DEA Tidal Composites

Version:: 1.0.0
Type:: Derivative, Raster
Resolution:: 10 m
Coverage:: 2016 to 2023
Data updates:: Yearly frequency, Ongoing

Overview

About

Digital Earth Australia (DEA) Tidal Composites are cloud-free imagery mosaics of Australia’s coasts, estuaries and reefs at low and high tide respectively. Calculated using a geometric median of Sentinel-2 imagery from the highest and lowest 15 % of observed tides, DEA Tidal Composites deliver an annually updated snapshot of high and low tide Australian coastal environments.

Streaming data from AWS is strongly recommended

DEA Tidal Composite data is extremely large with files up to 15 GB in size. We strongly recommend streaming rather than downloading the data. Please see the instructions on the Access tab: How to stream data from AWS

Access the data

For help accessing the data, see the Access tab.

See it on a map

DEA Maps

Explore data availability

DEA Explorer

Web Map Service (WMS)

Web services

AWS data

Data sources

How to stream data from AWS

Data sources

GitHub repository

Code examples

Key specifications

For more specifications, see the Specifications tab.

Technical name	Geoscience Australia Sentinel-2 Tidal Composites Calendar Year Collection 3
Bands	25 bands: low_coastal_aerosol, low_blue, and more
DOI	10.26186/150381
Currency	See currency and the latest and next update dates
Parent products	Sentinel-2A Analysis Ready Data, Sentinel-2B Analysis Ready Data, Sentinel-2C Analysis Ready Data
Collection	Geoscience Australia Sentinel-2 Collection 3
Licence	Creative Commons Attribution 4.0 International Licence

Cite this product

Data citation

Newey, V., Bishop-Taylor, R., Phillips, C., Sagar, S. (2025). Digital Earth Australia Tidal Composites. Geoscience Australia, Canberra.

Paper citation

Sagar, S., Phillips, C., Bala, B., Roberts, D., Lymburner, L., 2018. Generating continental scale pixel-based surface reflectance composites in coastal regions with the use of a multi-resolution tidal model. Remote Sensing. 10, 480. https://doi.org/10.3390/rs10030480

Publications

Sagar, S., Phillips, C., Bala, B., Roberts, D., & Lymburner, L. (2018). Generating Continental Scale Pixel-Based Surface Reflectance Composites in Coastal Regions with the Use of a Multi-Resolution Tidal Model. Remote Sensing, 10, 480. https://doi.org/10.3390/rs10030480

Description

Background

Intertidal zones are coastal environments that are exposed to both air and water at different times due to the cycle of low and high tides. These zones can include sandy beaches, tidal flats, rocky shores, and reefs. Many of them are critical coastal habitats and ecosystems which support a wide range of species and ecosystem services. Increasingly, these dynamic environments are faced with threats such as land reclamation, coastal erosion, and rising sea levels.

The ever-changing nature of the tides makes it difficult to systematically capture consistent imagery of an intertidal zone, particularly across large regions and in remote areas of the country. This is why Geomedian statistical techniques where used. These are robust techniques which combine tide-attributed time-series satellite imagery to produce representative and artefact-free imagery ‘composites’ of Australia’s coastal high- and low tide environments.

This product provides a suite of cloud-free composite Sentinel-2 satellite datasets that enable imaging of Australian coastal intertidal zones at both high and low tide. Using a geometric median (geomedian), the highest and lowest 15 % of satellite-observed tide heights from the Digital Earth Australia (DEA) Sentinel-2 imagery archive are combined to deliver annual snapshots of Australian coastal high and low tide environments.

Sentinel-2 satellite images are tidally attributed though pairing with pixel-based tidal modelling, generated from a selected ensemble of the best performing global tide models under local conditions. The ensemble tidal modelling approach (see below) was implemented to account for the varying performance and biases of existing global ocean tide models across the complex tidal regimes and coastal regions of Australia. Tidal attribution allows the imagery archive to be sorted by tide height rather than date, enabling you to selectively view the intertidal zone at any stage of the tidal cycle.

DEA Tidal Composites is an annually updated data suite, generated from rolling 3-year epochs, at a 10 m spatial resolution. It is spatially and temporally aligned to the DEA Intertidal product suite.

DEA Tidal Composites includes both low- and high-tide imagery products and their associated quality assurance layers. The low tide and high tide layers represent composites of the synthetic geomedian surface reflectance from Sentinel-2A, -2B, and -2C analysis-ready data streams. The geomedian calculation maintains the spectral relationships between bands (Roberts et al., 2017), ensuring that the DEA Tidal Composites product delivers robust and valid surface reflectance spectra suitable for uses such as habitat mapping (Li et al., 2012) and delivers a cloud-free and noise-reduced visualisation of the shallow water and intertidal coastal regions of Australia (Sagar et al., 2018). Quality assurance layers are provided for the low tide and high tide datasets. These include the tide-height thresholds above and below which associated images were included in the compositing process and they also include the count of clear input images that contributed to each pixel in the composites.

Applications

Here are some of the ways this data product can be used.

Mapping cover types within the intertidal zone.
Visualising the full observed extent of the tidal range around the Australian continental coastline.
Monitoring for change in Australian coastal environments.

Technical information

Features

This product is a 25-band mosaic, consistent with Sentinel-2. It is continental (coastal) in coverage and includes geomedian surface reflectance along with pixel-level metadata for each of the high and low tide mosaics.

The file naming convention is as follows:

{Organisation}_{Platform}_{Product}_{Reporting period}_{Collection}_{Tile reference}_{Data date}--{Data period}_{Product status}_{Band name}.{File extension}

Datasets

Annual files for each of the product bands are available in DEA’s Amazon S3 bucket in two formats: 32 km² tiles and continental mosaics. Multi-band continental imagery composites are also available. For access and usage information, see the Access tab.

32 km² grid tiles are available as downloadable GeoTIFF files, for example:

ga_s2_tidal_composites_cyear_3_x080y125_2022--P1Y_final_low-red-edge-3.tif

Single-band annual continental data mosaics are delivered to support access and navigability of DEA Tidal Composites data in geospatial information system (GIS) environments. These datasets, delivered in cloud-optimised GeoTIFF (COG) format, are recommended for fast and efficient data streaming of single-band layers of the DEA Tidal Composites product. Here’s an example of the COG file naming convention:

ga_s2_tidal_composites_cyear_3_2022_low-red-edge-3.tif

Multi-band annual continental data mosaics are provided for fast and efficient streaming of true colour (red/green/blue) and false colour (green/SWIR/NIR) imagery composites. Delivered in virtual raster format (VRT), these files stream and compile multiple single-band COG datasets and are used to simplify imagery exploration in GIS environments. Here’s an example of the VRT file naming convention:

ga_s2_tidal_composites_cyear_3_2022_vrt-low-truecolour.vrt

Ensemble tide modelling

The Ensemble Tidal Modelling approach was implemented to account for the varying performance and biases of existing global ocean tide models across the complex tidal regimes and coastal regions of Australia. The ensemble process utilises ancillary data to select and weight tidal models at any given coastal location based on how well each model correlates with local satellite-observed patterns of tidal inundation and water levels measured by satellite altimetry. A single ensemble tidal output was generated by combining the top three locally optimal models and then this was used for all downstream product workflows.

Ensemble tide modelling was implemented in the eo-tides Python package which integrates satellite Earth observation data with tide modelling, leveraging tide modelling functionality from the pyTMD package. The ensemble was based on 10 commonly-used global ocean tidal models:

Empirical Ocean Tide Model (EOT20; Hart-Davis et al., 2021)
Finite Element Solution tide models (FES2012, FES2014, FES2022; Carrère et al., 2012; Lyard et al., 2021; Carrère et al., 2022)
TOPEX/POSEIDON global tide models (TPXO8, TPXO9, TPXO10; Egbert and Erofeeva., 2002, 2010)
Global Ocean Tide models (GOT4.10, GOT5.5, GOT5.6; Ray, 2013, Padman et al., 2018)

Product layers

See the attributes of these layers in the Specifications tab.

Low tide composites (multiple ‘low_’ bands)

The 11 bands whose names start with low_ are delivered in the spectral resolution of the Sentinel-2 band set. Each band represents synthetic data, derived from the geomedian calculation of the input Sentinel-2 satellite data from the lowest 15 % of satellite-observed tide heights during each 3-year analysis epoch. Maintenance of the spectral relationships between geomedian bands ensures they can be combined to produce low tide imagery and analysis in coastal environments.

High tide composites (multiple ‘high_’ bands)

The 11 bands whose names start with high_ are delivered in the spectral resolution of the Sentinel-2 band set. Each band represents synthetic data, derived from the geomedian calculation of the input Sentinel-2 satellite data from the highest 15 % of satellite-observed tide heights during each 3-year analysis epoch. Maintenance of the spectral relationships between geomedian bands ensures they can be combined to produce high-tide imagery and analysis in coastal environments.

Quality assurance: Low threshold (qa_low_threshold)

A pixel-based quality assurance layer for identifying the maximum tide height included in the low tide composite. Usually, this value corresponds to the lowest 15th percentile satellite-observed tide height. Pixels with less than 20 clear observations in this 15th percentile range are gapfilled up to a count of 20 observations using the next lowest satellite-observed tide height observations. This is done to ensure sufficient data density to produce a clear composite image. When a pixel is gapfilled, the highest gapfilled tide height is reported for that pixel in this ‘low threshold’ layer. This ‘low threshold’ layer is only valid for marine and coastal pixels.

Quality assurance: High threshold (qa_high_threshold)

A pixel-based quality assurance layer for identifying the minimum tide height included in the high-tide composite. Usually, this value corresponds to the highest 15th percentile satellite-observed tide height. Pixels with less than 20 clear observations in this 15th percentile range are gapfilled up to a count of 20 observations using the next highest satellite-observed tide height observations. This is done to ensure sufficient data density to produce a clear composite image. When a pixel is gapfilled, the lowest gapfilled tide height is reported for that pixel in this ‘high threshold’ layer. This ‘high threshold’ layer is only valid for marine and coastal pixels.

Quality assurance: Count clear (qa_count_clear)

This pixel-based quality assurance layer represents the number of clear observations per pixel that are used in both the high and low tide composites. This layer typically identifies 15 % of all observations. When the observation count in 15 % of all observations is less than 20, the nearest tide-height observations (if available) are used to gapfill up to a count of 20 clear observations.

Processing steps

1. Load and pre-process data

Load analysis ready satellite data from Sentinel-2A, -2B, and -2C for the epoch of interest.
Remove sunglinted pixels by masking out pixels with glint angles of less than 20 degrees.
Proceed with tide modelling and geomedian calculation only if the full time-series of input satellite images has 50 or more observations.

2. Calculate high and low tide geomedian composites

Model tide heights for the spatial extent and timesteps of the loaded satellite data array.
Attribute tide heights to the valid satellite observations.
Rank all observations by ascending tide height.
Select the observations in the top and bottom 15 % of satellite-observed tide heights by identifying their associated tide height rankings.
If the number of observations in the top and bottom 15 % is less than 20, gapfill up to the count of 20 observations by taking the next highest or lowest tide heights from the full stack of satellite observations respectively.
Calculate a geomedian on each subset and count the contributing number of clear observations.

Software

This work was enabled by a range of Python libraries and packages whose code repositories include:

DEA Intertidal — DEA Intertidal product generation workflows.
eo-tides — Tools for integrating satellite Earth observations with tide modelling.
DEA Tools — Earth observation data manipulation tools.
PyTMD — Python-based tidal prediction software.
odc-algo — Algorithms for use with Open Data Cube workflows.

References

Carrère L., F. Lyard, M. Cancet, A. Guillot, L. Roblou, 2012. FES2012: A new global tidal model taking advantage of nearly 20 years of altimetry, Proceedings of meeting “20 Years of Altimetry”, Venice 2012

Carrère L., F. Lyard, M. Cancet, D. Allain, M. Dabat, E. Fouchet, E. Sahuc, Y. Faugere, G. Dibarboure, N. Picot, 2022. A new barotropic tide model for global ocean: FES2022, 2022 Ocean Surface Topography Science Team Meeting”, Venice 2022

Egbert, G. D., & Erofeeva, S. Y. (2002). Efficient Inverse Modeling of Barotropic Ocean Tides. Journal of Atmospheric and Oceanic Technology, 19(2), 183–204. https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2

Egbert, G.D., Erofeeva, S.Y., 2010. The OSU TOPEX/Poseiden Global Inverse Solution TPXO [WWW Document]. TPXO8-Atlas Version 10. URL http://volkov.oce.orst.edu/tides/global.html (accessed 2.15.16).

Hart-Davis, M.G., Piccioni, G., Dettmering, D., Schwatke, C., Passaro, M., Seitz, F., 2021. EOT20: a global ocean tide model from multi-mission satellite altimetry. Earth System Science Data 13, 3869–3884.

Li, F., Jupp, D. L. B., Thankappan, M., Lymburner, L., Mueller, N., Lewis, A., & Held, A. (2012). A physics-based atmospheric and BRDF correction for Landsat data over mountainous terrain. Remote Sensing of Environment, 124, 756–770. https://doi.org/10.1016/j.rse.2012.06.018

Lyard, F.H., Allain, D.J., Cancet, M., Carrère, L., Picot, N., 2021. FES2014 global ocean tide atlas: design and performance. Ocean Science 17, 615–649.

Padman, L., Siegfried, M.R., Fricker, H.A., 2018. Ocean Tide Influences on the Antarctic and Greenland Ice Sheets, Reviews of Geophysics, 56, 142-184.

Ray, R. D., 2013. Precise comparisons of bottom-pressure and altimetric ocean tides. Journal of Geophysical Research: Oceans, 118(9), 4570–4584.

Roberts, D., Mueller, N., & Mcintyre, A. (2017). High-Dimensional Pixel Composites From Earth Observation Time Series. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6254–6264. https://doi.org/10.1109/TGRS.2017.2723896

Rubel, F., & Kottek, M. (2010). Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorologische Zeitschrift, 19, 135–141. https://doi.org/10.1127/0941-2948/2010/0430

Sagar, S., Phillips, C., Bala, B., Roberts, D., & Lymburner, L. (2018). Generating Continental Scale Pixel-Based Surface Reflectance Composites in Coastal Regions with the Use of a Multi-Resolution Tidal Model. Remote Sensing, 10, 480. https://doi.org/10.3390/rs10030480

Sagar, S., Roberts, D., Bala, B., & Lymburner, L. (2017). Extracting the intertidal extent and topography of the Australian coastline from a 28year time series of Landsat observations. Remote Sensing of Environment, 195, 153–169. https://doi.org/10.1016/j.rse.2017.04.009

Quality

Limitations

Natural biases exist in the imaging of coastal tide ranges by orbiting spectral satellites such as Sentinel-2. Attempts to quantify these offsets are reported in the supporting DEA Intertidal Tide Attribute layers. These biases mean that while DEA Tidal Composites represent the upper and lower 15 % of all satellite observations of the local tide range, they do not represent the upper and lower 15 % of the full astronomical tide range. This means DEA Tidal Composites may not capture the extreme ends of the local tide range at some locations.
These biases may extend to seasonal and diurnal effects in the imagery, where the low- and high-tide imagery from which the composites are derived may not be evenly distributed across seasons in different geographic regions. We recommend users to refer to the accompanying graphs of satellite observations and their tidal distribution provided through the DEA Maps platform to assess potential impacts.
DEA Tidal Composites is delivered as an annually updated dataset, generated from rolling 3-year epochs of input data. In some locations, particularly those affected by regular cloud cover, the number of clear images from the upper and lower parts of the observed tide range may be few, resulting in artefacts in the imagery.
To maximise data density for clear geomedian outputs, we use a minimum number of 20 input observations. For pixels where less than 20 observations were available during the epoch, the resulting spectral geomedian values should be considered unreliable. The qa_count_clear layer identifies these pixel locations.
Tidal modelling is used to subset and select imagery to represent the tidal stages of oceanic and coastal intertidal regions. The geomedian values for terrestrial pixels produced as part of this subsetting process will vary based on these tidal subsets, but are not reflective of any specific terrestrial environment constraints.
Data input into the compositing process has been filtered by sun angle and satellite acquisition geometry to remove observations with a high likelihood of sun glint. However, some residual glint may still occur, particular in the north-eastern regions of the country, reducing the quality of the geomedian imagery.
Offshore shallow water regions such as the Great Barrier Reef can be impacted by a lower quality and number of ARD observations in the Sentinel-2 archive. These regions have been excluded from the current release of the DEA Tidal Composites product, resulting in notable gaps particularly across south-eastern extents of the reef. The impact and extent of this issue varies across each annual composite and is under investigation for future iterations of the product.

Accuracy

Tide modelling

The ensemble tide modelling for this product utilised the same input data, models, and temporal epochs as is used in the generation of the DEA Intertidal product suite. Therefore the DEA Intertidal Elevation Uncertainty dataset is useful to evaluate both a) highly dynamic coastal environments where tide modelling is less certain and b) geographic influences on tide modelling uncertainty. The latter exist in places where underlying inputs to tide modelling (such as local bathymetry) results in lower certainty in the global tide model. An outcome of this type of uncertainty is that the absolute modelled tide height values may be incorrect. For DEA Tidal Composites, this may result in a slight offset in absolute tide heights at some locations but is unlikely to alter the images that are selected in the upper and lower 15th percentile ranges of tide heights as the overall trends in tidal changes are still maintained by the tide modelling.

Geomedian calculation

Accuracies and limitations related to geomedian compositing of observations are discussed in Roberts et al. (2017).

Quality assurance

Data pre-processing

Only high-quality data was included as an input into DEA Tidal Composites. Data pre-processing was conducted, involving identifying and removing pixels that are impacted by clouds, cloud-shadow, and sunglint.

Code testing

Code used to generate DEA Tidal Composites is run against automated integration tests to ensure that data quality is maintained after the code has been updated. These tests verify that the entire product generation workflow is performing as expected and is a way that we track changes in product accuracy over time.

Specifications

Product ID

The Product ID is ga_s2_tidal_composites_cyear_3. This ID is used to load data from the Open Data Cube (ODC), for example dc.load(product="ga_s2_tidal_composites_cyear_3", ...)

Bands

Bands are distinct layers of data within a product that can be loaded using the Open Data Cube (on the DEA Sandbox or NCI) or DEA’s STAC API. Note that the Coordinate Reference System (CRS) of these bands is GDA94 / Australian Albers (EPSG:3577).

	Type	Units	Resolution	No-data	Aliases	Description
low_coastal_aerosol	int16	-	10 m	-999	low_band01	Low tide surface reflectance.
low_blue	int16	-	10 m	-999	low_band02	Low tide surface reflectance.
low_green	int16	-	10 m	-999	low_band03	Low tide surface reflectance.
low_red	int16	-	10 m	-999	low_band04	Low tide surface reflectance.
low_red_edge_1	int16	-	10 m	-999	low_band05	Low tide surface reflectance.
low_red_edge_2	int16	-	10 m	-999	low_band06	Low tide surface reflectance.
low_red_edge_3	int16	-	10 m	-999	low_band07	Low tide surface reflectance.
low_nir_1	int16	-	10 m	-999	low_band08	Low tide surface reflectance.
low_nir_2	int16	-	10 m	-999	low_band8a	Low tide surface reflectance.
low_swir_2	int16	-	10 m	-999	low_band11	Low tide surface reflectance.
low_swir_3	int16	-	10 m	-999	low_band12	Low tide surface reflectance.
high_coastal_aerosol	int16	-	10 m	-999	high_band01	High tide surface reflectance.
high_blue	int16	-	10 m	-999	high_band02	High tide surface reflectance.
high_green	int16	-	10 m	-999	high_band03	High tide surface reflectance.
high_red	int16	-	10 m	-999	high_band04	High tide surface reflectance.
high_red_edge_1	int16	-	10 m	-999	high_band05	High tide surface reflectance.
high_red_edge_2	int16	-	10 m	-999	high_band06	High tide surface reflectance.
high_red_edge_3	int16	-	10 m	-999	high_band07	High tide surface reflectance.
high_nir_1	int16	-	10 m	-999	high_band08	High tide surface reflectance.
high_nir_2	int16	-	10 m	-999	high_band8a	High tide surface reflectance.
high_swir_2	int16	-	10 m	-999	high_band11	High tide surface reflectance.
high_swir_3	int16	-	10 m	-999	high_band12	High tide surface reflectance.
qa_low_threshold	float32	Metres above MSL	10 m	nan	low_threshold	Maximum tide height threshold used to identify low tide satellite observations.
qa_high_threshold	float32	Metres above MSL	10 m	nan	high_threshold	Minimum tide height threshold used to identify high tide satellite observations.
qa_count_clear	int16	-	10 m	-999	count_clear	The count of clear and valid observations per pixel.

Product information

This metadata provides general information about the product.

Product ID	ga_s2_tidal_composites_cyear_3	Used to load data from the Open Data Cube.
Short name	DEA Tidal Composites	The name that is commonly used to refer to the product.
Technical name	Geoscience Australia Sentinel-2 Tidal Composites Calendar Year Collection 3	The full technical name that refers to the product and its specific provider, sensors, and collection.
Version	1.0.0	The version number of the product. See the History tab.
Lineage type	Derivative	Derivative products are derived from other products.
Spatial type	Raster	Raster data consists of a grid of pixels.
Spatial resolution	10 m	The size of the pixels in the raster.
Temporal coverage	2016 to 2023	The time span for which data is available.
Coordinate Reference System (CRS)	GDA94 / Australian Albers (EPSG:3577)	The method of mapping spatial data to the Earth’s surface.
Update frequency	Yearly	The expected frequency of data updates. Also called ‘Temporal resolution’.
Update activity	Ongoing	The activity status of data updates.
Currency	See the Currency Report	Currency is a measure based on data publishing and update frequency.
Latest and next update dates	See the Currency Report	See Table B of the report.
DOI	10.26186/150381	The Digital Object Identifier.
Catalogue ID	150381	The Data and Publications catalogue (eCat) ID.
Licence	Creative Commons Attribution 4.0 International Licence	See the Credits tab.

Product categorisation

This metadata describes how the product relates to other products.

Parent products	Sentinel-2A Analysis Ready Data, Sentinel-2B Analysis Ready Data, Sentinel-2C Analysis Ready Data
Collection	Geoscience Australia Sentinel-2 Collection 3
Tags	geoscience_australia_sentinel_2_collection_3, marine_and_coastal, coast, intertidal, coastal_change, geomedian, tidal_modelling, composite, high_and_low_tide_imagery, high_and_low_tide_composites, hltc

Access

Access the data

DEA Maps	See it on a map	Learn how to use DEA Maps.
DEA Explorer	Explore data availability	Learn how to use the DEA Explorer.
Web services	Web Map Service (WMS)	Learn how to use DEA’s web services.
Data sources	AWS data
Data sources	How to stream data from AWS
Code examples	GitHub repository

Streaming data from AWS is strongly recommended

DEA Tidal Composite data is extremely large with files up to 15 GB in size. We strongly recommend streaming rather than downloading the data. Please see the instructions below: How to stream data from AWS

How to stream data from AWS (Recommended)

The easiest way to access DEA Tidal Composite data is via our continental-scale cloud-optimised GeoTIFF mosaics (COGs). The COG file format is a type of GeoTIFF raster file (.tif) that allows you to quickly and efficiently ‘stream’ data directly from the Amazon S3 cloud without having to download files to your computer. This allows you to rapidly access data from the entire Australian continent without having to download large files.

See the following guides for how to access the data depending on your use case.

Stream continental COG mosaics in QGIS

Open the DEA Tidal Composites continental_mosaics directory in DEA’s Amazon S3 bucket.
Enter a directory of a particular year, e.g. 2018--P1Y
Right click one of the .tif files representing a particular band e.g. ga_s2_tidal_composites_cyear_3_2018_low-blue.tif > click Copy link address.
Open QGIS on your computer.
In QGIS, click Layer > Add Layer > Add Raster Layer.
1. For Source Type protocol, select HTTP(S) or cloud or otherwise.
2. For Type, select HTTP/HTTPS/FTP.
3. In the URI field, paste the link to the band that you copied to the clipboard.
Click Add to start streaming the layer. Data should appear on the map after a few seconds (or after several minutes on slow internet connections).

Learn more about streaming cloud datasets: How to read a Cloud Optimized GeoTIFF with QGIS.

Stream multi-band continental COG mosaics in QGIS

To make it easier to visualise DEA Tidal Composite bands in true and false colour, we provide several Virtual Raster files (.vrt) that can be loaded into QGIS. These Virtual Rasters stream data from the cloud automatically, avoiding the need to download multiple files. They also provide instructions for combining, streaming, and viewing multiple COG files simultaneously.

Open the DEA Tidal Composites continental_mosaics directory in DEA’s Amazon S3 bucket.
Enter a directory of a particular year, e.g. 2018--P1Y
Click to download the .vrt file of interest, e.g. ga_s2_tidal_composites_cyear_3_2018_vrt-low-truecolour.vrt
On your computer, drag the downloaded .vrt file into your QGIS project.
The multi-band dataset will stream seamlessly into your QGIS project.

Stream continental COG mosaics in Esri ArcPro

To connect Esri ArcPro to DEA’s Amazon S3 bucket, follow Esri’s tutorial: Connect to a cloud store. Use the following configurations for your cloud storage connection and leave the other fields blank:

Connection File Name — DEA data
Service Provider — AMAZON
Bucket Name (Container) — dea-public-data
Folder — derivative
Region (Environment) — Asia Pacific (Sydney)
Service Endpoint — s3.ap-southeast-2.amazonaws.com
Provider Options
- ARC_DEEP_CRAWL=NO
- AWS_NO_SIGN_REQUEST=TRUE

Note: When adding COG files to ArcPro, select No when asked whether to build statistics for the layer

If you encounter difficulty with any of these instructions, or with the COG files themselves, please contact us at earth.observation@ga.gov.au

History

Version history

Versions are numbered using the Semantic Versioning scheme (Major.Minor.Patch). Note that this list may include name changes and predecessor products.

v1.0.0	-	Current version
v2.0.0	of	DEA High and Low Tide Imagery (Landsat)

Changelog

DEA Tidal Composites 1.0.0

In May 2025, DEA Tidal Composites version 1.0.0, was released. This release involved deprecating the DEA High and Low Tide Composites product. Data from this legacy product will still be accessible; however, users are advised to transition to using DEA Tidal Composites instead, where possible.

The DEA Tidal Composites product suite extends the concepts developed in the Landsat-derived DEA High and Low Tide Composites product but instead uses 10 m Sentinel-2 data and tide modelling is completed at the pixel scale. Additionally, a rolling 3-year epoch is used to calculate the product on an annual time scale.

This shift to a more dynamic product suite is achieved through a pixel-based tide-modelling algorithm, improved data resolution and density through the use of Sentinel-2 data, and pre-processing improvements that include cloud, cloud-shadow, and glint-angle filtering to remove contaminated pixels.

Credits

Acknowledgments

The authors would like to sincerely thank users of the DEA High and Low Tide Composites data product for their feedback and insights which helped lead to the development of DEA Tidal Composites.

License and copyright

Released under Creative Commons Attribution 4.0 International Licence.