Search ga_ls8c_ard_3 across September 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:

Add field

First 100 results (query limit)

Time (local)	Label
2018-09-03 10:33:25	ga_ls8c_ard_3-0-0_103067_2018-09-03_final
2018-09-03 10:33:49	ga_ls8c_ard_3-0-0_103068_2018-09-03_final
2018-09-03 10:34:13	ga_ls8c_ard_3-0-0_103069_2018-09-03_final
2018-09-03 10:34:37	ga_ls8c_ard_3-0-0_103070_2018-09-03_final
2018-09-03 10:35:01	ga_ls8c_ard_3-0-0_103071_2018-09-03_final
2018-09-03 10:35:25	ga_ls8c_ard_3-0-0_103072_2018-09-03_final
2018-09-03 10:35:48	ga_ls8c_ard_3-0-0_103073_2018-09-03_final
2018-09-03 10:36:12	ga_ls8c_ard_3-0-0_103074_2018-09-03_final
2018-09-03 10:36:36	ga_ls8c_ard_3-0-0_103075_2018-09-03_final
2018-09-03 10:37:00	ga_ls8c_ard_3-0-0_103076_2018-09-03_final
2018-09-03 10:37:24	ga_ls8c_ard_3-0-0_103077_2018-09-03_final
2018-09-03 10:37:48	ga_ls8c_ard_3-0-0_103078_2018-09-03_final
2018-09-03 10:38:12	ga_ls8c_ard_3-0-0_103079_2018-09-03_final
2018-09-03 10:38:36	ga_ls8c_ard_3-0-0_103080_2018-09-03_final
2018-09-03 10:39:00	ga_ls8c_ard_3-0-0_103081_2018-09-03_final
2018-09-03 10:39:24	ga_ls8c_ard_3-0-0_103082_2018-09-03_final
2018-09-03 10:39:48	ga_ls8c_ard_3-0-0_103083_2018-09-03_final
2018-09-04 09:37:48	ga_ls8c_ard_3-0-0_094067_2018-09-04_final
2018-09-04 09:39:24	ga_ls8c_ard_3-0-0_094071_2018-09-04_final
2018-09-04 09:39:48	ga_ls8c_ard_3-0-0_094072_2018-09-04_final
2018-09-04 09:40:11	ga_ls8c_ard_3-0-0_094073_2018-09-04_final
2018-09-04 09:40:35	ga_ls8c_ard_3-0-0_094074_2018-09-04_final
2018-09-04 09:40:59	ga_ls8c_ard_3-0-0_094075_2018-09-04_final
2018-09-04 09:41:23	ga_ls8c_ard_3-0-0_094076_2018-09-04_final
2018-09-04 09:41:47	ga_ls8c_ard_3-0-0_094077_2018-09-04_final
2018-09-04 09:42:11	ga_ls8c_ard_3-0-0_094078_2018-09-04_final
2018-09-04 09:42:35	ga_ls8c_ard_3-0-0_094079_2018-09-04_final
2018-09-04 09:42:59	ga_ls8c_ard_3-0-0_094080_2018-09-04_final
2018-09-04 09:43:23	ga_ls8c_ard_3-0-0_094081_2018-09-04_final
2018-09-04 09:43:47	ga_ls8c_ard_3-0-0_094082_2018-09-04_final
2018-09-04 09:44:11	ga_ls8c_ard_3-0-0_094083_2018-09-04_final
2018-09-04 09:44:35	ga_ls8c_ard_3-0-0_094084_2018-09-04_final
2018-09-04 09:44:59	ga_ls8c_ard_3-0-0_094085_2018-09-04_final
2018-09-04 09:45:23	ga_ls8c_ard_3-0-0_094086_2018-09-04_final
2018-09-04 09:45:47	ga_ls8c_ard_3-0-0_094087_2018-09-04_final
2018-09-04 09:46:11	ga_ls8c_ard_3-0-0_094088_2018-09-04_final
2018-09-04 11:16:41	ga_ls8c_ard_3-0-0_110067_2018-09-04_final
2018-09-04 11:17:05	ga_ls8c_ard_3-0-0_110068_2018-09-04_final
2018-09-04 11:17:29	ga_ls8c_ard_3-0-0_110069_2018-09-04_final
2018-09-04 11:17:53	ga_ls8c_ard_3-0-0_110070_2018-09-04_final
2018-09-04 11:18:17	ga_ls8c_ard_3-0-0_110071_2018-09-04_final
2018-09-04 11:18:41	ga_ls8c_ard_3-0-0_110072_2018-09-04_final
2018-09-04 11:19:05	ga_ls8c_ard_3-0-0_110073_2018-09-04_final
2018-09-04 11:19:28	ga_ls8c_ard_3-0-0_110074_2018-09-04_final
2018-09-04 11:19:52	ga_ls8c_ard_3-0-0_110075_2018-09-04_final
2018-09-04 11:20:16	ga_ls8c_ard_3-0-0_110076_2018-09-04_final
2018-09-04 11:20:40	ga_ls8c_ard_3-0-0_110077_2018-09-04_final
2018-09-04 11:21:04	ga_ls8c_ard_3-0-0_110078_2018-09-04_final
2018-09-04 11:21:28	ga_ls8c_ard_3-0-0_110079_2018-09-04_final
2018-09-04 11:21:52	ga_ls8c_ard_3-0-0_110080_2018-09-04_final
2018-09-04 11:22:16	ga_ls8c_ard_3-0-0_110081_2018-09-04_final
2018-09-04 11:22:40	ga_ls8c_ard_3-0-0_110082_2018-09-04_final
2018-09-04 11:23:04	ga_ls8c_ard_3-0-0_110083_2018-09-04_final
2018-09-04 11:23:28	ga_ls8c_ard_3-0-0_110084_2018-09-04_final
2018-09-04 11:23:52	ga_ls8c_ard_3-0-0_110085_2018-09-04_final
2018-09-05 10:21:04	ga_ls8c_ard_3-0-0_101067_2018-09-05_final
2018-09-05 10:21:28	ga_ls8c_ard_3-0-0_101068_2018-09-05_final
2018-09-05 10:21:52	ga_ls8c_ard_3-0-0_101069_2018-09-05_final
2018-09-05 10:22:16	ga_ls8c_ard_3-0-0_101070_2018-09-05_final
2018-09-05 10:22:40	ga_ls8c_ard_3-0-0_101071_2018-09-05_final
2018-09-05 10:23:04	ga_ls8c_ard_3-0-0_101072_2018-09-05_final
2018-09-05 10:23:28	ga_ls8c_ard_3-0-0_101073_2018-09-05_final
2018-09-05 10:23:51	ga_ls8c_ard_3-0-0_101074_2018-09-05_final
2018-09-05 10:24:15	ga_ls8c_ard_3-0-0_101075_2018-09-05_final
2018-09-05 10:24:39	ga_ls8c_ard_3-0-0_101076_2018-09-05_final
2018-09-05 10:25:03	ga_ls8c_ard_3-0-0_101077_2018-09-05_final
2018-09-05 10:25:27	ga_ls8c_ard_3-0-0_101078_2018-09-05_final
2018-09-05 10:25:51	ga_ls8c_ard_3-0-0_101079_2018-09-05_final
2018-09-05 10:26:15	ga_ls8c_ard_3-0-0_101080_2018-09-05_final
2018-09-05 10:26:39	ga_ls8c_ard_3-0-0_101081_2018-09-05_final
2018-09-05 10:27:03	ga_ls8c_ard_3-1-0_101082_2018-09-05_final
2018-09-05 10:27:27	ga_ls8c_ard_3-0-0_101083_2018-09-05_final
2018-09-05 10:27:51	ga_ls8c_ard_3-0-0_101084_2018-09-05_final
2018-09-06 09:25:27	ga_ls8c_ard_3-0-0_092067_2018-09-05_final
2018-09-06 09:25:51	ga_ls8c_ard_3-0-0_092068_2018-09-05_final
2018-09-26 09:05:38	ga_ls8c_ard_3-0-0_088079_2018-09-25_final
2018-09-26 09:06:02	ga_ls8c_ard_3-0-0_088080_2018-09-25_final
2018-09-26 09:06:26	ga_ls8c_ard_3-1-0_088081_2018-09-25_final
2018-09-26 09:06:49	ga_ls8c_ard_3-0-0_088082_2018-09-25_final
2018-09-26 09:07:13	ga_ls8c_ard_3-0-0_088083_2018-09-25_final
2018-09-26 09:07:37	ga_ls8c_ard_3-0-0_088084_2018-09-25_final
2018-09-27 09:44:07	ga_ls8c_ard_3-0-0_095067_2018-09-27_final
2018-09-27 09:45:18	ga_ls8c_ard_3-0-0_095070_2018-09-27_final
2018-09-27 09:45:42	ga_ls8c_ard_3-0-0_095071_2018-09-27_final
2018-09-27 09:46:06	ga_ls8c_ard_3-0-0_095072_2018-09-27_final
2018-09-27 09:46:30	ga_ls8c_ard_3-0-0_095073_2018-09-27_final
2018-09-27 09:46:54	ga_ls8c_ard_3-0-0_095074_2018-09-27_final
2018-09-27 09:47:18	ga_ls8c_ard_3-0-0_095075_2018-09-27_final
2018-09-27 09:47:42	ga_ls8c_ard_3-0-0_095076_2018-09-27_final
2018-09-27 09:48:06	ga_ls8c_ard_3-0-0_095077_2018-09-27_final
2018-09-27 09:48:30	ga_ls8c_ard_3-0-0_095078_2018-09-27_final
2018-09-27 09:48:54	ga_ls8c_ard_3-0-0_095079_2018-09-27_final
2018-09-27 09:49:18	ga_ls8c_ard_3-0-0_095080_2018-09-27_final
2018-09-27 09:49:42	ga_ls8c_ard_3-0-0_095081_2018-09-27_final
2018-09-27 09:50:06	ga_ls8c_ard_3-0-0_095082_2018-09-27_final
2018-09-27 09:50:30	ga_ls8c_ard_3-0-0_095083_2018-09-27_final
2018-09-27 09:50:54	ga_ls8c_ard_3-0-0_095084_2018-09-27_final
2018-09-27 09:51:18	ga_ls8c_ard_3-0-0_095085_2018-09-27_final
2018-09-27 09:51:42	ga_ls8c_ard_3-0-0_095086_2018-09-27_final
2018-09-27 09:52:05	ga_ls8c_ard_3-0-0_095087_2018-09-27_final

Return to ga_ls8c_ard_3 across September 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

September

All days