Search ga_ls8c_ard_3 across 2022

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
2022-01-06 10:53:02	ga_ls8c_ard_3-2-1_106068_2022-01-06_final
2022-01-06 10:53:26	ga_ls8c_ard_3-2-1_106069_2022-01-06_final
2022-01-06 10:53:50	ga_ls8c_ard_3-2-1_106070_2022-01-06_final
2022-01-06 10:54:14	ga_ls8c_ard_3-2-1_106071_2022-01-06_final
2022-01-06 10:54:38	ga_ls8c_ard_3-2-1_106072_2022-01-06_final
2022-01-06 10:55:50	ga_ls8c_ard_3-2-1_106075_2022-01-06_final
2022-01-06 10:56:14	ga_ls8c_ard_3-2-1_106076_2022-01-06_final
2022-01-06 10:56:37	ga_ls8c_ard_3-2-1_106077_2022-01-06_final
2022-01-06 10:57:01	ga_ls8c_ard_3-2-1_106078_2022-01-06_final
2022-01-06 10:57:25	ga_ls8c_ard_3-2-1_106079_2022-01-06_final
2022-01-06 10:57:49	ga_ls8c_ard_3-2-1_106080_2022-01-06_final
2022-01-06 10:58:13	ga_ls8c_ard_3-2-1_106081_2022-01-06_final
2022-01-06 10:58:37	ga_ls8c_ard_3-2-1_106082_2022-01-06_final
2022-01-06 10:59:01	ga_ls8c_ard_3-2-1_106083_2022-01-06_final
2022-01-07 09:57:01	ga_ls8c_ard_3-2-1_097067_2022-01-07_final
2022-01-07 10:02:12	ga_ls8c_ard_3-2-1_097080_2022-01-07_final
2022-01-07 10:02:36	ga_ls8c_ard_3-2-1_097081_2022-01-07_final
2022-01-07 10:03:00	ga_ls8c_ard_3-2-1_097082_2022-01-07_final
2022-01-07 10:03:24	ga_ls8c_ard_3-2-1_097083_2022-01-07_final
2022-01-07 10:03:48	ga_ls8c_ard_3-2-1_097084_2022-01-07_final
2022-01-07 10:04:12	ga_ls8c_ard_3-2-1_097085_2022-01-07_final
2022-01-07 11:37:53	ga_ls8c_ard_3-2-1_113072_2022-01-07_final
2022-01-07 11:38:17	ga_ls8c_ard_3-2-1_113073_2022-01-07_final
2022-01-07 11:38:41	ga_ls8c_ard_3-2-1_113074_2022-01-07_final
2022-01-07 11:39:05	ga_ls8c_ard_3-2-1_113075_2022-01-07_final
2022-01-07 11:39:29	ga_ls8c_ard_3-2-1_113076_2022-01-07_final
2022-01-07 11:39:53	ga_ls8c_ard_3-2-1_113077_2022-01-07_final
2022-01-07 11:40:17	ga_ls8c_ard_3-2-1_113078_2022-01-07_final
2022-01-07 11:41:05	ga_ls8c_ard_3-2-1_113080_2022-01-07_final
2022-01-07 11:41:29	ga_ls8c_ard_3-2-1_113081_2022-01-07_final
2022-01-07 11:41:53	ga_ls8c_ard_3-2-1_113082_2022-01-07_final
2022-01-07 11:42:17	ga_ls8c_ard_3-2-1_113083_2022-01-07_final
2022-01-08 10:40:16	ga_ls8c_ard_3-2-1_104067_2022-01-08_final
2022-01-08 10:40:40	ga_ls8c_ard_3-2-1_104068_2022-01-08_final
2022-01-08 10:41:04	ga_ls8c_ard_3-2-1_104069_2022-01-08_final
2022-01-08 10:41:28	ga_ls8c_ard_3-2-1_104070_2022-01-08_final
2022-01-08 10:41:52	ga_ls8c_ard_3-2-1_104071_2022-01-08_final
2022-01-08 10:42:16	ga_ls8c_ard_3-2-1_104072_2022-01-08_final
2022-01-08 10:42:40	ga_ls8c_ard_3-2-1_104073_2022-01-08_final
2022-01-08 10:43:04	ga_ls8c_ard_3-2-1_104074_2022-01-08_final
2022-01-08 10:43:28	ga_ls8c_ard_3-2-1_104075_2022-01-08_final
2022-01-08 10:43:52	ga_ls8c_ard_3-2-1_104076_2022-01-08_final
2022-01-08 10:44:16	ga_ls8c_ard_3-2-1_104077_2022-01-08_final
2022-01-08 10:44:40	ga_ls8c_ard_3-2-1_104078_2022-01-08_final
2022-01-08 10:45:04	ga_ls8c_ard_3-2-1_104079_2022-01-08_final
2022-01-08 10:45:27	ga_ls8c_ard_3-2-1_104080_2022-01-08_final
2022-01-08 10:45:51	ga_ls8c_ard_3-2-1_104081_2022-01-08_final
2022-01-08 10:46:15	ga_ls8c_ard_3-2-1_104082_2022-01-08_final
2022-01-08 14:10:01	ga_ls8c_ard_3-2-1_136097_2022-01-08_final
2022-01-09 08:10:57	ga_ls8c_ard_3-2-1_079080_2022-01-08_final
2022-01-09 08:18:08	ga_ls8c_ard_3-2-1_079098_2022-01-08_final
2022-01-09 09:46:15	ga_ls8c_ard_3-2-1_095071_2022-01-09_final
2022-01-09 09:46:39	ga_ls8c_ard_3-2-1_095072_2022-01-09_final
2022-01-09 09:47:02	ga_ls8c_ard_3-2-1_095073_2022-01-09_final
2022-01-09 09:47:26	ga_ls8c_ard_3-2-1_095074_2022-01-09_final
2022-01-09 09:47:50	ga_ls8c_ard_3-2-1_095075_2022-01-09_final
2022-01-09 09:48:14	ga_ls8c_ard_3-2-1_095076_2022-01-09_final
2022-01-09 09:48:38	ga_ls8c_ard_3-2-1_095077_2022-01-09_final
2022-01-09 09:49:02	ga_ls8c_ard_3-2-1_095078_2022-01-09_final
2022-01-09 09:49:26	ga_ls8c_ard_3-2-1_095079_2022-01-09_final
2022-01-09 09:49:50	ga_ls8c_ard_3-2-1_095080_2022-01-09_final
2022-01-09 09:50:14	ga_ls8c_ard_3-2-1_095081_2022-01-09_final
2022-01-09 09:50:38	ga_ls8c_ard_3-2-1_095082_2022-01-09_final
2022-01-09 09:51:02	ga_ls8c_ard_3-2-1_095083_2022-01-09_final
2022-01-09 09:51:26	ga_ls8c_ard_3-2-1_095084_2022-01-09_final
2022-01-09 09:51:50	ga_ls8c_ard_3-2-1_095085_2022-01-09_final
2022-01-09 09:52:14	ga_ls8c_ard_3-2-1_095086_2022-01-09_final
2022-01-09 09:52:38	ga_ls8c_ard_3-2-1_095087_2022-01-09_final
2022-01-09 11:23:56	ga_ls8c_ard_3-2-1_111068_2022-01-09_final
2022-01-09 11:24:20	ga_ls8c_ard_3-2-1_111069_2022-01-09_final
2022-01-09 11:24:44	ga_ls8c_ard_3-2-1_111070_2022-01-09_final
2022-01-09 11:25:08	ga_ls8c_ard_3-2-1_111071_2022-01-09_final
2022-01-09 11:25:32	ga_ls8c_ard_3-2-1_111072_2022-01-09_final
2022-01-09 11:25:56	ga_ls8c_ard_3-2-1_111073_2022-01-09_final
2022-01-09 11:26:19	ga_ls8c_ard_3-2-1_111074_2022-01-09_final
2022-01-09 11:26:43	ga_ls8c_ard_3-2-1_111075_2022-01-09_final
2022-01-09 11:27:07	ga_ls8c_ard_3-2-1_111076_2022-01-09_final
2022-01-09 11:27:31	ga_ls8c_ard_3-2-1_111077_2022-01-09_final
2022-01-09 11:27:55	ga_ls8c_ard_3-2-1_111078_2022-01-09_final
2022-01-09 11:28:19	ga_ls8c_ard_3-2-1_111079_2022-01-09_final
2022-01-09 11:28:43	ga_ls8c_ard_3-2-1_111080_2022-01-09_final
2022-01-09 11:29:07	ga_ls8c_ard_3-2-1_111081_2022-01-09_final
2022-01-09 11:29:31	ga_ls8c_ard_3-2-1_111082_2022-01-09_final
2022-01-09 11:29:55	ga_ls8c_ard_3-2-1_111083_2022-01-09_final
2022-01-09 11:30:19	ga_ls8c_ard_3-2-1_111084_2022-01-09_final
2022-01-10 10:27:54	ga_ls8c_ard_3-2-1_102067_2022-01-10_final
2022-01-10 10:28:18	ga_ls8c_ard_3-2-1_102068_2022-01-10_final
2022-01-10 10:28:42	ga_ls8c_ard_3-2-1_102069_2022-01-10_final
2022-01-10 10:29:06	ga_ls8c_ard_3-2-1_102070_2022-01-10_final
2022-01-10 10:29:30	ga_ls8c_ard_3-2-1_102071_2022-01-10_final
2022-01-10 10:29:54	ga_ls8c_ard_3-2-1_102072_2022-01-10_final
2022-01-10 10:30:18	ga_ls8c_ard_3-2-1_102073_2022-01-10_final
2022-01-10 10:30:42	ga_ls8c_ard_3-2-1_102074_2022-01-10_final
2022-01-10 10:31:06	ga_ls8c_ard_3-2-1_102075_2022-01-10_final
2022-01-10 10:31:30	ga_ls8c_ard_3-2-1_102076_2022-01-10_final
2022-01-10 10:31:54	ga_ls8c_ard_3-2-1_102077_2022-01-10_final
2022-01-10 10:32:18	ga_ls8c_ard_3-2-1_102078_2022-01-10_final
2022-01-10 10:32:42	ga_ls8c_ard_3-2-1_102079_2022-01-10_final
2022-01-10 10:33:06	ga_ls8c_ard_3-2-1_102080_2022-01-10_final
2022-01-10 10:33:30	ga_ls8c_ard_3-2-1_102081_2022-01-10_final

Return to ga_ls8c_ard_3 across 2022 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

2022

All months