2021 Cloud Hackathon – data_access_direct

Timing

Exercise: 20 minutes

Summary

In the previous exercises we searched for and discovered cloud data assets that met certain search criteria (i.e., intersects with our region of interest and for a specified date range). The end goal was to find and save web links to the data assets we want to use in our workflow. The links we found allow us to download data via HTTPS (Hypertext Transfer Protocol Secure). However, NASA allows for direct in-region S3 bucket access for the same assets. In addition to saving the HTTPS links, we also created and saved the S3 links for those same cloud assets and we will use them here. In this exercise we will demonstrate how to perform direct in-region S3 bucket access for Harmonized Landsat Sentinel-2 (HLS) cloud data assets.

Direct S3 Access

NASA Earthdata Cloud provides two pathways for accessing data from the cloud. The first is via HTTPS. The other is through direct S3 bucket access. Below are some benefits and considerations when choosing to use direct S3 bucket access for NASA cloud assets.

Benefits

Retrieving data can be much quicker
No need to download data! Work with data in a more efficient manner, “next to it, in the cloud”
Increased capacity to do parallel processing, due to working in the cloud
You are working completely within the AWS cloud ecosystem and thus have access to the might of all AWS offerings (e.g., infrastructure, S3 API, services, etc.)

Considerations

If your workflow is in the cloud, choose S3 over HTTPS
Access only works within AWS us-west-2 region
Need an AWS S3 “token” to access S3 Bucket
Token expires after 1 hour (currently)
Token only works at the DAAC that generates it, e.g.,
- PO.DAAC token generator: https://archive.podaac.earthdata.nasa.gov/s3credentials
- LP DAAC token generator: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials
Direct S3 access on its own does not solve ‘cloud’ problems, but it is one key technology in solving big data problems
Still have to load things in to memory, parallelize the computation, if working with really large data volumes. There are a lot of tools that allow you to do that, but are not discussed in this tutorial

What you will learn from this tutorial

how to retrieve temporary S3 credentials for in-region direct S3 bucket access
how to configure our notebook environment for in-region direct S3 bucket access
how to access a single HLS file via in-region direct S3 bucket access
how to create an HLS time series data array from cloud assets via in-region direct S3 bucket access
how to plot results

This exercise can be found in the 2021 Cloud Hackathon Book

Import Required Packages

%matplotlib inline
import matplotlib.pyplot as plt
from datetime import datetime
import os
import requests
import boto3
import numpy as np
import xarray as xr
import rasterio as rio
from rasterio.session import AWSSession
from rasterio.plot import show
import rioxarray
import geoviews as gv
import hvplot.xarray
import holoviews as hv
gv.extension('bokeh', 'matplotlib')

Configure Local Environment and Get Temporary Credentials

To perform direct S3 data access one needs to acquire temporary S3 credentials. The credentials give users direct access to S3 buckets in NASA Earthdata Cloud. AWS credentials should not be shared, so take precautions when using them in notebooks our scripts. Note, these temporary credentials are valid for only 1 hour. For more information regarding the temporary credentials visit https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME. A netrc file is required to aquire these credentials. Use the NASA Earthdata Authentication to create a netrc file in your home directory.

s3_cred_endpoint = 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials'

def get_temp_creds():
    temp_creds_url = s3_cred_endpoint
    return requests.get(temp_creds_url).json()

temp_creds_req = get_temp_creds()
#temp_creds_req                      # !!! BEWARE, removing the # on this line will print your temporary S3 credentials.

Insert the credentials into our `boto3` session and configure our `rasterio` environment for data access

Create a boto3 Session object using your temporary credentials. This Session is used to pass credentials and configuration to AWS so we can interact wit S3 objects from applicable buckets.

session = boto3.Session(aws_access_key_id=temp_creds_req['accessKeyId'], 
                        aws_secret_access_key=temp_creds_req['secretAccessKey'],
                        aws_session_token=temp_creds_req['sessionToken'],
                        region_name='us-west-2')

For this exercise, we are going to open up a context manager for the notebook using the rasterio.env module to store the required GDAL and AWS configurations we need to access the data in Earthdata Cloud. While the context manager is open (rio_env.__enter__()) we will be able to run the open or get data commands that would typically be executed within a with statement, thus allowing us to more freely interact with the data. We’ll close the context (rio_env.__exit__()) at the end of the notebook.

GDAL environment variables must be configured to access Earthdata Cloud data assets. Geospatial data access Python packages like rasterio and rioxarray depend on GDAL, leveraging GDAL’s “Virtual File Systems” to read remote files. GDAL has a lot of environment variables that control it’s behavior. Changing these settings can mean the difference being able to access a file or not. They can also have an impact on the performance.

rio_env = rio.Env(AWSSession(session),
                  GDAL_DISABLE_READDIR_ON_OPEN='EMPTY_DIR',
                  GDAL_HTTP_COOKIEFILE=os.path.expanduser('~/cookies.txt'),
                  GDAL_HTTP_COOKIEJAR=os.path.expanduser('~/cookies.txt'))
rio_env.__enter__()

<rasterio.env.Env at 0x7f1619fbb0a0>

Read in S3 Links

In the CMR-STAC API tutorial we saved off multiple text file containing links, both HTTPS and S3 links, to Harmonized Landsat Sentinel-2 (HLS) cloud data assets. We will now read in one of those file and show how to access those data assets.

List the available files in the data directory

NOTE: If you are running the notebook from the tutorials-templates directory, please use the following path to connect to the data directoy: “../tutorials/data”

[f for f in os.listdir('./data') if '.txt' in f]

['HTTPS_T13TGF_B02_Links.txt',
 'S3_T13TGF_B05_Links.txt',
 'HTTPS_T13TGF_Fmask_Links.txt',
 'S3_T13TGF_B8A_Links.txt',
 'HTTPS_T13TGF_B04_Links.txt',
 'S3_T13TGF_B04_Links.txt',
 'S3_T13TGF_Fmask_Links.txt',
 'HTTPS_T13TGF_B8A_Links.txt',
 'HTTPS_T13TGF_B05_Links.txt',
 'S3_T13TGF_B02_Links.txt']

We will save our list of links and a single link as Python objects for use later.

NOTE: If you are running the notebook from the tutorials-templates directory, please use the following path to connect to the data directoy: “../tutorials/data”

s3_links = open('./data/S3_T13TGF_B04_Links.txt').read().splitlines()
s3_links

['s3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021133T172406.v2.0/HLS.L30.T13TGF.2021133T172406.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021133T173859.v2.0/HLS.S30.T13TGF.2021133T173859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021140T173021.v2.0/HLS.L30.T13TGF.2021140T173021.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021140T172859.v2.0/HLS.S30.T13TGF.2021140T172859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021145T172901.v2.0/HLS.S30.T13TGF.2021145T172901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021155T172901.v2.0/HLS.S30.T13TGF.2021155T172901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021156T173029.v2.0/HLS.L30.T13TGF.2021156T173029.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021158T173901.v2.0/HLS.S30.T13TGF.2021158T173901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021163T173909.v2.0/HLS.S30.T13TGF.2021163T173909.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021165T172422.v2.0/HLS.L30.T13TGF.2021165T172422.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021165T172901.v2.0/HLS.S30.T13TGF.2021165T172901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021173T173909.v2.0/HLS.S30.T13TGF.2021173T173909.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021185T172901.v2.0/HLS.S30.T13TGF.2021185T172901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021188T173037.v2.0/HLS.L30.T13TGF.2021188T173037.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021190T172859.v2.0/HLS.S30.T13TGF.2021190T172859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021193T173909.v2.0/HLS.S30.T13TGF.2021193T173909.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021198T173911.v2.0/HLS.S30.T13TGF.2021198T173911.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021200T172859.v2.0/HLS.S30.T13TGF.2021200T172859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021203T173909.v2.0/HLS.S30.T13TGF.2021203T173909.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021204T173042.v2.0/HLS.L30.T13TGF.2021204T173042.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021208T173911.v2.0/HLS.S30.T13TGF.2021208T173911.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021210T172859.v2.0/HLS.S30.T13TGF.2021210T172859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021215T172901.v2.0/HLS.S30.T13TGF.2021215T172901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021218T173911.v2.0/HLS.S30.T13TGF.2021218T173911.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021220T173049.v2.0/HLS.L30.T13TGF.2021220T173049.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021220T172859.v2.0/HLS.S30.T13TGF.2021220T172859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021223T173909.v2.0/HLS.S30.T13TGF.2021223T173909.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021228T173911.v2.0/HLS.S30.T13TGF.2021228T173911.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021229T172441.v2.0/HLS.L30.T13TGF.2021229T172441.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021230T172859.v2.0/HLS.S30.T13TGF.2021230T172859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021235T172901.v2.0/HLS.S30.T13TGF.2021235T172901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021243T173859.v2.0/HLS.S30.T13TGF.2021243T173859.v2.0.B04.tif']

s3_link = s3_links[0]
s3_link

's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021133T172406.v2.0/HLS.L30.T13TGF.2021133T172406.v2.0.B04.tif'

Read in a single HLS file

We’ll access the HLS S3 object using the rioxarray Python package. The package is an extension of xarray and rasterio, allowing users to read in and interact with geospatial data using xarray data structures. We will also be leveraging the tight integration between xarray and dask to lazily read in data via the chunks parameter. This allows us to connect to the HLS S3 object, reading only metadata, an not load the data into memory until we request it via the loads() function.

hls_da = rioxarray.open_rasterio(s3_link, chuncks=True)
hls_da

When GeoTIFFS/Cloud Optimized GeoTIFFS are read in, a band coordinate variable is automatically created (see the print out above). In this exercise we will not use that coordinate variable, so we will remove it using the squeeze() function to avoid confusion.

hls_da = hls_da.squeeze('band', drop=True)
hls_da

<xarray.DataArray (y: 3660, x: 3660)>
[13395600 values with dtype=int16]
Coordinates:
  * x            (x) float64 7e+05 7e+05 7e+05 ... 8.097e+05 8.097e+05 8.097e+05
  * y            (y) float64 4.6e+06 4.6e+06 4.6e+06 ... 4.49e+06 4.49e+06
    spatial_ref  int64 0
Attributes:
    _FillValue:    -9999.0
    scale_factor:  0.0001
    add_offset:    0.0
    long_name:     Red

xarray.DataArray

y: 3660
x: 3660

...
```
[13395600 values with dtype=int16]
```
Coordinates: (3)
- x
  (x)
  float64
  7e+05 7e+05 ... 8.097e+05 8.097e+05
```
array([699975., 700005., 700035., ..., 809685., 809715., 809745.])
```
- y
  (y)
  float64
  4.6e+06 4.6e+06 ... 4.49e+06
```
array([4600005., 4599975., 4599945., ..., 4490295., 4490265., 4490235.])
```
- spatial_ref
  ()
  int64
  0
  crs_wkt :
  PROJCS["UTM Zone 13, Northern Hemisphere",GEOGCS["Unknown datum based upon the WGS 84 ellipsoid",DATUM["Not_specified_based_on_WGS_84_spheroid",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]
  semi_major_axis :
  6378137.0
  semi_minor_axis :
  6356752.314245179
  inverse_flattening :
  298.257223563
  reference_ellipsoid_name :
  WGS 84
  longitude_of_prime_meridian :
  0.0
  prime_meridian_name :
  Greenwich
  geographic_crs_name :
  Unknown datum based upon the WGS 84 ellipsoid
  horizontal_datum_name :
  Not_specified_based_on_WGS_84_spheroid
  projected_crs_name :
  UTM Zone 13, Northern Hemisphere
  grid_mapping_name :
  transverse_mercator
  latitude_of_projection_origin :
  0.0
  longitude_of_central_meridian :
  -105.0
  false_easting :
  500000.0
  false_northing :
  0.0
  scale_factor_at_central_meridian :
  0.9996
  spatial_ref :
  PROJCS["UTM Zone 13, Northern Hemisphere",GEOGCS["Unknown datum based upon the WGS 84 ellipsoid",DATUM["Not_specified_based_on_WGS_84_spheroid",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]
  GeoTransform :
  699960.0 30.0 0.0 4600020.0 0.0 -30.0
```
array(0)
```
Attributes: (4)
_FillValue :
-9999.0
scale_factor :
0.0001
add_offset :
0.0
long_name :
Red

Plot the HLS S3 object

hls_da.hvplot.image(x='x', y='y', cmap='fire', rasterize=True, width=800, height=600, colorbar=True)    # colormaps -> https://holoviews.org/user_guide/Colormaps.html

We can print out the data value as a numpy array by typing .values

hls_da.values

array([[-9999, -9999, -9999, ...,  1527,  1440,  1412],
       [-9999, -9999, -9999, ...,  1493,  1476,  1407],
       [-9999, -9999, -9999, ...,  1466,  1438,  1359],
       ...,
       [-9999, -9999, -9999, ...,  1213,  1295,  1159],
       [-9999, -9999, -9999, ...,  1042,  1232,  1185],
       [-9999, -9999, -9999, ...,   954,  1127,  1133]], dtype=int16)

Up to this point, we have not saved anything but metadata into memory. To save or load the data into memory we can call the .load() function.

hls_da_data = hls_da.load()
hls_da_data

<xarray.DataArray (y: 3660, x: 3660)>
array([[-9999, -9999, -9999, ...,  1527,  1440,  1412],
       [-9999, -9999, -9999, ...,  1493,  1476,  1407],
       [-9999, -9999, -9999, ...,  1466,  1438,  1359],
       ...,
       [-9999, -9999, -9999, ...,  1213,  1295,  1159],
       [-9999, -9999, -9999, ...,  1042,  1232,  1185],
       [-9999, -9999, -9999, ...,   954,  1127,  1133]], dtype=int16)
Coordinates:
  * x            (x) float64 7e+05 7e+05 7e+05 ... 8.097e+05 8.097e+05 8.097e+05
  * y            (y) float64 4.6e+06 4.6e+06 4.6e+06 ... 4.49e+06 4.49e+06
    spatial_ref  int64 0
Attributes:
    _FillValue:    -9999.0
    scale_factor:  0.0001
    add_offset:    0.0
    long_name:     Red

xarray.DataArray

y: 3660
x: 3660

-9999 -9999 -9999 -9999 -9999 -9999 ... 1676 1486 1112 954 1127 1133

array([[-9999, -9999, -9999, ...,  1527,  1440,  1412],
       [-9999, -9999, -9999, ...,  1493,  1476,  1407],
       [-9999, -9999, -9999, ...,  1466,  1438,  1359],
       ...,
       [-9999, -9999, -9999, ...,  1213,  1295,  1159],
       [-9999, -9999, -9999, ...,  1042,  1232,  1185],
       [-9999, -9999, -9999, ...,   954,  1127,  1133]], dtype=int16)

Coordinates: (3)
- x
  (x)
  float64
  7e+05 7e+05 ... 8.097e+05 8.097e+05
```
array([699975., 700005., 700035., ..., 809685., 809715., 809745.])
```
- y
  (y)
  float64
  4.6e+06 4.6e+06 ... 4.49e+06
```
array([4600005., 4599975., 4599945., ..., 4490295., 4490265., 4490235.])
```
- spatial_ref
  ()
  int64
  0
  crs_wkt :
  PROJCS["UTM Zone 13, Northern Hemisphere",GEOGCS["Unknown datum based upon the WGS 84 ellipsoid",DATUM["Not_specified_based_on_WGS_84_spheroid",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]
  semi_major_axis :
  6378137.0
  semi_minor_axis :
  6356752.314245179
  inverse_flattening :
  298.257223563
  reference_ellipsoid_name :
  WGS 84
  longitude_of_prime_meridian :
  0.0
  prime_meridian_name :
  Greenwich
  geographic_crs_name :
  Unknown datum based upon the WGS 84 ellipsoid
  horizontal_datum_name :
  Not_specified_based_on_WGS_84_spheroid
  projected_crs_name :
  UTM Zone 13, Northern Hemisphere
  grid_mapping_name :
  transverse_mercator
  latitude_of_projection_origin :
  0.0
  longitude_of_central_meridian :
  -105.0
  false_easting :
  500000.0
  false_northing :
  0.0
  scale_factor_at_central_meridian :
  0.9996
  spatial_ref :
  PROJCS["UTM Zone 13, Northern Hemisphere",GEOGCS["Unknown datum based upon the WGS 84 ellipsoid",DATUM["Not_specified_based_on_WGS_84_spheroid",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]
  GeoTransform :
  699960.0 30.0 0.0 4600020.0 0.0 -30.0
```
array(0)
```
Attributes: (4)
_FillValue :
-9999.0
scale_factor :
0.0001
add_offset :
0.0
long_name :
Red

del(hls_da_data)

Read in HLS as a time series

Now we’ll read in multiple HLS S3 objects as a time series xarray. Let’s print the links list again to see what we’re working with.

s3_links

['s3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021133T172406.v2.0/HLS.L30.T13TGF.2021133T172406.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021133T173859.v2.0/HLS.S30.T13TGF.2021133T173859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021140T173021.v2.0/HLS.L30.T13TGF.2021140T173021.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021140T172859.v2.0/HLS.S30.T13TGF.2021140T172859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021145T172901.v2.0/HLS.S30.T13TGF.2021145T172901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021155T172901.v2.0/HLS.S30.T13TGF.2021155T172901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021156T173029.v2.0/HLS.L30.T13TGF.2021156T173029.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021158T173901.v2.0/HLS.S30.T13TGF.2021158T173901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021163T173909.v2.0/HLS.S30.T13TGF.2021163T173909.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021165T172422.v2.0/HLS.L30.T13TGF.2021165T172422.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021165T172901.v2.0/HLS.S30.T13TGF.2021165T172901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021173T173909.v2.0/HLS.S30.T13TGF.2021173T173909.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021185T172901.v2.0/HLS.S30.T13TGF.2021185T172901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021188T173037.v2.0/HLS.L30.T13TGF.2021188T173037.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021190T172859.v2.0/HLS.S30.T13TGF.2021190T172859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021193T173909.v2.0/HLS.S30.T13TGF.2021193T173909.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021198T173911.v2.0/HLS.S30.T13TGF.2021198T173911.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021200T172859.v2.0/HLS.S30.T13TGF.2021200T172859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021203T173909.v2.0/HLS.S30.T13TGF.2021203T173909.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021204T173042.v2.0/HLS.L30.T13TGF.2021204T173042.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021208T173911.v2.0/HLS.S30.T13TGF.2021208T173911.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021210T172859.v2.0/HLS.S30.T13TGF.2021210T172859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021215T172901.v2.0/HLS.S30.T13TGF.2021215T172901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021218T173911.v2.0/HLS.S30.T13TGF.2021218T173911.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021220T173049.v2.0/HLS.L30.T13TGF.2021220T173049.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021220T172859.v2.0/HLS.S30.T13TGF.2021220T172859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021223T173909.v2.0/HLS.S30.T13TGF.2021223T173909.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021228T173911.v2.0/HLS.S30.T13TGF.2021228T173911.v2.0.B04.tif',
 's3://lp-prod-protected/HLSL30.020/HLS.L30.T13TGF.2021229T172441.v2.0/HLS.L30.T13TGF.2021229T172441.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021230T172859.v2.0/HLS.S30.T13TGF.2021230T172859.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021235T172901.v2.0/HLS.S30.T13TGF.2021235T172901.v2.0.B04.tif',
 's3://lp-prod-protected/HLSS30.020/HLS.S30.T13TGF.2021243T173859.v2.0/HLS.S30.T13TGF.2021243T173859.v2.0.B04.tif']

Currently, the utilities and packages used in Python to read in GeoTIFF/COG file do not recognize associated dates stored in the internal metadata. To account for the dates for each file we must create a time variable and add it as a dimension in our final time series xarray. We’ll create a function that extracts the date from the file link and create an xarray variable with a time array of datetime objects.

def time_index_from_filenames(file_links):
    '''
    Helper function to create a pandas DatetimeIndex
    '''
    return [datetime.strptime(f.split('.')[-5], '%Y%jT%H%M%S') for f in file_links]

time = xr.Variable('time', time_index_from_filenames(s3_links))

We’ll now specify a chunk size to use that matches the internal tiling of HLS files. This will help improve performance.

chunks=dict(band=1, x=512, y=512)

Now, we will create our time series.

hls_ts_da = xr.concat([rioxarray.open_rasterio(f, chunks=chunks).squeeze('band', drop=True) for f in s3_links], dim=time)
hls_ts_da

<xarray.DataArray (time: 32, y: 3660, x: 3660)>
dask.array<concatenate, shape=(32, 3660, 3660), dtype=int16, chunksize=(1, 512, 512), chunktype=numpy.ndarray>
Coordinates:
  * x            (x) float64 7e+05 7e+05 7e+05 ... 8.097e+05 8.097e+05 8.097e+05
  * y            (y) float64 4.6e+06 4.6e+06 4.6e+06 ... 4.49e+06 4.49e+06
    spatial_ref  int64 0
  * time         (time) datetime64[ns] 2021-05-13T17:24:06 ... 2021-08-31T17:...
Attributes:
    _FillValue:    -9999.0
    scale_factor:  0.0001
    add_offset:    0.0
    long_name:     Red

xarray.DataArray

time: 32
y: 3660
x: 3660

dask.array<chunksize=(1, 512, 512), meta=np.ndarray>

	Array	Chunk
Bytes	817.60 MiB	512.00 kiB
Shape	(32, 3660, 3660)	(1, 512, 512)
Count	8224 Tasks	2048 Chunks
Type	int16	numpy.ndarray

Coordinates: (4)
- x
  (x)
  float64
  7e+05 7e+05 ... 8.097e+05 8.097e+05
```
array([699975., 700005., 700035., ..., 809685., 809715., 809745.])
```
- y
  (y)
  float64
  4.6e+06 4.6e+06 ... 4.49e+06
```
array([4600005., 4599975., 4599945., ..., 4490295., 4490265., 4490235.])
```
- spatial_ref
  ()
  int64
  0
  crs_wkt :
  PROJCS["UTM Zone 13, Northern Hemisphere",GEOGCS["Unknown datum based upon the WGS 84 ellipsoid",DATUM["Not_specified_based_on_WGS_84_spheroid",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]
  semi_major_axis :
  6378137.0
  semi_minor_axis :
  6356752.314245179
  inverse_flattening :
  298.257223563
  reference_ellipsoid_name :
  WGS 84
  longitude_of_prime_meridian :
  0.0
  prime_meridian_name :
  Greenwich
  geographic_crs_name :
  Unknown datum based upon the WGS 84 ellipsoid
  horizontal_datum_name :
  Not_specified_based_on_WGS_84_spheroid
  projected_crs_name :
  UTM Zone 13, Northern Hemisphere
  grid_mapping_name :
  transverse_mercator
  latitude_of_projection_origin :
  0.0
  longitude_of_central_meridian :
  -105.0
  false_easting :
  500000.0
  false_northing :
  0.0
  scale_factor_at_central_meridian :
  0.9996
  spatial_ref :
  PROJCS["UTM Zone 13, Northern Hemisphere",GEOGCS["Unknown datum based upon the WGS 84 ellipsoid",DATUM["Not_specified_based_on_WGS_84_spheroid",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]
  GeoTransform :
  699960.0 30.0 0.0 4600020.0 0.0 -30.0
```
array(0)
```
- time
  (time)
  datetime64[ns]
  2021-05-13T17:24:06 ... 2021-08-...
```
array(['2021-05-13T17:24:06.000000000', '2021-05-13T17:38:59.000000000',
       '2021-05-20T17:30:21.000000000', '2021-05-20T17:28:59.000000000',
       '2021-05-25T17:29:01.000000000', '2021-06-04T17:29:01.000000000',
       '2021-06-05T17:30:29.000000000', '2021-06-07T17:39:01.000000000',
       '2021-06-12T17:39:09.000000000', '2021-06-14T17:24:22.000000000',
       '2021-06-14T17:29:01.000000000', '2021-06-22T17:39:09.000000000',
       '2021-07-04T17:29:01.000000000', '2021-07-07T17:30:37.000000000',
       '2021-07-09T17:28:59.000000000', '2021-07-12T17:39:09.000000000',
       '2021-07-17T17:39:11.000000000', '2021-07-19T17:28:59.000000000',
       '2021-07-22T17:39:09.000000000', '2021-07-23T17:30:42.000000000',
       '2021-07-27T17:39:11.000000000', '2021-07-29T17:28:59.000000000',
       '2021-08-03T17:29:01.000000000', '2021-08-06T17:39:11.000000000',
       '2021-08-08T17:30:49.000000000', '2021-08-08T17:28:59.000000000',
       '2021-08-11T17:39:09.000000000', '2021-08-16T17:39:11.000000000',
       '2021-08-17T17:24:41.000000000', '2021-08-18T17:28:59.000000000',
       '2021-08-23T17:29:01.000000000', '2021-08-31T17:38:59.000000000'],
      dtype='datetime64[ns]')
```
Attributes: (4)
_FillValue :
-9999.0
scale_factor :
0.0001
add_offset :
0.0
long_name :
Red

Since we used the chunks parameter while reading the data, the hls_ts_da object is not read into memory yet. To do that we’ll use the load() function.

Now, we’ll see what we have. Use hvplot to plot our time series

hls_ts_da_data = hls_ts_da.load()

hls_ts_da_data.hvplot.image(x='x', y='y', rasterize=True, width=800, height=600, colorbar=True, cmap='fire').opts(clim=(hls_ts_da_data.values.min(), hls_ts_da_data.values.max()))

# Exit our context
rio_env.__exit__()

Concluding Remarks

The above exercise demonstrated how to perform in-region direct S3 bucket access for HLS cloud data assets. HLS cloud data assets are stored as Cloud Optimized GeoTIFFs, a format that has been the benifactor of data discovery and access advancements within the Python ecosystem. Knowing what the data storage format is (e.g., COG, netcdf4, or zarr store) and/or what data access protocol you’re using is critical in determining what Python data access method you will use. For COG data, rioxarray package is often prefered due to is ability to bring the geospatial data format into an xarray object. For netcdf4 files, the standard xarray package incombination with s3fs allow users to perform in-region direct access reads into an xarray object. Finally, if you are using OPeNDAP to connect to data, specialized packages like pydap have been integrated into xarray for streamline access directly to an xarray object.

05. Direct S3 Data Access with rioxarray

Timing

Summary

Direct S3 Access

Benefits

Considerations

What you will learn from this tutorial

Import Required Packages

Configure Local Environment and Get Temporary Credentials

Insert the credentials into our `boto3` session and configure our `rasterio` environment for data access

Read in S3 Links

List the available files in the data directory

Read in a single HLS file

Plot the HLS S3 object

Read in HLS as a time series

Concluding Remarks

Resources

05. Direct S3 Data Access with rioxarray

Timing

Summary

Direct S3 Access

Benefits

Considerations

What you will learn from this tutorial

Import Required Packages

Configure Local Environment and Get Temporary Credentials

Insert the credentials into our boto3 session and configure our rasterio environment for data access

Read in S3 Links

List the available files in the data directory

Read in a single HLS file

Plot the HLS S3 object

Read in HLS as a time series

Concluding Remarks

Resources

Insert the credentials into our `boto3` session and configure our `rasterio` environment for data access