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
2022-01-17 11:23:56	ga_ls9c_ard_3-2-1_111068_2022-01-17_final.stac-item
2022-02-02 11:23:58	ga_ls9c_ard_3-2-1_111068_2022-02-02_final.stac-item
2022-02-18 11:23:52	ga_ls9c_ard_3-2-1_111068_2022-02-18_final.stac-item
2022-03-06 11:23:43	ga_ls9c_ard_3-2-1_111068_2022-03-06_final.stac-item
2022-03-22 11:23:40	ga_ls9c_ard_3-2-1_111068_2022-03-22_final.stac-item
2022-04-07 11:23:37	ga_ls9c_ard_3-2-1_111068_2022-04-07_final.stac-item
2022-07-12 11:23:30	ga_ls9c_ard_3-2-1_111068_2022-07-12_final.stac-item
2022-10-02 09:37:36	ga_ls9c_ard_3-2-1_093080_2022-10-02_final.stac-item
2022-10-02 09:38:00	ga_ls9c_ard_3-2-1_093081_2022-10-02_final.stac-item
2022-10-02 09:38:24	ga_ls9c_ard_3-2-1_093082_2022-10-02_final.stac-item
2022-10-03 10:21:39	ga_ls9c_ard_3-2-1_100082_2022-10-03_final.stac-item
2022-10-03 10:22:03	ga_ls9c_ard_3-2-1_100083_2022-10-03_final.stac-item
2022-10-03 10:22:27	ga_ls9c_ard_3-2-1_100084_2022-10-03_final.stac-item
2022-10-04 11:02:55	ga_ls9c_ard_3-2-1_107077_2022-10-04_final.stac-item
2022-10-04 11:04:07	ga_ls9c_ard_3-2-1_107080_2022-10-04_final.stac-item
2022-10-04 11:04:31	ga_ls9c_ard_3-2-1_107081_2022-10-04_final.stac-item
2022-10-05 10:04:54	ga_ls9c_ard_3-2-1_098071_2022-10-05_final.stac-item
2022-10-05 10:08:05	ga_ls9c_ard_3-2-1_098079_2022-10-05_final.stac-item
2022-10-05 11:45:22	ga_ls9c_ard_3-2-1_114075_2022-10-05_final.stac-item
2022-10-05 11:46:58	ga_ls9c_ard_3-2-1_114079_2022-10-05_final.stac-item
2022-10-05 11:47:46	ga_ls9c_ard_3-2-1_114081_2022-10-05_final.stac-item
2022-10-06 09:10:28	ga_ls9c_ard_3-2-1_089074_2022-10-05_final.stac-item
2022-10-06 09:10:52	ga_ls9c_ard_3-2-1_089075_2022-10-05_final.stac-item
2022-10-07 09:53:44	ga_ls9c_ard_3-2-1_096074_2022-10-07_final.stac-item
2022-10-08 10:37:00	ga_ls9c_ard_3-2-1_103074_2022-10-08_final.stac-item
2022-10-09 09:41:48	ga_ls9c_ard_3-2-1_094075_2022-10-09_final.stac-item
2022-10-10 10:26:16	ga_ls9c_ard_3-2-1_101078_2022-10-10_final.stac-item
2022-10-10 10:27:52	ga_ls9c_ard_3-2-1_101082_2022-10-10_final.stac-item
2022-10-10 10:28:16	ga_ls9c_ard_3-2-1_101083_2022-10-10_final.stac-item
2022-10-11 11:08:21	ga_ls9c_ard_3-2-1_108075_2022-10-11_final.stac-item
2022-10-11 11:09:56	ga_ls9c_ard_3-2-1_108079_2022-10-11_final.stac-item
2022-10-12 10:10:20	ga_ls9c_ard_3-2-1_099069_2022-10-12_final.stac-item
2022-10-12 11:51:37	ga_ls9c_ard_3-2-1_115075_2022-10-12_final.stac-item
2022-10-13 09:16:43	ga_ls9c_ard_3-2-1_090074_2022-10-12_final.stac-item
2022-10-13 10:58:48	ga_ls9c_ard_3-2-1_106082_2022-10-13_final.stac-item
2022-10-14 11:39:40	ga_ls9c_ard_3-2-1_113076_2022-10-14_final.stac-item
2022-10-14 11:40:04	ga_ls9c_ard_3-2-1_113077_2022-10-14_final.stac-item
2022-10-14 11:42:04	ga_ls9c_ard_3-2-1_113082_2022-10-14_final.stac-item
2022-10-15 10:42:28	ga_ls9c_ard_3-2-1_104072_2022-10-15_final.stac-item
2022-10-15 10:46:27	ga_ls9c_ard_3-2-1_104082_2022-10-15_final.stac-item
2022-10-16 09:48:02	ga_ls9c_ard_3-2-1_095075_2022-10-16_final.stac-item
2022-10-18 09:36:05	ga_ls9c_ard_3-2-1_093076_2022-10-18_final.stac-item
2022-10-19 10:18:57	ga_ls9c_ard_3-2-1_100075_2022-10-19_final.stac-item
2022-10-21 10:08:12	ga_ls9c_ard_3-2-1_098079_2022-10-21_final.stac-item
2022-10-21 11:46:41	ga_ls9c_ard_3-2-1_114078_2022-10-21_final.stac-item
2022-10-21 11:47:29	ga_ls9c_ard_3-2-1_114080_2022-10-21_final.stac-item
2022-10-22 09:10:35	ga_ls9c_ard_3-2-1_089074_2022-10-21_final.stac-item
2022-10-22 09:16:58	ga_ls9c_ard_3-2-1_089090_2022-10-21_final.stac-item
2022-10-22 10:46:40	ga_ls9c_ard_3-2-1_105067_2022-10-22_final.stac-item
2022-10-24 10:37:06	ga_ls9c_ard_3-2-1_103074_2022-10-24_final.stac-item
2022-10-25 09:41:52	ga_ls9c_ard_3-2-1_094075_2022-10-25_final.stac-item
2022-10-25 09:42:16	ga_ls9c_ard_3-2-1_094076_2022-10-25_final.stac-item
2022-10-25 09:44:16	ga_ls9c_ard_3-2-1_094081_2022-10-25_final.stac-item
2022-10-25 11:22:45	ga_ls9c_ard_3-2-1_110080_2022-10-25_final.stac-item
2022-10-26 10:25:56	ga_ls9c_ard_3-2-1_101077_2022-10-26_final.stac-item
2022-10-27 09:29:55	ga_ls9c_ard_3-2-1_092076_2022-10-26_final.stac-item
2022-10-27 09:33:54	ga_ls9c_ard_3-2-1_092086_2022-10-27_final.stac-item
2022-10-27 11:07:36	ga_ls9c_ard_3-2-1_108073_2022-10-27_final.stac-item
2022-10-27 11:08:48	ga_ls9c_ard_3-2-1_108076_2022-10-27_final.stac-item
2022-10-28 10:11:58	ga_ls9c_ard_3-2-1_099073_2022-10-28_final.stac-item
2022-10-28 11:53:39	ga_ls9c_ard_3-2-1_115080_2022-10-28_final.stac-item
2022-10-29 09:20:44	ga_ls9c_ard_3-2-1_090084_2022-10-28_final.stac-item
2022-10-29 10:58:01	ga_ls9c_ard_3-2-1_106080_2022-10-29_final.stac-item
2022-10-30 10:02:24	ga_ls9c_ard_3-2-1_097080_2022-10-30_final.stac-item
2022-10-30 10:03:35	ga_ls9c_ard_3-2-1_097083_2022-10-30_final.stac-item
2022-10-30 10:03:59	ga_ls9c_ard_3-2-1_097084_2022-10-30_final.stac-item
2022-10-30 11:39:41	ga_ls9c_ard_3-2-1_113076_2022-10-30_final.stac-item
2022-10-31 10:45:39	ga_ls9c_ard_3-2-1_104080_2022-10-31_final.stac-item
2022-11-01 09:47:37	ga_ls9c_ard_3-2-1_095074_2022-11-01_final.stac-item
2022-11-01 09:50:49	ga_ls9c_ard_3-2-1_095082_2022-11-01_final.stac-item
2022-11-01 09:51:13	ga_ls9c_ard_3-2-1_095083_2022-11-01_final.stac-item
2022-11-01 09:51:37	ga_ls9c_ard_3-2-1_095084_2022-11-01_final.stac-item
2022-11-01 09:52:01	ga_ls9c_ard_3-2-1_095085_2022-11-01_final.stac-item
2022-11-01 09:52:25	ga_ls9c_ard_3-2-1_095086_2022-11-01_final.stac-item
2022-11-01 11:28:06	ga_ls9c_ard_3-2-1_111078_2022-11-01_final.stac-item
2022-11-06 10:10:33	ga_ls9c_ard_3-2-1_098085_2022-11-06_final.stac-item
2022-11-06 11:45:51	ga_ls9c_ard_3-2-1_114076_2022-11-06_final.stac-item
2022-11-06 11:47:27	ga_ls9c_ard_3-2-1_114080_2022-11-06_final.stac-item
2022-11-07 10:47:50	ga_ls9c_ard_3-2-1_105070_2022-11-07_final.stac-item
2022-11-07 10:51:02	ga_ls9c_ard_3-2-1_105078_2022-11-07_final.stac-item
2022-11-08 09:57:49	ga_ls9c_ard_3-2-1_096084_2022-11-08_final.stac-item
2022-11-09 10:37:06	ga_ls9c_ard_3-2-1_103074_2022-11-09_final.stac-item
2022-11-10 09:41:53	ga_ls9c_ard_3-2-1_094075_2022-11-10_final.stac-item
2022-11-10 09:43:52	ga_ls9c_ard_3-2-1_094080_2022-11-10_final.stac-item
2022-11-10 09:44:40	ga_ls9c_ard_3-2-1_094082_2022-11-10_final.stac-item
2022-11-11 10:27:32	ga_ls9c_ard_3-2-1_101081_2022-11-11_final.stac-item
2022-11-11 10:27:56	ga_ls9c_ard_3-2-1_101082_2022-11-11_final.stac-item
2022-11-12 11:09:36	ga_ls9c_ard_3-2-1_108078_2022-11-12_final.stac-item
2022-11-19 09:34:07	ga_ls9c_ard_3-2-1_093071_2022-11-19_final.stac-item
2022-11-21 09:28:32	ga_ls9c_ard_3-2-1_091088_2022-11-20_final.stac-item
2022-11-21 09:28:56	ga_ls9c_ard_3-2-1_091089_2022-11-20_final.stac-item
2022-11-22 10:04:37	ga_ls9c_ard_3-2-1_098070_2022-11-22_final.stac-item
2022-11-22 11:45:05	ga_ls9c_ard_3-2-1_114074_2022-11-22_final.stac-item
2022-11-22 11:45:29	ga_ls9c_ard_3-2-1_114075_2022-11-22_final.stac-item
2022-11-23 09:10:35	ga_ls9c_ard_3-2-1_089074_2022-11-22_final.stac-item
2022-11-23 10:47:52	ga_ls9c_ard_3-2-1_105070_2022-11-23_final.stac-item
2022-11-23 10:51:04	ga_ls9c_ard_3-2-1_105078_2022-11-23_final.stac-item
2022-11-29 11:51:14	ga_ls9c_ard_3-2-1_115074_2022-11-29_final.stac-item
2022-11-29 11:53:37	ga_ls9c_ard_3-2-1_115080_2022-11-29_final.stac-item
2022-11-30 09:19:07	ga_ls9c_ard_3-2-1_090080_2022-11-29_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

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

All years