Search ga_ls9c_ard_3 whole collection

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

78 results

Time (local)	Label
2021-11-06 10:19:46	ga_ls9c_ard_3-2-1_100084_2021-11-06_final.stac-item
2021-11-08 09:58:14	ga_ls9c_ard_3-2-1_097077_2021-11-08_final.stac-item
2021-11-11 10:17:51	ga_ls9c_ard_3-2-1_100073_2021-11-11_final.stac-item
2021-11-11 10:22:14	ga_ls9c_ard_3-2-1_100084_2021-11-11_final.stac-item
2021-11-16 10:20:19	ga_ls9c_ard_3-2-1_100073_2021-11-16_final.stac-item
2021-11-16 10:24:42	ga_ls9c_ard_3-2-1_100084_2021-11-16_final.stac-item
2021-11-22 11:05:07	ga_ls9c_ard_3-2-1_107079_2021-11-22_final.stac-item
2021-12-04 09:22:53	ga_ls9c_ard_3-2-1_091074_2021-12-03_final.stac-item
2021-12-09 09:41:47	ga_ls9c_ard_3-2-1_094075_2021-12-09_final.stac-item
2021-12-14 11:39:09	ga_ls9c_ard_3-2-1_113075_2021-12-14_final.stac-item
2021-12-16 09:50:16	ga_ls9c_ard_3-2-1_095081_2021-12-16_final.stac-item
2021-12-16 11:26:22	ga_ls9c_ard_3-2-1_111074_2021-12-16_final.stac-item
2021-12-19 10:20:22	ga_ls9c_ard_3-2-1_100079_2021-12-19_final.stac-item
2021-12-20 09:22:46	ga_ls9c_ard_3-2-1_091074_2021-12-19_final.stac-item
2021-12-21 10:06:02	ga_ls9c_ard_3-2-1_098074_2021-12-21_final.stac-item
2021-12-28 10:14:36	ga_ls9c_ard_3-2-1_099080_2021-12-28_final.stac-item
2021-12-30 11:39:08	ga_ls9c_ard_3-2-1_113075_2021-12-30_final.stac-item
2021-12-31 10:41:55	ga_ls9c_ard_3-2-1_104071_2021-12-31_final.stac-item
2022-01-01 09:50:17	ga_ls9c_ard_3-2-1_095081_2022-01-01_final.stac-item
2022-01-04 10:20:22	ga_ls9c_ard_3-2-1_100079_2022-01-04_final.stac-item
2022-01-06 10:03:13	ga_ls9c_ard_3-2-1_098067_2022-01-06_final.stac-item
2022-01-09 10:34:30	ga_ls9c_ard_3-2-1_103068_2022-01-09_final.stac-item
2022-01-13 10:14:33	ga_ls9c_ard_3-2-1_099080_2022-01-13_final.stac-item
2022-01-16 10:41:52	ga_ls9c_ard_3-2-1_104071_2022-01-16_final.stac-item
2022-01-21 09:26:20	ga_ls9c_ard_3-2-1_091083_2022-01-20_final.stac-item
2022-01-24 09:53:39	ga_ls9c_ard_3-2-1_096074_2022-01-24_final.stac-item
2022-01-26 11:21:23	ga_ls9c_ard_3-2-1_110077_2022-01-26_final.stac-item
2022-02-01 10:40:42	ga_ls9c_ard_3-2-1_104068_2022-02-01_final.stac-item
2022-02-04 09:38:18	ga_ls9c_ard_3-2-1_093082_2022-02-04_final.stac-item
2022-02-06 09:26:20	ga_ls9c_ard_3-2-1_091083_2022-02-05_final.stac-item
2022-02-08 10:48:27	ga_ls9c_ard_3-2-1_105072_2022-02-08_final.stac-item
2022-02-26 10:34:19	ga_ls9c_ard_3-2-1_103068_2022-02-26_final.stac-item
2022-03-07 10:29:42	ga_ls9c_ard_3-2-1_102072_2022-03-07_final.stac-item
2022-03-21 10:41:37	ga_ls9c_ard_3-2-1_104071_2022-03-21_final.stac-item
2022-04-06 10:41:33	ga_ls9c_ard_3-2-1_104071_2022-04-06_final.stac-item
2022-04-07 11:29:36	ga_ls9c_ard_3-2-1_111083_2022-04-07_final.stac-item
2022-04-09 11:13:14	ga_ls9c_ard_3-2-1_109073_2022-04-09_final.stac-item
2022-05-11 09:34:53	ga_ls9c_ard_3-2-1_093075_2022-05-11_final.stac-item
2022-05-15 09:13:42	ga_ls9c_ard_3-2-1_089084_2022-05-14_final.stac-item
2022-05-15 10:50:12	ga_ls9c_ard_3-2-1_105078_2022-05-15_final.stac-item
2022-05-20 09:28:58	ga_ls9c_ard_3-2-1_092076_2022-05-19_final.stac-item
2022-05-21 10:12:38	ga_ls9c_ard_3-2-1_099077_2022-05-21_final.stac-item
2022-05-27 09:34:45	ga_ls9c_ard_3-2-1_093075_2022-05-27_final.stac-item
2022-05-29 09:22:00	ga_ls9c_ard_3-2-1_091074_2022-05-28_final.stac-item
2022-05-29 09:27:59	ga_ls9c_ard_3-2-1_091089_2022-05-28_final.stac-item
2022-05-31 09:11:38	ga_ls9c_ard_3-2-1_089079_2022-05-30_final.stac-item
2022-05-31 10:47:44	ga_ls9c_ard_3-2-1_105072_2022-05-31_final.stac-item
2022-05-31 10:50:07	ga_ls9c_ard_3-2-1_105078_2022-05-31_final.stac-item
2022-06-04 10:22:13	ga_ls9c_ard_3-2-1_101070_2022-06-04_final.stac-item
2022-06-06 10:12:39	ga_ls9c_ard_3-2-1_099077_2022-06-06_final.stac-item
2022-06-07 09:17:02	ga_ls9c_ard_3-2-1_090077_2022-06-06_final.stac-item
2022-06-09 10:39:58	ga_ls9c_ard_3-2-1_104068_2022-06-09_final.stac-item
2022-06-10 11:25:38	ga_ls9c_ard_3-2-1_111074_2022-06-10_final.stac-item
2022-06-12 09:37:37	ga_ls9c_ard_3-2-1_093082_2022-06-12_final.stac-item
2022-06-14 09:22:05	ga_ls9c_ard_3-2-1_091074_2022-06-13_final.stac-item
2022-06-14 09:28:04	ga_ls9c_ard_3-2-1_091089_2022-06-13_final.stac-item
2022-06-14 11:04:58	ga_ls9c_ard_3-2-1_107084_2022-06-14_final.stac-item
2022-06-16 09:11:45	ga_ls9c_ard_3-2-1_089079_2022-06-15_final.stac-item
2022-06-23 09:17:20	ga_ls9c_ard_3-2-1_090077_2022-06-22_final.stac-item
2022-06-23 10:53:01	ga_ls9c_ard_3-2-1_106069_2022-06-23_final.stac-item
2022-06-26 11:25:58	ga_ls9c_ard_3-2-1_111074_2022-06-26_final.stac-item
2022-06-30 09:22:21	ga_ls9c_ard_3-2-1_091074_2022-06-29_final.stac-item
2022-07-16 09:22:18	ga_ls9c_ard_3-2-1_091074_2022-07-15_final.stac-item
2022-08-02 10:05:49	ga_ls9c_ard_3-2-1_098074_2022-08-02_final.stac-item
2022-08-13 09:48:11	ga_ls9c_ard_3-2-1_095076_2022-08-13_final.stac-item
2022-08-18 10:05:57	ga_ls9c_ard_3-2-1_098074_2022-08-18_final.stac-item
2022-08-27 11:39:05	ga_ls9c_ard_3-2-1_113075_2022-08-27_final.stac-item
2022-08-29 09:48:14	ga_ls9c_ard_3-2-1_095076_2022-08-29_final.stac-item
2022-08-29 09:50:14	ga_ls9c_ard_3-2-1_095081_2022-08-29_final.stac-item
2022-09-05 11:33:18	ga_ls9c_ard_3-2-1_112076_2022-09-05_final.stac-item
2022-09-12 11:39:06	ga_ls9c_ard_3-2-1_113075_2022-09-12_final.stac-item
2022-09-14 09:50:16	ga_ls9c_ard_3-2-1_095081_2022-09-14_final.stac-item
2022-09-18 11:03:40	ga_ls9c_ard_3-2-1_107079_2022-09-18_final.stac-item
2022-09-21 11:33:24	ga_ls9c_ard_3-2-1_112076_2022-09-21_final.stac-item
2022-09-23 09:44:33	ga_ls9c_ard_3-2-1_094082_2022-09-23_final.stac-item
2022-09-30 11:28:26	ga_ls9c_ard_3-2-1_111079_2022-09-30_final.stac-item
2022-09-30 11:30:02	ga_ls9c_ard_3-2-1_111083_2022-09-30_final.stac-item
2022-10-06 10:48:33	ga_ls9c_ard_3-2-1_105072_2022-10-06_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