Search ga_ls8c_ard_3 whole collection

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

85 results

Time (local)	Label
2022-09-23 10:28:43	ga_ls8c_ard_3-2-1_102068_2022-09-23_final
2022-09-23 10:29:07	ga_ls8c_ard_3-2-1_102069_2022-09-23_final
2022-09-23 10:29:31	ga_ls8c_ard_3-2-1_102070_2022-09-23_final
2022-09-23 10:29:55	ga_ls8c_ard_3-2-1_102071_2022-09-23_final
2022-09-23 10:30:19	ga_ls8c_ard_3-2-1_102072_2022-09-23_final
2022-09-23 10:30:43	ga_ls8c_ard_3-2-1_102073_2022-09-23_final
2022-09-23 10:31:07	ga_ls8c_ard_3-2-1_102074_2022-09-23_final
2022-09-23 10:31:31	ga_ls8c_ard_3-2-1_102075_2022-09-23_final
2022-09-23 10:31:55	ga_ls8c_ard_3-2-1_102076_2022-09-23_final
2022-09-23 10:32:19	ga_ls8c_ard_3-2-1_102077_2022-09-23_final
2022-09-23 10:32:43	ga_ls8c_ard_3-2-1_102078_2022-09-23_final
2022-09-23 10:33:07	ga_ls8c_ard_3-2-1_102079_2022-09-23_final
2022-09-23 10:33:30	ga_ls8c_ard_3-2-1_102080_2022-09-23_final
2022-09-23 10:33:54	ga_ls8c_ard_3-2-1_102081_2022-09-23_final
2022-09-23 10:34:18	ga_ls8c_ard_3-2-1_102082_2022-09-23_final
2022-09-24 09:34:18	ga_ls8c_ard_3-2-1_093071_2022-09-24_final
2022-09-24 09:34:42	ga_ls8c_ard_3-2-1_093072_2022-09-24_final
2022-09-24 09:35:06	ga_ls8c_ard_3-2-1_093073_2022-09-24_final
2022-09-24 09:35:29	ga_ls8c_ard_3-2-1_093074_2022-09-24_final
2022-09-24 09:35:53	ga_ls8c_ard_3-2-1_093075_2022-09-24_final
2022-09-24 09:36:17	ga_ls8c_ard_3-2-1_093076_2022-09-24_final
2022-09-24 09:36:41	ga_ls8c_ard_3-2-1_093077_2022-09-24_final
2022-09-24 09:37:05	ga_ls8c_ard_3-2-1_093078_2022-09-24_final
2022-09-24 09:37:29	ga_ls8c_ard_3-2-1_093079_2022-09-24_final
2022-09-24 09:37:53	ga_ls8c_ard_3-2-1_093080_2022-09-24_final
2022-09-24 09:38:17	ga_ls8c_ard_3-2-1_093081_2022-09-24_final
2022-09-24 09:38:41	ga_ls8c_ard_3-2-1_093082_2022-09-24_final
2022-09-24 09:39:05	ga_ls8c_ard_3-2-1_093083_2022-09-24_final
2022-09-24 09:39:29	ga_ls8c_ard_3-2-1_093084_2022-09-24_final
2022-09-24 09:39:53	ga_ls8c_ard_3-2-1_093085_2022-09-24_final
2022-09-24 09:40:17	ga_ls8c_ard_3-2-1_093086_2022-09-24_final
2022-09-24 09:40:41	ga_ls8c_ard_3-2-1_093087_2022-09-24_final
2022-09-24 09:41:05	ga_ls8c_ard_3-2-1_093088_2022-09-24_final
2022-09-24 11:12:23	ga_ls8c_ard_3-2-1_109069_2022-09-24_final
2022-09-24 11:12:47	ga_ls8c_ard_3-2-1_109070_2022-09-24_final
2022-09-24 11:13:11	ga_ls8c_ard_3-2-1_109071_2022-09-24_final
2022-09-24 11:13:35	ga_ls8c_ard_3-2-1_109072_2022-09-24_final
2022-09-24 11:13:59	ga_ls8c_ard_3-2-1_109073_2022-09-24_final
2022-09-24 11:14:23	ga_ls8c_ard_3-2-1_109074_2022-09-24_final
2022-09-24 11:14:46	ga_ls8c_ard_3-2-1_109075_2022-09-24_final
2022-09-24 11:15:10	ga_ls8c_ard_3-2-1_109076_2022-09-24_final
2022-09-24 11:15:34	ga_ls8c_ard_3-2-1_109077_2022-09-24_final
2022-09-24 11:15:58	ga_ls8c_ard_3-2-1_109078_2022-09-24_final
2022-09-24 11:16:22	ga_ls8c_ard_3-2-1_109079_2022-09-24_final
2022-09-24 11:16:46	ga_ls8c_ard_3-2-1_109080_2022-09-24_final
2022-09-24 11:17:10	ga_ls8c_ard_3-2-1_109081_2022-09-24_final
2022-09-24 11:17:34	ga_ls8c_ard_3-2-1_109082_2022-09-24_final
2022-09-24 11:17:58	ga_ls8c_ard_3-2-1_109083_2022-09-24_final
2022-09-24 11:18:22	ga_ls8c_ard_3-2-1_109084_2022-09-24_final
2022-09-25 10:17:33	ga_ls8c_ard_3-2-1_100071_2022-09-25_final
2022-09-25 10:17:57	ga_ls8c_ard_3-2-1_100072_2022-09-25_final
2022-09-25 10:18:21	ga_ls8c_ard_3-2-1_100073_2022-09-25_final
2022-09-25 10:18:45	ga_ls8c_ard_3-2-1_100074_2022-09-25_final
2022-09-25 10:19:09	ga_ls8c_ard_3-2-1_100075_2022-09-25_final
2022-09-25 10:19:33	ga_ls8c_ard_3-2-1_100076_2022-09-25_final
2022-09-25 10:19:57	ga_ls8c_ard_3-2-1_100077_2022-09-25_final
2022-09-25 10:20:21	ga_ls8c_ard_3-2-1_100078_2022-09-25_final
2022-09-25 10:20:45	ga_ls8c_ard_3-2-1_100079_2022-09-25_final
2022-09-25 10:21:09	ga_ls8c_ard_3-2-1_100080_2022-09-25_final
2022-09-25 10:21:33	ga_ls8c_ard_3-2-1_100081_2022-09-25_final
2022-09-25 10:21:57	ga_ls8c_ard_3-2-1_100082_2022-09-25_final
2022-09-25 10:22:21	ga_ls8c_ard_3-2-1_100083_2022-09-25_final
2022-09-25 10:22:45	ga_ls8c_ard_3-2-1_100084_2022-09-25_final
2022-09-25 11:58:26	ga_ls8c_ard_3-2-1_116076_2022-09-25_final
2022-09-25 11:58:50	ga_ls8c_ard_3-2-1_116077_2022-09-25_final
2022-09-25 11:59:14	ga_ls8c_ard_3-2-1_116078_2022-09-25_final
2022-09-26 09:22:20	ga_ls8c_ard_3-2-1_091072_2022-09-25_final
2022-09-26 09:22:44	ga_ls8c_ard_3-2-1_091073_2022-09-25_final
2022-09-26 09:23:08	ga_ls8c_ard_3-2-1_091074_2022-09-25_final
2022-09-26 09:23:32	ga_ls8c_ard_3-2-1_091075_2022-09-25_final
2022-09-26 09:23:56	ga_ls8c_ard_3-2-1_091076_2022-09-25_final
2022-09-26 09:24:20	ga_ls8c_ard_3-2-1_091077_2022-09-25_final
2022-09-26 09:24:44	ga_ls8c_ard_3-2-1_091078_2022-09-25_final
2022-09-26 09:25:08	ga_ls8c_ard_3-2-1_091079_2022-09-25_final
2022-09-26 09:25:31	ga_ls8c_ard_3-2-1_091080_2022-09-25_final
2022-09-26 09:25:55	ga_ls8c_ard_3-2-1_091081_2022-09-25_final
2022-09-26 09:26:19	ga_ls8c_ard_3-2-1_091082_2022-09-25_final
2022-09-26 09:26:43	ga_ls8c_ard_3-2-1_091083_2022-09-25_final
2022-09-26 09:27:07	ga_ls8c_ard_3-2-1_091084_2022-09-25_final
2022-09-26 09:27:31	ga_ls8c_ard_3-2-1_091085_2022-09-25_final
2022-09-26 09:27:55	ga_ls8c_ard_3-2-1_091086_2022-09-25_final
2022-09-26 09:28:19	ga_ls8c_ard_3-2-1_091087_2022-09-25_final
2022-09-26 09:28:43	ga_ls8c_ard_3-2-1_091088_2022-09-25_final
2022-09-26 09:29:07	ga_ls8c_ard_3-2-1_091089_2022-09-25_final
2022-09-26 09:29:31	ga_ls8c_ard_3-2-1_091090_2022-09-25_final

Return to ga_ls8c_ard_3 whole collection 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

All years