Search ga_ls8c_ard_3 across November 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
2019-11-06 10:03:20	ga_ls8c_ard_3-1-0_098067_2019-11-06_final
2019-11-06 10:03:44	ga_ls8c_ard_3-1-0_098068_2019-11-06_final
2019-11-06 10:04:08	ga_ls8c_ard_3-1-0_098069_2019-11-06_final
2019-11-06 10:04:31	ga_ls8c_ard_3-1-0_098070_2019-11-06_final
2019-11-06 10:04:55	ga_ls8c_ard_3-1-0_098071_2019-11-06_final
2019-11-06 10:05:19	ga_ls8c_ard_3-1-0_098072_2019-11-06_final
2019-11-06 10:05:43	ga_ls8c_ard_3-1-0_098073_2019-11-06_final
2019-11-06 10:06:07	ga_ls8c_ard_3-1-0_098074_2019-11-06_final
2019-11-06 10:06:31	ga_ls8c_ard_3-1-0_098075_2019-11-06_final
2019-11-06 10:06:55	ga_ls8c_ard_3-1-0_098076_2019-11-06_final
2019-11-06 10:07:19	ga_ls8c_ard_3-1-0_098077_2019-11-06_final
2019-11-06 10:07:43	ga_ls8c_ard_3-1-0_098078_2019-11-06_final
2019-11-06 10:08:07	ga_ls8c_ard_3-1-0_098079_2019-11-06_final
2019-11-06 10:08:31	ga_ls8c_ard_3-1-0_098080_2019-11-06_final
2019-11-06 10:08:55	ga_ls8c_ard_3-1-0_098081_2019-11-06_final
2019-11-06 10:09:19	ga_ls8c_ard_3-1-0_098082_2019-11-06_final
2019-11-06 10:09:43	ga_ls8c_ard_3-1-0_098083_2019-11-06_final
2019-11-06 10:10:07	ga_ls8c_ard_3-1-0_098084_2019-11-06_final
2019-11-06 10:10:31	ga_ls8c_ard_3-1-0_098085_2019-11-06_final
2019-11-07 09:10:30	ga_ls8c_ard_3-1-0_089074_2019-11-06_final
2019-11-07 09:10:54	ga_ls8c_ard_3-1-0_089075_2019-11-06_final
2019-11-07 09:11:18	ga_ls8c_ard_3-1-0_089076_2019-11-06_final
2019-11-07 09:11:42	ga_ls8c_ard_3-1-0_089077_2019-11-06_final
2019-11-07 09:12:06	ga_ls8c_ard_3-1-0_089078_2019-11-06_final
2019-11-07 09:12:29	ga_ls8c_ard_3-1-0_089079_2019-11-06_final
2019-11-07 09:12:53	ga_ls8c_ard_3-1-0_089080_2019-11-06_final
2019-11-07 09:13:17	ga_ls8c_ard_3-1-0_089081_2019-11-06_final
2019-11-07 09:13:41	ga_ls8c_ard_3-1-0_089082_2019-11-06_final
2019-11-07 09:14:05	ga_ls8c_ard_3-1-0_089083_2019-11-06_final
2019-11-07 09:14:29	ga_ls8c_ard_3-1-0_089084_2019-11-06_final
2019-11-07 09:14:53	ga_ls8c_ard_3-1-0_089085_2019-11-06_final
2019-11-07 09:15:17	ga_ls8c_ard_3-1-0_089086_2019-11-06_final
2019-11-07 09:15:41	ga_ls8c_ard_3-1-0_089087_2019-11-06_final
2019-11-07 09:16:05	ga_ls8c_ard_3-1-0_089088_2019-11-06_final
2019-11-07 09:16:29	ga_ls8c_ard_3-1-0_089089_2019-11-06_final
2019-11-07 09:16:53	ga_ls8c_ard_3-1-0_089090_2019-11-06_final
2019-11-22 10:02:52	ga_ls8c_ard_3-2-1_098066_2019-11-22_final
2019-11-22 10:03:16	ga_ls8c_ard_3-1-0_098067_2019-11-22_final
2019-11-22 10:03:40	ga_ls8c_ard_3-1-0_098068_2019-11-22_final
2019-11-22 10:04:04	ga_ls8c_ard_3-1-0_098069_2019-11-22_final
2019-11-22 10:04:28	ga_ls8c_ard_3-1-0_098070_2019-11-22_final
2019-11-22 10:04:52	ga_ls8c_ard_3-1-0_098071_2019-11-22_final
2019-11-22 10:05:16	ga_ls8c_ard_3-1-0_098072_2019-11-22_final
2019-11-22 10:05:39	ga_ls8c_ard_3-1-0_098073_2019-11-22_final
2019-11-22 10:06:03	ga_ls8c_ard_3-1-0_098074_2019-11-22_final
2019-11-22 10:06:27	ga_ls8c_ard_3-1-0_098075_2019-11-22_final
2019-11-22 10:06:51	ga_ls8c_ard_3-1-0_098076_2019-11-22_final
2019-11-22 10:07:15	ga_ls8c_ard_3-1-0_098077_2019-11-22_final
2019-11-22 10:07:39	ga_ls8c_ard_3-1-0_098078_2019-11-22_final
2019-11-22 10:08:03	ga_ls8c_ard_3-1-0_098079_2019-11-22_final
2019-11-22 10:08:27	ga_ls8c_ard_3-1-0_098080_2019-11-22_final
2019-11-22 10:08:51	ga_ls8c_ard_3-1-0_098081_2019-11-22_final
2019-11-22 10:09:15	ga_ls8c_ard_3-1-0_098082_2019-11-22_final
2019-11-22 10:09:39	ga_ls8c_ard_3-1-0_098083_2019-11-22_final
2019-11-22 10:10:03	ga_ls8c_ard_3-1-0_098084_2019-11-22_final
2019-11-22 10:10:27	ga_ls8c_ard_3-1-0_098085_2019-11-22_final
2019-11-22 10:10:51	ga_ls8c_ard_3-1-0_098086_2019-11-22_final
2019-11-22 11:42:09	ga_ls8c_ard_3-1-0_114067_2019-11-22_final
2019-11-22 11:44:33	ga_ls8c_ard_3-1-0_114073_2019-11-22_final
2019-11-22 11:44:56	ga_ls8c_ard_3-1-0_114074_2019-11-22_final
2019-11-22 11:45:20	ga_ls8c_ard_3-1-0_114075_2019-11-22_final
2019-11-22 11:45:44	ga_ls8c_ard_3-1-0_114076_2019-11-22_final
2019-11-22 11:46:08	ga_ls8c_ard_3-1-0_114077_2019-11-22_final
2019-11-22 11:46:32	ga_ls8c_ard_3-1-0_114078_2019-11-22_final
2019-11-22 11:46:56	ga_ls8c_ard_3-1-0_114079_2019-11-22_final
2019-11-22 11:47:20	ga_ls8c_ard_3-1-0_114080_2019-11-22_final
2019-11-22 11:47:44	ga_ls8c_ard_3-1-0_114081_2019-11-22_final
2019-11-23 09:10:26	ga_ls8c_ard_3-1-0_089074_2019-11-22_final
2019-11-23 09:10:50	ga_ls8c_ard_3-1-0_089075_2019-11-22_final
2019-11-23 09:11:14	ga_ls8c_ard_3-1-0_089076_2019-11-22_final
2019-11-23 09:11:38	ga_ls8c_ard_3-1-0_089077_2019-11-22_final
2019-11-23 09:12:02	ga_ls8c_ard_3-1-0_089078_2019-11-22_final
2019-11-23 09:12:26	ga_ls8c_ard_3-1-0_089079_2019-11-22_final
2019-11-23 09:12:50	ga_ls8c_ard_3-1-0_089080_2019-11-22_final
2019-11-23 09:13:14	ga_ls8c_ard_3-1-0_089081_2019-11-22_final
2019-11-23 09:13:38	ga_ls8c_ard_3-1-0_089082_2019-11-22_final
2019-11-23 09:14:02	ga_ls8c_ard_3-1-0_089083_2019-11-22_final
2019-11-23 09:14:26	ga_ls8c_ard_3-1-0_089084_2019-11-22_final
2019-11-23 09:14:50	ga_ls8c_ard_3-1-0_089085_2019-11-22_final
2019-11-23 09:15:14	ga_ls8c_ard_3-1-0_089086_2019-11-22_final
2019-11-23 09:15:38	ga_ls8c_ard_3-1-0_089087_2019-11-22_final
2019-11-23 09:16:01	ga_ls8c_ard_3-1-0_089088_2019-11-22_final
2019-11-23 09:16:25	ga_ls8c_ard_3-1-0_089089_2019-11-22_final
2019-11-23 09:16:49	ga_ls8c_ard_3-1-0_089090_2019-11-22_final
2019-11-23 10:46:32	ga_ls8c_ard_3-1-0_105067_2019-11-23_final
2019-11-23 10:46:56	ga_ls8c_ard_3-1-0_105068_2019-11-23_final
2019-11-23 10:47:20	ga_ls8c_ard_3-1-0_105069_2019-11-23_final
2019-11-23 10:47:43	ga_ls8c_ard_3-1-0_105070_2019-11-23_final
2019-11-23 10:48:07	ga_ls8c_ard_3-1-0_105071_2019-11-23_final
2019-11-23 10:48:31	ga_ls8c_ard_3-1-0_105072_2019-11-23_final
2019-11-23 10:48:55	ga_ls8c_ard_3-1-0_105073_2019-11-23_final
2019-11-23 10:49:19	ga_ls8c_ard_3-1-0_105074_2019-11-23_final
2019-11-23 10:49:43	ga_ls8c_ard_3-1-0_105075_2019-11-23_final
2019-11-23 10:50:07	ga_ls8c_ard_3-1-0_105076_2019-11-23_final
2019-11-23 10:50:31	ga_ls8c_ard_3-1-0_105077_2019-11-23_final
2019-11-23 10:50:55	ga_ls8c_ard_3-1-0_105078_2019-11-23_final
2019-11-23 10:51:19	ga_ls8c_ard_3-1-0_105079_2019-11-23_final
2019-11-23 10:51:43	ga_ls8c_ard_3-1-0_105080_2019-11-23_final
2019-11-23 10:52:07	ga_ls8c_ard_3-1-0_105081_2019-11-23_final
2019-11-23 10:52:31	ga_ls8c_ard_3-1-0_105082_2019-11-23_final

Return to ga_ls8c_ard_3 across November 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

November

All days