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The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels. For these ARD products, this value comes from the Fmask algorithm. from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels. For these ARD products, this value comes from the Fmask algorithm. to (exclusive):

Time when dataset was created (processed) From

Time when dataset was created (processed) to (exclusive)

The raw CRS string as it appears in metadata:

One of - final|interim|nrt (near real time):

ID of a dataset type:

Ground sampling distance of the sensor’s best resolution band in metres; represents the size (or spatial resolution) of one pixel. from:

Ground sampling distance of the sensor’s best resolution band in metres; represents the size (or spatial resolution) of one pixel. to (exclusive):

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north from:

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north to (exclusive):

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon. from:

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon. to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm to (exclusive):

File format (GeoTiff, NetCDF):

GQA Circular error probable (90%) from:

GQA Circular error probable (90%) to (exclusive):

Mean of the absolute values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the absolute values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the absolute values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the absolute values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the absolute values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the absolute values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Absolute value of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Absolute value of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Absolute value of the total GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Absolute value of the total GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Absolute value of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Absolute value of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Circular error probable (90%) of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Circular error probable (90%) of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Dataset UUID:

User who indexed the dataset:

When dataset was indexed:

Label:

A Landsat Collection 1 Level-1 product identifier including the Collection processing levels, processing date, collection number, and collection tier category (e.g. ‘LE07_L1TP_098084_20210307_20210308_02_RT’) :

A Landsat scene ID including WRS path and row, year and day, ground station identifier and archive version number (e.g. ‘LC80900852021066LGN00’) :

Latitude range from:

Latitude range to (exclusive):

Longitude range from:

Longitude range to (exclusive):

Full metadata document:

Metadata type name of dataset:

ID of a metadata type:

Spatial reference code from the provider. For Landsat region_code is a scene path row: '{:03d}{:03d}.format(path,row)' For Sentinel it is MGRS code. In general it is a unique string identifier that datasets covering roughly the same spatial region share. :

Acquisition time range From

Acquisition time range to (exclusive)

Dataset URI:

Add field

44 results

Time (local)	Label
2022-03-01 09:27:29	ga_ls9c_ard_3-2-1_092071_2022-02-28_final.stac-item
2022-06-21 09:27:14	ga_ls9c_ard_3-2-1_092071_2022-06-20_final.stac-item
2022-07-07 09:27:19	ga_ls9c_ard_3-2-1_092071_2022-07-06_final.stac-item
2022-07-23 09:27:24	ga_ls9c_ard_3-2-1_092071_2022-07-22_final.stac-item
2022-08-08 09:27:36	ga_ls9c_ard_3-2-1_092071_2022-08-07_final.stac-item
2022-08-24 09:27:42	ga_ls9c_ard_3-2-1_092071_2022-08-23_final.stac-item
2022-09-09 09:27:41	ga_ls9c_ard_3-2-1_092071_2022-09-08_final.stac-item
2022-09-25 09:27:49	ga_ls9c_ard_3-2-1_092071_2022-09-24_final.stac-item
2022-10-11 09:27:52	ga_ls9c_ard_3-2-1_092071_2022-10-10_final.stac-item
2022-10-27 09:27:55	ga_ls9c_ard_3-2-1_092071_2022-10-26_final.stac-item
2022-11-12 09:27:56	ga_ls9c_ard_3-2-1_092071_2022-11-11_final.stac-item
2022-11-28 09:27:54	ga_ls9c_ard_3-2-1_092071_2022-11-27_final.stac-item
2022-12-14 09:27:55	ga_ls9c_ard_3-2-1_092071_2022-12-13_final.stac-item
2022-12-30 09:27:54	ga_ls9c_ard_3-2-1_092071_2022-12-29_final.stac-item
2023-01-15 09:27:51	ga_ls9c_ard_3-2-1_092071_2023-01-14_final.stac-item
2023-01-31 09:27:55	ga_ls9c_ard_3-2-1_092071_2023-01-30_final.stac-item
2023-02-16 09:27:55	ga_ls9c_ard_3-2-1_092071_2023-02-15_final.stac-item
2023-03-04 09:27:42	ga_ls9c_ard_3-2-1_092071_2023-03-03_final.stac-item
2023-03-20 09:27:35	ga_ls9c_ard_3-2-1_092071_2023-03-19_final.stac-item
2023-04-05 09:27:25	ga_ls9c_ard_3-2-1_092071_2023-04-04_final.stac-item
2023-04-21 09:27:19	ga_ls9c_ard_3-2-1_092071_2023-04-20_final.stac-item
2023-05-07 09:27:07	ga_ls9c_ard_3-2-1_092071_2023-05-06_final.stac-item
2023-05-23 09:26:58	ga_ls9c_ard_3-2-1_092071_2023-05-22_final.stac-item
2023-06-08 09:26:53	ga_ls9c_ard_3-2-1_092071_2023-06-07_final.stac-item
2023-06-24 09:26:47	ga_ls9c_ard_3-2-1_092071_2023-06-23_final.stac-item
2023-07-10 09:27:02	ga_ls9c_ard_3-2-1_092071_2023-07-09_final.stac-item
2023-07-26 09:27:04	ga_ls9c_ard_3-2-1_092071_2023-07-25_final.stac-item
2023-08-11 09:27:14	ga_ls9c_ard_3-2-1_092071_2023-08-10_final.stac-item
2023-08-27 09:27:22	ga_ls9c_ard_3-2-1_092071_2023-08-26_final.stac-item
2023-09-12 09:27:32	ga_ls9c_ard_3-2-1_092071_2023-09-11_final.stac-item
2023-09-28 09:27:27	ga_ls9c_ard_3-2-1_092071_2023-09-27_final.stac-item
2023-10-14 09:27:41	ga_ls9c_ard_3-2-1_092071_2023-10-13_final.stac-item
2023-10-30 09:27:39	ga_ls9c_ard_3-2-1_092071_2023-10-29_final.stac-item
2023-11-15 09:27:38	ga_ls9c_ard_3-2-1_092071_2023-11-14_final.stac-item
2023-12-01 09:27:46	ga_ls9c_ard_3-2-1_092071_2023-11-30_final.stac-item
2023-12-17 09:27:47	ga_ls9c_ard_3-2-1_092071_2023-12-16_final.stac-item
2024-01-02 09:27:44	ga_ls9c_ard_3-2-1_092071_2024-01-01_final.stac-item
2024-01-18 09:27:44	ga_ls9c_ard_3-2-1_092071_2024-01-17_final.stac-item
2024-02-03 09:27:46	ga_ls9c_ard_3-2-1_092071_2024-02-02_final.stac-item
2024-02-19 09:27:38	ga_ls9c_ard_3-2-1_092071_2024-02-18_final.stac-item
2024-03-06 09:27:39	ga_ls9c_ard_3-2-1_092071_2024-03-05_final.stac-item
2024-03-22 09:27:36	ga_ls9c_ard_3-2-1_092071_2024-03-21_final.stac-item
2024-04-07 09:27:27	ga_ls9c_ard_3-2-1_092071_2024-04-06_final.stac-item
2024-04-23 09:27:07	ga_ls9c_ard_3-2-1_092071_2024-04-22_nrt.stac-item

Return to ga_ls9c_ard_3 whole collection overview

ga_ls9c_ard_3: Geoscience Australia Landsat 9 Operational Land Imager and Thermal Infra-Red Scanner Analysis Ready Data Collection 3

Baseline satellite data

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

All years