Search ga_ls8c_ard_3 across 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
2019-07-28 09:45:38	ga_ls8c_ard_3-1-0_095070_2019-07-28_final
2019-07-28 09:46:02	ga_ls8c_ard_3-1-0_095071_2019-07-28_final
2019-07-28 09:46:26	ga_ls8c_ard_3-1-0_095072_2019-07-28_final
2019-07-28 09:46:50	ga_ls8c_ard_3-1-0_095073_2019-07-28_final
2019-07-28 09:47:13	ga_ls8c_ard_3-1-0_095074_2019-07-28_final
2019-07-28 09:47:37	ga_ls8c_ard_3-1-0_095075_2019-07-28_final
2019-07-28 09:48:01	ga_ls8c_ard_3-1-0_095076_2019-07-28_final
2019-07-28 09:48:25	ga_ls8c_ard_3-1-0_095077_2019-07-28_final
2019-07-28 09:48:49	ga_ls8c_ard_3-1-0_095078_2019-07-28_final
2019-07-28 09:49:13	ga_ls8c_ard_3-1-0_095079_2019-07-28_final
2019-07-28 09:49:37	ga_ls8c_ard_3-1-0_095080_2019-07-28_final
2019-07-28 09:50:01	ga_ls8c_ard_3-1-0_095081_2019-07-28_final
2019-07-28 09:50:25	ga_ls8c_ard_3-1-0_095082_2019-07-28_final
2019-07-28 09:50:49	ga_ls8c_ard_3-1-0_095083_2019-07-28_final
2019-07-28 09:51:13	ga_ls8c_ard_3-1-0_095084_2019-07-28_final
2019-07-28 09:51:37	ga_ls8c_ard_3-1-0_095085_2019-07-28_final
2019-07-28 09:52:01	ga_ls8c_ard_3-1-0_095086_2019-07-28_final
2019-07-28 09:52:25	ga_ls8c_ard_3-1-0_095087_2019-07-28_final
2019-07-28 09:52:49	ga_ls8c_ard_3-1-0_095088_2019-07-28_final
2019-07-28 11:23:19	ga_ls8c_ard_3-1-0_111067_2019-07-28_final
2019-07-28 11:23:43	ga_ls8c_ard_3-1-0_111068_2019-07-28_final
2019-07-28 11:24:07	ga_ls8c_ard_3-1-0_111069_2019-07-28_final
2019-07-28 11:24:31	ga_ls8c_ard_3-1-0_111070_2019-07-28_final
2019-07-28 11:24:55	ga_ls8c_ard_3-1-0_111071_2019-07-28_final
2019-07-28 11:25:19	ga_ls8c_ard_3-1-0_111072_2019-07-28_final
2019-07-28 11:25:43	ga_ls8c_ard_3-1-0_111073_2019-07-28_final
2019-07-28 11:26:07	ga_ls8c_ard_3-1-0_111074_2019-07-28_final
2019-07-28 11:26:30	ga_ls8c_ard_3-1-0_111075_2019-07-28_final
2019-07-28 11:26:54	ga_ls8c_ard_3-1-0_111076_2019-07-28_final
2019-07-28 11:27:18	ga_ls8c_ard_3-1-0_111077_2019-07-28_final
2019-07-28 11:27:42	ga_ls8c_ard_3-1-0_111078_2019-07-28_final
2019-07-28 11:28:06	ga_ls8c_ard_3-1-0_111079_2019-07-28_final
2019-07-28 11:28:30	ga_ls8c_ard_3-1-0_111080_2019-07-28_final
2019-07-28 11:28:54	ga_ls8c_ard_3-1-0_111081_2019-07-28_final
2019-07-28 11:29:18	ga_ls8c_ard_3-1-0_111082_2019-07-28_final
2019-07-28 11:29:42	ga_ls8c_ard_3-1-0_111083_2019-07-28_final
2019-07-28 11:30:06	ga_ls8c_ard_3-1-0_111084_2019-07-28_final
2019-07-28 11:30:30	ga_ls8c_ard_3-1-0_111085_2019-07-28_final
2019-07-29 10:27:42	ga_ls8c_ard_3-1-0_102067_2019-07-29_final
2019-07-29 10:28:06	ga_ls8c_ard_3-1-0_102068_2019-07-29_final
2019-07-29 10:28:30	ga_ls8c_ard_3-1-0_102069_2019-07-29_final
2019-07-29 10:28:54	ga_ls8c_ard_3-1-0_102070_2019-07-29_final
2019-07-29 10:29:18	ga_ls8c_ard_3-1-0_102071_2019-07-29_final
2019-07-29 10:29:42	ga_ls8c_ard_3-1-0_102072_2019-07-29_final
2019-07-29 10:30:06	ga_ls8c_ard_3-1-0_102073_2019-07-29_final
2019-07-29 10:30:30	ga_ls8c_ard_3-1-0_102074_2019-07-29_final
2019-07-29 10:30:54	ga_ls8c_ard_3-1-0_102075_2019-07-29_final
2019-07-29 10:31:17	ga_ls8c_ard_3-1-0_102076_2019-07-29_final
2019-07-29 10:31:41	ga_ls8c_ard_3-1-0_102077_2019-07-29_final
2019-07-29 10:32:05	ga_ls8c_ard_3-1-0_102078_2019-07-29_final
2019-07-29 10:32:29	ga_ls8c_ard_3-1-0_102079_2019-07-29_final
2019-07-29 10:32:53	ga_ls8c_ard_3-1-0_102080_2019-07-29_final
2019-07-29 10:33:17	ga_ls8c_ard_3-1-0_102081_2019-07-29_final
2019-07-29 10:33:41	ga_ls8c_ard_3-1-0_102082_2019-07-29_final
2019-07-29 10:34:05	ga_ls8c_ard_3-1-0_102083_2019-07-29_final
2019-07-30 09:32:05	ga_ls8c_ard_3-1-0_093067_2019-07-30_final
2019-07-30 09:32:29	ga_ls8c_ard_3-1-0_093068_2019-07-30_final
2019-07-30 09:33:41	ga_ls8c_ard_3-1-0_093071_2019-07-30_final
2019-07-30 09:34:05	ga_ls8c_ard_3-1-0_093072_2019-07-30_final
2019-07-30 09:34:29	ga_ls8c_ard_3-1-0_093073_2019-07-30_final
2019-07-30 09:34:53	ga_ls8c_ard_3-1-0_093074_2019-07-30_final
2019-07-30 09:35:17	ga_ls8c_ard_3-1-0_093075_2019-07-30_final
2019-07-30 09:35:41	ga_ls8c_ard_3-1-0_093076_2019-07-30_final
2019-07-30 09:36:05	ga_ls8c_ard_3-1-0_093077_2019-07-30_final
2019-07-30 09:36:28	ga_ls8c_ard_3-1-0_093078_2019-07-30_final
2019-07-30 09:36:52	ga_ls8c_ard_3-1-0_093079_2019-07-30_final
2019-07-30 09:37:16	ga_ls8c_ard_3-1-0_093080_2019-07-30_final
2019-07-30 09:37:40	ga_ls8c_ard_3-1-0_093081_2019-07-30_final
2019-07-30 09:38:04	ga_ls8c_ard_3-1-0_093082_2019-07-30_final
2019-07-30 09:38:28	ga_ls8c_ard_3-1-0_093083_2019-07-30_final
2019-07-30 09:38:52	ga_ls8c_ard_3-1-0_093084_2019-07-30_final
2019-07-30 09:39:16	ga_ls8c_ard_3-1-0_093085_2019-07-30_final
2019-07-30 09:39:40	ga_ls8c_ard_3-1-0_093086_2019-07-30_final
2019-07-30 09:40:04	ga_ls8c_ard_3-1-0_093087_2019-07-30_final
2019-07-30 09:40:28	ga_ls8c_ard_3-1-0_093088_2019-07-30_final
2019-07-30 11:10:58	ga_ls8c_ard_3-1-0_109067_2019-07-30_final
2019-07-30 11:11:22	ga_ls8c_ard_3-1-0_109068_2019-07-30_final
2019-07-30 11:11:46	ga_ls8c_ard_3-1-0_109069_2019-07-30_final
2019-07-30 11:12:10	ga_ls8c_ard_3-1-0_109070_2019-07-30_final
2019-07-30 11:12:34	ga_ls8c_ard_3-1-0_109071_2019-07-30_final
2019-07-30 11:12:58	ga_ls8c_ard_3-1-0_109072_2019-07-30_final
2019-07-30 11:13:22	ga_ls8c_ard_3-1-0_109073_2019-07-30_final
2019-07-30 11:13:46	ga_ls8c_ard_3-1-0_109074_2019-07-30_final
2019-07-30 11:14:10	ga_ls8c_ard_3-1-0_109075_2019-07-30_final
2019-07-30 11:14:34	ga_ls8c_ard_3-1-0_109076_2019-07-30_final
2019-07-30 11:14:58	ga_ls8c_ard_3-1-0_109077_2019-07-30_final
2019-07-30 11:15:21	ga_ls8c_ard_3-1-0_109078_2019-07-30_final
2019-07-30 11:15:45	ga_ls8c_ard_3-1-0_109079_2019-07-30_final
2019-07-30 11:16:09	ga_ls8c_ard_3-1-0_109080_2019-07-30_final
2019-07-30 11:16:33	ga_ls8c_ard_3-1-0_109081_2019-07-30_final
2019-07-30 11:16:57	ga_ls8c_ard_3-1-0_109082_2019-07-30_final
2019-07-30 11:17:21	ga_ls8c_ard_3-1-0_109083_2019-07-30_final
2019-07-30 11:17:45	ga_ls8c_ard_3-1-0_109084_2019-07-30_final
2019-07-30 11:18:09	ga_ls8c_ard_3-1-0_109085_2019-07-30_final
2019-08-29 09:46:12	ga_ls8c_ard_3-1-0_095071_2019-08-29_final
2019-08-29 09:46:36	ga_ls8c_ard_3-1-0_095072_2019-08-29_final
2019-08-29 09:47:00	ga_ls8c_ard_3-1-0_095073_2019-08-29_final
2019-08-29 09:47:47	ga_ls8c_ard_3-1-0_095075_2019-08-29_final
2019-08-29 09:48:59	ga_ls8c_ard_3-1-0_095078_2019-08-29_final
2019-08-29 09:50:35	ga_ls8c_ard_3-1-0_095082_2019-08-29_final

Return to ga_ls8c_ard_3 across 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

All months