yes	NaN	NaN	NaN	NaN	NaN	NaN
4	R4	St4	10:13:00	2024-07-21	-89.43162	28.90630	NaN	0.5	8.34838	30.13989	...	100ml	yes	yes	yes	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 22 columns

Filter the data to select only the sampling locations with latitude and longitude coordinates.

In [45]:

df_filtered = df.dropna(subset=["Lon", "Lat"]).reset_index(drop=True)
df_filtered.head()

df_filtered = df.dropna(subset=["Lon", "Lat"]).reset_index(drop=True) df_filtered.head()

Out[45]:

	Station	Station.1	Time	Date	Lon	Lat	Depth (m)	Secchi (m)	Salinity	Water Temp	...	Absorption	CDOM	LISST	Nano	Surface \npH	Sufface \nO2	Bottom \nO2	FL-ECO	FL-CDOM	Notes
0	R1	St1	09:39:00	2024-07-21	-89.45114	28.89887	NaN	2	28.71	30.74161	...	125ml	yes	yes	no	NaN	NaN	NaN	NaN	NaN	River Mouth
1	R2	St2	09:47:00	2024-07-21	-89.45306	28.90001	NaN	2	25.91325	30.91475	...	150ml	yes	yes	yes	NaN	NaN	NaN	NaN	NaN	River plume, seaside
2	R3	St3	09:59:00	2024-07-21	-89.43833	28.89487	NaN	0.75	24.44862	30.59565	...	100ml	yes	yes	yes	NaN	NaN	NaN	NaN	NaN	NaN
3	R4	St4	10:13:00	2024-07-21	-89.43162	28.90630	NaN	0.5	8.34838	30.13989	...	100ml	yes	yes	yes	NaN	NaN	NaN	NaN	NaN	NaN
4	R5	St5	10:58:00	2024-07-21	-89.37324	28.98095	NaN	0.5	2.4625	30.3054	...	100ml	yes	yes	yes	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 22 columns

Download the KML file containing the cruise path.

In [46]:

url = (
    "https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Path.kml"
)
kml_path = "data/Hypoxia_Path.kml"
hypercoast.download_file(url, kml_path)

url = ( "https://github.com/opengeos/datasets/releases/download/hypercoast/Hypoxia_Path.kml" ) kml_path = "data/Hypoxia_Path.kml" hypercoast.download_file(url, kml_path)

Out[46]:

'/home/runner/work/HyperCoast/HyperCoast/docs/workshops/data/Hypoxia_Path.kml'

We will use the PACE AOP dataset acquired on July 30, 2024, to visualize the cruise sampling locations. The dataset should have been downloaded in the previous section.

In [47]:

filepath = "data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc"

filepath = "data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc"

Read the PACE AOP dataset as an xarray Dataset.

In [48]:

dataset = hypercoast.read_pace(filepath)
# dataset

dataset = hypercoast.read_pace(filepath) # dataset

Visualize the cruise sampling locations and PACE data on the map.

In [49]:

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")
style = {"weight": 2, "color": "red"}
m.add_kml(kml_path, style=style, layer_name="Hypoxia Path", info_mode=None)
m.add_points_from_xy(
    df_filtered,
    x="Lon",
    y="Lat",
    max_cluster_radius=50,
    layer_name="Hypoxia Data Points",
)
m.set_center(-91.46118, 28.89758, zoom=8)
m

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") style = {"weight": 2, "color": "red"} m.add_kml(kml_path, style=style, layer_name="Hypoxia Path", info_mode=None) m.add_points_from_xy( df_filtered, x="Lon", y="Lat", max_cluster_radius=50, layer_name="Hypoxia Data Points", ) m.set_center(-91.46118, 28.89758, zoom=8) m

Out[49]:

Visualize in-situ data¶

This section demonstrates how to visualize in-situ data on the map. First, let's download a hypothetical in-situ dataset.

In [50]:

url = "https://github.com/opengeos/datasets/releases/download/hypercoast/pace_sample_points.csv"
data = pd.read_csv(url)
data.head()

url = "https://github.com/opengeos/datasets/releases/download/hypercoast/pace_sample_points.csv" data = pd.read_csv(url) data.head()

Out[50]:

	band	wavelength	(30.1926 -90.1318)	(30.1594 -90.2856)	(29.3295 -92.3071)	(28.8783 -90.4559)	(30.5481 -87.9840)	(29.5305 -85.0671)	(28.8254 -85.6659)	(29.4587 -83.8477)	(26.8878 -87.7643)	(24.6570 -86.4954)	(26.8045 -82.4854)
0	0	339.0	NaN	NaN	0.000100	NaN	NaN	NaN	NaN	NaN	0.001718	0.000710	NaN
1	1	341.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001554	0.000846	NaN
2	2	344.0	NaN	NaN	0.001816	NaN	NaN	NaN	NaN	NaN	0.004047	0.003598	NaN
3	3	346.0	0.000346	NaN	0.002393	NaN	NaN	NaN	NaN	NaN	0.005045	0.004646	NaN
4	4	348.0	NaN	NaN	0.001578	NaN	NaN	NaN	NaN	NaN	0.004258	0.003984	NaN

Again, we will use the PACE AOP dataset acquired on July 30, 2024, to visualize the in-situ data. The dataset should have been downloaded in the previous section.

In [51]:

filepath = "data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc"

filepath = "data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc"

Read the PACE dataset as an xarray Dataset.

In [52]:

dataset = hypercoast.read_pace(filepath)

dataset = hypercoast.read_pace(filepath)

Visualize the in-situ data on the map.

In [53]:

m = hypercoast.Map(center=[27.235094, -87.791748], zoom=6)

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.add_field_data(
    data,
    x_col="wavelength",
    y_col_prefix="(",
    x_label="Wavelength (nm)",
    y_label="Reflectance",
    use_marker_cluster=True,
)
m.set_center(-87.791748, 27.235094, zoom=6)
m

m = hypercoast.Map(center=[27.235094, -87.791748], zoom=6) 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.add_field_data( data, x_col="wavelength", y_col_prefix="(", x_label="Wavelength (nm)", y_label="Reflectance", use_marker_cluster=True, ) m.set_center(-87.791748, 27.235094, zoom=6) m

Out[53]:

Click on any marker to display the in-situ data.

Analyze PACE data¶

To anyalyze the PACE data with algorithms, such as K-means clustering, principal component analysis (PCA), or Spectral Angle Mapper (SAM), follow the notebook at https://hypercoast.org/examples/pace_cyano.