From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.

SWOT Hydrology Dataset Exploration in the Cloud

Accessing and Visualizing SWOT Datasets

Requirement:

This tutorial can only be run in an AWS cloud instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via earthaccess python library; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.

Learning Objectives:

  • Access SWOT HR data prodcuts (archived in NASA Earthdata Cloud) within the AWS cloud, without downloading to local machine

  • Visualize accessed data for a quick check

SWOT Level 2 KaRIn High Rate Version 2.0 Datasets:

  1. River Vector Shapefile - SWOT_L2_HR_RIVERSP_2.0

  2. Lake Vector Shapefile - SWOT_L2_HR_LAKESP_2.0

  3. Water Mask Pixel Cloud NetCDF - SWOT_L2_HR_PIXC_2.0

  4. Water Mask Pixel Cloud Vector Attribute NetCDF - SWOT_L2_HR_PIXCVec_2.0

  5. Raster NetCDF - SWOT_L2_HR_Raster_2.0

  6. Single Look Complex Data product - SWOT_L1B_HR_SLC_2.0

Notebook Author: Cassie Nickles, NASA PO.DAAC (Feb 2024) || Other Contributors: Zoe Walschots (PO.DAAC Summer Intern 2023), Catalina Taglialatela (NASA PO.DAAC), Luis Lopez (NASA NSIDC DAAC)

Last updated: 7 Feb 2024

Libraries Needed

[1]:
import s3fs
import fiona
import xarray as xr
import pandas as pd
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
import hvplot.xarray
import earthaccess

pd.set_option('display.max_columns', None) #make sure all columns displayed for shapefiles

Earthdata Login

An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. If you don’t already have one, please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up. We use earthaccess to authenticate your login credentials below.

[2]:
auth = earthaccess.login()

Single File Access

1. River Vector Shapefiles

The s3 access link can be found using earthaccess data search. Since this collection consists of Reach and Node files, we need to extract only the granule for the Reach file. We do this by filtering for the ‘Reach’ title in the data link.

Alternatively, Earthdata Search (see tutorial) can be used to search in a map graphic user interface.

For additional tips on spatial searching of SWOT HR L2 data, see also PO.DAAC Cookbook - SWOT Chapter tips section.

Search for the data of interest

[3]:
#Retrieves granule from the day we want, in this case by passing to `earthdata.search_data` function the data collection shortname, temporal bounds, and filter by wildcards
river_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_RIVERSP_2.0',
                                        #temporal = ('2024-02-01 00:00:00', '2024-02-29 23:59:59'), # can also specify by time
                                        granule_name = '*Reach*_009_NA*') # here we filter by Reach files (not node), pass=009, continent code=NA
Granules found: 7

Set up an s3fs session for Direct Cloud Access

s3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the data access information.

[4]:
fs_s3 = earthaccess.get_s3fs_session(results=river_results)

Create Fiona session to work with zip and embedded shapefiles in the AWS Cloud

The native format for this data is a .zip file, and we want the .shp file within the .zip file, so we will create a Fiona AWS session using the credentials from setting up the s3fs session above to access the shapefiles within the zip files. If we don’t do this, the alternative would be to download the data to the cloud environment (e.g. EC2 instance, user S3 bucket) and extract the .zip file there.

[5]:
fiona_session=fiona.session.AWSSession(
        aws_access_key_id=fs_s3.storage_options["key"],
        aws_secret_access_key=fs_s3.storage_options["secret"],
        aws_session_token=fs_s3.storage_options["token"]
    )
[6]:
# Get the link for the first zip file
river_link = earthaccess.results.DataGranule.data_links(river_results[0], access='direct')[0]

# We use the zip+ prefix so fiona knows that we are operating on a zip file
river_shp_url = f"zip+{river_link}"

with fiona.Env(session=fiona_session):
    SWOT_HR_shp1 = gpd.read_file(river_shp_url)

#view the attribute table
SWOT_HR_shp1
[6]:
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931 rows × 127 columns

Quickly plot the SWOT river data

[7]:
# Simple plot
fig, ax = plt.subplots(figsize=(7,5))
SWOT_HR_shp1.plot(ax=ax, color='black')
[7]:
<Axes: >
../../_images/SWOT-Hydrology_BASIC_Product_Overview_SWOT_HR_cloud_in_Earthdata_14_1.png
[8]:
# # Another way to plot geopandas dataframes is with `explore`, which also plots a basemap
# SWOT_HR_shp1.explore()

2. Lake Vector Shapefiles

The lake vector shapefiles can be accessed in the same way as the river shapefiles above.

For additional tips on spatial searching of SWOT HR L2 data, see also PO.DAAC Cookbook - SWOT Chapter tips section.

Search for data of interest

[8]:
lake_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_LAKESP_2.0',
                                        #temporal = ('2024-02-01 00:00:00', '2024-02-29 23:59:59'), # can also specify by time
                                        granule_name = '*Prior*_009_NA*') # here we filter by files with 'Prior' in the name (This collection has three options: Obs, Unassigned, and Prior), pass #8 and continent code=NA
Granules found: 3

Set up an s3fs session for Direct Cloud Access

s3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the data access information.

[9]:
fs_s3 = earthaccess.get_s3fs_session(results=lake_results)

Create Fiona session to work with zip and embedded shapefiles in the AWS Cloud

The native format for this data is a .zip file, and we want the .shp file within the .zip file, so we will create a Fiona AWS session using the credentials from setting up the s3fs session above to access the shapefiles within the zip files. If we don’t do this, the alternative would be to download the data to the cloud environment (e.g. EC2 instance, user S3 bucket) and extract the .zip file there.

[10]:
fiona_session=fiona.session.AWSSession(
        aws_access_key_id=fs_s3.storage_options["key"],
        aws_secret_access_key=fs_s3.storage_options["secret"],
        aws_session_token=fs_s3.storage_options["token"]
    )
[11]:
# Get the link for the first zip file
lake_link = earthaccess.results.DataGranule.data_links(lake_results[0], access='direct')[0]

# We use the zip+ prefix so fiona knows that we are operating on a zip file
lake_shp_url = f"zip+{lake_link}"

with fiona.Env(session=fiona_session):
    SWOT_HR_shp2 = gpd.read_file(lake_shp_url)

#view the attribute table
SWOT_HR_shp2
[11]:
lake_id reach_id obs_id overlap n_overlap time time_tai time_str wse wse_u wse_r_u wse_std area_total area_tot_u area_detct area_det_u layovr_val xtrk_dist ds1_l ds1_l_u ds1_q ds1_q_u ds2_l ds2_l_u ds2_q ds2_q_u quality_f dark_frac ice_clim_f ice_dyn_f partial_f xovr_cal_q geoid_hght solid_tide load_tidef load_tideg pole_tide dry_trop_c wet_trop_c iono_c xovr_cal_c lake_name p_res_id p_lon p_lat p_ref_wse p_ref_area p_date_t0 p_ds_t0 p_storage geometry
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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
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56495 7420501022 no_data 742233R999968;742233R000007;742233R000003 57;8;4 3 7.558788e+08 7.558788e+08 2023-12-14T14:19:39Z 3.105560e+02 9.900000e-02 7.300000e-02 6.540000e-01 3.935470e-01 2.443000e-03 1.602010e-01 2.443000e-03 1.100000e-02 5.434642e+04 -1.000000e+12 -1.000000e+12 -1.000000e+12 -1.000000e+12 -1.000000e+12 -1.000000e+12 -1.000000e+12 -1.000000e+12 0 -1.000000e+12 2 -999 0 0 -2.887526e+01 -1.217310e-01 -1.435000e-02 -1.513400e-02 6.583000e-03 -2.268862e+00 -5.500000e-02 -2.866000e-03 1.664425e+00 LAKE TUSTIN -99999999 -93.665611 44.206288 -1.000000e+12 0.546300 no_data -1.000000e+12 -1.000000e+12 MULTIPOLYGON (((-93.66006 44.20938, -93.66012 ...

56496 rows × 51 columns

Quickly plot the SWOT lakes data

[12]:
fig, ax = plt.subplots(figsize=(7,5))
SWOT_HR_shp2.plot(ax=ax, color='black')
[12]:
<Axes: >
../../_images/SWOT-Hydrology_BASIC_Product_Overview_SWOT_HR_cloud_in_Earthdata_26_1.png

Accessing the remaining files is different than the shp files above. We do not need to read the shapefiles within a zip file using something like Fiona session (or to download and unzip in the cloud) because the following SWOT HR collections are stored in netCDF files in the cloud. For the rest of the products, we will open via xarray, not geopandas.

3. Water Mask Pixel Cloud NetCDF

Search for data collection and time of interest

For additional tips on spatial searching of SWOT HR L2 data, see also PO.DAAC Cookbook - SWOT Chapter tips section.

[13]:
pixc_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_PIXC_2.0',
                                        #granule_name = '*_009_*', # pass number 9 if we want to filter further
                                        #temporal = ('2024-02-01 00:00:00', '2024-02-29 23:59:59'), # can also specify by time
                                        bounding_box = (-106.62, 38.809, -106.54, 38.859)) # Lake Travis near Austin, TX
Granules found: 39

Open data using xarray

The pixel cloud netCDF files are formatted with three groups titled, “pixel cloud”, “tvp”, or “noise” (more detail here). In order to access the coordinates and variables within the file, a group must be specified when calling xarray open_dataset.

[14]:
ds_PIXC = xr.open_mfdataset(earthaccess.open([pixc_results[0]]), group = 'pixel_cloud', engine='h5netcdf')
ds_PIXC
Opening 1 granules, approx size: 0.22 GB
using endpoint: https://archive.swot.podaac.earthdata.nasa.gov/s3credentials
[14]:
<xarray.Dataset>
Dimensions:                                (points: 2357958, complex_depth: 2,
                                            num_pixc_lines: 3278)
Coordinates:
    latitude                               (points) float64 dask.array<chunksize=(471592,), meta=np.ndarray>
    longitude                              (points) float64 dask.array<chunksize=(471592,), meta=np.ndarray>
Dimensions without coordinates: points, complex_depth, num_pixc_lines
Data variables: (12/61)
    azimuth_index                          (points) float64 dask.array<chunksize=(785986,), meta=np.ndarray>
    range_index                            (points) float64 dask.array<chunksize=(785986,), meta=np.ndarray>
    interferogram                          (points, complex_depth) float32 dask.array<chunksize=(785986, 1), meta=np.ndarray>
    power_plus_y                           (points) float32 dask.array<chunksize=(785986,), meta=np.ndarray>
    power_minus_y                          (points) float32 dask.array<chunksize=(785986,), meta=np.ndarray>
    coherent_power                         (points) float32 dask.array<chunksize=(785986,), meta=np.ndarray>
    ...                                     ...
    pixc_line_qual                         (num_pixc_lines) float64 dask.array<chunksize=(3278,), meta=np.ndarray>
    pixc_line_to_tvp                       (num_pixc_lines) float32 dask.array<chunksize=(3278,), meta=np.ndarray>
    data_window_first_valid                (num_pixc_lines) float64 dask.array<chunksize=(3278,), meta=np.ndarray>
    data_window_last_valid                 (num_pixc_lines) float64 dask.array<chunksize=(3278,), meta=np.ndarray>
    data_window_first_cross_track          (num_pixc_lines) float32 dask.array<chunksize=(3278,), meta=np.ndarray>
    data_window_last_cross_track           (num_pixc_lines) float32 dask.array<chunksize=(3278,), meta=np.ndarray>
Attributes:
    description:                 cloud of geolocated interferogram pixels
    interferogram_size_azimuth:  3278
    interferogram_size_range:    5623
    looks_to_efflooks:           1.5509709858820855
    num_azimuth_looks:           7.0
    azimuth_offset:              7

Simple plot of the results

[15]:
# This could take a few minutes to plot
plt.scatter(x=ds_PIXC.longitude, y=ds_PIXC.latitude, c=ds_PIXC.height)
plt.colorbar().set_label('Height (m)')
../../_images/SWOT-Hydrology_BASIC_Product_Overview_SWOT_HR_cloud_in_Earthdata_35_0.png

4. Water Mask Pixel Cloud Vector Attribute NetCDF

Search for data of interest

[16]:
pixcvec_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_PIXCVEC_2.0',
                                        #granule_name = '*_009_*', # pass number 9 if we want to filter further
                                        #temporal = ('2024-02-01 00:00:00', '2024-02-29 23:59:59'), # can also specify by time
                                        bounding_box = (-106.62, 38.809, -106.54, 38.859)) # Lake Travis near Austin, TX
Granules found: 26

Open data using xarray

[17]:
ds_PIXCVEC = xr.open_mfdataset(earthaccess.open([pixcvec_results[0]]), decode_cf=False,  engine='h5netcdf')
ds_PIXCVEC
Opening 1 granules, approx size: 0.21 GB
using endpoint: https://archive.swot.podaac.earthdata.nasa.gov/s3credentials
[17]:
<xarray.Dataset>
Dimensions:               (points: 2831150, nchar_reach_id: 11,
                           nchar_node_id: 14, nchar_lake_id: 10,
                           nchar_obs_id: 13)
Dimensions without coordinates: points, nchar_reach_id, nchar_node_id,
                                nchar_lake_id, nchar_obs_id
Data variables:
    azimuth_index         (points) int32 dask.array<chunksize=(2831150,), meta=np.ndarray>
    range_index           (points) int32 dask.array<chunksize=(2831150,), meta=np.ndarray>
    latitude_vectorproc   (points) float64 dask.array<chunksize=(2831150,), meta=np.ndarray>
    longitude_vectorproc  (points) float64 dask.array<chunksize=(2831150,), meta=np.ndarray>
    height_vectorproc     (points) float32 dask.array<chunksize=(2831150,), meta=np.ndarray>
    reach_id              (points, nchar_reach_id) |S1 dask.array<chunksize=(2831150, 11), meta=np.ndarray>
    node_id               (points, nchar_node_id) |S1 dask.array<chunksize=(2831150, 14), meta=np.ndarray>
    lake_id               (points, nchar_lake_id) |S1 dask.array<chunksize=(2831150, 10), meta=np.ndarray>
    obs_id                (points, nchar_obs_id) |S1 dask.array<chunksize=(2831150, 13), meta=np.ndarray>
    ice_clim_f            (points) int8 dask.array<chunksize=(2831150,), meta=np.ndarray>
    ice_dyn_f             (points) int8 dask.array<chunksize=(2831150,), meta=np.ndarray>
Attributes: (12/45)
    Conventions:                     CF-1.7
    title:                           Level 2 KaRIn high rate pixel cloud vect...
    short_name:                      L2_HR_PIXCVec
    institution:                     CNES
    source:                          Level 1B KaRIn High Rate Single Look Com...
    history:                         2023-12-03T05:59:43.712142Z: Creation
    ...                              ...
    xref_prior_river_db_file:
    xref_prior_lake_db_file:         SWOT_LakeDatabase_Nom_106_20000101T00000...
    xref_reforbittrack_files:        SWOT_RefOrbitTrackTileBoundary_Nom_20000...
    xref_param_l2_hr_laketile_file:  SWOT_Param_L2_HR_LakeTile_20000101T00000...
    ellipsoid_semi_major_axis:       6378137.0
    ellipsoid_flattening:            0.0033528106647474805

Simple plot

[18]:
pixcvec_htvals = ds_PIXCVEC.height_vectorproc.compute()
pixcvec_latvals = ds_PIXCVEC.latitude_vectorproc.compute()
pixcvec_lonvals = ds_PIXCVEC.longitude_vectorproc.compute()

#Before plotting, we set all fill values to nan so that the graph shows up better spatially
pixcvec_htvals[pixcvec_htvals > 15000] = np.nan
pixcvec_latvals[pixcvec_latvals < 1] = np.nan
pixcvec_lonvals[pixcvec_lonvals > -1] = np.nan
[19]:
plt.scatter(x=pixcvec_lonvals, y=pixcvec_latvals, c=pixcvec_htvals)
plt.colorbar().set_label('Height (m)')
../../_images/SWOT-Hydrology_BASIC_Product_Overview_SWOT_HR_cloud_in_Earthdata_42_0.png

5. Raster NetCDF

Search for data of interest

For additional tips on spatial searching of SWOT HR L2 data, see also PO.DAAC Cookbook - SWOT Chapter tips section.

[20]:
raster_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_Raster_2.0',
                                        #temporal = ('2024-02-01 00:00:00', '2024-02-29 23:59:59'), # can also specify by time
                                        granule_name = '*100m*', # here we filter by files with '100m' in the name (This collection has two resolution options: 100m & 250m)
                                        bounding_box = (-106.62, 38.809, -106.54, 38.859)) # Lake Travis near Austin, TX
Granules found: 44

Open data with xarray

[21]:
ds_raster = xr.open_mfdataset(earthaccess.open([raster_results[0]]), engine='h5netcdf')
ds_raster
Opening 1 granules, approx size: 0.04 GB
using endpoint: https://archive.swot.podaac.earthdata.nasa.gov/s3credentials
[21]:
<xarray.Dataset>
Dimensions:                  (x: 1520, y: 1519)
Coordinates:
  * x                        (x) float64 2.969e+05 2.97e+05 ... 4.488e+05
  * y                        (y) float64 4.274e+06 4.274e+06 ... 4.426e+06
Data variables: (12/39)
    crs                      object ...
    longitude                (y, x) float64 dask.array<chunksize=(507, 507), meta=np.ndarray>
    latitude                 (y, x) float64 dask.array<chunksize=(507, 507), meta=np.ndarray>
    wse                      (y, x) float32 dask.array<chunksize=(760, 760), meta=np.ndarray>
    wse_qual                 (y, x) float32 dask.array<chunksize=(1519, 1520), meta=np.ndarray>
    wse_qual_bitwise         (y, x) float64 dask.array<chunksize=(760, 760), meta=np.ndarray>
    ...                       ...
    load_tide_fes            (y, x) float32 dask.array<chunksize=(760, 760), meta=np.ndarray>
    load_tide_got            (y, x) float32 dask.array<chunksize=(760, 760), meta=np.ndarray>
    pole_tide                (y, x) float32 dask.array<chunksize=(760, 760), meta=np.ndarray>
    model_dry_tropo_cor      (y, x) float32 dask.array<chunksize=(760, 760), meta=np.ndarray>
    model_wet_tropo_cor      (y, x) float32 dask.array<chunksize=(760, 760), meta=np.ndarray>
    iono_cor_gim_ka          (y, x) float32 dask.array<chunksize=(760, 760), meta=np.ndarray>
Attributes: (12/49)
    Conventions:                   CF-1.7
    title:                         Level 2 KaRIn High Rate Raster Data Product
    source:                        Ka-band radar interferometer
    history:                       2023-12-03T08:26:57Z : Creation
    platform:                      SWOT
    references:                    V1.1.1
    ...                            ...
    x_min:                         296900.0
    x_max:                         448800.0
    y_min:                         4274000.0
    y_max:                         4425800.0
    institution:                   CNES
    product_version:               04

Quick interactive plot with hvplot

[22]:
ds_raster.wse.hvplot.image(y='y', x='x')
[22]:

6. SLC

Search for data collection and time of interest

For additional tips on spatial searching of SWOT HR L2 data, see also PO.DAAC Cookbook - SWOT Chapter tips section.

[23]:
slc_results = earthaccess.search_data(short_name = 'SWOT_L1B_HR_SLC_2.0',
                                        #temporal = ('2024-02-01 00:00:00', '2024-02-29 23:59:59'), # can also specify by time
                                        #granule_name = '*_009_*', # here we can additionally filter by pass number 009
                                        bounding_box = (-106.62, 38.809, -106.54, 38.859)) # Lake Travis near Austin, TX
Granules found: 46

Open data using xarray

The L1B_HR_SLC product file contains five NetCDF data group called the slc, xfactor, noise, tvp, and grdem groups. More info can be found in the product description document within the dataset table for each group.

[24]:
ds_SLC = xr.open_mfdataset(earthaccess.open([slc_results[0]]), group = 'slc', engine='h5netcdf')
ds_SLC
Opening 1 granules, approx size: 2.25 GB
using endpoint: https://archive.swot.podaac.earthdata.nasa.gov/s3credentials
[24]:
<xarray.Dataset>
Dimensions:      (num_lines: 22956, num_pixels: 5623, complex_depth: 2)
Dimensions without coordinates: num_lines, num_pixels, complex_depth
Data variables:
    slc_plus_y   (num_lines, num_pixels, complex_depth) float32 dask.array<chunksize=(3280, 804, 1), meta=np.ndarray>
    slc_minus_y  (num_lines, num_pixels, complex_depth) float32 dask.array<chunksize=(3280, 804, 1), meta=np.ndarray>
    slc_qual     (num_lines) float32 dask.array<chunksize=(22956,), meta=np.ndarray>
Attributes:
    description:  Single look complex images for plus_y and minus_y channels