Search ga_ls8c_ard_3 across October 2019

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
2019-10-04 09:22:03	ga_ls8c_ard_3-1-0_091072_2019-10-03_final
2019-10-04 09:22:27	ga_ls8c_ard_3-1-0_091073_2019-10-03_final
2019-10-04 09:22:51	ga_ls8c_ard_3-1-0_091074_2019-10-03_final
2019-10-04 09:23:15	ga_ls8c_ard_3-1-0_091075_2019-10-03_final
2019-10-04 09:23:39	ga_ls8c_ard_3-1-0_091076_2019-10-03_final
2019-10-04 09:24:02	ga_ls8c_ard_3-1-0_091077_2019-10-03_final
2019-10-04 09:24:26	ga_ls8c_ard_3-1-0_091078_2019-10-03_final
2019-10-04 09:24:50	ga_ls8c_ard_3-1-0_091079_2019-10-03_final
2019-10-06 09:10:29	ga_ls8c_ard_3-1-0_089074_2019-10-05_final
2019-10-06 09:10:53	ga_ls8c_ard_3-1-0_089075_2019-10-05_final
2019-10-06 09:11:17	ga_ls8c_ard_3-1-0_089076_2019-10-05_final
2019-10-06 09:11:41	ga_ls8c_ard_3-1-0_089077_2019-10-05_final
2019-10-06 09:12:05	ga_ls8c_ard_3-1-0_089078_2019-10-05_final
2019-10-06 09:12:29	ga_ls8c_ard_3-1-0_089079_2019-10-05_final
2019-10-06 09:12:53	ga_ls8c_ard_3-1-0_089080_2019-10-05_final
2019-10-06 09:13:17	ga_ls8c_ard_3-1-0_089081_2019-10-05_final
2019-10-06 09:13:41	ga_ls8c_ard_3-1-0_089082_2019-10-05_final
2019-10-06 09:14:05	ga_ls8c_ard_3-1-0_089083_2019-10-05_final
2019-10-06 09:14:29	ga_ls8c_ard_3-1-0_089084_2019-10-05_final
2019-10-06 09:14:53	ga_ls8c_ard_3-1-0_089085_2019-10-05_final
2019-10-06 09:15:17	ga_ls8c_ard_3-1-0_089086_2019-10-05_final
2019-10-06 09:15:41	ga_ls8c_ard_3-1-0_089087_2019-10-05_final
2019-10-06 09:16:05	ga_ls8c_ard_3-1-0_089088_2019-10-05_final
2019-10-06 09:16:29	ga_ls8c_ard_3-1-0_089089_2019-10-05_final
2019-10-06 09:16:53	ga_ls8c_ard_3-1-0_089090_2019-10-05_final
2019-10-06 10:46:35	ga_ls8c_ard_3-1-0_105067_2019-10-06_final
2019-10-06 10:46:59	ga_ls8c_ard_3-1-0_105068_2019-10-06_final
2019-10-06 10:47:23	ga_ls8c_ard_3-1-0_105069_2019-10-06_final
2019-10-06 10:47:47	ga_ls8c_ard_3-1-0_105070_2019-10-06_final
2019-10-06 10:48:11	ga_ls8c_ard_3-1-0_105071_2019-10-06_final
2019-10-06 10:48:35	ga_ls8c_ard_3-1-0_105072_2019-10-06_final
2019-10-06 10:48:58	ga_ls8c_ard_3-1-0_105073_2019-10-06_final
2019-10-06 10:49:22	ga_ls8c_ard_3-1-0_105074_2019-10-06_final
2019-10-06 10:49:46	ga_ls8c_ard_3-1-0_105075_2019-10-06_final
2019-10-06 10:50:10	ga_ls8c_ard_3-1-0_105076_2019-10-06_final
2019-10-06 10:50:34	ga_ls8c_ard_3-1-0_105077_2019-10-06_final
2019-10-06 10:50:58	ga_ls8c_ard_3-1-0_105078_2019-10-06_final
2019-10-06 10:51:22	ga_ls8c_ard_3-1-0_105079_2019-10-06_final
2019-10-06 10:51:46	ga_ls8c_ard_3-1-0_105080_2019-10-06_final
2019-10-06 10:52:10	ga_ls8c_ard_3-1-0_105081_2019-10-06_final
2019-10-06 10:52:34	ga_ls8c_ard_3-1-0_105082_2019-10-06_final
2019-10-06 10:52:58	ga_ls8c_ard_3-1-0_105083_2019-10-06_final
2019-10-07 08:24:27	ga_ls8c_ard_3-2-1_080098_2019-10-06_final
2019-10-07 09:50:58	ga_ls8c_ard_3-1-0_096067_2019-10-07_final
2019-10-07 09:51:22	ga_ls8c_ard_3-1-0_096068_2019-10-07_final
2019-10-07 09:51:46	ga_ls8c_ard_3-1-0_096069_2019-10-07_final
2019-10-07 09:52:10	ga_ls8c_ard_3-1-0_096070_2019-10-07_final
2019-10-07 09:52:33	ga_ls8c_ard_3-1-0_096071_2019-10-07_final
2019-10-07 09:52:57	ga_ls8c_ard_3-1-0_096072_2019-10-07_final
2019-10-07 09:53:21	ga_ls8c_ard_3-1-0_096073_2019-10-07_final
2019-10-07 09:53:45	ga_ls8c_ard_3-1-0_096074_2019-10-07_final
2019-10-07 09:54:09	ga_ls8c_ard_3-1-0_096075_2019-10-07_final
2019-10-07 09:54:33	ga_ls8c_ard_3-1-0_096076_2019-10-07_final
2019-10-07 09:54:57	ga_ls8c_ard_3-1-0_096077_2019-10-07_final
2019-10-07 09:55:21	ga_ls8c_ard_3-1-0_096078_2019-10-07_final
2019-10-07 09:55:45	ga_ls8c_ard_3-1-0_096079_2019-10-07_final
2019-10-07 09:56:09	ga_ls8c_ard_3-1-0_096080_2019-10-07_final
2019-10-07 09:56:33	ga_ls8c_ard_3-1-0_096081_2019-10-07_final
2019-10-07 09:56:57	ga_ls8c_ard_3-1-0_096082_2019-10-07_final
2019-10-07 09:57:21	ga_ls8c_ard_3-1-0_096083_2019-10-07_final
2019-10-07 09:57:45	ga_ls8c_ard_3-1-0_096084_2019-10-07_final
2019-10-07 09:58:09	ga_ls8c_ard_3-1-0_096085_2019-10-07_final
2019-10-07 09:58:33	ga_ls8c_ard_3-1-0_096086_2019-10-07_final
2019-10-07 09:58:57	ga_ls8c_ard_3-1-0_096087_2019-10-07_final
2019-10-07 11:29:51	ga_ls8c_ard_3-1-0_112067_2019-10-07_final
2019-10-07 11:30:15	ga_ls8c_ard_3-1-0_112068_2019-10-07_final
2019-10-07 11:30:39	ga_ls8c_ard_3-1-0_112069_2019-10-07_final
2019-10-07 11:31:03	ga_ls8c_ard_3-1-0_112070_2019-10-07_final
2019-10-07 11:31:27	ga_ls8c_ard_3-1-0_112071_2019-10-07_final
2019-10-07 11:31:50	ga_ls8c_ard_3-1-0_112072_2019-10-07_final
2019-10-07 11:32:14	ga_ls8c_ard_3-1-0_112073_2019-10-07_final
2019-10-07 11:32:38	ga_ls8c_ard_3-1-0_112074_2019-10-07_final
2019-10-07 11:33:02	ga_ls8c_ard_3-1-0_112075_2019-10-07_final
2019-10-07 11:33:26	ga_ls8c_ard_3-1-0_112076_2019-10-07_final
2019-10-07 11:33:50	ga_ls8c_ard_3-1-0_112077_2019-10-07_final
2019-10-07 11:34:14	ga_ls8c_ard_3-1-0_112078_2019-10-07_final
2019-10-07 11:34:38	ga_ls8c_ard_3-1-0_112079_2019-10-07_final
2019-10-07 11:35:02	ga_ls8c_ard_3-1-0_112080_2019-10-07_final
2019-10-07 11:35:26	ga_ls8c_ard_3-1-0_112081_2019-10-07_final
2019-10-07 11:35:50	ga_ls8c_ard_3-1-0_112082_2019-10-07_final
2019-10-07 11:36:14	ga_ls8c_ard_3-1-0_112083_2019-10-07_final
2019-10-07 11:36:38	ga_ls8c_ard_3-1-0_112084_2019-10-07_final
2019-10-07 11:37:02	ga_ls8c_ard_3-1-0_112085_2019-10-07_final
2019-10-08 10:34:14	ga_ls8c_ard_3-1-0_103067_2019-10-08_final
2019-10-08 10:34:38	ga_ls8c_ard_3-1-0_103068_2019-10-08_final
2019-10-08 10:35:01	ga_ls8c_ard_3-1-0_103069_2019-10-08_final
2019-10-08 10:35:25	ga_ls8c_ard_3-1-0_103070_2019-10-08_final
2019-10-08 10:35:49	ga_ls8c_ard_3-1-0_103071_2019-10-08_final
2019-10-08 10:36:13	ga_ls8c_ard_3-1-0_103072_2019-10-08_final
2019-10-08 10:36:37	ga_ls8c_ard_3-1-0_103073_2019-10-08_final
2019-10-08 10:37:01	ga_ls8c_ard_3-1-0_103074_2019-10-08_final
2019-10-08 10:37:25	ga_ls8c_ard_3-1-0_103075_2019-10-08_final
2019-10-08 10:37:49	ga_ls8c_ard_3-1-0_103076_2019-10-08_final
2019-10-08 10:38:13	ga_ls8c_ard_3-1-0_103077_2019-10-08_final
2019-10-08 10:38:37	ga_ls8c_ard_3-1-0_103078_2019-10-08_final
2019-10-08 10:39:01	ga_ls8c_ard_3-1-0_103079_2019-10-08_final
2019-10-08 10:39:25	ga_ls8c_ard_3-1-0_103080_2019-10-08_final
2019-10-08 10:39:49	ga_ls8c_ard_3-1-0_103081_2019-10-08_final
2019-10-08 10:40:13	ga_ls8c_ard_3-1-0_103082_2019-10-08_final
2019-10-08 10:40:37	ga_ls8c_ard_3-1-0_103083_2019-10-08_final

Return to ga_ls8c_ard_3 across October 2019 overview

ga_ls8c_ard_3: Geoscience Australia Landsat 8 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

2019

October

All days