Search ga_ls8c_ard_3 across January 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
2018-01-03 10:03:05	ga_ls8c_ard_3-0-0_098067_2018-01-03_final
2018-01-03 10:03:29	ga_ls8c_ard_3-0-0_098068_2018-01-03_final
2018-01-03 10:03:53	ga_ls8c_ard_3-0-0_098069_2018-01-03_final
2018-01-03 10:04:17	ga_ls8c_ard_3-0-0_098070_2018-01-03_final
2018-01-03 10:04:41	ga_ls8c_ard_3-0-0_098071_2018-01-03_final
2018-01-03 10:05:05	ga_ls8c_ard_3-0-0_098072_2018-01-03_final
2018-01-03 10:05:29	ga_ls8c_ard_3-0-0_098073_2018-01-03_final
2018-01-03 10:05:53	ga_ls8c_ard_3-0-0_098074_2018-01-03_final
2018-01-03 10:06:17	ga_ls8c_ard_3-0-0_098075_2018-01-03_final
2018-01-03 10:06:41	ga_ls8c_ard_3-0-0_098076_2018-01-03_final
2018-01-03 10:07:05	ga_ls8c_ard_3-0-0_098077_2018-01-03_final
2018-01-03 10:07:29	ga_ls8c_ard_3-0-0_098078_2018-01-03_final
2018-01-03 10:07:53	ga_ls8c_ard_3-0-0_098079_2018-01-03_final
2018-01-03 10:08:17	ga_ls8c_ard_3-0-0_098080_2018-01-03_final
2018-01-03 10:08:41	ga_ls8c_ard_3-0-0_098081_2018-01-03_final
2018-01-03 10:09:05	ga_ls8c_ard_3-0-0_098082_2018-01-03_final
2018-01-03 10:09:28	ga_ls8c_ard_3-0-0_098083_2018-01-03_final
2018-01-03 10:09:52	ga_ls8c_ard_3-0-0_098084_2018-01-03_final
2018-01-03 10:10:16	ga_ls8c_ard_3-0-0_098085_2018-01-03_final
2018-01-03 10:10:40	ga_ls8c_ard_3-0-0_098086_2018-01-03_final
2018-01-04 09:10:15	ga_ls8c_ard_3-0-0_089074_2018-01-03_final
2018-01-04 09:10:39	ga_ls8c_ard_3-0-0_089075_2018-01-03_final
2018-01-04 09:11:03	ga_ls8c_ard_3-0-0_089076_2018-01-03_final
2018-01-04 09:11:27	ga_ls8c_ard_3-0-0_089077_2018-01-03_final
2018-01-04 09:11:51	ga_ls8c_ard_3-0-0_089078_2018-01-03_final
2018-01-04 09:12:15	ga_ls8c_ard_3-0-0_089079_2018-01-03_final
2018-01-04 09:12:39	ga_ls8c_ard_3-0-0_089080_2018-01-03_final
2018-01-04 09:13:03	ga_ls8c_ard_3-0-0_089081_2018-01-03_final
2018-01-04 09:13:27	ga_ls8c_ard_3-0-0_089082_2018-01-03_final
2018-01-04 09:13:51	ga_ls8c_ard_3-0-0_089083_2018-01-03_final
2018-01-04 09:14:15	ga_ls8c_ard_3-0-0_089084_2018-01-03_final
2018-01-04 09:14:39	ga_ls8c_ard_3-0-0_089085_2018-01-03_final
2018-01-04 09:15:03	ga_ls8c_ard_3-0-0_089086_2018-01-03_final
2018-01-04 09:15:27	ga_ls8c_ard_3-0-0_089087_2018-01-03_final
2018-01-04 09:15:51	ga_ls8c_ard_3-0-0_089088_2018-01-03_final
2018-01-04 09:16:15	ga_ls8c_ard_3-0-0_089089_2018-01-03_final
2018-01-04 09:16:38	ga_ls8c_ard_3-0-0_089090_2018-01-03_final
2018-01-04 10:46:21	ga_ls8c_ard_3-0-0_105067_2018-01-04_final
2018-01-04 10:46:45	ga_ls8c_ard_3-0-0_105068_2018-01-04_final
2018-01-04 10:47:09	ga_ls8c_ard_3-0-0_105069_2018-01-04_final
2018-01-04 10:47:33	ga_ls8c_ard_3-0-0_105070_2018-01-04_final
2018-01-04 10:47:56	ga_ls8c_ard_3-0-0_105071_2018-01-04_final
2018-01-04 10:48:20	ga_ls8c_ard_3-0-0_105072_2018-01-04_final
2018-01-04 10:48:44	ga_ls8c_ard_3-0-0_105073_2018-01-04_final
2018-01-04 10:49:08	ga_ls8c_ard_3-0-0_105074_2018-01-04_final
2018-01-30 09:44:19	ga_ls8c_ard_3-0-0_095067_2018-01-30_final
2018-01-30 09:45:31	ga_ls8c_ard_3-0-0_095070_2018-01-30_final
2018-01-30 09:45:54	ga_ls8c_ard_3-0-0_095071_2018-01-30_final
2018-01-30 09:46:18	ga_ls8c_ard_3-0-0_095072_2018-01-30_final
2018-01-30 09:46:42	ga_ls8c_ard_3-0-0_095073_2018-01-30_final
2018-01-30 09:47:06	ga_ls8c_ard_3-0-0_095074_2018-01-30_final
2018-01-30 09:47:30	ga_ls8c_ard_3-0-0_095075_2018-01-30_final
2018-01-30 09:47:54	ga_ls8c_ard_3-0-0_095076_2018-01-30_final
2018-01-30 09:48:18	ga_ls8c_ard_3-0-0_095077_2018-01-30_final
2018-01-30 09:48:42	ga_ls8c_ard_3-0-0_095078_2018-01-30_final
2018-01-30 09:49:06	ga_ls8c_ard_3-0-0_095079_2018-01-30_final
2018-01-30 09:49:30	ga_ls8c_ard_3-0-0_095080_2018-01-30_final
2018-01-30 09:49:54	ga_ls8c_ard_3-0-0_095081_2018-01-30_final
2018-01-30 09:50:18	ga_ls8c_ard_3-0-0_095082_2018-01-30_final
2018-01-30 09:50:42	ga_ls8c_ard_3-0-0_095083_2018-01-30_final
2018-01-30 09:51:06	ga_ls8c_ard_3-0-0_095084_2018-01-30_final
2018-01-30 09:51:30	ga_ls8c_ard_3-0-0_095085_2018-01-30_final
2018-01-30 09:51:54	ga_ls8c_ard_3-0-0_095086_2018-01-30_final
2018-01-30 09:52:18	ga_ls8c_ard_3-0-0_095087_2018-01-30_final
2018-01-30 09:52:42	ga_ls8c_ard_3-0-0_095088_2018-01-30_final
2018-01-30 11:23:12	ga_ls8c_ard_3-0-0_111067_2018-01-30_final
2018-01-30 11:23:36	ga_ls8c_ard_3-0-0_111068_2018-01-30_final
2018-01-30 11:24:00	ga_ls8c_ard_3-0-0_111069_2018-01-30_final
2018-01-30 11:24:24	ga_ls8c_ard_3-0-0_111070_2018-01-30_final
2018-01-30 11:24:47	ga_ls8c_ard_3-0-0_111071_2018-01-30_final
2018-01-30 11:25:11	ga_ls8c_ard_3-0-0_111072_2018-01-30_final
2018-01-30 11:25:35	ga_ls8c_ard_3-0-0_111073_2018-01-30_final
2018-01-30 11:25:59	ga_ls8c_ard_3-0-0_111074_2018-01-30_final
2018-01-30 11:26:23	ga_ls8c_ard_3-0-0_111075_2018-01-30_final
2018-01-30 11:26:47	ga_ls8c_ard_3-0-0_111076_2018-01-30_final
2018-01-30 11:27:11	ga_ls8c_ard_3-0-0_111077_2018-01-30_final
2018-01-30 11:27:35	ga_ls8c_ard_3-0-0_111078_2018-01-30_final
2018-01-30 11:27:59	ga_ls8c_ard_3-0-0_111079_2018-01-30_final
2018-01-30 11:28:23	ga_ls8c_ard_3-0-0_111080_2018-01-30_final
2018-01-30 11:28:47	ga_ls8c_ard_3-0-0_111081_2018-01-30_final
2018-01-30 11:29:11	ga_ls8c_ard_3-0-0_111082_2018-01-30_final
2018-01-30 11:29:35	ga_ls8c_ard_3-0-0_111083_2018-01-30_final
2018-01-30 11:29:59	ga_ls8c_ard_3-0-0_111084_2018-01-30_final
2018-01-30 11:30:23	ga_ls8c_ard_3-0-0_111085_2018-01-30_final
2018-01-31 10:27:34	ga_ls8c_ard_3-0-0_102067_2018-01-31_final
2018-01-31 10:27:58	ga_ls8c_ard_3-1-0_102068_2018-01-31_final
2018-01-31 10:28:22	ga_ls8c_ard_3-0-0_102069_2018-01-31_final
2018-01-31 10:28:46	ga_ls8c_ard_3-0-0_102070_2018-01-31_final
2018-01-31 10:29:09	ga_ls8c_ard_3-0-0_102071_2018-01-31_final
2018-01-31 10:29:33	ga_ls8c_ard_3-0-0_102072_2018-01-31_final
2018-01-31 10:29:57	ga_ls8c_ard_3-0-0_102073_2018-01-31_final
2018-01-31 10:30:21	ga_ls8c_ard_3-0-0_102074_2018-01-31_final
2018-01-31 10:30:45	ga_ls8c_ard_3-0-0_102075_2018-01-31_final
2018-01-31 10:31:09	ga_ls8c_ard_3-0-0_102076_2018-01-31_final
2018-01-31 10:31:33	ga_ls8c_ard_3-0-0_102077_2018-01-31_final
2018-01-31 10:31:57	ga_ls8c_ard_3-0-0_102078_2018-01-31_final
2018-01-31 10:32:21	ga_ls8c_ard_3-0-0_102079_2018-01-31_final
2018-01-31 10:32:45	ga_ls8c_ard_3-0-0_102080_2018-01-31_final
2018-01-31 10:33:09	ga_ls8c_ard_3-0-0_102081_2018-01-31_final
2018-01-31 10:33:33	ga_ls8c_ard_3-0-0_102082_2018-01-31_final

Return to ga_ls8c_ard_3 across January 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

January

All days