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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

43 results

Time (local)	Label
2021-11-07 10:56:32	ga_ls9c_ard_3-2-1_106076_2021-11-07_final.stac-item
2021-12-12 10:13:50	ga_ls9c_ard_3-2-1_099078_2021-12-12_final.stac-item
2021-12-18 09:34:43	ga_ls9c_ard_3-2-1_093073_2021-12-18_final.stac-item
2022-01-04 10:17:11	ga_ls9c_ard_3-2-1_100071_2022-01-04_final.stac-item
2022-01-14 10:53:25	ga_ls9c_ard_3-2-1_106069_2022-01-14_final.stac-item
2022-02-15 10:52:59	ga_ls9c_ard_3-2-1_106068_2022-02-15_final.stac-item
2022-02-18 11:25:03	ga_ls9c_ard_3-2-1_111071_2022-02-18_final.stac-item
2022-02-22 11:04:17	ga_ls9c_ard_3-2-1_107081_2022-02-22_final.stac-item
2022-04-09 09:33:57	ga_ls9c_ard_3-2-1_093072_2022-04-09_final.stac-item
2022-04-11 09:22:46	ga_ls9c_ard_3-2-1_091075_2022-04-10_final.stac-item
2022-05-15 09:10:55	ga_ls9c_ard_3-2-1_089077_2022-05-14_final.stac-item
2022-08-18 11:47:38	ga_ls9c_ard_3-2-1_114081_2022-08-18_final.stac-item
2022-09-02 09:27:30	ga_ls9c_ard_3-2-1_091086_2022-09-01_final.stac-item
2022-09-12 11:37:54	ga_ls9c_ard_3-2-1_113072_2022-09-12_final.stac-item
2022-09-20 10:49:20	ga_ls9c_ard_3-2-1_105074_2022-09-20_final.stac-item
2022-10-03 10:19:39	ga_ls9c_ard_3-2-1_100077_2022-10-03_final.stac-item
2022-10-23 09:56:38	ga_ls9c_ard_3-2-1_096081_2022-10-23_final.stac-item
2022-11-06 10:03:22	ga_ls9c_ard_3-2-1_098067_2022-11-06_final.stac-item
2022-11-15 10:01:14	ga_ls9c_ard_3-2-1_097077_2022-11-15_final.stac-item
2022-11-30 09:21:06	ga_ls9c_ard_3-2-1_090085_2022-11-29_final.stac-item
2022-12-10 11:31:55	ga_ls9c_ard_3-2-1_112072_2022-12-10_final.stac-item
2022-12-29 10:27:55	ga_ls9c_ard_3-2-1_101082_2022-12-29_final.stac-item
2023-01-05 10:28:55	ga_ls9c_ard_3-2-1_102069_2023-01-05_final.stac-item
2023-01-09 10:05:23	ga_ls9c_ard_3-2-1_098072_2023-01-09_final.stac-item
2023-01-10 09:10:57	ga_ls9c_ard_3-2-1_089075_2023-01-09_final.stac-item
2023-04-05 09:28:37	ga_ls9c_ard_3-2-1_092074_2023-04-04_final.stac-item
2023-05-15 10:18:55	ga_ls9c_ard_3-2-1_100077_2023-05-15_final.stac-item
2023-05-28 11:23:58	ga_ls9c_ard_3-2-1_111070_2023-05-28_final.stac-item
2023-05-30 09:33:31	ga_ls9c_ard_3-2-1_093072_2023-05-30_final.stac-item
2023-05-30 11:14:00	ga_ls9c_ard_3-2-1_109076_2023-05-30_final.stac-item
2023-08-13 09:20:04	ga_ls9c_ard_3-2-1_090084_2023-08-12_final.stac-item
2023-09-17 09:46:29	ga_ls9c_ard_3-2-1_095072_2023-09-17_final.stac-item
2023-09-26 09:40:14	ga_ls9c_ard_3-2-1_094072_2023-09-26_final.stac-item
2023-10-03 11:25:46	ga_ls9c_ard_3-2-1_111073_2023-10-03_final.stac-item
2023-10-05 11:13:50	ga_ls9c_ard_3-2-1_109074_2023-10-05_final.stac-item
2023-12-06 09:46:15	ga_ls9c_ard_3-2-1_095071_2023-12-06_final.stac-item
2023-12-15 09:40:08	ga_ls9c_ard_3-2-1_094071_2023-12-15_final.stac-item
2023-12-15 11:20:13	ga_ls9c_ard_3-2-1_110074_2023-12-15_final.stac-item
2024-02-11 10:20:00	ga_ls9c_ard_3-2-1_100078_2024-02-11_final.stac-item
2024-02-23 09:07:36	ga_ls9c_ard_3-2-1_088083_2024-02-22_final.stac-item
2024-03-22 09:28:00	ga_ls9c_ard_3-2-1_092072_2024-03-21_final.stac-item
2024-03-29 11:14:32	ga_ls9c_ard_3-2-1_109076_2024-03-29_final.stac-item
2024-05-21 11:31:44	ga_ls9c_ard_3-2-1_112074_2024-05-21_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