Search ga_ls8c_ard_3 across November 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
2018-11-13 09:05:52	ga_ls8c_ard_3-0-0_088079_2018-11-12_final
2018-11-13 09:06:16	ga_ls8c_ard_3-0-0_088080_2018-11-12_final
2018-11-13 09:06:40	ga_ls8c_ard_3-1-0_088081_2018-11-12_final
2018-11-13 09:07:04	ga_ls8c_ard_3-0-0_088082_2018-11-12_final
2018-11-13 09:07:28	ga_ls8c_ard_3-0-0_088083_2018-11-12_final
2018-11-13 09:07:52	ga_ls8c_ard_3-0-0_088084_2018-11-12_final
2018-11-13 10:39:58	ga_ls8c_ard_3-0-0_104067_2018-11-13_final
2018-11-13 10:40:22	ga_ls8c_ard_3-0-0_104068_2018-11-13_final
2018-11-13 10:40:46	ga_ls8c_ard_3-0-0_104069_2018-11-13_final
2018-11-13 10:41:10	ga_ls8c_ard_3-0-0_104070_2018-11-13_final
2018-11-13 10:41:34	ga_ls8c_ard_3-0-0_104071_2018-11-13_final
2018-11-13 10:41:58	ga_ls8c_ard_3-0-0_104072_2018-11-13_final
2018-11-13 10:42:22	ga_ls8c_ard_3-0-0_104073_2018-11-13_final
2018-11-13 10:42:46	ga_ls8c_ard_3-0-0_104074_2018-11-13_final
2018-11-13 10:43:09	ga_ls8c_ard_3-0-0_104075_2018-11-13_final
2018-11-13 10:43:33	ga_ls8c_ard_3-0-0_104076_2018-11-13_final
2018-11-13 10:43:57	ga_ls8c_ard_3-0-0_104077_2018-11-13_final
2018-11-13 10:44:21	ga_ls8c_ard_3-0-0_104078_2018-11-13_final
2018-11-13 10:44:45	ga_ls8c_ard_3-0-0_104079_2018-11-13_final
2018-11-13 10:45:09	ga_ls8c_ard_3-0-0_104080_2018-11-13_final
2018-11-13 10:45:33	ga_ls8c_ard_3-0-0_104081_2018-11-13_final
2018-11-13 10:45:57	ga_ls8c_ard_3-0-0_104082_2018-11-13_final
2018-11-13 10:46:21	ga_ls8c_ard_3-0-0_104083_2018-11-13_final
2018-11-13 14:09:43	ga_ls8c_ard_3-2-1_136097_2018-11-13_final
2018-11-14 08:10:39	ga_ls8c_ard_3-2-1_079080_2018-11-13_final
2018-11-14 08:17:50	ga_ls8c_ard_3-2-1_079098_2018-11-13_final
2018-11-14 09:44:21	ga_ls8c_ard_3-0-0_095067_2018-11-14_final
2018-11-14 09:45:32	ga_ls8c_ard_3-0-0_095070_2018-11-14_final
2018-11-14 09:45:56	ga_ls8c_ard_3-0-0_095071_2018-11-14_final
2018-11-14 09:46:20	ga_ls8c_ard_3-0-0_095072_2018-11-14_final
2018-11-14 09:46:44	ga_ls8c_ard_3-0-0_095073_2018-11-14_final
2018-11-14 09:47:08	ga_ls8c_ard_3-0-0_095074_2018-11-14_final
2018-11-14 09:47:32	ga_ls8c_ard_3-0-0_095075_2018-11-14_final
2018-11-14 09:47:56	ga_ls8c_ard_3-0-0_095076_2018-11-14_final
2018-11-14 09:48:20	ga_ls8c_ard_3-0-0_095077_2018-11-14_final
2018-11-14 09:48:44	ga_ls8c_ard_3-0-0_095078_2018-11-14_final
2018-11-14 09:49:08	ga_ls8c_ard_3-0-0_095079_2018-11-14_final
2018-11-14 09:49:32	ga_ls8c_ard_3-0-0_095080_2018-11-14_final
2018-11-14 09:49:56	ga_ls8c_ard_3-0-0_095081_2018-11-14_final
2018-11-14 09:50:20	ga_ls8c_ard_3-0-0_095082_2018-11-14_final
2018-11-14 09:50:44	ga_ls8c_ard_3-0-0_095083_2018-11-14_final
2018-11-14 09:51:08	ga_ls8c_ard_3-0-0_095084_2018-11-14_final
2018-11-14 09:51:32	ga_ls8c_ard_3-0-0_095085_2018-11-14_final
2018-11-14 09:51:56	ga_ls8c_ard_3-0-0_095086_2018-11-14_final
2018-11-14 09:52:20	ga_ls8c_ard_3-0-0_095087_2018-11-14_final
2018-11-14 09:52:44	ga_ls8c_ard_3-0-0_095088_2018-11-14_final
2018-11-14 11:23:14	ga_ls8c_ard_3-0-0_111067_2018-11-14_final
2018-11-14 11:23:38	ga_ls8c_ard_3-0-0_111068_2018-11-14_final
2018-11-14 11:24:02	ga_ls8c_ard_3-0-0_111069_2018-11-14_final
2018-11-14 11:24:26	ga_ls8c_ard_3-0-0_111070_2018-11-14_final
2018-11-14 11:24:49	ga_ls8c_ard_3-0-0_111071_2018-11-14_final
2018-11-14 11:25:13	ga_ls8c_ard_3-0-0_111072_2018-11-14_final
2018-11-14 11:25:37	ga_ls8c_ard_3-0-0_111073_2018-11-14_final
2018-11-14 11:26:01	ga_ls8c_ard_3-0-0_111074_2018-11-14_final
2018-11-14 11:26:25	ga_ls8c_ard_3-0-0_111075_2018-11-14_final
2018-11-14 11:26:49	ga_ls8c_ard_3-0-0_111076_2018-11-14_final
2018-11-14 11:27:13	ga_ls8c_ard_3-0-0_111077_2018-11-14_final
2018-11-14 11:27:37	ga_ls8c_ard_3-0-0_111078_2018-11-14_final
2018-11-14 11:28:01	ga_ls8c_ard_3-0-0_111079_2018-11-14_final
2018-11-14 11:28:25	ga_ls8c_ard_3-0-0_111080_2018-11-14_final
2018-11-14 11:28:49	ga_ls8c_ard_3-0-0_111081_2018-11-14_final
2018-11-14 11:29:13	ga_ls8c_ard_3-0-0_111082_2018-11-14_final
2018-11-14 11:29:37	ga_ls8c_ard_3-0-0_111083_2018-11-14_final
2018-11-14 11:30:01	ga_ls8c_ard_3-0-0_111084_2018-11-14_final
2018-11-14 11:30:25	ga_ls8c_ard_3-0-0_111085_2018-11-14_final
2018-11-15 10:27:36	ga_ls8c_ard_3-0-0_102067_2018-11-15_final
2018-11-15 10:28:00	ga_ls8c_ard_3-0-0_102068_2018-11-15_final
2018-11-15 10:28:24	ga_ls8c_ard_3-0-0_102069_2018-11-15_final
2018-11-15 10:28:48	ga_ls8c_ard_3-0-0_102070_2018-11-15_final
2018-11-15 10:29:12	ga_ls8c_ard_3-0-0_102071_2018-11-15_final
2018-11-15 10:29:36	ga_ls8c_ard_3-0-0_102072_2018-11-15_final
2018-11-15 10:30:00	ga_ls8c_ard_3-0-0_102073_2018-11-15_final
2018-11-15 10:30:24	ga_ls8c_ard_3-0-0_102074_2018-11-15_final
2018-11-15 10:30:48	ga_ls8c_ard_3-0-0_102075_2018-11-15_final
2018-11-15 10:31:12	ga_ls8c_ard_3-0-0_102076_2018-11-15_final
2018-11-15 10:31:36	ga_ls8c_ard_3-0-0_102077_2018-11-15_final
2018-11-15 10:32:00	ga_ls8c_ard_3-0-0_102078_2018-11-15_final
2018-11-15 10:32:24	ga_ls8c_ard_3-0-0_102079_2018-11-15_final
2018-11-15 10:32:48	ga_ls8c_ard_3-0-0_102080_2018-11-15_final
2018-11-15 10:33:12	ga_ls8c_ard_3-0-0_102081_2018-11-15_final
2018-11-15 10:33:35	ga_ls8c_ard_3-0-0_102082_2018-11-15_final
2018-11-15 10:33:59	ga_ls8c_ard_3-0-0_102083_2018-11-15_final
2018-11-16 09:31:59	ga_ls8c_ard_3-0-0_093067_2018-11-16_final
2018-11-16 09:32:23	ga_ls8c_ard_3-0-0_093068_2018-11-16_final
2018-11-23 09:38:10	ga_ls8c_ard_3-0-0_094067_2018-11-23_final
2018-11-23 09:39:45	ga_ls8c_ard_3-0-0_094071_2018-11-23_final
2018-11-23 09:40:09	ga_ls8c_ard_3-0-0_094072_2018-11-23_final
2018-11-23 09:40:33	ga_ls8c_ard_3-0-0_094073_2018-11-23_final
2018-11-23 09:40:57	ga_ls8c_ard_3-0-0_094074_2018-11-23_final
2018-11-23 09:41:21	ga_ls8c_ard_3-0-0_094075_2018-11-23_final
2018-11-23 09:41:45	ga_ls8c_ard_3-0-0_094076_2018-11-23_final
2018-11-23 09:42:09	ga_ls8c_ard_3-0-0_094077_2018-11-23_final
2018-11-23 09:42:33	ga_ls8c_ard_3-0-0_094078_2018-11-23_final
2018-11-23 09:42:57	ga_ls8c_ard_3-0-0_094079_2018-11-23_final
2018-11-23 09:43:21	ga_ls8c_ard_3-0-0_094080_2018-11-23_final
2018-11-23 09:43:45	ga_ls8c_ard_3-0-0_094081_2018-11-23_final
2018-11-23 09:44:09	ga_ls8c_ard_3-0-0_094082_2018-11-23_final
2018-11-23 09:44:33	ga_ls8c_ard_3-0-0_094083_2018-11-23_final
2018-11-23 09:44:57	ga_ls8c_ard_3-0-0_094084_2018-11-23_final
2018-11-25 09:25:48	ga_ls8c_ard_3-0-0_092067_2018-11-24_final

Return to ga_ls8c_ard_3 across November 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

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

2018

November

All days