Search ga_ls8c_ard_3 across February 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
2019-02-21 11:56:42	ga_ls8c_ard_3-0-0_116074_2019-02-21_final
2019-02-21 11:57:06	ga_ls8c_ard_3-0-0_116075_2019-02-21_final
2019-02-21 11:57:30	ga_ls8c_ard_3-0-0_116076_2019-02-21_final
2019-02-21 11:57:54	ga_ls8c_ard_3-0-0_116077_2019-02-21_final
2019-02-21 11:58:18	ga_ls8c_ard_3-0-0_116078_2019-02-21_final
2019-02-22 09:19:48	ga_ls8c_ard_3-0-0_091068_2019-02-21_final
2019-02-22 09:21:24	ga_ls8c_ard_3-0-0_091072_2019-02-21_final
2019-02-22 09:21:48	ga_ls8c_ard_3-0-0_091073_2019-02-21_final
2019-02-22 09:22:12	ga_ls8c_ard_3-0-0_091074_2019-02-21_final
2019-02-22 09:22:36	ga_ls8c_ard_3-0-0_091075_2019-02-21_final
2019-02-22 09:23:00	ga_ls8c_ard_3-0-0_091076_2019-02-21_final
2019-02-22 09:23:24	ga_ls8c_ard_3-0-0_091077_2019-02-21_final
2019-02-22 09:23:48	ga_ls8c_ard_3-0-0_091078_2019-02-21_final
2019-02-22 09:24:11	ga_ls8c_ard_3-0-0_091079_2019-02-21_final
2019-02-22 09:24:35	ga_ls8c_ard_3-0-0_091080_2019-02-21_final
2019-02-22 09:24:59	ga_ls8c_ard_3-0-0_091081_2019-02-21_final
2019-02-22 09:25:23	ga_ls8c_ard_3-0-0_091082_2019-02-21_final
2019-02-22 09:25:47	ga_ls8c_ard_3-0-0_091083_2019-02-21_final
2019-02-22 09:26:11	ga_ls8c_ard_3-0-0_091084_2019-02-21_final
2019-02-22 09:26:35	ga_ls8c_ard_3-0-0_091085_2019-02-21_final
2019-02-22 09:26:59	ga_ls8c_ard_3-0-0_091086_2019-02-21_final
2019-02-22 09:27:23	ga_ls8c_ard_3-0-0_091087_2019-02-21_final
2019-02-22 09:27:47	ga_ls8c_ard_3-0-0_091088_2019-02-21_final
2019-02-22 09:28:11	ga_ls8c_ard_3-0-0_091089_2019-02-21_final
2019-02-22 09:28:35	ga_ls8c_ard_3-0-0_091090_2019-02-21_final
2019-02-22 09:28:59	ga_ls8c_ard_3-0-0_091091_2019-02-21_final
2019-02-22 10:58:17	ga_ls8c_ard_3-0-0_107067_2019-02-22_final
2019-02-22 10:58:41	ga_ls8c_ard_3-0-0_107068_2019-02-22_final
2019-02-22 10:59:05	ga_ls8c_ard_3-0-0_107069_2019-02-22_final
2019-02-22 10:59:29	ga_ls8c_ard_3-0-0_107070_2019-02-22_final
2019-02-22 11:00:17	ga_ls8c_ard_3-0-0_107072_2019-02-22_final
2019-02-22 11:00:41	ga_ls8c_ard_3-0-0_107073_2019-02-22_final
2019-02-22 11:01:05	ga_ls8c_ard_3-0-0_107074_2019-02-22_final
2019-02-22 11:01:29	ga_ls8c_ard_3-0-0_107075_2019-02-22_final
2019-02-22 11:01:53	ga_ls8c_ard_3-0-0_107076_2019-02-22_final
2019-02-22 11:02:17	ga_ls8c_ard_3-0-0_107077_2019-02-22_final
2019-02-22 11:02:41	ga_ls8c_ard_3-0-0_107078_2019-02-22_final
2019-02-22 11:03:04	ga_ls8c_ard_3-0-0_107079_2019-02-22_final
2019-02-22 11:03:28	ga_ls8c_ard_3-0-0_107080_2019-02-22_final
2019-02-22 11:03:52	ga_ls8c_ard_3-0-0_107081_2019-02-22_final
2019-02-22 11:04:16	ga_ls8c_ard_3-1-0_107082_2019-02-22_final
2019-02-22 11:04:40	ga_ls8c_ard_3-0-0_107083_2019-02-22_final
2019-02-22 11:05:04	ga_ls8c_ard_3-0-0_107084_2019-02-22_final
2019-02-22 11:05:28	ga_ls8c_ard_3-0-0_107085_2019-02-22_final
2019-02-23 10:02:40	ga_ls8c_ard_3-0-0_098067_2019-02-23_final
2019-02-23 10:03:04	ga_ls8c_ard_3-0-0_098068_2019-02-23_final
2019-02-23 10:03:28	ga_ls8c_ard_3-0-0_098069_2019-02-23_final
2019-02-23 10:03:51	ga_ls8c_ard_3-0-0_098070_2019-02-23_final
2019-02-23 10:04:15	ga_ls8c_ard_3-0-0_098071_2019-02-23_final
2019-02-23 10:04:39	ga_ls8c_ard_3-0-0_098072_2019-02-23_final
2019-02-23 10:05:03	ga_ls8c_ard_3-0-0_098073_2019-02-23_final
2019-02-23 10:05:27	ga_ls8c_ard_3-0-0_098074_2019-02-23_final
2019-02-23 10:05:51	ga_ls8c_ard_3-0-0_098075_2019-02-23_final
2019-02-23 10:06:15	ga_ls8c_ard_3-0-0_098076_2019-02-23_final
2019-02-23 10:06:39	ga_ls8c_ard_3-0-0_098077_2019-02-23_final
2019-02-23 10:07:03	ga_ls8c_ard_3-0-0_098078_2019-02-23_final
2019-02-23 10:07:27	ga_ls8c_ard_3-0-0_098079_2019-02-23_final
2019-02-23 10:07:51	ga_ls8c_ard_3-0-0_098080_2019-02-23_final
2019-02-23 10:08:15	ga_ls8c_ard_3-0-0_098081_2019-02-23_final
2019-02-23 10:08:39	ga_ls8c_ard_3-0-0_098082_2019-02-23_final
2019-02-23 10:09:03	ga_ls8c_ard_3-0-0_098083_2019-02-23_final
2019-02-23 10:09:27	ga_ls8c_ard_3-0-0_098084_2019-02-23_final
2019-02-23 10:09:51	ga_ls8c_ard_3-0-0_098085_2019-02-23_final
2019-02-23 10:10:15	ga_ls8c_ard_3-0-0_098086_2019-02-23_final
2019-02-23 11:41:33	ga_ls8c_ard_3-0-0_114067_2019-02-23_final
2019-02-23 11:43:56	ga_ls8c_ard_3-0-0_114073_2019-02-23_final
2019-02-23 11:44:20	ga_ls8c_ard_3-0-0_114074_2019-02-23_final
2019-02-23 11:44:44	ga_ls8c_ard_3-0-0_114075_2019-02-23_final
2019-02-23 11:45:08	ga_ls8c_ard_3-0-0_114076_2019-02-23_final
2019-02-23 11:45:32	ga_ls8c_ard_3-0-0_114077_2019-02-23_final
2019-02-23 11:45:56	ga_ls8c_ard_3-0-0_114078_2019-02-23_final
2019-02-23 11:46:20	ga_ls8c_ard_3-0-0_114079_2019-02-23_final
2019-02-23 11:46:44	ga_ls8c_ard_3-0-0_114080_2019-02-23_final
2019-02-23 11:47:08	ga_ls8c_ard_3-0-0_114081_2019-02-23_final
2019-02-24 09:09:50	ga_ls8c_ard_3-0-0_089074_2019-02-23_final
2019-02-24 09:10:14	ga_ls8c_ard_3-0-0_089075_2019-02-23_final
2019-02-24 09:10:38	ga_ls8c_ard_3-0-0_089076_2019-02-23_final
2019-02-24 09:11:01	ga_ls8c_ard_3-0-0_089077_2019-02-23_final
2019-02-24 09:11:25	ga_ls8c_ard_3-0-0_089078_2019-02-23_final
2019-02-24 09:11:49	ga_ls8c_ard_3-0-0_089079_2019-02-23_final
2019-02-24 09:12:13	ga_ls8c_ard_3-0-0_089080_2019-02-23_final
2019-02-24 09:12:37	ga_ls8c_ard_3-0-0_089081_2019-02-23_final
2019-02-24 09:13:01	ga_ls8c_ard_3-0-0_089082_2019-02-23_final
2019-02-24 09:13:25	ga_ls8c_ard_3-0-0_089083_2019-02-23_final
2019-02-24 09:13:49	ga_ls8c_ard_3-0-0_089084_2019-02-23_final
2019-02-24 09:14:13	ga_ls8c_ard_3-0-0_089085_2019-02-23_final
2019-02-24 09:14:37	ga_ls8c_ard_3-0-0_089086_2019-02-23_final
2019-02-24 09:15:01	ga_ls8c_ard_3-0-0_089087_2019-02-23_final
2019-02-24 09:15:25	ga_ls8c_ard_3-0-0_089088_2019-02-23_final
2019-02-24 09:15:49	ga_ls8c_ard_3-0-0_089089_2019-02-23_final
2019-02-24 09:16:13	ga_ls8c_ard_3-0-0_089090_2019-02-23_final
2019-02-24 10:45:55	ga_ls8c_ard_3-0-0_105067_2019-02-24_final
2019-02-24 10:46:19	ga_ls8c_ard_3-0-0_105068_2019-02-24_final
2019-02-24 10:46:43	ga_ls8c_ard_3-0-0_105069_2019-02-24_final
2019-02-24 10:47:07	ga_ls8c_ard_3-0-0_105070_2019-02-24_final
2019-02-24 10:47:31	ga_ls8c_ard_3-0-0_105071_2019-02-24_final
2019-02-24 10:47:55	ga_ls8c_ard_3-0-0_105072_2019-02-24_final
2019-02-24 10:48:19	ga_ls8c_ard_3-0-0_105073_2019-02-24_final
2019-02-24 10:48:43	ga_ls8c_ard_3-0-0_105074_2019-02-24_final
2019-02-24 10:49:07	ga_ls8c_ard_3-0-0_105075_2019-02-24_final

Return to ga_ls8c_ard_3 across February 2019 overview

ga_ls8c_ard_3: Geoscience Australia Landsat 8 Operational Land Imager and Thermal Infra-Red Scanner Analysis Ready Data Collection 3

GA Collection 3

USGS Products

Collection 2 NBAR

Collection 2 NBAR Tiled

Pixel Quality

Collection 2 Level1

Tidal

Water Observations

Fractional Cover

Sentinel 2

Ingested Pixel Quality

Geomedians

TMAD

Telemetry data

Summary

Other Products

2019

February

All days