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

First 100 results (query limit)

Time (local)	Label
2021-11-03 11:34:01	ga_ls9c_ard_3-2-1_112076_2021-11-03_final.stac-item
2021-11-06 10:19:46	ga_ls9c_ard_3-2-1_100084_2021-11-06_final.stac-item
2021-11-10 11:18:09	ga_ls9c_ard_3-2-1_110077_2021-11-10_final.stac-item
2021-11-11 10:22:14	ga_ls9c_ard_3-2-1_100084_2021-11-11_final.stac-item
2021-11-12 09:23:08	ga_ls9c_ard_3-2-1_091083_2021-11-11_final.stac-item
2021-11-16 10:24:42	ga_ls9c_ard_3-2-1_100084_2021-11-16_final.stac-item
2021-11-17 09:25:40	ga_ls9c_ard_3-2-1_091083_2021-11-16_final.stac-item
2021-11-18 10:03:10	ga_ls9c_ard_3-2-1_097077_2021-11-18_final.stac-item
2021-11-22 11:05:07	ga_ls9c_ard_3-2-1_107079_2021-11-22_final.stac-item
2021-11-22 11:07:07	ga_ls9c_ard_3-2-1_107084_2021-11-22_final.stac-item
2021-12-09 09:41:47	ga_ls9c_ard_3-2-1_094075_2021-12-09_final.stac-item
2021-12-15 10:40:43	ga_ls9c_ard_3-2-1_104068_2021-12-15_final.stac-item
2021-12-19 10:17:59	ga_ls9c_ard_3-2-1_100073_2021-12-19_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 11:05:38	ga_ls9c_ard_3-2-1_107084_2021-12-20_final.stac-item
2021-12-25 09:41:43	ga_ls9c_ard_3-2-1_094075_2021-12-25_final.stac-item
2021-12-31 10:40:43	ga_ls9c_ard_3-2-1_104068_2021-12-31_final.stac-item
2022-01-04 10:20:22	ga_ls9c_ard_3-2-1_100079_2022-01-04_final.stac-item
2022-01-07 09:12:23	ga_ls9c_ard_3-2-1_089079_2022-01-06_final.stac-item
2022-01-10 09:41:40	ga_ls9c_ard_3-2-1_094075_2022-01-10_final.stac-item
2022-01-10 11:21:21	ga_ls9c_ard_3-2-1_110077_2022-01-10_final.stac-item
2022-01-22 10:09:36	ga_ls9c_ard_3-2-1_098083_2022-01-22_final.stac-item
2022-02-04 09:38:18	ga_ls9c_ard_3-2-1_093082_2022-02-04_final.stac-item
2022-02-07 10:03:12	ga_ls9c_ard_3-2-1_098067_2022-02-07_final.stac-item
2022-02-09 09:53:38	ga_ls9c_ard_3-2-1_096074_2022-02-09_final.stac-item
2022-02-20 11:17:52	ga_ls9c_ard_3-2-1_109084_2022-02-20_final.stac-item
2022-02-28 10:22:44	ga_ls9c_ard_3-2-1_101070_2022-02-28_final.stac-item
2022-03-08 11:17:45	ga_ls9c_ard_3-2-1_109084_2022-03-08_final.stac-item
2022-03-09 10:20:08	ga_ls9c_ard_3-2-1_100079_2022-03-09_final.stac-item
2022-03-31 09:44:10	ga_ls9c_ard_3-2-1_094082_2022-03-31_final.stac-item
2022-04-16 09:44:03	ga_ls9c_ard_3-2-1_094082_2022-04-16_final.stac-item
2022-05-05 11:51:19	ga_ls9c_ard_3-2-1_115076_2022-05-05_final.stac-item
2022-05-09 09:47:40	ga_ls9c_ard_3-2-1_095076_2022-05-09_final.stac-item
2022-05-18 09:43:45	ga_ls9c_ard_3-2-1_094082_2022-05-18_final.stac-item
2022-06-03 09:43:45	ga_ls9c_ard_3-2-1_094082_2022-06-03_final.stac-item
2022-06-07 09:18:14	ga_ls9c_ard_3-2-1_090080_2022-06-06_final.stac-item
2022-06-10 09:47:33	ga_ls9c_ard_3-2-1_095076_2022-06-10_final.stac-item
2022-06-12 09:34:49	ga_ls9c_ard_3-2-1_093075_2022-06-12_final.stac-item
2022-06-12 09:37:37	ga_ls9c_ard_3-2-1_093082_2022-06-12_final.stac-item
2022-06-13 10:19:41	ga_ls9c_ard_3-2-1_100079_2022-06-13_final.stac-item
2022-06-13 10:21:41	ga_ls9c_ard_3-2-1_100084_2022-06-13_final.stac-item
2022-06-19 09:41:07	ga_ls9c_ard_3-2-1_094075_2022-06-19_final.stac-item
2022-06-21 09:29:13	ga_ls9c_ard_3-2-1_092076_2022-06-20_final.stac-item
2022-06-23 10:53:01	ga_ls9c_ard_3-2-1_106069_2022-06-23_final.stac-item
2022-06-28 09:35:07	ga_ls9c_ard_3-2-1_093075_2022-06-28_final.stac-item
2022-06-29 10:19:59	ga_ls9c_ard_3-2-1_100079_2022-06-29_final.stac-item
2022-06-30 09:28:21	ga_ls9c_ard_3-2-1_091089_2022-06-29_final.stac-item
2022-07-01 11:44:54	ga_ls9c_ard_3-2-1_114075_2022-07-01_final.stac-item
2022-07-09 09:17:20	ga_ls9c_ard_3-2-1_090077_2022-07-08_final.stac-item
2022-07-09 10:53:02	ga_ls9c_ard_3-2-1_106069_2022-07-09_final.stac-item
2022-07-16 09:28:17	ga_ls9c_ard_3-2-1_091089_2022-07-15_final.stac-item
2022-07-17 10:09:10	ga_ls9c_ard_3-2-1_098083_2022-07-17_final.stac-item
2022-07-19 11:34:56	ga_ls9c_ard_3-2-1_112081_2022-07-19_final.stac-item
2022-07-23 11:05:29	ga_ls9c_ard_3-2-1_108069_2022-07-23_final.stac-item
2022-07-25 09:17:28	ga_ls9c_ard_3-2-1_090077_2022-07-24_final.stac-item
2022-07-27 10:41:38	ga_ls9c_ard_3-2-1_104071_2022-07-27_final.stac-item
2022-07-30 09:35:17	ga_ls9c_ard_3-2-1_093075_2022-07-30_final.stac-item
2022-07-31 10:20:09	ga_ls9c_ard_3-2-1_100079_2022-07-31_final.stac-item
2022-08-01 09:28:32	ga_ls9c_ard_3-2-1_091089_2022-07-31_final.stac-item
2022-08-02 10:09:25	ga_ls9c_ard_3-2-1_098083_2022-08-02_final.stac-item
2022-08-02 11:45:06	ga_ls9c_ard_3-2-1_114075_2022-08-02_final.stac-item
2022-08-03 10:48:18	ga_ls9c_ard_3-2-1_105072_2022-08-03_final.stac-item
2022-08-04 11:35:10	ga_ls9c_ard_3-2-1_112081_2022-08-04_final.stac-item
2022-08-10 09:17:39	ga_ls9c_ard_3-2-1_090077_2022-08-09_final.stac-item
2022-08-13 09:48:11	ga_ls9c_ard_3-2-1_095076_2022-08-13_final.stac-item
2022-08-17 09:28:40	ga_ls9c_ard_3-2-1_091089_2022-08-16_final.stac-item
2022-08-18 10:09:33	ga_ls9c_ard_3-2-1_098083_2022-08-18_final.stac-item
2022-08-20 09:57:12	ga_ls9c_ard_3-2-1_096083_2022-08-20_final.stac-item
2022-08-20 11:35:17	ga_ls9c_ard_3-2-1_112081_2022-08-20_final.stac-item
2022-08-26 10:53:25	ga_ls9c_ard_3-2-1_106069_2022-08-26_final.stac-item
2022-08-29 09:48:14	ga_ls9c_ard_3-2-1_095076_2022-08-29_final.stac-item
2022-09-18 09:26:23	ga_ls9c_ard_3-2-1_091083_2022-09-17_final.stac-item
2022-09-22 10:34:35	ga_ls9c_ard_3-2-1_103068_2022-09-22_final.stac-item
2022-09-25 11:06:18	ga_ls9c_ard_3-2-1_108070_2022-09-25_final.stac-item
2022-09-30 11:28:26	ga_ls9c_ard_3-2-1_111079_2022-09-30_final.stac-item
2022-10-01 10:28:50	ga_ls9c_ard_3-2-1_102069_2022-10-01_final.stac-item
2022-10-01 10:29:14	ga_ls9c_ard_3-2-1_102070_2022-10-01_final.stac-item
2022-10-01 10:31:37	ga_ls9c_ard_3-2-1_102076_2022-10-01_final.stac-item
2022-10-01 10:33:13	ga_ls9c_ard_3-2-1_102080_2022-10-01_final.stac-item
2022-10-01 10:33:37	ga_ls9c_ard_3-2-1_102081_2022-10-01_final.stac-item
2022-10-01 10:34:01	ga_ls9c_ard_3-2-1_102082_2022-10-01_final.stac-item
2022-10-05 10:05:18	ga_ls9c_ard_3-2-1_098072_2022-10-05_final.stac-item
2022-10-06 09:12:04	ga_ls9c_ard_3-2-1_089078_2022-10-05_final.stac-item
2022-10-06 09:13:15	ga_ls9c_ard_3-2-1_089081_2022-10-05_final.stac-item
2022-10-06 09:14:03	ga_ls9c_ard_3-2-1_089083_2022-10-05_final.stac-item
2022-10-06 10:48:33	ga_ls9c_ard_3-2-1_105072_2022-10-06_final.stac-item
2022-10-07 09:55:19	ga_ls9c_ard_3-2-1_096078_2022-10-07_final.stac-item
2022-10-07 09:56:07	ga_ls9c_ard_3-2-1_096080_2022-10-07_final.stac-item
2022-10-07 11:35:24	ga_ls9c_ard_3-2-1_112081_2022-10-07_final.stac-item
2022-10-11 09:32:39	ga_ls9c_ard_3-2-1_092083_2022-10-11_final.stac-item
2022-10-11 11:06:45	ga_ls9c_ard_3-2-1_108071_2022-10-11_final.stac-item
2022-10-11 11:11:56	ga_ls9c_ard_3-2-1_108084_2022-10-11_final.stac-item
2022-10-12 10:16:43	ga_ls9c_ard_3-2-1_099085_2022-10-12_final.stac-item
2022-10-14 10:01:59	ga_ls9c_ard_3-2-1_097079_2022-10-14_final.stac-item
2022-10-16 11:27:19	ga_ls9c_ard_3-2-1_111076_2022-10-16_final.stac-item
2022-10-16 11:28:55	ga_ls9c_ard_3-2-1_111080_2022-10-16_final.stac-item
2022-10-18 11:14:34	ga_ls9c_ard_3-2-1_109075_2022-10-18_final.stac-item
2022-10-20 09:26:32	ga_ls9c_ard_3-2-1_091083_2022-10-19_final.stac-item
2022-10-20 09:27:19	ga_ls9c_ard_3-2-1_091085_2022-10-19_final.stac-item
2022-10-20 09:27:43	ga_ls9c_ard_3-2-1_091086_2022-10-19_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