Search ga_ls8c_ard_3 across October 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
2018-10-02 10:02:42	ga_ls8c_ard_3-0-0_098067_2018-10-02_final
2018-10-02 10:03:06	ga_ls8c_ard_3-0-0_098068_2018-10-02_final
2018-10-02 10:03:29	ga_ls8c_ard_3-0-0_098069_2018-10-02_final
2018-10-02 10:03:53	ga_ls8c_ard_3-0-0_098070_2018-10-02_final
2018-10-02 10:04:17	ga_ls8c_ard_3-0-0_098071_2018-10-02_final
2018-10-02 10:04:41	ga_ls8c_ard_3-0-0_098072_2018-10-02_final
2018-10-02 10:05:05	ga_ls8c_ard_3-0-0_098073_2018-10-02_final
2018-10-02 10:05:29	ga_ls8c_ard_3-0-0_098074_2018-10-02_final
2018-10-02 10:05:53	ga_ls8c_ard_3-0-0_098075_2018-10-02_final
2018-10-02 10:06:17	ga_ls8c_ard_3-0-0_098076_2018-10-02_final
2018-10-02 10:06:41	ga_ls8c_ard_3-0-0_098077_2018-10-02_final
2018-10-02 10:07:05	ga_ls8c_ard_3-0-0_098078_2018-10-02_final
2018-10-02 10:07:29	ga_ls8c_ard_3-0-0_098079_2018-10-02_final
2018-10-02 10:07:53	ga_ls8c_ard_3-0-0_098080_2018-10-02_final
2018-10-02 10:08:17	ga_ls8c_ard_3-0-0_098081_2018-10-02_final
2018-10-02 10:08:41	ga_ls8c_ard_3-0-0_098082_2018-10-02_final
2018-10-02 10:09:05	ga_ls8c_ard_3-0-0_098083_2018-10-02_final
2018-10-02 10:09:29	ga_ls8c_ard_3-0-0_098084_2018-10-02_final
2018-10-02 10:09:53	ga_ls8c_ard_3-0-0_098085_2018-10-02_final
2018-10-02 10:10:17	ga_ls8c_ard_3-0-0_098086_2018-10-02_final
2018-10-03 09:09:52	ga_ls8c_ard_3-0-0_089074_2018-10-02_final
2018-10-03 09:10:16	ga_ls8c_ard_3-0-0_089075_2018-10-02_final
2018-10-03 09:10:40	ga_ls8c_ard_3-0-0_089076_2018-10-02_final
2018-10-03 09:11:04	ga_ls8c_ard_3-0-0_089077_2018-10-02_final
2018-10-03 09:11:28	ga_ls8c_ard_3-0-0_089078_2018-10-02_final
2018-10-03 09:11:52	ga_ls8c_ard_3-0-0_089079_2018-10-02_final
2018-10-03 09:12:16	ga_ls8c_ard_3-0-0_089080_2018-10-02_final
2018-10-03 09:12:40	ga_ls8c_ard_3-0-0_089081_2018-10-02_final
2018-10-03 09:13:04	ga_ls8c_ard_3-0-0_089082_2018-10-02_final
2018-10-03 09:13:28	ga_ls8c_ard_3-0-0_089083_2018-10-02_final
2018-10-03 09:13:52	ga_ls8c_ard_3-0-0_089084_2018-10-02_final
2018-10-03 09:14:16	ga_ls8c_ard_3-0-0_089085_2018-10-02_final
2018-10-03 09:14:40	ga_ls8c_ard_3-0-0_089086_2018-10-02_final
2018-10-03 09:15:04	ga_ls8c_ard_3-0-0_089087_2018-10-02_final
2018-10-03 09:15:28	ga_ls8c_ard_3-0-0_089088_2018-10-02_final
2018-10-03 09:15:52	ga_ls8c_ard_3-0-0_089089_2018-10-02_final
2018-10-03 09:16:16	ga_ls8c_ard_3-0-0_089090_2018-10-02_final
2018-10-18 10:02:24	ga_ls8c_ard_3-2-1_098066_2018-10-18_final
2018-10-18 10:02:48	ga_ls8c_ard_3-0-0_098067_2018-10-18_final
2018-10-18 10:03:12	ga_ls8c_ard_3-0-0_098068_2018-10-18_final
2018-10-18 10:03:36	ga_ls8c_ard_3-0-0_098069_2018-10-18_final
2018-10-18 10:04:00	ga_ls8c_ard_3-0-0_098070_2018-10-18_final
2018-10-18 10:04:24	ga_ls8c_ard_3-0-0_098071_2018-10-18_final
2018-10-18 10:04:48	ga_ls8c_ard_3-0-0_098072_2018-10-18_final
2018-10-18 10:05:12	ga_ls8c_ard_3-0-0_098073_2018-10-18_final
2018-10-18 10:05:36	ga_ls8c_ard_3-0-0_098074_2018-10-18_final
2018-10-18 10:06:00	ga_ls8c_ard_3-0-0_098075_2018-10-18_final
2018-10-18 10:06:24	ga_ls8c_ard_3-0-0_098076_2018-10-18_final
2018-10-18 10:06:48	ga_ls8c_ard_3-0-0_098077_2018-10-18_final
2018-10-18 10:07:12	ga_ls8c_ard_3-0-0_098078_2018-10-18_final
2018-10-18 10:07:35	ga_ls8c_ard_3-0-0_098079_2018-10-18_final
2018-10-18 10:07:59	ga_ls8c_ard_3-0-0_098080_2018-10-18_final
2018-10-18 10:08:23	ga_ls8c_ard_3-0-0_098081_2018-10-18_final
2018-10-18 10:08:47	ga_ls8c_ard_3-0-0_098082_2018-10-18_final
2018-10-18 10:09:11	ga_ls8c_ard_3-0-0_098083_2018-10-18_final
2018-10-18 10:09:35	ga_ls8c_ard_3-0-0_098084_2018-10-18_final
2018-10-18 10:09:59	ga_ls8c_ard_3-0-0_098085_2018-10-18_final
2018-10-18 10:10:23	ga_ls8c_ard_3-0-0_098086_2018-10-18_final
2018-10-18 11:41:41	ga_ls8c_ard_3-0-0_114067_2018-10-18_final
2018-10-18 11:44:29	ga_ls8c_ard_3-0-0_114074_2018-10-18_final
2018-10-18 11:44:53	ga_ls8c_ard_3-0-0_114075_2018-10-18_final
2018-10-18 11:45:17	ga_ls8c_ard_3-0-0_114076_2018-10-18_final
2018-10-18 11:45:41	ga_ls8c_ard_3-0-0_114077_2018-10-18_final
2018-10-18 11:46:05	ga_ls8c_ard_3-0-0_114078_2018-10-18_final
2018-10-18 11:46:29	ga_ls8c_ard_3-0-0_114079_2018-10-18_final
2018-10-18 11:46:53	ga_ls8c_ard_3-0-0_114080_2018-10-18_final
2018-10-18 11:47:16	ga_ls8c_ard_3-0-0_114081_2018-10-18_final
2018-10-19 09:09:59	ga_ls8c_ard_3-0-0_089074_2018-10-18_final
2018-10-19 09:10:23	ga_ls8c_ard_3-0-0_089075_2018-10-18_final
2018-10-19 09:10:47	ga_ls8c_ard_3-0-0_089076_2018-10-18_final
2018-10-19 09:11:11	ga_ls8c_ard_3-0-0_089077_2018-10-18_final
2018-10-19 09:11:35	ga_ls8c_ard_3-0-0_089078_2018-10-18_final
2018-10-19 09:11:59	ga_ls8c_ard_3-0-0_089079_2018-10-18_final
2018-10-19 09:12:22	ga_ls8c_ard_3-0-0_089080_2018-10-18_final
2018-10-19 09:12:46	ga_ls8c_ard_3-0-0_089081_2018-10-18_final
2018-10-19 09:13:10	ga_ls8c_ard_3-0-0_089082_2018-10-18_final
2018-10-19 09:13:34	ga_ls8c_ard_3-0-0_089083_2018-10-18_final
2018-10-19 09:13:58	ga_ls8c_ard_3-0-0_089084_2018-10-18_final
2018-10-19 09:14:22	ga_ls8c_ard_3-0-0_089085_2018-10-18_final
2018-10-19 09:14:46	ga_ls8c_ard_3-0-0_089086_2018-10-18_final
2018-10-19 09:15:10	ga_ls8c_ard_3-0-0_089087_2018-10-18_final
2018-10-19 09:15:34	ga_ls8c_ard_3-0-0_089088_2018-10-18_final
2018-10-19 09:15:58	ga_ls8c_ard_3-0-0_089089_2018-10-18_final
2018-10-19 09:16:22	ga_ls8c_ard_3-0-0_089090_2018-10-18_final
2018-10-19 10:46:04	ga_ls8c_ard_3-0-0_105067_2018-10-19_final
2018-10-19 10:46:28	ga_ls8c_ard_3-0-0_105068_2018-10-19_final
2018-10-19 10:46:52	ga_ls8c_ard_3-0-0_105069_2018-10-19_final
2018-10-19 10:47:16	ga_ls8c_ard_3-0-0_105070_2018-10-19_final
2018-10-19 10:47:40	ga_ls8c_ard_3-0-0_105071_2018-10-19_final
2018-10-19 10:48:04	ga_ls8c_ard_3-0-0_105072_2018-10-19_final
2018-10-19 10:48:28	ga_ls8c_ard_3-0-0_105073_2018-10-19_final
2018-10-19 10:48:52	ga_ls8c_ard_3-0-0_105074_2018-10-19_final
2018-10-19 10:49:16	ga_ls8c_ard_3-0-0_105075_2018-10-19_final
2018-10-19 10:49:40	ga_ls8c_ard_3-0-0_105076_2018-10-19_final
2018-10-19 10:50:04	ga_ls8c_ard_3-0-0_105077_2018-10-19_final
2018-10-19 10:50:28	ga_ls8c_ard_3-0-0_105078_2018-10-19_final
2018-10-19 10:50:52	ga_ls8c_ard_3-0-0_105079_2018-10-19_final
2018-10-19 10:51:15	ga_ls8c_ard_3-0-0_105080_2018-10-19_final
2018-10-19 10:51:39	ga_ls8c_ard_3-0-0_105081_2018-10-19_final
2018-10-19 10:52:03	ga_ls8c_ard_3-0-0_105082_2018-10-19_final

Return to ga_ls8c_ard_3 across October 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

October

All days