Search ga_ls8c_ard_3 across January 2022

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
2022-01-01 10:34:30	ga_ls8c_ard_3-2-1_103068_2022-01-01_final
2022-01-01 10:34:54	ga_ls8c_ard_3-2-1_103069_2022-01-01_final
2022-01-01 10:35:18	ga_ls8c_ard_3-2-1_103070_2022-01-01_final
2022-01-01 10:35:42	ga_ls8c_ard_3-2-1_103071_2022-01-01_final
2022-01-01 10:36:05	ga_ls8c_ard_3-2-1_103072_2022-01-01_final
2022-01-01 10:36:29	ga_ls8c_ard_3-2-1_103073_2022-01-01_final
2022-01-01 10:36:53	ga_ls8c_ard_3-2-1_103074_2022-01-01_final
2022-01-01 10:37:17	ga_ls8c_ard_3-2-1_103075_2022-01-01_final
2022-01-01 10:37:41	ga_ls8c_ard_3-2-1_103076_2022-01-01_final
2022-01-01 10:38:05	ga_ls8c_ard_3-2-1_103077_2022-01-01_final
2022-01-01 10:38:29	ga_ls8c_ard_3-2-1_103078_2022-01-01_final
2022-01-01 10:38:53	ga_ls8c_ard_3-2-1_103079_2022-01-01_final
2022-01-01 10:39:17	ga_ls8c_ard_3-2-1_103080_2022-01-01_final
2022-01-01 10:39:41	ga_ls8c_ard_3-2-1_103081_2022-01-01_final
2022-01-01 10:40:05	ga_ls8c_ard_3-2-1_103082_2022-01-01_final
2022-01-01 14:03:50	ga_ls8c_ard_3-2-1_135097_2022-01-01_final
2022-01-02 09:40:04	ga_ls8c_ard_3-2-1_094071_2022-01-02_final
2022-01-02 09:40:28	ga_ls8c_ard_3-2-1_094072_2022-01-02_final
2022-01-02 09:40:52	ga_ls8c_ard_3-2-1_094073_2022-01-02_final
2022-01-02 09:41:16	ga_ls8c_ard_3-2-1_094074_2022-01-02_final
2022-01-02 09:41:40	ga_ls8c_ard_3-2-1_094075_2022-01-02_final
2022-01-02 09:42:04	ga_ls8c_ard_3-2-1_094076_2022-01-02_final
2022-01-02 09:42:28	ga_ls8c_ard_3-2-1_094077_2022-01-02_final
2022-01-02 09:42:52	ga_ls8c_ard_3-2-1_094078_2022-01-02_final
2022-01-02 09:43:16	ga_ls8c_ard_3-2-1_094079_2022-01-02_final
2022-01-13 09:21:55	ga_ls8c_ard_3-2-1_091072_2022-01-12_final
2022-01-13 09:22:19	ga_ls8c_ard_3-2-1_091073_2022-01-12_final
2022-01-13 09:22:43	ga_ls8c_ard_3-2-1_091074_2022-01-12_final
2022-01-13 09:23:06	ga_ls8c_ard_3-2-1_091075_2022-01-12_final
2022-01-13 09:23:30	ga_ls8c_ard_3-2-1_091076_2022-01-12_final
2022-01-13 09:23:54	ga_ls8c_ard_3-2-1_091077_2022-01-12_final
2022-01-13 09:24:18	ga_ls8c_ard_3-2-1_091078_2022-01-12_final
2022-01-13 09:24:42	ga_ls8c_ard_3-2-1_091079_2022-01-12_final
2022-01-13 09:25:06	ga_ls8c_ard_3-2-1_091080_2022-01-12_final
2022-01-13 09:25:30	ga_ls8c_ard_3-2-1_091081_2022-01-12_final
2022-01-13 09:25:54	ga_ls8c_ard_3-2-1_091082_2022-01-12_final
2022-01-13 09:26:18	ga_ls8c_ard_3-2-1_091083_2022-01-12_final
2022-01-13 09:26:42	ga_ls8c_ard_3-2-1_091084_2022-01-12_final
2022-01-13 09:27:06	ga_ls8c_ard_3-2-1_091085_2022-01-12_final
2022-01-13 09:27:30	ga_ls8c_ard_3-2-1_091086_2022-01-12_final
2022-01-13 09:27:54	ga_ls8c_ard_3-2-1_091087_2022-01-12_final
2022-01-13 09:28:18	ga_ls8c_ard_3-2-1_091088_2022-01-12_final
2022-01-13 09:28:42	ga_ls8c_ard_3-2-1_091089_2022-01-12_final
2022-01-13 09:29:06	ga_ls8c_ard_3-2-1_091090_2022-01-12_final
2022-01-13 11:00:00	ga_ls8c_ard_3-2-1_107070_2022-01-13_final
2022-01-13 11:00:24	ga_ls8c_ard_3-2-1_107071_2022-01-13_final
2022-01-13 11:00:48	ga_ls8c_ard_3-2-1_107072_2022-01-13_final
2022-01-13 11:01:12	ga_ls8c_ard_3-2-1_107073_2022-01-13_final
2022-01-13 11:01:36	ga_ls8c_ard_3-2-1_107074_2022-01-13_final
2022-01-13 11:01:59	ga_ls8c_ard_3-2-1_107075_2022-01-13_final
2022-01-13 11:02:23	ga_ls8c_ard_3-2-1_107076_2022-01-13_final
2022-01-13 11:02:47	ga_ls8c_ard_3-2-1_107077_2022-01-13_final
2022-01-13 11:03:11	ga_ls8c_ard_3-2-1_107078_2022-01-13_final
2022-01-13 11:03:35	ga_ls8c_ard_3-2-1_107079_2022-01-13_final
2022-01-13 11:03:59	ga_ls8c_ard_3-2-1_107080_2022-01-13_final
2022-01-13 11:04:23	ga_ls8c_ard_3-2-1_107081_2022-01-13_final
2022-01-13 11:04:47	ga_ls8c_ard_3-2-1_107082_2022-01-13_final
2022-01-13 11:05:11	ga_ls8c_ard_3-2-1_107083_2022-01-13_final
2022-01-13 11:05:35	ga_ls8c_ard_3-2-1_107084_2022-01-13_final
2022-01-14 10:03:11	ga_ls8c_ard_3-2-1_098067_2022-01-14_final
2022-01-14 10:03:34	ga_ls8c_ard_3-2-1_098068_2022-01-14_final
2022-01-14 10:03:58	ga_ls8c_ard_3-2-1_098069_2022-01-14_final
2022-01-14 10:04:22	ga_ls8c_ard_3-2-1_098070_2022-01-14_final
2022-01-14 10:04:46	ga_ls8c_ard_3-2-1_098071_2022-01-14_final
2022-01-14 10:05:10	ga_ls8c_ard_3-2-1_098072_2022-01-14_final
2022-01-14 10:05:34	ga_ls8c_ard_3-2-1_098073_2022-01-14_final
2022-01-14 10:05:58	ga_ls8c_ard_3-2-1_098074_2022-01-14_final
2022-01-14 10:06:22	ga_ls8c_ard_3-2-1_098075_2022-01-14_final
2022-01-14 10:06:46	ga_ls8c_ard_3-2-1_098076_2022-01-14_final
2022-01-14 10:07:10	ga_ls8c_ard_3-2-1_098077_2022-01-14_final
2022-01-14 10:07:34	ga_ls8c_ard_3-2-1_098078_2022-01-14_final
2022-01-14 10:07:58	ga_ls8c_ard_3-2-1_098079_2022-01-14_final
2022-01-14 10:08:22	ga_ls8c_ard_3-2-1_098080_2022-01-14_final
2022-01-14 10:08:46	ga_ls8c_ard_3-2-1_098081_2022-01-14_final
2022-01-14 10:09:10	ga_ls8c_ard_3-2-1_098082_2022-01-14_final
2022-01-14 10:09:34	ga_ls8c_ard_3-2-1_098083_2022-01-14_final
2022-01-14 10:09:57	ga_ls8c_ard_3-2-1_098084_2022-01-14_final
2022-01-14 10:10:21	ga_ls8c_ard_3-2-1_098085_2022-01-14_final
2022-01-14 11:44:51	ga_ls8c_ard_3-2-1_114074_2022-01-14_final
2022-01-14 11:45:15	ga_ls8c_ard_3-2-1_114075_2022-01-14_final
2022-01-14 11:45:39	ga_ls8c_ard_3-2-1_114076_2022-01-14_final
2022-01-14 11:46:03	ga_ls8c_ard_3-2-1_114077_2022-01-14_final
2022-01-14 11:46:27	ga_ls8c_ard_3-2-1_114078_2022-01-14_final
2022-01-14 11:46:51	ga_ls8c_ard_3-2-1_114079_2022-01-14_final
2022-01-14 11:47:15	ga_ls8c_ard_3-2-1_114080_2022-01-14_final
2022-01-14 11:47:39	ga_ls8c_ard_3-2-1_114081_2022-01-14_final
2022-01-15 09:10:21	ga_ls8c_ard_3-2-1_089074_2022-01-14_final
2022-01-15 09:10:44	ga_ls8c_ard_3-2-1_089075_2022-01-14_final
2022-01-15 09:11:08	ga_ls8c_ard_3-2-1_089076_2022-01-14_final
2022-01-15 09:11:32	ga_ls8c_ard_3-2-1_089077_2022-01-14_final
2022-01-15 09:11:56	ga_ls8c_ard_3-2-1_089078_2022-01-14_final
2022-01-15 09:12:20	ga_ls8c_ard_3-2-1_089079_2022-01-14_final
2022-01-15 09:12:44	ga_ls8c_ard_3-2-1_089080_2022-01-14_final
2022-01-15 09:13:08	ga_ls8c_ard_3-2-1_089081_2022-01-14_final
2022-01-15 09:13:32	ga_ls8c_ard_3-2-1_089082_2022-01-14_final
2022-01-15 09:13:56	ga_ls8c_ard_3-2-1_089083_2022-01-14_final
2022-01-15 09:14:20	ga_ls8c_ard_3-2-1_089084_2022-01-14_final
2022-01-15 09:14:44	ga_ls8c_ard_3-2-1_089085_2022-01-14_final
2022-01-15 09:15:08	ga_ls8c_ard_3-2-1_089086_2022-01-14_final
2022-01-15 09:16:20	ga_ls8c_ard_3-2-1_089089_2022-01-14_final

Return to ga_ls8c_ard_3 across January 2022 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

2022

January

All days