Integrating external data from a CSV f393e43e738d4ba0bbf5b2127d01b6b7


It is often useful to combine external data (e.g. from a CSV or other dataset) with data loaded from Digital Earth Australia. For example, we may want to combine data from a tide or river guage with satellite data to determine water levels at the exact moment each satellite observation was made. This can allow us to manipulate and extract satellite data and obtain additional insights using data from our external source.


This example notebook loads in a time series of external tide modelling data from a CSV, and combines it with satellite data loaded from Digital Earth Australia. This workflow could be applied to any external time series dataset (e.g. river guages, tide guagues, rainfall measurements etc). The notebook demonstrates how to:

  1. Load a time series of Landsat 8 data

  2. Load in external time series data from a CSV

  3. Convert this data to an xarray.Dataset and link it to each satellite observation by interpolating values at each satellite timestep

  4. Add this new data as a variable in our satellite dataset, and use this to filter satellite imagery to high and low tide imagery

  5. Demonstrate how to swap dimensions between time and the new tide_height variable

Getting started

To run this analysis, run all the cells in the notebook, starting with the “Load packages” cell.

Load packages

%matplotlib inline

import datacube
import pandas as pd
import matplotlib.pyplot as plt


Connect to the datacube

dc = datacube.Datacube(app='Integrating_external_data')

Loading satellite data

First we load in a year of Landsat 8 data. We will use Moreton Bay in Queensland for this demonstration, as we have a CSV of tide height data for this location that we wish to combine with satellite data. We load a single band nbart_nir which clearly differentiates between water (low values) and land (higher values). This will let us verify that we can use external tide data to identify low and high tide satellite observations.

# Set up a location for the analysis
query = {
    'x': (153.38, 153.42),
    'y': (-27.63, -27.67),
    'time': ('2015-01-01', '2015-12-31'),
    'measurements': ['nbart_nir'],
    'output_crs': 'EPSG:3577',
    'resolution': (-30, 30)

# Load Landsat 8 data
ds = dc.load(product='ga_ls8c_ard_3', group_by='solar_day', **query)


Dimensions:    (time: 23, x: 154, y: 170)
  * time       (time) datetime64[ns] 2015-01-11T23:42:10.081625 ... 2015-12-29T23:42:14.365276
  * y          (y) float64 -3.171e+06 -3.172e+06 ... -3.177e+06 -3.177e+06
  * x          (x) float64 2.074e+06 2.074e+06 2.074e+06 ... 2.078e+06 2.078e+06
Data variables:
    nbart_nir  (time, y, x) int16 6511 6509 6451 6434 ... 1976 1967 2150 2254
    crs:      EPSG:3577

Integrating external data

Load in a CSV of external data with timestamps

In the code below, we aim to take a CSV file of external data (in this case, half-hourly tide heights for a location in Moreton Bay, Queensland for a five year period between 2014 and 2018 generated using the OTPS TPXO8 tidal model), and link this data back to our satellite data timeseries.

We can load the existing tide height data using the pandas module which we imported earlier. The data has a column of time values, which we will set as the index column (roughly equivelent to a dimension in xarray).

tide_data = pd.read_csv('../Supplementary_data/Integrating_external_data/moretonbay_-27.55_153.35_2014-2018_tides.csv',


2014-01-01 00:00:00 1.179
2014-01-01 00:30:00 1.012
2014-01-01 01:00:00 0.805
2014-01-01 01:30:00 0.569
2014-01-01 02:00:00 0.316

Filtering by external data

Now that we have a new variable tide_height in our dataset, we can use xarray indexing methods to manipulate our data using tide heights (e.g. filter by tide to select low or high tide images):

# Select a subset of low tide images (tide less than -0.6 m)
low_tide_ds = ds.sel(time = ds.tide_height < -0.60)

# Select a subset of high tide images (tide greater than 0.9 m)
high_tide_ds = ds.sel(time = ds.tide_height > 0.9)

We can plot an image from these subsets to verify that the satellite images were observed at low tide:

plt.title("Low tide")


And at high tide. Note that many tidal flat areas in the image above (blue-green) now appear inundated by water (dark blue/purple):

plt.title("High tide")


Swapping dimensions based on external data

By default, xarray uses time as one of the main dimensions in the dataset (in addition to x and y). Now that we have a new tide_height variable, we can change this to be an actual dimension in the dataset in place of time. This enables additional more advanced operations, such as calculating rolling statistics or aggregating by tide_heights.

In the example below, you can see that the dataset now has three dimensions (tide_height, x and y). The dimension time is no longer a dimension in the dataset.

print(ds.swap_dims({'time': 'tide_height'}))

Dimensions:      (tide_height: 23, x: 154, y: 170)
    time         (tide_height) datetime64[ns] 2015-01-11T23:42:10.081625 ... 2015-12-29T23:42:14.365276
  * y            (y) float64 -3.171e+06 -3.172e+06 ... -3.177e+06 -3.177e+06
  * x            (x) float64 2.074e+06 2.074e+06 ... 2.078e+06 2.078e+06
  * tide_height  (tide_height) float64 0.02013 -0.2608 -0.2658 ... 1.007 0.4098
Data variables:
    nbart_nir    (tide_height, y, x) int16 6511 6509 6451 ... 1967 2150 2254
    crs:      EPSG:3577

Additional information

License: The code in this notebook is licensed under the Apache License, Version 2.0. Digital Earth Australia data is licensed under the Creative Commons by Attribution 4.0 license.

Contact: If you need assistance, please post a question on the Open Data Cube Slack channel or on the GIS Stack Exchange using the open-data-cube tag (you can view previously asked questions here). If you would like to report an issue with this notebook, you can file one on Github.

Last modified: December 2019

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Tags: sandbox compatible, NCI compatible, time series analysis, landsat 8, external data, interpolation, indexing, swapping dimensions, csv, intertidal, tide modelling