For an updated notebook using the latest data, see this notebook in the PO.DAAC Cookbook

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 1.1 (where available) Datasets:

  1. River Vector Shapefile - SWOT_L2_HR_RIVERSP_1.1

  2. Lake Vector Shapefile - SWOT_L2_HR_LAKESP_1.1

  3. Water Mask Pixel Cloud NetCDF - SWOT_L2_HR_PIXC_1.1

  4. Water Mask Pixel Cloud Vector Attribute NetCDF - SWOT_L2_HR_PIXCVec_1.1

  5. Raster NetCDF - SWOT_L2_HR_Raster_1.1

  6. Single Look Complex Data product - SWOT_L1B_HR_SLC_1.1

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

Last updated: 4 Dec 2023

Libraries Needed

import glob
import os
import requests
import s3fs
import fiona
import netCDF4 as nc
import h5netcdf
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
from earthaccess import Auth, DataCollections, DataGranules, Store

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.

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

# Retrieves granule from the day we want, in this case by passing to `earthdata.search_data` function the data collection shortname, temporal bounds, and for restricted data one must specify the search count
river_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_RIVERSP_1.1', 
                                        temporal = ('2023-04-08 00:00:00', '2023-04-22 23:59:59'),
                                        granule_name = '*Reach*_024_NA*') # here we filter by Reach files (not node), pass #24 and continent code=NA for North America
                                        # granule_name = '*Reach*_013_NA*') # here we filter by Reach files (not node), pass #13 and continent code=NA
Granules found: 15

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.

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.

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"]
    )
# 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 
reach_id time time_tai time_str p_lat p_lon river_name wse wse_u wse_r_u ... p_wid_var p_n_nodes p_dist_out p_length p_maf p_dam_id p_n_ch_max p_n_ch_mod p_low_slp geometry
0 71185400013 7.342856e+08 7.342856e+08 2023-04-08T16:12:43Z 55.405348 -106.628388 no_data 3.864838e+02 1.139410e+00 1.135850e+00 ... 7863771.149 48 61917.017 9521.873154 -1.000000e+12 0 10 2 0 LINESTRING (-106.60903 55.44509, -106.60930 55...
1 71185400021 7.342856e+08 7.342856e+08 2023-04-08T16:12:43Z 55.452342 -106.601114 no_data -1.000000e+12 -1.000000e+12 -1.000000e+12 ... 0.000 10 53346.297 1902.305299 -1.000000e+12 0 5 1 0 LINESTRING (-106.59293 55.45986, -106.59320 55...
2 71185400033 -1.000000e+12 -1.000000e+12 no_data 55.632220 -106.451323 no_data -1.000000e+12 -1.000000e+12 -1.000000e+12 ... 758315.173 14 28676.430 2858.149671 -1.000000e+12 0 7 2 0 LINESTRING (-106.47121 55.62881, -106.47073 55...
3 71185400041 7.342856e+08 7.342856e+08 2023-04-08T16:12:43Z 55.361687 -106.646694 no_data 3.861999e+02 9.139000e-02 1.588000e-02 ... 0.000 5 62976.523 1059.505878 -1.000000e+12 0 5 1 0 LINESTRING (-106.64608 55.36668, -106.64607 55...
4 71185400053 7.342856e+08 7.342856e+08 2023-04-08T16:12:43Z 55.350062 -106.647210 no_data 3.861795e+02 1.022600e-01 4.855000e-02 ... 3214.190 8 64492.945 1516.422084 -1.000000e+12 0 1 1 0 LINESTRING (-106.64728 55.35669, -106.64736 55...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
594 75211000291 -1.000000e+12 -1.000000e+12 no_data 26.100287 -98.270345 Rio Bravo -1.000000e+12 -1.000000e+12 -1.000000e+12 ... 123.027 53 238333.030 10660.888100 -1.000000e+12 0 1 1 0 LINESTRING (-98.25015 26.07251, -98.25039 26.0...
595 75211000301 -1.000000e+12 -1.000000e+12 no_data 26.115209 -98.305631 Rio Grande -1.000000e+12 -1.000000e+12 -1.000000e+12 ... 242.204 53 248976.010 10642.980241 -1.000000e+12 0 1 1 0 LINESTRING (-98.27467 26.11517, -98.27497 26.1...
596 75211000683 7.342861e+08 7.342861e+08 2023-04-08T16:21:20Z 25.955223 -97.159176 Rio Grande 2.871000e-01 9.005000e-02 3.080000e-03 ... 436.214 18 9238.006 3611.160551 -1.000000e+12 0 1 1 0 LINESTRING (-97.14980 25.95092, -97.15011 25.9...
597 75211000691 7.342861e+08 7.342861e+08 2023-04-08T16:21:20Z 25.957129 -97.209134 Rio Grande 3.374000e-01 9.102000e-02 1.360000e-02 ... 348.855 53 19926.935 10688.929343 -1.000000e+12 0 1 1 0 LINESTRING (-97.16943 25.96060, -97.16972 25.9...
598 75211000701 7.342861e+08 7.342861e+08 2023-04-08T16:21:20Z 25.945001 -97.279869 Rio Grande 4.375000e-01 9.212000e-02 1.965000e-02 ... 203.786 53 30608.499 10681.563344 -1.000000e+12 0 1 1 0 LINESTRING (-97.25170 25.94769, -97.25200 25.9...

599 rows × 127 columns

Quickly plot the SWOT river data

# Simple plot
fig, ax = plt.subplots(figsize=(7,5))
SWOT_HR_shp1.plot(ax=ax, color='black')

# # Another way to plot geopandas dataframes is with `explore`, which also plots a basemap
SWOT_HR_shp1.explore()
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