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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

52 results

Time (local)	Label
2021-11-08 09:57:02	ga_ls9c_ard_3-2-1_097074_2021-11-08_final.stac-item
2021-11-13 09:59:30	ga_ls9c_ard_3-2-1_097074_2021-11-13_final.stac-item
2021-11-18 10:01:58	ga_ls9c_ard_3-2-1_097074_2021-11-18_final.stac-item
2021-12-30 09:59:51	ga_ls9c_ard_3-2-1_097074_2021-12-30_final.stac-item
2022-04-21 09:59:19	ga_ls9c_ard_3-2-1_097074_2022-04-21_final.stac-item
2022-05-07 09:59:15	ga_ls9c_ard_3-2-1_097074_2022-05-07_final.stac-item
2022-05-23 09:59:04	ga_ls9c_ard_3-2-1_097074_2022-05-23_final.stac-item
2022-06-08 09:59:06	ga_ls9c_ard_3-2-1_097074_2022-06-08_final.stac-item
2022-06-24 09:59:26	ga_ls9c_ard_3-2-1_097074_2022-06-24_final.stac-item
2022-07-10 09:59:23	ga_ls9c_ard_3-2-1_097074_2022-07-10_final.stac-item
2022-07-26 09:59:33	ga_ls9c_ard_3-2-1_097074_2022-07-26_final.stac-item
2022-08-11 09:59:44	ga_ls9c_ard_3-2-1_097074_2022-08-11_final.stac-item
2022-08-27 09:59:48	ga_ls9c_ard_3-2-1_097074_2022-08-27_final.stac-item
2022-09-12 09:59:49	ga_ls9c_ard_3-2-1_097074_2022-09-12_final.stac-item
2022-09-28 09:59:55	ga_ls9c_ard_3-2-1_097074_2022-09-28_final.stac-item
2022-10-14 09:59:59	ga_ls9c_ard_3-2-1_097074_2022-10-14_final.stac-item
2022-10-30 10:00:00	ga_ls9c_ard_3-2-1_097074_2022-10-30_final.stac-item
2022-11-15 10:00:02	ga_ls9c_ard_3-2-1_097074_2022-11-15_final.stac-item
2022-12-01 09:59:58	ga_ls9c_ard_3-2-1_097074_2022-12-01_final.stac-item
2022-12-17 10:00:00	ga_ls9c_ard_3-2-1_097074_2022-12-17_final.stac-item
2023-01-02 10:00:01	ga_ls9c_ard_3-2-1_097074_2023-01-02_final.stac-item
2023-01-18 09:59:54	ga_ls9c_ard_3-2-1_097074_2023-01-18_final.stac-item
2023-02-03 10:00:03	ga_ls9c_ard_3-2-1_097074_2023-02-03_final.stac-item
2023-02-19 09:59:57	ga_ls9c_ard_3-2-1_097074_2023-02-19_final.stac-item
2023-03-07 09:59:45	ga_ls9c_ard_3-2-1_097074_2023-03-07_final.stac-item
2023-03-23 09:59:39	ga_ls9c_ard_3-2-1_097074_2023-03-23_final.stac-item
2023-04-08 09:59:30	ga_ls9c_ard_3-2-1_097074_2023-04-08_final.stac-item
2023-04-24 09:59:23	ga_ls9c_ard_3-2-1_097074_2023-04-24_final.stac-item
2023-05-10 09:59:13	ga_ls9c_ard_3-2-1_097074_2023-05-10_final.stac-item
2023-05-26 09:59:02	ga_ls9c_ard_3-2-1_097074_2023-05-26_final.stac-item
2023-06-11 09:58:57	ga_ls9c_ard_3-2-1_097074_2023-06-11_final.stac-item
2023-06-27 09:58:55	ga_ls9c_ard_3-2-1_097074_2023-06-27_final.stac-item
2023-07-13 09:59:09	ga_ls9c_ard_3-2-1_097074_2023-07-13_final.stac-item
2023-07-29 09:59:13	ga_ls9c_ard_3-2-1_097074_2023-07-29_final.stac-item
2023-08-14 09:59:20	ga_ls9c_ard_3-2-1_097074_2023-08-14_final.stac-item
2023-08-30 09:59:30	ga_ls9c_ard_3-2-1_097074_2023-08-30_final.stac-item
2023-09-15 09:59:38	ga_ls9c_ard_3-2-1_097074_2023-09-15_final.stac-item
2023-10-01 09:59:37	ga_ls9c_ard_3-2-1_097074_2023-10-01_final.stac-item
2023-10-17 09:59:48	ga_ls9c_ard_3-2-1_097074_2023-10-17_final.stac-item
2023-11-02 09:59:41	ga_ls9c_ard_3-2-1_097074_2023-11-02_final.stac-item
2023-11-18 09:59:46	ga_ls9c_ard_3-2-1_097074_2023-11-18_final.stac-item
2023-12-04 09:59:50	ga_ls9c_ard_3-2-1_097074_2023-12-04_final.stac-item
2023-12-20 09:59:54	ga_ls9c_ard_3-2-1_097074_2023-12-20_final.stac-item
2024-01-05 09:59:48	ga_ls9c_ard_3-2-1_097074_2024-01-05_final.stac-item
2024-01-21 09:59:48	ga_ls9c_ard_3-2-1_097074_2024-01-21_final.stac-item
2024-02-06 09:59:53	ga_ls9c_ard_3-2-1_097074_2024-02-06_final.stac-item
2024-02-22 09:59:39	ga_ls9c_ard_3-2-1_097074_2024-02-22_final.stac-item
2024-03-09 09:59:45	ga_ls9c_ard_3-2-1_097074_2024-03-09_final.stac-item
2024-03-25 09:59:39	ga_ls9c_ard_3-2-1_097074_2024-03-25_final.stac-item
2024-04-10 09:59:31	ga_ls9c_ard_3-2-1_097074_2024-04-10_final.stac-item
2024-04-26 09:59:11	ga_ls9c_ard_3-2-1_097074_2024-04-26_final.stac-item
2024-05-12 09:59:09	ga_ls9c_ard_3-2-1_097074_2024-05-12_final.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

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

All years