Search ga_ls9c_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
2022-10-23 11:35:31	ga_ls9c_ard_3-2-1_112081_2022-10-23_final.stac-item
2022-10-23 11:36:19	ga_ls9c_ard_3-2-1_112083_2022-10-23_final.stac-item
2022-10-25 09:40:41	ga_ls9c_ard_3-2-1_094072_2022-10-25_final.stac-item
2022-10-25 09:41:29	ga_ls9c_ard_3-2-1_094074_2022-10-25_final.stac-item
2022-10-30 10:00:24	ga_ls9c_ard_3-2-1_097075_2022-10-30_final.stac-item
2022-10-30 10:01:36	ga_ls9c_ard_3-2-1_097078_2022-10-30_final.stac-item
2022-10-31 10:44:03	ga_ls9c_ard_3-2-1_104076_2022-10-31_final.stac-item
2022-10-31 10:44:51	ga_ls9c_ard_3-2-1_104078_2022-10-31_final.stac-item
2022-11-03 09:34:51	ga_ls9c_ard_3-2-1_093073_2022-11-03_final.stac-item
2022-11-03 09:35:39	ga_ls9c_ard_3-2-1_093075_2022-11-03_final.stac-item
2022-11-03 09:37:14	ga_ls9c_ard_3-2-1_093079_2022-11-03_final.stac-item
2022-11-03 09:39:38	ga_ls9c_ard_3-2-1_093085_2022-11-03_final.stac-item
2022-11-03 11:12:32	ga_ls9c_ard_3-2-1_109070_2022-11-03_final.stac-item
2022-11-03 11:16:31	ga_ls9c_ard_3-2-1_109080_2022-11-03_final.stac-item
2022-11-03 11:16:55	ga_ls9c_ard_3-2-1_109081_2022-11-03_final.stac-item
2022-11-03 11:18:07	ga_ls9c_ard_3-2-1_109084_2022-11-03_final.stac-item
2022-11-04 10:20:30	ga_ls9c_ard_3-2-1_100079_2022-11-04_final.stac-item
2022-11-04 10:20:54	ga_ls9c_ard_3-2-1_100080_2022-11-04_final.stac-item
2022-11-05 09:22:54	ga_ls9c_ard_3-2-1_091074_2022-11-04_final.stac-item
2022-11-05 09:24:05	ga_ls9c_ard_3-2-1_091077_2022-11-04_final.stac-item
2022-11-05 09:25:41	ga_ls9c_ard_3-2-1_091081_2022-11-04_final.stac-item
2022-11-05 09:26:05	ga_ls9c_ard_3-2-1_091082_2022-11-04_final.stac-item
2022-11-12 09:31:07	ga_ls9c_ard_3-2-1_092079_2022-11-12_final.stac-item
2022-11-12 09:32:43	ga_ls9c_ard_3-2-1_092083_2022-11-12_final.stac-item
2022-11-12 09:33:07	ga_ls9c_ard_3-2-1_092084_2022-11-12_final.stac-item
2022-11-12 09:35:07	ga_ls9c_ard_3-2-1_092089_2022-11-12_final.stac-item
2022-11-12 11:06:25	ga_ls9c_ard_3-2-1_108070_2022-11-12_final.stac-item
2022-11-14 09:16:46	ga_ls9c_ard_3-2-1_090074_2022-11-13_final.stac-item
2022-11-14 09:21:58	ga_ls9c_ard_3-2-1_090087_2022-11-13_final.stac-item
2022-11-14 09:22:45	ga_ls9c_ard_3-2-1_090089_2022-11-13_final.stac-item
2022-11-14 10:54:04	ga_ls9c_ard_3-2-1_106070_2022-11-14_final.stac-item
2022-11-15 10:00:02	ga_ls9c_ard_3-2-1_097074_2022-11-15_final.stac-item
2022-11-17 09:47:40	ga_ls9c_ard_3-2-1_095074_2022-11-17_final.stac-item
2022-11-17 09:48:52	ga_ls9c_ard_3-2-1_095077_2022-11-17_final.stac-item
2022-11-17 11:24:10	ga_ls9c_ard_3-2-1_111068_2022-11-17_final.stac-item
2022-11-17 11:24:34	ga_ls9c_ard_3-2-1_111069_2022-11-17_final.stac-item
2022-11-26 09:40:16	ga_ls9c_ard_3-2-1_094071_2022-11-26_final.stac-item
2022-11-26 09:45:03	ga_ls9c_ard_3-2-1_094083_2022-11-26_final.stac-item
2022-11-26 09:46:39	ga_ls9c_ard_3-2-1_094087_2022-11-26_final.stac-item
2022-11-26 11:21:57	ga_ls9c_ard_3-2-1_110078_2022-11-26_final.stac-item
2022-11-27 10:27:55	ga_ls9c_ard_3-2-1_101082_2022-11-27_final.stac-item
2022-11-28 09:35:05	ga_ls9c_ard_3-2-1_092089_2022-11-28_final.stac-item
2022-11-29 10:09:57	ga_ls9c_ard_3-2-1_099068_2022-11-29_final.stac-item
2022-11-29 10:14:20	ga_ls9c_ard_3-2-1_099079_2022-11-29_final.stac-item
2022-11-29 10:16:20	ga_ls9c_ard_3-2-1_099084_2022-11-29_final.stac-item
2022-11-30 09:20:42	ga_ls9c_ard_3-2-1_090084_2022-11-29_final.stac-item
2022-11-30 10:59:11	ga_ls9c_ard_3-2-1_106083_2022-11-30_final.stac-item
2022-12-01 09:59:34	ga_ls9c_ard_3-2-1_097073_2022-12-01_final.stac-item
2022-12-01 11:38:27	ga_ls9c_ard_3-2-1_113073_2022-12-01_final.stac-item
2022-12-02 10:44:25	ga_ls9c_ard_3-2-1_104077_2022-12-02_final.stac-item
2022-12-03 09:47:37	ga_ls9c_ard_3-2-1_095074_2022-12-03_final.stac-item
2022-12-03 09:49:37	ga_ls9c_ard_3-2-1_095079_2022-12-03_final.stac-item
2022-12-03 11:24:30	ga_ls9c_ard_3-2-1_111069_2022-12-03_final.stac-item
2022-12-03 11:27:18	ga_ls9c_ard_3-2-1_111076_2022-12-03_final.stac-item
2022-12-03 11:28:30	ga_ls9c_ard_3-2-1_111079_2022-12-03_final.stac-item
2022-12-03 11:28:54	ga_ls9c_ard_3-2-1_111080_2022-12-03_final.stac-item
2022-12-04 10:28:29	ga_ls9c_ard_3-2-1_102068_2022-12-04_final.stac-item
2022-12-04 10:30:29	ga_ls9c_ard_3-2-1_102073_2022-12-04_final.stac-item
2022-12-04 10:30:53	ga_ls9c_ard_3-2-1_102074_2022-12-04_final.stac-item
2022-12-04 10:34:05	ga_ls9c_ard_3-2-1_102082_2022-12-04_final.stac-item
2022-12-05 09:34:28	ga_ls9c_ard_3-2-1_093072_2022-12-05_final.stac-item
2022-12-05 09:35:40	ga_ls9c_ard_3-2-1_093075_2022-12-05_final.stac-item
2022-12-05 11:14:09	ga_ls9c_ard_3-2-1_109074_2022-12-05_final.stac-item
2022-12-07 09:22:31	ga_ls9c_ard_3-2-1_091073_2022-12-06_final.stac-item
2022-12-07 09:22:55	ga_ls9c_ard_3-2-1_091074_2022-12-06_final.stac-item
2022-12-07 09:25:19	ga_ls9c_ard_3-2-1_091080_2022-12-06_final.stac-item
2022-12-07 09:27:19	ga_ls9c_ard_3-2-1_091085_2022-12-06_final.stac-item
2022-12-07 09:29:18	ga_ls9c_ard_3-2-1_091090_2022-12-06_final.stac-item
2022-12-07 11:05:48	ga_ls9c_ard_3-2-1_107084_2022-12-07_final.stac-item
2022-12-08 10:05:23	ga_ls9c_ard_3-2-1_098072_2022-12-08_final.stac-item
2022-12-08 10:09:23	ga_ls9c_ard_3-2-1_098082_2022-12-08_final.stac-item
2022-12-09 09:13:46	ga_ls9c_ard_3-2-1_089082_2022-12-08_final.stac-item
2022-12-09 09:14:10	ga_ls9c_ard_3-2-1_089083_2022-12-08_final.stac-item
2022-12-09 10:46:40	ga_ls9c_ard_3-2-1_105067_2022-12-09_final.stac-item
2022-12-09 10:51:03	ga_ls9c_ard_3-2-1_105078_2022-12-09_final.stac-item
2022-12-10 09:55:50	ga_ls9c_ard_3-2-1_096079_2022-12-10_final.stac-item
2022-12-10 09:57:25	ga_ls9c_ard_3-2-1_096083_2022-12-10_final.stac-item
2022-12-10 09:58:13	ga_ls9c_ard_3-2-1_096085_2022-12-10_final.stac-item
2022-12-10 09:58:37	ga_ls9c_ard_3-2-1_096086_2022-12-10_final.stac-item
2022-12-10 11:35:07	ga_ls9c_ard_3-2-1_112080_2022-12-10_final.stac-item
2022-12-10 11:35:54	ga_ls9c_ard_3-2-1_112082_2022-12-10_final.stac-item
2022-12-12 09:44:40	ga_ls9c_ard_3-2-1_094082_2022-12-12_final.stac-item
2022-12-12 11:20:45	ga_ls9c_ard_3-2-1_110075_2022-12-12_final.stac-item
2022-12-12 11:23:57	ga_ls9c_ard_3-2-1_110083_2022-12-12_final.stac-item
2022-12-13 10:23:56	ga_ls9c_ard_3-2-1_101072_2022-12-13_final.stac-item
2022-12-13 10:28:20	ga_ls9c_ard_3-2-1_101083_2022-12-13_final.stac-item
2022-12-14 09:27:55	ga_ls9c_ard_3-2-1_092071_2022-12-13_final.stac-item
2022-12-14 09:30:42	ga_ls9c_ard_3-2-1_092078_2022-12-14_final.stac-item
2022-12-14 11:09:35	ga_ls9c_ard_3-2-1_108078_2022-12-14_final.stac-item
2022-12-15 11:52:27	ga_ls9c_ard_3-2-1_115077_2022-12-15_final.stac-item
2022-12-16 09:22:20	ga_ls9c_ard_3-2-1_090088_2022-12-15_final.stac-item
2022-12-17 10:00:00	ga_ls9c_ard_3-2-1_097074_2022-12-17_final.stac-item
2022-12-17 10:01:12	ga_ls9c_ard_3-2-1_097077_2022-12-17_final.stac-item
2022-12-17 10:04:00	ga_ls9c_ard_3-2-1_097084_2022-12-17_final.stac-item
2022-12-18 10:42:28	ga_ls9c_ard_3-2-1_104072_2022-12-18_final.stac-item
2022-12-19 09:47:38	ga_ls9c_ard_3-2-1_095074_2022-12-19_final.stac-item
2022-12-19 11:24:31	ga_ls9c_ard_3-2-1_111069_2022-12-19_final.stac-item
2022-12-26 09:56:59	ga_ls9c_ard_3-2-1_096082_2022-12-26_final.stac-item
2022-12-26 11:32:41	ga_ls9c_ard_3-2-1_112074_2022-12-26_final.stac-item
2022-12-27 10:39:51	ga_ls9c_ard_3-2-1_103081_2022-12-27_final.stac-item

Return to ga_ls9c_ard_3 whole collection overview

ga_ls9c_ard_3: Geoscience Australia Landsat 9 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