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

First 100 results (query limit)

Time (local)	Label
2022-07-19 10:43:05	ga_ls8c_ard_3-2-1_104074_2022-07-19_final
2022-07-26 10:51:22	ga_ls8c_ard_3-2-1_105079_2022-07-26_final
2022-07-26 10:51:45	ga_ls8c_ard_3-2-1_105080_2022-07-26_final
2022-07-29 11:24:41	ga_ls8c_ard_3-2-1_110085_2022-07-29_final
2022-07-30 10:27:29	ga_ls8c_ard_3-2-1_101081_2022-07-30_final
2022-08-03 10:00:48	ga_ls8c_ard_3-2-1_097076_2022-08-03_final
2022-08-03 11:39:17	ga_ls8c_ard_3-2-1_113075_2022-08-03_final
2022-08-15 10:26:48	ga_ls8c_ard_3-2-1_101079_2022-08-15_final
2022-08-19 10:00:54	ga_ls8c_ard_3-2-1_097076_2022-08-19_final
2022-08-22 10:33:00	ga_ls8c_ard_3-2-1_102079_2022-08-22_final
2022-08-23 11:15:51	ga_ls8c_ard_3-2-1_109078_2022-08-23_final
2022-08-25 11:03:54	ga_ls8c_ard_3-2-1_107079_2022-08-25_final
2022-08-27 10:51:56	ga_ls8c_ard_3-2-1_105080_2022-08-27_final
2022-09-12 10:51:13	ga_ls8c_ard_3-2-1_105078_2022-09-12_final
2022-09-16 10:27:19	ga_ls8c_ard_3-2-1_101080_2022-09-16_final
2022-09-18 10:12:58	ga_ls8c_ard_3-2-1_099075_2022-09-18_final
2022-09-20 11:39:53	ga_ls8c_ard_3-2-1_113076_2022-09-20_final
2022-09-21 10:45:04	ga_ls8c_ard_3-2-1_104078_2022-09-21_final
2022-09-24 11:16:46	ga_ls8c_ard_3-2-1_109080_2022-09-24_final
2022-09-25 11:58:50	ga_ls8c_ard_3-2-1_116077_2022-09-25_final
2022-09-29 11:34:06	ga_ls8c_ard_3-2-1_112077_2022-09-29_final
2022-09-30 10:37:17	ga_ls8c_ard_3-2-1_103074_2022-09-30_final
2022-10-02 10:26:30	ga_ls8c_ard_3-2-1_101078_2022-10-02_final
2022-10-04 10:12:56	ga_ls8c_ard_3-2-1_099075_2022-10-04_final
2022-10-06 10:03:21	ga_ls8c_ard_3-2-1_097082_2022-10-06_final
2022-10-06 11:39:51	ga_ls8c_ard_3-2-1_113076_2022-10-06_final
2022-10-08 09:48:59	ga_ls8c_ard_3-2-1_095077_2022-10-08_final
2022-10-10 09:38:35	ga_ls8c_ard_3-2-1_093082_2022-10-10_final
2022-10-14 10:51:29	ga_ls8c_ard_3-2-1_105079_2022-10-14_final
2022-10-15 11:34:22	ga_ls8c_ard_3-2-1_112078_2022-10-15_final
2022-10-30 10:51:38	ga_ls8c_ard_3-2-1_105079_2022-10-30_final
2022-10-31 09:55:37	ga_ls8c_ard_3-2-1_096078_2022-10-31_final
2022-11-02 09:46:51	ga_ls8c_ard_3-2-1_094087_2022-11-02_final
2022-11-11 11:16:43	ga_ls8c_ard_3-2-1_109080_2022-11-11_final
2022-11-18 11:22:01	ga_ls8c_ard_3-2-1_110078_2022-11-18_final
2022-11-20 09:30:23	ga_ls8c_ard_3-2-1_092077_2022-11-20_final
2022-11-25 09:48:57	ga_ls8c_ard_3-2-1_095077_2022-11-25_final
2022-11-25 11:28:38	ga_ls8c_ard_3-2-1_111079_2022-11-25_final
2022-11-28 10:21:03	ga_ls8c_ard_3-2-1_100080_2022-11-28_final
2022-12-02 09:56:20	ga_ls8c_ard_3-2-1_096080_2022-12-02_final
2022-12-13 09:38:32	ga_ls8c_ard_3-2-1_093082_2022-12-13_final
2022-12-14 10:21:24	ga_ls8c_ard_3-2-1_100081_2022-12-14_final
2022-12-22 09:31:02	ga_ls8c_ard_3-2-1_092079_2022-12-22_final
2022-12-27 09:49:36	ga_ls8c_ard_3-2-1_095079_2022-12-27_final
2022-12-29 11:16:32	ga_ls8c_ard_3-2-1_109080_2022-12-29_final
2022-12-30 10:18:31	ga_ls8c_ard_3-2-1_100074_2022-12-30_final
2023-01-03 11:34:17	ga_ls8c_ard_3-2-1_112078_2023-01-03_final
2023-02-01 09:24:28	ga_ls8c_ard_3-2-1_091078_2023-01-31_final
2023-02-08 11:10:18	ga_ls8c_ard_3-2-1_108080_2023-02-08_final
2023-02-13 09:49:53	ga_ls8c_ard_3-2-1_095080_2023-02-13_final
2023-02-15 11:16:23	ga_ls8c_ard_3-2-1_109080_2023-02-15_final
2023-02-18 10:09:09	ga_ls8c_ard_3-2-1_098082_2023-02-18_final
2023-02-20 11:34:26	ga_ls8c_ard_3-2-1_112079_2023-02-20_final
2023-02-24 09:30:20	ga_ls8c_ard_3-2-1_092078_2023-02-24_final
2023-03-01 09:49:15	ga_ls8c_ard_3-2-1_095079_2023-03-01_final
2023-03-02 10:32:54	ga_ls8c_ard_3-2-1_102080_2023-03-02_final
2023-03-04 10:20:56	ga_ls8c_ard_3-2-1_100081_2023-03-04_final
2023-03-08 11:34:38	ga_ls8c_ard_3-2-1_112080_2023-03-08_final
2023-03-10 09:44:35	ga_ls8c_ard_3-2-1_094083_2023-03-10_final
2023-03-13 10:14:41	ga_ls8c_ard_3-2-1_099081_2023-03-13_final
2023-03-15 10:02:43	ga_ls8c_ard_3-2-1_097082_2023-03-15_final
2023-03-17 09:49:08	ga_ls8c_ard_3-2-1_095079_2023-03-17_final
2023-03-17 11:28:25	ga_ls8c_ard_3-2-1_111080_2023-03-17_final
2023-03-24 09:55:13	ga_ls8c_ard_3-2-1_096079_2023-03-24_final
2023-03-24 11:33:42	ga_ls8c_ard_3-2-1_112078_2023-03-24_final
2023-03-26 11:21:20	ga_ls8c_ard_3-2-1_110078_2023-03-26_final
2023-04-02 11:28:14	ga_ls8c_ard_3-2-1_111080_2023-04-02_final
2023-04-03 10:32:36	ga_ls8c_ard_3-2-1_102080_2023-04-03_nrt
2023-04-09 11:32:16	ga_ls8c_ard_3-2-1_112075_2023-04-09_final
2023-04-11 09:41:23	ga_ls8c_ard_3-2-1_094076_2023-04-11_final
2023-04-12 10:26:14	ga_ls8c_ard_3-2-1_101080_2023-04-12_final
2023-04-14 10:14:16	ga_ls8c_ard_3-2-1_099081_2023-04-14_final
2023-04-16 11:38:47	ga_ls8c_ard_3-2-1_113076_2023-04-16_final
2023-04-17 10:43:09	ga_ls8c_ard_3-2-1_104076_2023-04-17_final
2023-04-17 10:45:09	ga_ls8c_ard_3-2-1_104081_2023-04-17_final
2023-04-18 09:48:43	ga_ls8c_ard_3-2-1_095079_2023-04-18_final
2023-04-20 09:37:32	ga_ls8c_ard_3-2-1_093082_2023-04-20_final
2023-04-24 10:49:43	ga_ls8c_ard_3-2-1_105077_2023-04-24_final
2023-04-26 10:36:33	ga_ls8c_ard_3-2-1_103075_2023-04-26_final
2023-04-27 09:41:20	ga_ls8c_ard_3-2-1_094076_2023-04-27_final
2023-04-27 09:42:55	ga_ls8c_ard_3-2-1_094080_2023-04-27_final
2023-04-27 11:21:48	ga_ls8c_ard_3-2-1_110080_2023-04-27_final
2023-04-28 10:26:11	ga_ls8c_ard_3-2-1_101080_2023-04-28_final
2023-04-28 10:26:35	ga_ls8c_ard_3-2-1_101081_2023-04-28_final
2023-04-29 11:09:26	ga_ls8c_ard_3-2-1_108080_2023-04-29_final
2023-05-03 10:43:28	ga_ls8c_ard_3-2-1_104077_2023-05-03_final
2023-05-03 10:43:52	ga_ls8c_ard_3-2-1_104078_2023-05-03_final
2023-05-04 11:27:07	ga_ls8c_ard_3-2-1_111078_2023-05-04_final
2023-05-06 11:14:44	ga_ls8c_ard_3-2-1_109078_2023-05-06_final
2023-05-08 11:02:44	ga_ls8c_ard_3-2-1_107079_2023-05-08_final
2023-05-09 10:08:18	ga_ls8c_ard_3-2-1_098082_2023-05-09_final
2023-05-19 10:42:53	ga_ls8c_ard_3-2-1_104076_2023-05-19_final
2023-05-20 11:28:09	ga_ls8c_ard_3-2-1_111081_2023-05-20_final
2023-05-21 10:31:21	ga_ls8c_ard_3-2-1_102078_2023-05-21_final
2023-05-22 11:14:37	ga_ls8c_ard_3-2-1_109078_2023-05-22_final
2023-06-03 10:02:05	ga_ls8c_ard_3-2-1_097082_2023-06-03_nrt
2023-06-03 11:39:22	ga_ls8c_ard_3-2-1_113078_2023-06-03_nrt
2023-06-03 11:39:46	ga_ls8c_ard_3-2-1_113079_2023-06-03_nrt
2023-06-04 10:44:10	ga_ls8c_ard_3-2-1_104079_2023-06-04_nrt
2023-06-04 10:44:57	ga_ls8c_ard_3-2-1_104081_2023-06-04_nrt

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

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

All years