Search ga_ls8c_ard_3 across September 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
2019-09-06 10:34:04	ga_ls8c_ard_3-1-0_103067_2019-09-06_final
2019-09-06 10:34:28	ga_ls8c_ard_3-1-0_103068_2019-09-06_final
2019-09-06 10:34:52	ga_ls8c_ard_3-1-0_103069_2019-09-06_final
2019-09-06 10:35:16	ga_ls8c_ard_3-1-0_103070_2019-09-06_final
2019-09-06 10:35:40	ga_ls8c_ard_3-1-0_103071_2019-09-06_final
2019-09-06 10:36:03	ga_ls8c_ard_3-1-0_103072_2019-09-06_final
2019-09-06 10:36:27	ga_ls8c_ard_3-1-0_103073_2019-09-06_final
2019-09-06 10:36:51	ga_ls8c_ard_3-1-0_103074_2019-09-06_final
2019-09-06 10:37:15	ga_ls8c_ard_3-1-0_103075_2019-09-06_final
2019-09-06 10:37:39	ga_ls8c_ard_3-1-0_103076_2019-09-06_final
2019-09-06 10:38:03	ga_ls8c_ard_3-1-0_103077_2019-09-06_final
2019-09-06 10:38:27	ga_ls8c_ard_3-1-0_103078_2019-09-06_final
2019-09-06 10:38:51	ga_ls8c_ard_3-1-0_103079_2019-09-06_final
2019-09-06 10:39:15	ga_ls8c_ard_3-1-0_103080_2019-09-06_final
2019-09-06 10:39:39	ga_ls8c_ard_3-1-0_103081_2019-09-06_final
2019-09-06 10:40:03	ga_ls8c_ard_3-1-0_103082_2019-09-06_final
2019-09-06 10:40:27	ga_ls8c_ard_3-1-0_103083_2019-09-06_final
2019-09-07 09:38:27	ga_ls8c_ard_3-1-0_094067_2019-09-07_final
2019-09-07 09:40:03	ga_ls8c_ard_3-1-0_094071_2019-09-07_final
2019-09-07 09:40:27	ga_ls8c_ard_3-1-0_094072_2019-09-07_final
2019-09-07 09:40:50	ga_ls8c_ard_3-1-0_094073_2019-09-07_final
2019-09-07 09:41:14	ga_ls8c_ard_3-1-0_094074_2019-09-07_final
2019-09-07 09:41:38	ga_ls8c_ard_3-1-0_094075_2019-09-07_final
2019-09-07 09:42:02	ga_ls8c_ard_3-1-0_094076_2019-09-07_final
2019-09-07 09:42:26	ga_ls8c_ard_3-1-0_094077_2019-09-07_final
2019-09-07 09:42:50	ga_ls8c_ard_3-1-0_094078_2019-09-07_final
2019-09-07 09:43:14	ga_ls8c_ard_3-1-0_094079_2019-09-07_final
2019-09-07 09:43:38	ga_ls8c_ard_3-1-0_094080_2019-09-07_final
2019-09-07 09:44:02	ga_ls8c_ard_3-1-0_094081_2019-09-07_final
2019-09-07 09:44:26	ga_ls8c_ard_3-1-0_094082_2019-09-07_final
2019-09-07 09:44:50	ga_ls8c_ard_3-1-0_094083_2019-09-07_final
2019-09-07 09:45:14	ga_ls8c_ard_3-1-0_094084_2019-09-07_final
2019-09-07 09:45:38	ga_ls8c_ard_3-1-0_094085_2019-09-07_final
2019-09-07 09:46:02	ga_ls8c_ard_3-1-0_094086_2019-09-07_final
2019-09-07 09:46:26	ga_ls8c_ard_3-1-0_094087_2019-09-07_final
2019-09-07 09:46:50	ga_ls8c_ard_3-1-0_094088_2019-09-07_final
2019-09-07 11:17:20	ga_ls8c_ard_3-1-0_110067_2019-09-07_final
2019-09-07 11:17:44	ga_ls8c_ard_3-1-0_110068_2019-09-07_final
2019-09-07 11:18:08	ga_ls8c_ard_3-1-0_110069_2019-09-07_final
2019-09-07 11:18:32	ga_ls8c_ard_3-1-0_110070_2019-09-07_final
2019-09-07 11:18:56	ga_ls8c_ard_3-1-0_110071_2019-09-07_final
2019-09-07 11:19:20	ga_ls8c_ard_3-1-0_110072_2019-09-07_final
2019-09-07 11:19:44	ga_ls8c_ard_3-1-0_110073_2019-09-07_final
2019-09-07 11:20:07	ga_ls8c_ard_3-1-0_110074_2019-09-07_final
2019-09-07 11:20:31	ga_ls8c_ard_3-1-0_110075_2019-09-07_final
2019-09-07 11:20:55	ga_ls8c_ard_3-1-0_110076_2019-09-07_final
2019-09-07 11:21:19	ga_ls8c_ard_3-1-0_110077_2019-09-07_final
2019-09-07 11:21:43	ga_ls8c_ard_3-1-0_110078_2019-09-07_final
2019-09-07 11:22:07	ga_ls8c_ard_3-1-0_110079_2019-09-07_final
2019-09-07 11:22:31	ga_ls8c_ard_3-1-0_110080_2019-09-07_final
2019-09-07 11:22:55	ga_ls8c_ard_3-1-0_110081_2019-09-07_final
2019-09-07 11:23:19	ga_ls8c_ard_3-1-0_110082_2019-09-07_final
2019-09-07 11:23:43	ga_ls8c_ard_3-1-0_110083_2019-09-07_final
2019-09-07 11:24:07	ga_ls8c_ard_3-1-0_110084_2019-09-07_final
2019-09-07 11:24:31	ga_ls8c_ard_3-1-0_110085_2019-09-07_final
2019-09-08 10:21:43	ga_ls8c_ard_3-1-0_101067_2019-09-08_final
2019-09-08 10:22:07	ga_ls8c_ard_3-1-0_101068_2019-09-08_final
2019-09-08 10:22:31	ga_ls8c_ard_3-1-0_101069_2019-09-08_final
2019-09-08 10:22:55	ga_ls8c_ard_3-1-0_101070_2019-09-08_final
2019-09-08 10:23:19	ga_ls8c_ard_3-1-0_101071_2019-09-08_final
2019-09-08 10:23:43	ga_ls8c_ard_3-1-0_101072_2019-09-08_final
2019-09-08 10:24:07	ga_ls8c_ard_3-1-0_101073_2019-09-08_final
2019-09-08 10:24:31	ga_ls8c_ard_3-1-0_101074_2019-09-08_final
2019-09-08 10:24:55	ga_ls8c_ard_3-1-0_101075_2019-09-08_final
2019-09-08 10:25:18	ga_ls8c_ard_3-1-0_101076_2019-09-08_final
2019-09-08 10:25:42	ga_ls8c_ard_3-1-0_101077_2019-09-08_final
2019-09-08 10:26:06	ga_ls8c_ard_3-1-0_101078_2019-09-08_final
2019-09-08 10:26:30	ga_ls8c_ard_3-1-0_101079_2019-09-08_final
2019-09-08 10:26:54	ga_ls8c_ard_3-1-0_101080_2019-09-08_final
2019-09-08 10:27:18	ga_ls8c_ard_3-1-0_101081_2019-09-08_final
2019-09-08 10:27:42	ga_ls8c_ard_3-1-0_101082_2019-09-08_final
2019-09-08 10:28:06	ga_ls8c_ard_3-1-0_101083_2019-09-08_final
2019-09-08 10:28:30	ga_ls8c_ard_3-1-0_101084_2019-09-08_final
2019-09-09 09:26:06	ga_ls8c_ard_3-1-0_092067_2019-09-08_final
2019-09-09 09:26:30	ga_ls8c_ard_3-1-0_092068_2019-09-08_final
2019-09-29 09:06:17	ga_ls8c_ard_3-1-0_088079_2019-09-28_final
2019-09-29 09:06:41	ga_ls8c_ard_3-1-0_088080_2019-09-28_final
2019-09-29 09:07:05	ga_ls8c_ard_3-1-0_088081_2019-09-28_final
2019-09-29 09:07:29	ga_ls8c_ard_3-1-0_088082_2019-09-28_final
2019-09-29 09:07:53	ga_ls8c_ard_3-1-0_088083_2019-09-28_final
2019-09-29 09:08:17	ga_ls8c_ard_3-1-0_088084_2019-09-28_final
2019-09-30 09:44:46	ga_ls8c_ard_3-1-0_095067_2019-09-30_final
2019-09-30 09:45:57	ga_ls8c_ard_3-1-0_095070_2019-09-30_final
2019-09-30 09:46:21	ga_ls8c_ard_3-1-0_095071_2019-09-30_final
2019-09-30 09:46:45	ga_ls8c_ard_3-1-0_095072_2019-09-30_final
2019-09-30 09:47:09	ga_ls8c_ard_3-1-0_095073_2019-09-30_final
2019-09-30 09:47:33	ga_ls8c_ard_3-1-0_095074_2019-09-30_final
2019-09-30 09:47:57	ga_ls8c_ard_3-1-0_095075_2019-09-30_final
2019-09-30 09:48:21	ga_ls8c_ard_3-1-0_095076_2019-09-30_final
2019-09-30 09:48:45	ga_ls8c_ard_3-1-0_095077_2019-09-30_final
2019-09-30 09:49:09	ga_ls8c_ard_3-1-0_095078_2019-09-30_final
2019-09-30 09:49:33	ga_ls8c_ard_3-1-0_095079_2019-09-30_final
2019-09-30 09:49:57	ga_ls8c_ard_3-1-0_095080_2019-09-30_final
2019-09-30 09:50:21	ga_ls8c_ard_3-1-0_095081_2019-09-30_final
2019-09-30 09:50:45	ga_ls8c_ard_3-1-0_095082_2019-09-30_final
2019-09-30 09:51:09	ga_ls8c_ard_3-1-0_095083_2019-09-30_final
2019-09-30 09:51:33	ga_ls8c_ard_3-1-0_095084_2019-09-30_final
2019-09-30 09:51:56	ga_ls8c_ard_3-1-0_095085_2019-09-30_final
2019-09-30 09:52:20	ga_ls8c_ard_3-1-0_095086_2019-09-30_final
2019-09-30 09:52:44	ga_ls8c_ard_3-1-0_095087_2019-09-30_final

Return to ga_ls8c_ard_3 across September 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

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

2019

September

All days