Search ga_ls8c_ard_3 across October 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
2019-10-17 10:28:04	ga_ls8c_ard_3-1-0_102067_2019-10-17_final
2019-10-17 10:28:28	ga_ls8c_ard_3-1-0_102068_2019-10-17_final
2019-10-17 10:28:52	ga_ls8c_ard_3-1-0_102069_2019-10-17_final
2019-10-17 10:29:16	ga_ls8c_ard_3-1-0_102070_2019-10-17_final
2019-10-17 10:29:40	ga_ls8c_ard_3-1-0_102071_2019-10-17_final
2019-10-17 10:30:03	ga_ls8c_ard_3-1-0_102072_2019-10-17_final
2019-10-17 10:30:27	ga_ls8c_ard_3-1-0_102073_2019-10-17_final
2019-10-17 10:30:51	ga_ls8c_ard_3-1-0_102074_2019-10-17_final
2019-10-17 10:31:15	ga_ls8c_ard_3-1-0_102075_2019-10-17_final
2019-10-17 10:31:39	ga_ls8c_ard_3-1-0_102076_2019-10-17_final
2019-10-17 10:32:03	ga_ls8c_ard_3-1-0_102077_2019-10-17_final
2019-10-17 10:32:27	ga_ls8c_ard_3-1-0_102078_2019-10-17_final
2019-10-17 10:32:51	ga_ls8c_ard_3-1-0_102079_2019-10-17_final
2019-10-17 10:33:15	ga_ls8c_ard_3-1-0_102080_2019-10-17_final
2019-10-17 10:33:39	ga_ls8c_ard_3-1-0_102081_2019-10-17_final
2019-10-17 10:34:03	ga_ls8c_ard_3-1-0_102082_2019-10-17_final
2019-10-17 10:34:27	ga_ls8c_ard_3-1-0_102083_2019-10-17_final
2019-10-18 09:32:27	ga_ls8c_ard_3-1-0_093067_2019-10-18_final
2019-10-18 09:32:50	ga_ls8c_ard_3-1-0_093068_2019-10-18_final
2019-10-18 09:34:02	ga_ls8c_ard_3-1-0_093071_2019-10-18_final
2019-10-18 09:34:26	ga_ls8c_ard_3-1-0_093072_2019-10-18_final
2019-10-18 09:34:50	ga_ls8c_ard_3-1-0_093073_2019-10-18_final
2019-10-18 09:35:14	ga_ls8c_ard_3-1-0_093074_2019-10-18_final
2019-10-18 09:35:38	ga_ls8c_ard_3-1-0_093075_2019-10-18_final
2019-10-18 09:36:02	ga_ls8c_ard_3-1-0_093076_2019-10-18_final
2019-10-18 09:36:26	ga_ls8c_ard_3-1-0_093077_2019-10-18_final
2019-10-18 09:36:50	ga_ls8c_ard_3-1-0_093078_2019-10-18_final
2019-10-18 09:37:14	ga_ls8c_ard_3-1-0_093079_2019-10-18_final
2019-10-18 09:37:38	ga_ls8c_ard_3-1-0_093080_2019-10-18_final
2019-10-18 09:38:02	ga_ls8c_ard_3-1-0_093081_2019-10-18_final
2019-10-18 09:38:26	ga_ls8c_ard_3-1-0_093082_2019-10-18_final
2019-10-18 09:38:50	ga_ls8c_ard_3-1-0_093083_2019-10-18_final
2019-10-18 09:39:14	ga_ls8c_ard_3-1-0_093084_2019-10-18_final
2019-10-18 09:39:37	ga_ls8c_ard_3-1-0_093085_2019-10-18_final
2019-10-18 09:40:01	ga_ls8c_ard_3-1-0_093086_2019-10-18_final
2019-10-18 09:40:25	ga_ls8c_ard_3-1-0_093087_2019-10-18_final
2019-10-18 09:40:49	ga_ls8c_ard_3-1-0_093088_2019-10-18_final
2019-10-18 11:11:20	ga_ls8c_ard_3-1-0_109067_2019-10-18_final
2019-10-18 11:11:44	ga_ls8c_ard_3-1-0_109068_2019-10-18_final
2019-10-18 11:12:07	ga_ls8c_ard_3-1-0_109069_2019-10-18_final
2019-10-18 11:12:31	ga_ls8c_ard_3-1-0_109070_2019-10-18_final
2019-10-18 11:12:55	ga_ls8c_ard_3-1-0_109071_2019-10-18_final
2019-10-18 11:13:19	ga_ls8c_ard_3-1-0_109072_2019-10-18_final
2019-10-18 11:13:43	ga_ls8c_ard_3-1-0_109073_2019-10-18_final
2019-10-18 11:14:07	ga_ls8c_ard_3-1-0_109074_2019-10-18_final
2019-10-18 11:14:31	ga_ls8c_ard_3-1-0_109075_2019-10-18_final
2019-10-18 11:14:55	ga_ls8c_ard_3-1-0_109076_2019-10-18_final
2019-10-18 11:15:19	ga_ls8c_ard_3-1-0_109077_2019-10-18_final
2019-10-18 11:15:43	ga_ls8c_ard_3-1-0_109078_2019-10-18_final
2019-10-18 11:16:07	ga_ls8c_ard_3-1-0_109079_2019-10-18_final
2019-10-18 11:16:31	ga_ls8c_ard_3-1-0_109080_2019-10-18_final
2019-10-18 11:16:55	ga_ls8c_ard_3-1-0_109081_2019-10-18_final
2019-10-18 11:17:19	ga_ls8c_ard_3-1-0_109082_2019-10-18_final
2019-10-18 11:17:43	ga_ls8c_ard_3-1-0_109083_2019-10-18_final
2019-10-18 11:18:07	ga_ls8c_ard_3-1-0_109084_2019-10-18_final
2019-10-18 11:18:31	ga_ls8c_ard_3-1-0_109085_2019-10-18_final
2019-10-19 08:42:48	ga_ls8c_ard_3-2-1_084082_2019-10-18_final
2019-10-19 10:15:42	ga_ls8c_ard_3-1-0_100067_2019-10-19_final
2019-10-19 10:16:06	ga_ls8c_ard_3-1-0_100068_2019-10-19_final
2019-10-19 10:16:30	ga_ls8c_ard_3-1-0_100069_2019-10-19_final
2019-10-19 10:16:54	ga_ls8c_ard_3-1-0_100070_2019-10-19_final
2019-10-19 10:17:18	ga_ls8c_ard_3-1-0_100071_2019-10-19_final
2019-10-19 10:17:42	ga_ls8c_ard_3-1-0_100072_2019-10-19_final
2019-10-19 10:18:06	ga_ls8c_ard_3-1-0_100073_2019-10-19_final
2019-10-19 10:18:30	ga_ls8c_ard_3-1-0_100074_2019-10-19_final
2019-10-19 10:18:54	ga_ls8c_ard_3-1-0_100075_2019-10-19_final
2019-10-19 10:19:18	ga_ls8c_ard_3-1-0_100076_2019-10-19_final
2019-10-19 10:19:42	ga_ls8c_ard_3-1-0_100077_2019-10-19_final
2019-10-19 10:20:06	ga_ls8c_ard_3-1-0_100078_2019-10-19_final
2019-10-19 10:20:30	ga_ls8c_ard_3-1-0_100079_2019-10-19_final
2019-10-19 10:20:53	ga_ls8c_ard_3-1-0_100080_2019-10-19_final
2019-10-19 10:21:17	ga_ls8c_ard_3-1-0_100081_2019-10-19_final
2019-10-19 10:21:41	ga_ls8c_ard_3-1-0_100082_2019-10-19_final
2019-10-19 10:22:05	ga_ls8c_ard_3-1-0_100083_2019-10-19_final
2019-10-19 10:22:29	ga_ls8c_ard_3-1-0_100084_2019-10-19_final
2019-10-19 10:22:53	ga_ls8c_ard_3-1-0_100085_2019-10-19_final
2019-10-19 11:57:23	ga_ls8c_ard_3-1-0_116074_2019-10-19_final
2019-10-19 11:57:47	ga_ls8c_ard_3-1-0_116075_2019-10-19_final
2019-10-19 11:58:11	ga_ls8c_ard_3-1-0_116076_2019-10-19_final
2019-10-19 11:58:35	ga_ls8c_ard_3-1-0_116077_2019-10-19_final
2019-10-19 11:58:59	ga_ls8c_ard_3-1-0_116078_2019-10-19_final
2019-10-20 09:20:29	ga_ls8c_ard_3-1-0_091068_2019-10-19_final
2019-10-25 09:38:38	ga_ls8c_ard_3-1-0_094067_2019-10-25_final
2019-10-25 09:40:13	ga_ls8c_ard_3-1-0_094071_2019-10-25_final
2019-10-25 09:40:37	ga_ls8c_ard_3-1-0_094072_2019-10-25_final
2019-10-25 09:41:01	ga_ls8c_ard_3-1-0_094073_2019-10-25_final
2019-10-25 09:41:25	ga_ls8c_ard_3-1-0_094074_2019-10-25_final
2019-10-25 09:41:49	ga_ls8c_ard_3-1-0_094075_2019-10-25_final
2019-10-25 09:42:13	ga_ls8c_ard_3-1-0_094076_2019-10-25_final
2019-10-25 09:42:37	ga_ls8c_ard_3-1-0_094077_2019-10-25_final
2019-10-25 09:43:01	ga_ls8c_ard_3-1-0_094078_2019-10-25_final
2019-10-25 09:43:25	ga_ls8c_ard_3-1-0_094079_2019-10-25_final
2019-10-25 09:43:49	ga_ls8c_ard_3-1-0_094080_2019-10-25_final
2019-10-25 09:44:13	ga_ls8c_ard_3-1-0_094081_2019-10-25_final
2019-10-25 09:44:37	ga_ls8c_ard_3-1-0_094082_2019-10-25_final
2019-10-25 09:45:01	ga_ls8c_ard_3-1-0_094083_2019-10-25_final
2019-10-25 09:45:24	ga_ls8c_ard_3-1-0_094084_2019-10-25_final
2019-10-25 09:45:48	ga_ls8c_ard_3-1-0_094085_2019-10-25_final
2019-10-25 09:46:12	ga_ls8c_ard_3-1-0_094086_2019-10-25_final
2019-10-27 09:26:16	ga_ls8c_ard_3-1-0_092067_2019-10-26_final

Return to ga_ls8c_ard_3 across October 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

October

All days