Part 2: Using the `icepyx` python library to access ICESat-2 data#

Tutorial Overview#

This tutorial is designed for the “Cloud Computing and Open-Source Scientific Software for Cryosphere Communities” Learning Workshop at the 2023 AGU Fall Meeting.

This notebook demonstrates how to search for, access, and analyse and plot a cloud-hosted ICESat-2 dataset using the icepyx package.

icepyx is a community and software library for searching, downloading, and reading ICESat-2 data. While opening data should be straightforward, there are some oddities in navigating the highly nested organization and hundreds of variables of the ICESat-2 data. icepyx provides tools to help with those oddities.

icepyx was started and initially developed by Jessica Scheick to provide easy programmatic access to ICESat-2 data (before earthaccess existed!) and facilitate collaborative development around ICESat-2 data products, including training, skill building, and support around practicing open science and contributing to open-source software. Thanks to contributions from countless community members, icepyx can (for ICESat-2 data):

search for available data granules (data files)
order and download data or access it directly in the cloud
order a subset of data: clipped in space, time, containing fewer variables, or a few other options provided by NSIDC
search through the available ICESat-2 data variables
read ICESat-2 data into xarray DataArrays, including merging data from multiple files

Under the hood, icepyx relies on earthaccess to help handle authentication, especially for obtaining S3 tokens to access ICESat-2 data in the cloud. All this happens without the user needing to take any action other than supplying their Earthdata Login credentials using one of the methods described in the earthaccess tutorial.

In this tutorial we will look at the ATL08 Land and Vegetation Height product.

Learning Objectives#

In this tutorial you will learn:

how to use icepyx to search for ICESat-2 data using spatial and temporal filters;
how to open and combine data multiple HDF5 groups into an xarray.Dataset using icepyx.Read;
how to begin your analysis, including selecting strong/weak beams and plotting.

Prerequisites#

The workflow described in this tutorial forms the initial steps of an Analysis in Place workflow that would be run on a AWS cloud compute resource. You will need:

a JupyterHub, such as CryoHub, or AWS EC2 instance in the us-west-2 region.
a NASA Earthdata Login. If you need to register for an Earthdata Login see the Getting an Earthdata Login section of the ICESat-2 Hackweek 2023 Jupyter Book.
A .netrc file, that contains your Earthdata Login credentials, in your home directory. See Configure Programmatic Access to NASA Servers to create a .netrc file.

Credits#

This notebook is based on an icepyx Tutorial originally created by Rachel Wegener, Univ. Maryland and updated by Amy Steiker, NSIDC, and Jessica Scheick, Univ. of New Hampshire. It was updated in May 2024 to utilize (at a minimum) v1.0.0 of icepyx.

Using `icepyx` to search and access ICESat-2 data#

We won’t dive into using icepyx to search for and download data in this tutorial, since we already discussed how to do that with earthaccess. The code to search and download is still provided below for the curious reader. The icepyx documentation shows more detail about different search parameters and how to inspect the results of a query.

import icepyx as ipx

import json
import math
import warnings

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

from shapely.geometry import shape, GeometryCollection

%matplotlib inline

We’ll search for ATL08 tracks that intersect with the La Primavera Biosphere Reserve, Jalisco, Mexico, located west of Guadalajara.

# Open a geojson of our area of interest
with open("./bosque_primavera.json") as f:
    features = json.load(f)["features"]

bosque = GeometryCollection([shape(feature["geometry"]).buffer(0) for feature in features])
bosque

# Use our search parameters to setup a search Query
short_name = 'ATL08'
spatial_extent = list(bosque.bounds)
date_range = ['2019-05-04','2019-05-04']
region = ipx.Query(short_name, spatial_extent, date_range)

# Display if any data files, or granules, matched our search
region.avail_granules(ids=True)

[['ATL08_20190504124152_05540301_006_02.h5']]

# We can also get the S3 urls
print(region.avail_granules(ids=True, cloud=True))
s3urls = region.avail_granules(ids=True, cloud=True)[1]

[['ATL08_20190504124152_05540301_006_02.h5'], ['s3://nsidc-cumulus-prod-protected/ATLAS/ATL08/006/2019/05/04/ATL08_20190504124152_05540301_006_02.h5']]

# Download the granules to a into a folder called 'bosque_primavera_ATL08'
region.download_granules('./bosque_primavera_ATL08')

Enter your Earthdata Login username:  icepyx_devteam
Enter your Earthdata password:  ········

Total number of data order requests is  1  for  1  granules.
Data request  1  of  1  is submitting to NSIDC
order ID:  5000005555328
Initial status of your order request at NSIDC is:  processing
Your order status is still  processing  at NSIDC. Please continue waiting... this may take a few moments.
Your order is: complete
Beginning download of zipped output...
Data request 5000005555328 of  1  order(s) is downloaded.
Download complete

Tip: If you don't want to type your Earthdata Login information every time they are required you can setup more automatic methods of authentication. Two common methods are 1) Add your earthdata password and username to as environment variables as EARTHDATA_USERNAME and EARTHDATA_PASSWORD. 2) setup a .netrc file in your home directory. See the Openscapes tutorial

When to Cloud#

The astute user has by now noticed that in this tutorial we downloaded a granule to read in rather than directly reading it from an S3 bucket. Recall from the previous tutorial that reading a single group was a time intensive step and did not include multiple groups. Due to the way ICESat-2 data is stored on disk (because of the file format - it doesn’t matter if it’s a local disk or cloud disk), accessing the data within the file is really slow via the virtual file system. Several efforts are under way to help address this issue, and icepyx will implement them as soon as they are available. Current efforts include:

storing ICESat-2 data in a cloud-optimized format
reading data using the h5coro library

Please let Amy, Jessica, or one of the workshop leads know if you’re interested in joining any of these conversations (or telling us what issues you’ve encountered). We’d love to have your input and use case!

Summary#

In this notebook we explored the opening and rendering ATL08 data with icepyx. We saw that icepyx will subset our downloaded data to our area of interest and also allows us to download only the variables we need. The ATL08 data has a folder-like structure with many variables to choose from. We focused on h_canopy and showed additional examples using the raw photons and h_te_best_fit for the ground height.

More information about ATL08 or icepyx can be found in:

The icepyx documentation
The Algorithm Theoretical Basis Document (ATBD)
Neuenschwander et. al. 2019, Remote Sens. Env. DOI

CryoCloud

Part 2: Using the icepyx python library to access ICESat-2 data

Contents

Part 2: Using the `icepyx` python library to access ICESat-2 data#

Tutorial Overview#

Learning Objectives#

Prerequisites#

Credits#

Using `icepyx` to search and access ICESat-2 data#

Reading a file with icepyx#

Create a `Read` object#

Select your variables#

Load the data!#

When to Cloud#

Some example plots#

Example 1: View the photon classifications#

Plot the canopy compared to the ground height#

Summary#

CryoCloud

Part 2: Using the icepyx python library to access ICESat-2 data

Contents

Part 2: Using the icepyx python library to access ICESat-2 data#

Tutorial Overview#

Learning Objectives#

Prerequisites#

Credits#

Using icepyx to search and access ICESat-2 data#

Reading a file with icepyx#

Create a Read object#

Select your variables#

Load the data!#

When to Cloud#

Some example plots#

Example 1: View the photon classifications#

Plot the canopy compared to the ground height#

Summary#

Part 2: Using the `icepyx` python library to access ICESat-2 data#

Using `icepyx` to search and access ICESat-2 data#

Create a `Read` object#