Search ga_ls8c_ard_3 across November 2019

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
2019-11-16 09:06:17	ga_ls8c_ard_3-1-0_088079_2019-11-15_final
2019-11-16 09:06:41	ga_ls8c_ard_3-1-0_088080_2019-11-15_final
2019-11-16 09:07:05	ga_ls8c_ard_3-1-0_088081_2019-11-15_final
2019-11-16 09:07:29	ga_ls8c_ard_3-1-0_088082_2019-11-15_final
2019-11-16 09:07:53	ga_ls8c_ard_3-1-0_088083_2019-11-15_final
2019-11-16 09:08:16	ga_ls8c_ard_3-1-0_088084_2019-11-15_final
2019-11-16 10:40:22	ga_ls8c_ard_3-1-0_104067_2019-11-16_final
2019-11-16 10:40:46	ga_ls8c_ard_3-1-0_104068_2019-11-16_final
2019-11-16 10:41:10	ga_ls8c_ard_3-1-0_104069_2019-11-16_final
2019-11-16 10:41:34	ga_ls8c_ard_3-1-0_104070_2019-11-16_final
2019-11-16 10:41:58	ga_ls8c_ard_3-1-0_104071_2019-11-16_final
2019-11-16 10:42:22	ga_ls8c_ard_3-1-0_104072_2019-11-16_final
2019-11-16 10:42:46	ga_ls8c_ard_3-1-0_104073_2019-11-16_final
2019-11-16 10:43:10	ga_ls8c_ard_3-1-0_104074_2019-11-16_final
2019-11-16 10:43:34	ga_ls8c_ard_3-1-0_104075_2019-11-16_final
2019-11-16 10:43:58	ga_ls8c_ard_3-1-0_104076_2019-11-16_final
2019-11-16 10:44:22	ga_ls8c_ard_3-1-0_104077_2019-11-16_final
2019-11-16 10:44:46	ga_ls8c_ard_3-1-0_104078_2019-11-16_final
2019-11-16 10:45:10	ga_ls8c_ard_3-1-0_104079_2019-11-16_final
2019-11-16 10:45:34	ga_ls8c_ard_3-1-0_104080_2019-11-16_final
2019-11-16 10:45:58	ga_ls8c_ard_3-1-0_104081_2019-11-16_final
2019-11-16 10:46:22	ga_ls8c_ard_3-1-0_104082_2019-11-16_final
2019-11-16 10:46:45	ga_ls8c_ard_3-1-0_104083_2019-11-16_final
2019-11-16 14:10:07	ga_ls8c_ard_3-2-1_136097_2019-11-16_final
2019-11-17 08:11:03	ga_ls8c_ard_3-2-1_079080_2019-11-16_final
2019-11-17 08:18:14	ga_ls8c_ard_3-2-1_079098_2019-11-16_final
2019-11-17 09:44:45	ga_ls8c_ard_3-1-0_095067_2019-11-17_final
2019-11-17 09:45:57	ga_ls8c_ard_3-1-0_095070_2019-11-17_final
2019-11-17 09:46:21	ga_ls8c_ard_3-1-0_095071_2019-11-17_final
2019-11-17 09:46:45	ga_ls8c_ard_3-1-0_095072_2019-11-17_final
2019-11-17 09:47:08	ga_ls8c_ard_3-1-0_095073_2019-11-17_final
2019-11-17 09:47:32	ga_ls8c_ard_3-1-0_095074_2019-11-17_final
2019-11-17 09:47:56	ga_ls8c_ard_3-1-0_095075_2019-11-17_final
2019-11-17 09:48:20	ga_ls8c_ard_3-1-0_095076_2019-11-17_final
2019-11-17 09:48:44	ga_ls8c_ard_3-1-0_095077_2019-11-17_final
2019-11-17 09:49:08	ga_ls8c_ard_3-1-0_095078_2019-11-17_final
2019-11-17 09:49:32	ga_ls8c_ard_3-1-0_095079_2019-11-17_final
2019-11-17 09:49:56	ga_ls8c_ard_3-1-0_095080_2019-11-17_final
2019-11-17 09:50:20	ga_ls8c_ard_3-1-0_095081_2019-11-17_final
2019-11-17 09:50:44	ga_ls8c_ard_3-1-0_095082_2019-11-17_final
2019-11-17 09:51:08	ga_ls8c_ard_3-1-0_095083_2019-11-17_final
2019-11-17 09:51:32	ga_ls8c_ard_3-1-0_095084_2019-11-17_final
2019-11-17 09:51:56	ga_ls8c_ard_3-1-0_095085_2019-11-17_final
2019-11-17 09:52:20	ga_ls8c_ard_3-1-0_095086_2019-11-17_final
2019-11-17 09:52:44	ga_ls8c_ard_3-1-0_095087_2019-11-17_final
2019-11-17 09:53:08	ga_ls8c_ard_3-1-0_095088_2019-11-17_final
2019-11-17 11:23:38	ga_ls8c_ard_3-1-0_111067_2019-11-17_final
2019-11-17 11:24:02	ga_ls8c_ard_3-1-0_111068_2019-11-17_final
2019-11-17 11:24:26	ga_ls8c_ard_3-1-0_111069_2019-11-17_final
2019-11-17 11:24:50	ga_ls8c_ard_3-1-0_111070_2019-11-17_final
2019-11-17 11:25:14	ga_ls8c_ard_3-1-0_111071_2019-11-17_final
2019-11-17 11:25:38	ga_ls8c_ard_3-1-0_111072_2019-11-17_final
2019-11-17 11:26:01	ga_ls8c_ard_3-1-0_111073_2019-11-17_final
2019-11-17 11:26:25	ga_ls8c_ard_3-1-0_111074_2019-11-17_final
2019-11-17 11:26:49	ga_ls8c_ard_3-1-0_111075_2019-11-17_final
2019-11-17 11:27:13	ga_ls8c_ard_3-1-0_111076_2019-11-17_final
2019-11-17 11:27:37	ga_ls8c_ard_3-1-0_111077_2019-11-17_final
2019-11-17 11:28:01	ga_ls8c_ard_3-1-0_111078_2019-11-17_final
2019-11-17 11:28:25	ga_ls8c_ard_3-1-0_111079_2019-11-17_final
2019-11-17 11:28:49	ga_ls8c_ard_3-1-0_111080_2019-11-17_final
2019-11-17 11:29:13	ga_ls8c_ard_3-1-0_111081_2019-11-17_final
2019-11-17 11:29:37	ga_ls8c_ard_3-1-0_111082_2019-11-17_final
2019-11-17 11:30:01	ga_ls8c_ard_3-1-0_111083_2019-11-17_final
2019-11-17 11:30:25	ga_ls8c_ard_3-1-0_111084_2019-11-17_final
2019-11-17 11:30:49	ga_ls8c_ard_3-1-0_111085_2019-11-17_final
2019-11-29 10:09:27	ga_ls8c_ard_3-1-0_099067_2019-11-29_final
2019-11-29 10:09:51	ga_ls8c_ard_3-1-0_099068_2019-11-29_final
2019-11-29 10:10:15	ga_ls8c_ard_3-1-0_099069_2019-11-29_final
2019-11-29 10:10:39	ga_ls8c_ard_3-1-0_099070_2019-11-29_final
2019-11-29 10:11:03	ga_ls8c_ard_3-1-0_099071_2019-11-29_final
2019-11-29 10:11:27	ga_ls8c_ard_3-1-0_099072_2019-11-29_final
2019-11-29 10:11:51	ga_ls8c_ard_3-1-0_099073_2019-11-29_final
2019-11-29 10:12:14	ga_ls8c_ard_3-1-0_099074_2019-11-29_final
2019-11-29 10:12:38	ga_ls8c_ard_3-1-0_099075_2019-11-29_final
2019-11-29 10:13:02	ga_ls8c_ard_3-1-0_099076_2019-11-29_final
2019-11-29 10:13:26	ga_ls8c_ard_3-1-0_099077_2019-11-29_final
2019-11-29 10:13:50	ga_ls8c_ard_3-1-0_099078_2019-11-29_final
2019-11-29 10:14:14	ga_ls8c_ard_3-1-0_099079_2019-11-29_final
2019-11-29 10:14:38	ga_ls8c_ard_3-1-0_099080_2019-11-29_final
2019-11-29 10:15:02	ga_ls8c_ard_3-1-0_099081_2019-11-29_final
2019-11-29 10:15:26	ga_ls8c_ard_3-1-0_099082_2019-11-29_final
2019-11-29 10:15:50	ga_ls8c_ard_3-1-0_099083_2019-11-29_final
2019-11-29 10:16:14	ga_ls8c_ard_3-1-0_099084_2019-11-29_final
2019-11-30 09:16:37	ga_ls8c_ard_3-1-0_090074_2019-11-29_final
2019-11-30 09:17:01	ga_ls8c_ard_3-1-0_090075_2019-11-29_final
2019-11-30 09:17:25	ga_ls8c_ard_3-1-0_090076_2019-11-29_final
2019-11-30 09:17:49	ga_ls8c_ard_3-1-0_090077_2019-11-29_final
2019-11-30 09:18:13	ga_ls8c_ard_3-1-0_090078_2019-11-29_final
2019-11-30 09:18:37	ga_ls8c_ard_3-1-0_090079_2019-11-29_final
2019-11-30 09:19:01	ga_ls8c_ard_3-1-0_090080_2019-11-29_final
2019-11-30 09:19:25	ga_ls8c_ard_3-1-0_090081_2019-11-29_final
2019-11-30 09:19:49	ga_ls8c_ard_3-1-0_090082_2019-11-29_final
2019-11-30 09:20:13	ga_ls8c_ard_3-1-0_090083_2019-11-29_final
2019-11-30 09:20:37	ga_ls8c_ard_3-1-0_090084_2019-11-29_final
2019-11-30 09:21:01	ga_ls8c_ard_3-1-0_090085_2019-11-29_final
2019-11-30 09:21:25	ga_ls8c_ard_3-1-0_090086_2019-11-29_final
2019-11-30 09:21:49	ga_ls8c_ard_3-1-0_090087_2019-11-29_final
2019-11-30 09:22:12	ga_ls8c_ard_3-1-0_090088_2019-11-29_final
2019-11-30 09:22:36	ga_ls8c_ard_3-1-0_090089_2019-11-29_final
2019-11-30 09:23:00	ga_ls8c_ard_3-1-0_090090_2019-11-29_final

Return to ga_ls8c_ard_3 across November 2019 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

2019

November

All days