Search ga_ls8c_ard_3 across May 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
2019-05-02 09:37:39	ga_ls8c_ard_3-0-0_094067_2019-05-02_final
2019-05-02 09:39:15	ga_ls8c_ard_3-0-0_094071_2019-05-02_final
2019-05-02 09:39:39	ga_ls8c_ard_3-0-0_094072_2019-05-02_final
2019-05-02 09:40:03	ga_ls8c_ard_3-0-0_094073_2019-05-02_final
2019-05-02 09:40:27	ga_ls8c_ard_3-0-0_094074_2019-05-02_final
2019-05-02 09:40:51	ga_ls8c_ard_3-0-0_094075_2019-05-02_final
2019-05-02 09:41:15	ga_ls8c_ard_3-0-0_094076_2019-05-02_final
2019-05-02 09:41:39	ga_ls8c_ard_3-0-0_094077_2019-05-02_final
2019-05-02 09:42:03	ga_ls8c_ard_3-0-0_094078_2019-05-02_final
2019-05-02 09:42:27	ga_ls8c_ard_3-0-0_094079_2019-05-02_final
2019-05-02 09:42:51	ga_ls8c_ard_3-0-0_094080_2019-05-02_final
2019-05-02 09:43:15	ga_ls8c_ard_3-0-0_094081_2019-05-02_final
2019-05-02 09:43:39	ga_ls8c_ard_3-0-0_094082_2019-05-02_final
2019-05-02 09:44:02	ga_ls8c_ard_3-0-0_094083_2019-05-02_final
2019-05-02 09:44:26	ga_ls8c_ard_3-0-0_094084_2019-05-02_final
2019-05-02 09:44:50	ga_ls8c_ard_3-0-0_094085_2019-05-02_final
2019-05-02 09:45:14	ga_ls8c_ard_3-0-0_094086_2019-05-02_final
2019-05-02 09:45:38	ga_ls8c_ard_3-0-0_094087_2019-05-02_final
2019-05-02 09:46:02	ga_ls8c_ard_3-0-0_094088_2019-05-02_final
2019-05-02 11:16:32	ga_ls8c_ard_3-0-0_110067_2019-05-02_final
2019-05-02 11:16:56	ga_ls8c_ard_3-0-0_110068_2019-05-02_final
2019-05-02 11:17:20	ga_ls8c_ard_3-0-0_110069_2019-05-02_final
2019-05-02 11:17:44	ga_ls8c_ard_3-0-0_110070_2019-05-02_final
2019-05-02 11:18:08	ga_ls8c_ard_3-0-0_110071_2019-05-02_final
2019-05-02 11:18:32	ga_ls8c_ard_3-0-0_110072_2019-05-02_final
2019-05-02 11:18:56	ga_ls8c_ard_3-0-0_110073_2019-05-02_final
2019-05-02 11:19:20	ga_ls8c_ard_3-0-0_110074_2019-05-02_final
2019-05-02 11:19:44	ga_ls8c_ard_3-0-0_110075_2019-05-02_final
2019-05-02 11:20:08	ga_ls8c_ard_3-0-0_110076_2019-05-02_final
2019-05-02 11:20:32	ga_ls8c_ard_3-0-0_110077_2019-05-02_final
2019-05-02 11:20:56	ga_ls8c_ard_3-0-0_110078_2019-05-02_final
2019-05-02 11:21:20	ga_ls8c_ard_3-0-0_110079_2019-05-02_final
2019-05-02 11:21:44	ga_ls8c_ard_3-0-0_110080_2019-05-02_final
2019-05-02 11:22:08	ga_ls8c_ard_3-0-0_110081_2019-05-02_final
2019-05-02 11:22:32	ga_ls8c_ard_3-0-0_110082_2019-05-02_final
2019-05-02 11:22:56	ga_ls8c_ard_3-0-0_110083_2019-05-02_final
2019-05-02 11:23:20	ga_ls8c_ard_3-0-0_110084_2019-05-02_final
2019-05-02 11:23:43	ga_ls8c_ard_3-0-0_110085_2019-05-02_final
2019-05-03 10:20:56	ga_ls8c_ard_3-0-0_101067_2019-05-03_final
2019-05-03 10:21:19	ga_ls8c_ard_3-0-0_101068_2019-05-03_final
2019-05-03 10:21:43	ga_ls8c_ard_3-0-0_101069_2019-05-03_final
2019-05-03 10:22:07	ga_ls8c_ard_3-0-0_101070_2019-05-03_final
2019-05-03 10:22:31	ga_ls8c_ard_3-0-0_101071_2019-05-03_final
2019-05-03 10:22:55	ga_ls8c_ard_3-0-0_101072_2019-05-03_final
2019-05-03 10:23:19	ga_ls8c_ard_3-0-0_101073_2019-05-03_final
2019-05-03 10:23:43	ga_ls8c_ard_3-0-0_101074_2019-05-03_final
2019-05-03 10:24:07	ga_ls8c_ard_3-0-0_101075_2019-05-03_final
2019-05-03 10:24:31	ga_ls8c_ard_3-0-0_101076_2019-05-03_final
2019-05-03 10:24:55	ga_ls8c_ard_3-0-0_101077_2019-05-03_final
2019-05-03 10:25:19	ga_ls8c_ard_3-0-0_101078_2019-05-03_final
2019-05-03 10:25:43	ga_ls8c_ard_3-0-0_101079_2019-05-03_final
2019-05-03 10:26:07	ga_ls8c_ard_3-0-0_101080_2019-05-03_final
2019-05-03 10:26:31	ga_ls8c_ard_3-0-0_101081_2019-05-03_final
2019-05-03 10:26:55	ga_ls8c_ard_3-0-0_101082_2019-05-03_final
2019-05-03 10:27:19	ga_ls8c_ard_3-0-0_101083_2019-05-03_final
2019-05-03 10:27:43	ga_ls8c_ard_3-0-0_101084_2019-05-03_final
2019-05-04 09:25:19	ga_ls8c_ard_3-0-0_092067_2019-05-03_final
2019-05-04 09:25:43	ga_ls8c_ard_3-0-0_092068_2019-05-03_final
2019-05-04 09:26:54	ga_ls8c_ard_3-0-0_092071_2019-05-03_final
2019-05-04 09:27:18	ga_ls8c_ard_3-0-0_092072_2019-05-03_final
2019-05-04 09:27:42	ga_ls8c_ard_3-0-0_092073_2019-05-03_final
2019-05-04 09:28:06	ga_ls8c_ard_3-0-0_092074_2019-05-03_final
2019-05-04 09:28:30	ga_ls8c_ard_3-0-0_092075_2019-05-03_final
2019-05-04 09:28:54	ga_ls8c_ard_3-0-0_092076_2019-05-03_final
2019-05-04 09:29:18	ga_ls8c_ard_3-0-0_092077_2019-05-03_final
2019-05-04 09:29:42	ga_ls8c_ard_3-0-0_092078_2019-05-03_final
2019-05-04 09:30:06	ga_ls8c_ard_3-0-0_092079_2019-05-04_final
2019-05-04 09:30:30	ga_ls8c_ard_3-0-0_092080_2019-05-04_final
2019-05-04 09:30:54	ga_ls8c_ard_3-0-0_092081_2019-05-04_final
2019-05-04 09:31:18	ga_ls8c_ard_3-0-0_092082_2019-05-04_final
2019-05-04 09:31:42	ga_ls8c_ard_3-0-0_092083_2019-05-04_final
2019-05-04 09:32:06	ga_ls8c_ard_3-0-0_092084_2019-05-04_final
2019-05-04 09:32:30	ga_ls8c_ard_3-0-0_092085_2019-05-04_final
2019-05-04 09:32:54	ga_ls8c_ard_3-0-0_092086_2019-05-04_final
2019-05-04 09:33:18	ga_ls8c_ard_3-0-0_092087_2019-05-04_final
2019-05-04 09:33:41	ga_ls8c_ard_3-0-0_092088_2019-05-04_final
2019-05-04 09:34:05	ga_ls8c_ard_3-0-0_092089_2019-05-04_final
2019-05-04 09:34:29	ga_ls8c_ard_3-0-0_092090_2019-05-04_final
2019-05-04 11:04:12	ga_ls8c_ard_3-0-0_108067_2019-05-04_final
2019-05-04 11:04:36	ga_ls8c_ard_3-0-0_108068_2019-05-04_final
2019-05-04 11:05:00	ga_ls8c_ard_3-0-0_108069_2019-05-04_final
2019-05-04 11:05:23	ga_ls8c_ard_3-0-0_108070_2019-05-04_final
2019-05-04 11:05:47	ga_ls8c_ard_3-0-0_108071_2019-05-04_final
2019-05-04 11:06:11	ga_ls8c_ard_3-0-0_108072_2019-05-04_final
2019-05-04 11:06:35	ga_ls8c_ard_3-0-0_108073_2019-05-04_final
2019-05-04 11:06:59	ga_ls8c_ard_3-1-0_108074_2019-05-04_final
2019-05-04 11:07:23	ga_ls8c_ard_3-0-0_108075_2019-05-04_final
2019-05-04 11:07:47	ga_ls8c_ard_3-0-0_108076_2019-05-04_final
2019-05-04 11:08:11	ga_ls8c_ard_3-0-0_108077_2019-05-04_final
2019-05-04 11:08:35	ga_ls8c_ard_3-0-0_108078_2019-05-04_final
2019-05-04 11:08:59	ga_ls8c_ard_3-0-0_108079_2019-05-04_final
2019-05-04 11:09:23	ga_ls8c_ard_3-0-0_108080_2019-05-04_final
2019-05-04 11:09:47	ga_ls8c_ard_3-0-0_108081_2019-05-04_final
2019-05-04 11:10:11	ga_ls8c_ard_3-0-0_108082_2019-05-04_final
2019-05-04 11:10:35	ga_ls8c_ard_3-0-0_108083_2019-05-04_final
2019-05-04 11:10:59	ga_ls8c_ard_3-0-0_108084_2019-05-04_final
2019-05-04 11:11:23	ga_ls8c_ard_3-0-0_108085_2019-05-04_final
2019-05-31 09:09:50	ga_ls8c_ard_3-0-0_089074_2019-05-30_final
2019-05-31 09:10:14	ga_ls8c_ard_3-0-0_089075_2019-05-30_final
2019-05-31 09:10:38	ga_ls8c_ard_3-0-0_089076_2019-05-30_final

Return to ga_ls8c_ard_3 across May 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

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

2019

May

All days