Search ga_ls8c_ard_3 across October 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
2022-10-01 09:40:28	ga_ls8c_ard_3-2-1_094071_2022-10-01_final
2022-10-01 09:40:51	ga_ls8c_ard_3-2-1_094072_2022-10-01_final
2022-10-01 09:41:15	ga_ls8c_ard_3-2-1_094073_2022-10-01_final
2022-10-01 09:41:39	ga_ls8c_ard_3-2-1_094074_2022-10-01_final
2022-10-01 09:42:03	ga_ls8c_ard_3-2-1_094075_2022-10-01_final
2022-10-01 09:42:27	ga_ls8c_ard_3-2-1_094076_2022-10-01_final
2022-10-01 09:42:51	ga_ls8c_ard_3-2-1_094077_2022-10-01_final
2022-10-01 09:43:15	ga_ls8c_ard_3-2-1_094078_2022-10-01_final
2022-10-01 09:43:39	ga_ls8c_ard_3-2-1_094079_2022-10-01_final
2022-10-01 09:44:03	ga_ls8c_ard_3-2-1_094080_2022-10-01_final
2022-10-01 09:44:27	ga_ls8c_ard_3-2-1_094081_2022-10-01_final
2022-10-01 09:44:51	ga_ls8c_ard_3-2-1_094082_2022-10-01_final
2022-10-01 09:45:15	ga_ls8c_ard_3-2-1_094083_2022-10-01_final
2022-10-01 09:45:39	ga_ls8c_ard_3-2-1_094084_2022-10-01_final
2022-10-01 09:46:03	ga_ls8c_ard_3-2-1_094085_2022-10-01_final
2022-10-01 09:46:27	ga_ls8c_ard_3-2-1_094086_2022-10-01_final
2022-10-01 09:46:51	ga_ls8c_ard_3-2-1_094087_2022-10-01_final
2022-10-01 11:18:33	ga_ls8c_ard_3-2-1_110069_2022-10-01_final
2022-10-01 11:18:57	ga_ls8c_ard_3-2-1_110070_2022-10-01_final
2022-10-01 11:19:21	ga_ls8c_ard_3-2-1_110071_2022-10-01_final
2022-10-01 11:19:44	ga_ls8c_ard_3-2-1_110072_2022-10-01_final
2022-10-01 11:20:08	ga_ls8c_ard_3-2-1_110073_2022-10-01_final
2022-10-01 11:20:32	ga_ls8c_ard_3-2-1_110074_2022-10-01_final
2022-10-01 11:20:56	ga_ls8c_ard_3-2-1_110075_2022-10-01_final
2022-10-01 11:21:20	ga_ls8c_ard_3-2-1_110076_2022-10-01_final
2022-10-01 11:21:44	ga_ls8c_ard_3-2-1_110077_2022-10-01_final
2022-10-01 11:22:08	ga_ls8c_ard_3-2-1_110078_2022-10-01_final
2022-10-01 11:22:32	ga_ls8c_ard_3-2-1_110079_2022-10-01_final
2022-10-01 11:22:56	ga_ls8c_ard_3-2-1_110080_2022-10-01_final
2022-10-01 11:23:20	ga_ls8c_ard_3-2-1_110081_2022-10-01_final
2022-10-01 11:23:44	ga_ls8c_ard_3-2-1_110082_2022-10-01_final
2022-10-01 11:24:08	ga_ls8c_ard_3-2-1_110083_2022-10-01_final
2022-10-01 11:24:32	ga_ls8c_ard_3-2-1_110084_2022-10-01_final
2022-10-01 11:24:56	ga_ls8c_ard_3-2-1_110085_2022-10-01_final
2022-10-02 10:22:55	ga_ls8c_ard_3-2-1_101069_2022-10-02_final
2022-10-02 10:23:19	ga_ls8c_ard_3-2-1_101070_2022-10-02_final
2022-10-02 10:23:43	ga_ls8c_ard_3-2-1_101071_2022-10-02_final
2022-10-02 10:24:07	ga_ls8c_ard_3-2-1_101072_2022-10-02_final
2022-10-02 10:24:31	ga_ls8c_ard_3-2-1_101073_2022-10-02_final
2022-10-02 10:24:55	ga_ls8c_ard_3-2-1_101074_2022-10-02_final
2022-10-02 10:25:19	ga_ls8c_ard_3-2-1_101075_2022-10-02_final
2022-10-02 10:25:43	ga_ls8c_ard_3-2-1_101076_2022-10-02_final
2022-10-02 10:26:07	ga_ls8c_ard_3-2-1_101077_2022-10-02_final
2022-10-02 10:26:30	ga_ls8c_ard_3-2-1_101078_2022-10-02_final
2022-10-02 10:26:54	ga_ls8c_ard_3-2-1_101079_2022-10-02_final
2022-10-02 10:27:18	ga_ls8c_ard_3-2-1_101080_2022-10-02_final
2022-10-02 10:27:42	ga_ls8c_ard_3-2-1_101081_2022-10-02_final
2022-10-02 10:28:06	ga_ls8c_ard_3-2-1_101082_2022-10-02_final
2022-10-02 10:28:30	ga_ls8c_ard_3-2-1_101083_2022-10-02_final
2022-10-03 09:28:05	ga_ls8c_ard_3-2-1_092071_2022-10-02_final
2022-10-03 09:28:29	ga_ls8c_ard_3-2-1_092072_2022-10-02_final
2022-10-03 09:28:53	ga_ls8c_ard_3-2-1_092073_2022-10-02_final
2022-10-03 09:29:17	ga_ls8c_ard_3-2-1_092074_2022-10-02_final
2022-10-03 09:29:41	ga_ls8c_ard_3-2-1_092075_2022-10-02_final
2022-10-03 09:30:05	ga_ls8c_ard_3-2-1_092076_2022-10-03_final
2022-10-03 09:30:29	ga_ls8c_ard_3-2-1_092077_2022-10-03_final
2022-10-03 09:30:53	ga_ls8c_ard_3-2-1_092078_2022-10-03_final
2022-10-03 09:31:17	ga_ls8c_ard_3-2-1_092079_2022-10-03_final
2022-10-03 09:31:41	ga_ls8c_ard_3-2-1_092080_2022-10-03_final
2022-10-03 09:32:05	ga_ls8c_ard_3-2-1_092081_2022-10-03_final
2022-10-03 09:32:29	ga_ls8c_ard_3-2-1_092082_2022-10-03_final
2022-10-03 09:32:53	ga_ls8c_ard_3-2-1_092083_2022-10-03_final
2022-10-03 09:33:17	ga_ls8c_ard_3-2-1_092084_2022-10-03_final
2022-10-03 09:33:40	ga_ls8c_ard_3-2-1_092085_2022-10-03_final
2022-10-03 09:34:04	ga_ls8c_ard_3-2-1_092086_2022-10-03_final
2022-10-03 09:34:28	ga_ls8c_ard_3-2-1_092087_2022-10-03_final
2022-10-03 09:34:52	ga_ls8c_ard_3-2-1_092088_2022-10-03_final
2022-10-03 09:35:16	ga_ls8c_ard_3-2-1_092089_2022-10-03_final
2022-10-03 09:35:40	ga_ls8c_ard_3-2-1_092090_2022-10-03_final
2022-10-03 11:06:10	ga_ls8c_ard_3-2-1_108069_2022-10-03_final
2022-10-03 11:06:34	ga_ls8c_ard_3-2-1_108070_2022-10-03_final
2022-10-03 11:06:58	ga_ls8c_ard_3-2-1_108071_2022-10-03_final
2022-10-03 11:07:22	ga_ls8c_ard_3-2-1_108072_2022-10-03_final
2022-10-03 11:07:46	ga_ls8c_ard_3-2-1_108073_2022-10-03_final
2022-10-03 11:08:10	ga_ls8c_ard_3-2-1_108074_2022-10-03_final
2022-10-03 11:08:34	ga_ls8c_ard_3-2-1_108075_2022-10-03_final
2022-10-03 11:08:58	ga_ls8c_ard_3-2-1_108076_2022-10-03_final
2022-10-03 11:09:22	ga_ls8c_ard_3-2-1_108077_2022-10-03_final
2022-10-03 11:09:46	ga_ls8c_ard_3-2-1_108078_2022-10-03_final
2022-10-03 11:10:10	ga_ls8c_ard_3-2-1_108079_2022-10-03_final
2022-10-03 11:10:34	ga_ls8c_ard_3-2-1_108080_2022-10-03_final
2022-10-03 11:10:58	ga_ls8c_ard_3-2-1_108081_2022-10-03_final
2022-10-03 11:11:22	ga_ls8c_ard_3-2-1_108082_2022-10-03_final
2022-10-03 11:11:46	ga_ls8c_ard_3-2-1_108083_2022-10-03_final
2022-10-03 11:12:10	ga_ls8c_ard_3-2-1_108084_2022-10-03_final
2022-10-07 09:06:55	ga_ls8c_ard_3-2-1_088080_2022-10-06_final
2022-10-07 09:07:19	ga_ls8c_ard_3-2-1_088081_2022-10-06_final
2022-10-07 09:07:43	ga_ls8c_ard_3-2-1_088082_2022-10-06_final
2022-10-07 09:08:07	ga_ls8c_ard_3-2-1_088083_2022-10-06_final
2022-10-07 10:41:49	ga_ls8c_ard_3-2-1_104070_2022-10-07_final
2022-10-07 10:42:37	ga_ls8c_ard_3-2-1_104072_2022-10-07_final
2022-10-07 10:43:01	ga_ls8c_ard_3-2-1_104073_2022-10-07_final
2022-10-08 09:49:22	ga_ls8c_ard_3-2-1_095078_2022-10-08_final
2022-10-08 09:49:46	ga_ls8c_ard_3-2-1_095079_2022-10-08_final
2022-10-08 11:24:16	ga_ls8c_ard_3-2-1_111068_2022-10-08_final
2022-10-08 11:24:40	ga_ls8c_ard_3-2-1_111069_2022-10-08_final
2022-10-08 11:25:04	ga_ls8c_ard_3-2-1_111070_2022-10-08_final
2022-10-08 11:25:28	ga_ls8c_ard_3-2-1_111071_2022-10-08_final
2022-10-08 11:25:52	ga_ls8c_ard_3-2-1_111072_2022-10-08_final
2022-10-08 11:26:16	ga_ls8c_ard_3-2-1_111073_2022-10-08_final

Return to ga_ls8c_ard_3 across October 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

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

2022

October

All days