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:

Add field

First 100 results (query limit)

Time (local)	Label
2018-05-01 09:25:02	ga_ls8c_ard_3-0-0_092067_2018-04-30_final
2018-05-01 09:25:26	ga_ls8c_ard_3-0-0_092068_2018-04-30_final
2018-05-01 09:26:38	ga_ls8c_ard_3-0-0_092071_2018-04-30_final
2018-05-01 09:27:02	ga_ls8c_ard_3-0-0_092072_2018-04-30_final
2018-05-15 09:37:15	ga_ls8c_ard_3-0-0_094067_2018-05-15_final
2018-05-15 09:38:50	ga_ls8c_ard_3-0-0_094071_2018-05-15_final
2018-05-15 09:39:14	ga_ls8c_ard_3-0-0_094072_2018-05-15_final
2018-05-15 09:39:38	ga_ls8c_ard_3-0-0_094073_2018-05-15_final
2018-05-15 09:40:02	ga_ls8c_ard_3-0-0_094074_2018-05-15_final
2018-05-15 09:40:26	ga_ls8c_ard_3-0-0_094075_2018-05-15_final
2018-05-15 09:40:50	ga_ls8c_ard_3-0-0_094076_2018-05-15_final
2018-05-15 09:41:14	ga_ls8c_ard_3-0-0_094077_2018-05-15_final
2018-05-15 09:41:38	ga_ls8c_ard_3-0-0_094078_2018-05-15_final
2018-05-15 09:42:02	ga_ls8c_ard_3-0-0_094079_2018-05-15_final
2018-05-15 09:42:26	ga_ls8c_ard_3-0-0_094080_2018-05-15_final
2018-05-15 09:42:50	ga_ls8c_ard_3-0-0_094081_2018-05-15_final
2018-05-15 09:43:14	ga_ls8c_ard_3-0-0_094082_2018-05-15_final
2018-05-15 09:43:38	ga_ls8c_ard_3-0-0_094083_2018-05-15_final
2018-05-15 09:44:02	ga_ls8c_ard_3-0-0_094084_2018-05-15_final
2018-05-15 09:44:26	ga_ls8c_ard_3-0-0_094085_2018-05-15_final
2018-05-15 09:44:50	ga_ls8c_ard_3-0-0_094086_2018-05-15_final
2018-05-15 09:45:14	ga_ls8c_ard_3-0-0_094087_2018-05-15_final
2018-05-15 09:45:37	ga_ls8c_ard_3-0-0_094088_2018-05-15_final
2018-05-15 11:16:08	ga_ls8c_ard_3-0-0_110067_2018-05-15_final
2018-05-15 11:16:32	ga_ls8c_ard_3-0-0_110068_2018-05-15_final
2018-05-15 11:16:56	ga_ls8c_ard_3-0-0_110069_2018-05-15_final
2018-05-15 11:17:19	ga_ls8c_ard_3-0-0_110070_2018-05-15_final
2018-05-15 11:17:43	ga_ls8c_ard_3-0-0_110071_2018-05-15_final
2018-05-15 11:18:07	ga_ls8c_ard_3-0-0_110072_2018-05-15_final
2018-05-15 11:18:31	ga_ls8c_ard_3-0-0_110073_2018-05-15_final
2018-05-15 11:18:55	ga_ls8c_ard_3-0-0_110074_2018-05-15_final
2018-05-15 11:19:19	ga_ls8c_ard_3-0-0_110075_2018-05-15_final
2018-05-15 11:19:43	ga_ls8c_ard_3-0-0_110076_2018-05-15_final
2018-05-15 11:20:07	ga_ls8c_ard_3-0-0_110077_2018-05-15_final
2018-05-15 11:20:31	ga_ls8c_ard_3-0-0_110078_2018-05-15_final
2018-05-15 11:20:55	ga_ls8c_ard_3-0-0_110079_2018-05-15_final
2018-05-15 11:21:19	ga_ls8c_ard_3-0-0_110080_2018-05-15_final
2018-05-15 11:21:43	ga_ls8c_ard_3-0-0_110081_2018-05-15_final
2018-05-15 11:22:07	ga_ls8c_ard_3-0-0_110082_2018-05-15_final
2018-05-15 11:22:31	ga_ls8c_ard_3-0-0_110083_2018-05-15_final
2018-05-15 11:22:55	ga_ls8c_ard_3-0-0_110084_2018-05-15_final
2018-05-15 11:23:19	ga_ls8c_ard_3-0-0_110085_2018-05-15_final
2018-05-16 10:20:30	ga_ls8c_ard_3-0-0_101067_2018-05-16_final
2018-05-16 10:20:54	ga_ls8c_ard_3-0-0_101068_2018-05-16_final
2018-05-16 10:21:18	ga_ls8c_ard_3-0-0_101069_2018-05-16_final
2018-05-16 10:21:41	ga_ls8c_ard_3-0-0_101070_2018-05-16_final
2018-05-16 10:22:05	ga_ls8c_ard_3-0-0_101071_2018-05-16_final
2018-05-16 10:22:29	ga_ls8c_ard_3-0-0_101072_2018-05-16_final
2018-05-16 10:22:53	ga_ls8c_ard_3-0-0_101073_2018-05-16_final
2018-05-16 10:23:17	ga_ls8c_ard_3-0-0_101074_2018-05-16_final
2018-05-16 10:23:41	ga_ls8c_ard_3-0-0_101075_2018-05-16_final
2018-05-16 10:24:05	ga_ls8c_ard_3-0-0_101076_2018-05-16_final
2018-05-16 10:24:29	ga_ls8c_ard_3-0-0_101077_2018-05-16_final
2018-05-16 10:24:53	ga_ls8c_ard_3-0-0_101078_2018-05-16_final
2018-05-16 10:25:17	ga_ls8c_ard_3-0-0_101079_2018-05-16_final
2018-05-16 10:25:41	ga_ls8c_ard_3-0-0_101080_2018-05-16_final
2018-05-16 10:26:05	ga_ls8c_ard_3-0-0_101081_2018-05-16_final
2018-05-16 10:26:29	ga_ls8c_ard_3-0-0_101082_2018-05-16_final
2018-05-16 10:26:53	ga_ls8c_ard_3-0-0_101083_2018-05-16_final
2018-05-16 10:27:17	ga_ls8c_ard_3-0-0_101084_2018-05-16_final
2018-05-17 09:24:52	ga_ls8c_ard_3-0-0_092067_2018-05-16_final
2018-05-17 09:25:16	ga_ls8c_ard_3-0-0_092068_2018-05-16_final
2018-05-17 09:26:27	ga_ls8c_ard_3-0-0_092071_2018-05-16_final
2018-05-17 09:26:51	ga_ls8c_ard_3-0-0_092072_2018-05-16_final
2018-05-17 09:27:15	ga_ls8c_ard_3-0-0_092073_2018-05-16_final
2018-05-17 09:27:39	ga_ls8c_ard_3-0-0_092074_2018-05-16_final
2018-05-17 09:28:03	ga_ls8c_ard_3-0-0_092075_2018-05-16_final
2018-05-17 09:28:27	ga_ls8c_ard_3-0-0_092076_2018-05-16_final
2018-05-17 09:28:51	ga_ls8c_ard_3-0-0_092077_2018-05-16_final
2018-05-17 09:29:15	ga_ls8c_ard_3-0-0_092078_2018-05-16_final
2018-05-17 09:29:39	ga_ls8c_ard_3-0-0_092079_2018-05-16_final
2018-05-17 09:30:03	ga_ls8c_ard_3-0-0_092080_2018-05-17_final
2018-05-17 09:30:27	ga_ls8c_ard_3-0-0_092081_2018-05-17_final
2018-05-17 09:30:51	ga_ls8c_ard_3-0-0_092082_2018-05-17_final
2018-05-17 09:31:15	ga_ls8c_ard_3-0-0_092083_2018-05-17_final
2018-05-17 09:31:39	ga_ls8c_ard_3-0-0_092084_2018-05-17_final
2018-05-17 09:32:03	ga_ls8c_ard_3-0-0_092085_2018-05-17_final
2018-05-17 09:32:26	ga_ls8c_ard_3-0-0_092086_2018-05-17_final
2018-05-17 09:32:50	ga_ls8c_ard_3-0-0_092087_2018-05-17_final
2018-05-17 09:33:14	ga_ls8c_ard_3-0-0_092088_2018-05-17_final
2018-05-17 09:33:38	ga_ls8c_ard_3-0-0_092089_2018-05-17_final
2018-05-17 09:34:02	ga_ls8c_ard_3-0-0_092090_2018-05-17_final
2018-05-17 11:03:45	ga_ls8c_ard_3-0-0_108067_2018-05-17_final
2018-05-17 11:04:09	ga_ls8c_ard_3-0-0_108068_2018-05-17_final
2018-05-17 11:04:32	ga_ls8c_ard_3-0-0_108069_2018-05-17_final
2018-05-17 11:04:56	ga_ls8c_ard_3-0-0_108070_2018-05-17_final
2018-05-17 11:05:20	ga_ls8c_ard_3-0-0_108071_2018-05-17_final
2018-05-17 11:05:44	ga_ls8c_ard_3-0-0_108072_2018-05-17_final
2018-05-17 11:06:08	ga_ls8c_ard_3-0-0_108073_2018-05-17_final
2018-05-17 11:06:32	ga_ls8c_ard_3-0-0_108074_2018-05-17_final
2018-05-17 11:06:56	ga_ls8c_ard_3-0-0_108075_2018-05-17_final
2018-05-17 11:07:20	ga_ls8c_ard_3-0-0_108076_2018-05-17_final
2018-05-17 11:07:44	ga_ls8c_ard_3-0-0_108077_2018-05-17_final
2018-05-17 11:08:08	ga_ls8c_ard_3-0-0_108078_2018-05-17_final
2018-05-17 11:08:32	ga_ls8c_ard_3-0-0_108079_2018-05-17_final
2018-05-17 11:08:56	ga_ls8c_ard_3-0-0_108080_2018-05-17_final
2018-05-17 11:09:20	ga_ls8c_ard_3-0-0_108081_2018-05-17_final
2018-05-17 11:09:44	ga_ls8c_ard_3-0-0_108082_2018-05-17_final
2018-05-17 11:10:08	ga_ls8c_ard_3-0-0_108083_2018-05-17_final
2018-05-17 11:10:32	ga_ls8c_ard_3-0-0_108084_2018-05-17_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

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

2018

May

All days