Search ga_ls8c_ard_3 across May 2018

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:

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First 100 results (query limit)

Time (local)	Label
2018-05-18 10:08:07	ga_ls8c_ard_3-0-0_099067_2018-05-18_final
2018-05-18 10:08:30	ga_ls8c_ard_3-0-0_099068_2018-05-18_final
2018-05-18 10:08:54	ga_ls8c_ard_3-0-0_099069_2018-05-18_final
2018-05-18 10:09:18	ga_ls8c_ard_3-0-0_099070_2018-05-18_final
2018-05-18 10:09:42	ga_ls8c_ard_3-0-0_099071_2018-05-18_final
2018-05-18 10:10:06	ga_ls8c_ard_3-0-0_099072_2018-05-18_final
2018-05-18 10:10:30	ga_ls8c_ard_3-0-0_099073_2018-05-18_final
2018-05-18 10:10:54	ga_ls8c_ard_3-0-0_099074_2018-05-18_final
2018-05-18 10:11:18	ga_ls8c_ard_3-0-0_099075_2018-05-18_final
2018-05-18 10:11:42	ga_ls8c_ard_3-0-0_099076_2018-05-18_final
2018-05-18 10:12:06	ga_ls8c_ard_3-0-0_099077_2018-05-18_final
2018-05-18 10:12:30	ga_ls8c_ard_3-0-0_099078_2018-05-18_final
2018-05-18 10:12:54	ga_ls8c_ard_3-0-0_099079_2018-05-18_final
2018-05-18 10:13:18	ga_ls8c_ard_3-0-0_099080_2018-05-18_final
2018-05-18 10:13:42	ga_ls8c_ard_3-0-0_099081_2018-05-18_final
2018-05-18 10:14:06	ga_ls8c_ard_3-1-0_099082_2018-05-18_final
2018-05-18 10:14:30	ga_ls8c_ard_3-0-0_099083_2018-05-18_final
2018-05-18 10:14:53	ga_ls8c_ard_3-0-0_099084_2018-05-18_final
2018-05-18 10:15:17	ga_ls8c_ard_3-0-0_099085_2018-05-18_final
2018-05-18 11:49:23	ga_ls8c_ard_3-0-0_115073_2018-05-18_final
2018-05-18 11:49:47	ga_ls8c_ard_3-0-0_115074_2018-05-18_final
2018-05-18 11:50:11	ga_ls8c_ard_3-0-0_115075_2018-05-18_final
2018-05-18 11:50:35	ga_ls8c_ard_3-0-0_115076_2018-05-18_final
2018-05-18 11:50:59	ga_ls8c_ard_3-0-0_115077_2018-05-18_final
2018-05-18 11:51:23	ga_ls8c_ard_3-0-0_115078_2018-05-18_final
2018-05-18 11:51:47	ga_ls8c_ard_3-0-0_115079_2018-05-18_final
2018-05-18 11:52:11	ga_ls8c_ard_3-0-0_115080_2018-05-18_final
2018-05-19 09:15:16	ga_ls8c_ard_3-0-0_090074_2018-05-18_final
2018-05-19 09:15:40	ga_ls8c_ard_3-0-0_090075_2018-05-18_final
2018-05-19 09:16:04	ga_ls8c_ard_3-0-0_090076_2018-05-18_final
2018-05-19 09:16:28	ga_ls8c_ard_3-0-0_090077_2018-05-18_final
2018-05-19 09:16:52	ga_ls8c_ard_3-0-0_090078_2018-05-18_final
2018-05-19 09:17:16	ga_ls8c_ard_3-0-0_090079_2018-05-18_final
2018-05-19 09:17:40	ga_ls8c_ard_3-0-0_090080_2018-05-18_final
2018-05-19 09:18:04	ga_ls8c_ard_3-0-0_090081_2018-05-18_final
2018-05-19 09:18:28	ga_ls8c_ard_3-0-0_090082_2018-05-18_final
2018-05-19 09:18:51	ga_ls8c_ard_3-0-0_090083_2018-05-18_final
2018-05-19 09:19:15	ga_ls8c_ard_3-0-0_090084_2018-05-18_final
2018-05-19 09:19:39	ga_ls8c_ard_3-0-0_090085_2018-05-18_final
2018-05-19 09:20:03	ga_ls8c_ard_3-0-0_090086_2018-05-18_final
2018-05-19 09:20:27	ga_ls8c_ard_3-0-0_090087_2018-05-18_final
2018-05-19 09:20:51	ga_ls8c_ard_3-0-0_090088_2018-05-18_final
2018-05-19 09:21:15	ga_ls8c_ard_3-0-0_090089_2018-05-18_final
2018-05-19 09:21:39	ga_ls8c_ard_3-0-0_090090_2018-05-18_final
2018-05-19 09:22:03	ga_ls8c_ard_3-0-0_090091_2018-05-18_final
2018-05-19 10:51:21	ga_ls8c_ard_3-0-0_106067_2018-05-19_final
2018-05-19 10:51:45	ga_ls8c_ard_3-0-0_106068_2018-05-19_final
2018-05-19 10:52:09	ga_ls8c_ard_3-0-0_106069_2018-05-19_final
2018-05-19 10:52:33	ga_ls8c_ard_3-0-0_106070_2018-05-19_final
2018-05-19 10:52:57	ga_ls8c_ard_3-0-0_106071_2018-05-19_final
2018-05-19 10:53:21	ga_ls8c_ard_3-0-0_106072_2018-05-19_final
2018-05-19 10:53:45	ga_ls8c_ard_3-0-0_106073_2018-05-19_final
2018-05-19 10:54:09	ga_ls8c_ard_3-0-0_106074_2018-05-19_final
2018-05-19 10:54:33	ga_ls8c_ard_3-0-0_106075_2018-05-19_final
2018-05-19 10:54:57	ga_ls8c_ard_3-0-0_106076_2018-05-19_final
2018-05-19 10:55:21	ga_ls8c_ard_3-0-0_106077_2018-05-19_final
2018-05-19 10:55:45	ga_ls8c_ard_3-0-0_106078_2018-05-19_final
2018-05-19 10:56:09	ga_ls8c_ard_3-0-0_106079_2018-05-19_final
2018-05-19 10:56:33	ga_ls8c_ard_3-0-0_106080_2018-05-19_final
2018-05-19 10:56:57	ga_ls8c_ard_3-0-0_106081_2018-05-19_final
2018-05-19 10:57:20	ga_ls8c_ard_3-0-0_106082_2018-05-19_final
2018-05-19 10:57:44	ga_ls8c_ard_3-0-0_106083_2018-05-19_final
2018-05-19 10:58:08	ga_ls8c_ard_3-0-0_106084_2018-05-19_final
2018-05-20 09:55:43	ga_ls8c_ard_3-0-0_097067_2018-05-20_final
2018-05-20 09:56:07	ga_ls8c_ard_3-0-0_097068_2018-05-20_final
2018-05-20 09:56:31	ga_ls8c_ard_3-0-0_097069_2018-05-20_final
2018-05-20 09:56:55	ga_ls8c_ard_3-0-0_097070_2018-05-20_final
2018-05-20 09:57:19	ga_ls8c_ard_3-0-0_097071_2018-05-20_final
2018-05-20 09:57:43	ga_ls8c_ard_3-0-0_097072_2018-05-20_final
2018-05-20 09:58:07	ga_ls8c_ard_3-0-0_097073_2018-05-20_final
2018-05-20 09:58:31	ga_ls8c_ard_3-0-0_097074_2018-05-20_final
2018-05-20 09:58:55	ga_ls8c_ard_3-0-0_097075_2018-05-20_final
2018-05-20 09:59:19	ga_ls8c_ard_3-0-0_097076_2018-05-20_final
2018-05-20 09:59:43	ga_ls8c_ard_3-0-0_097077_2018-05-20_final
2018-05-20 10:00:07	ga_ls8c_ard_3-0-0_097078_2018-05-20_final
2018-05-20 10:00:31	ga_ls8c_ard_3-0-0_097079_2018-05-20_final
2018-05-20 10:00:55	ga_ls8c_ard_3-0-0_097080_2018-05-20_final
2018-05-20 10:01:18	ga_ls8c_ard_3-0-0_097081_2018-05-20_final
2018-05-20 10:01:42	ga_ls8c_ard_3-0-0_097082_2018-05-20_final
2018-05-20 10:02:06	ga_ls8c_ard_3-0-0_097083_2018-05-20_final
2018-05-20 10:02:30	ga_ls8c_ard_3-0-0_097084_2018-05-20_final
2018-05-20 10:02:54	ga_ls8c_ard_3-0-0_097085_2018-05-20_final
2018-05-20 10:03:18	ga_ls8c_ard_3-0-0_097086_2018-05-20_final
2018-05-20 11:34:36	ga_ls8c_ard_3-0-0_113067_2018-05-20_final
2018-05-20 11:36:36	ga_ls8c_ard_3-0-0_113072_2018-05-20_final
2018-05-20 11:37:00	ga_ls8c_ard_3-0-0_113073_2018-05-20_final
2018-05-20 11:37:24	ga_ls8c_ard_3-0-0_113074_2018-05-20_final
2018-05-20 11:37:48	ga_ls8c_ard_3-0-0_113075_2018-05-20_final
2018-05-20 11:38:12	ga_ls8c_ard_3-0-0_113076_2018-05-20_final
2018-05-20 11:38:36	ga_ls8c_ard_3-0-0_113077_2018-05-20_final
2018-05-20 11:39:00	ga_ls8c_ard_3-0-0_113078_2018-05-20_final
2018-05-20 11:39:24	ga_ls8c_ard_3-0-0_113079_2018-05-20_final
2018-05-20 11:39:48	ga_ls8c_ard_3-0-0_113080_2018-05-20_final
2018-05-20 11:40:11	ga_ls8c_ard_3-0-0_113081_2018-05-20_final
2018-05-20 11:40:35	ga_ls8c_ard_3-0-0_113082_2018-05-20_final
2018-05-20 11:40:59	ga_ls8c_ard_3-0-0_113083_2018-05-20_final
2018-05-20 11:41:23	ga_ls8c_ard_3-0-0_113084_2018-05-20_final
2018-05-21 09:04:53	ga_ls8c_ard_3-0-0_088079_2018-05-20_final
2018-05-21 09:05:16	ga_ls8c_ard_3-0-0_088080_2018-05-20_final
2018-05-21 09:05:40	ga_ls8c_ard_3-1-0_088081_2018-05-20_final

Return to ga_ls8c_ard_3 across May 2018 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

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

2018

May

All days