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Software + Data

Software + Data#

This page contains information about the computing environment and software packages that we will be using throughout the rest of the book.

Computing environment#

We will make use of a number of software packages that are well-suited for working with geospatial and remote sensing data. These include:xarray, rioxarray, dask, geopandas

import os
import tempfile
import requests
import markdown
import pathlib


import xarray as xr
import rioxarray as rio
import geopandas as gpd
from shapely.geometry import Polygon

import planetary_computer
import adlfs
import pystac_client
from pystac_client import Client
import stackstac
import pystac


import matplotlib.pyplot as plt
from datetime import datetime
import numpy as np
import pandas as pd
import rich.table
import time
import glob

from IPython.display import Image

See the specific versions of packages used below. You can also access an environment.yml file at the github repo associated with this tutorial.

%load_ext watermark 

%watermark -v -m

The watermark extension is already loaded. To reload it, use:
  %reload_ext watermark
Python implementation: CPython
Python version       : 3.10.6
IPython version      : 8.5.0

Compiler    : Clang 13.0.1 
OS          : Darwin
Release     : 21.6.0
Machine     : x86_64
Processor   : i386
CPU cores   : 8
Architecture: 64bit

Data#

This tutorial works with two different datasets of Sentinel-1 RTC imagery.

  1. The first is processed by Alaska Satellite Facility (ASF) using their On-Demand platform and downloaded locally as directories for each scene containing GeoTIFF files and associated metadata. Access to ASF data and SAR data processing is available to anyone with NASA Earthdata login credentials, which you can learn more about here. The ASF-processed data used in this tutorial is available here.

  2. The second is processed and hosted by Microsoft Planetary Computer as cloud-optimized GeoTIFF (COGs).

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