Search ga_ls9c_ard_3 across October 2023

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
2023-10-01 09:58:25	ga_ls9c_ard_3-2-1_097071_2023-10-01_final.stac-item
2023-10-01 09:58:49	ga_ls9c_ard_3-2-1_097072_2023-10-01_final.stac-item
2023-10-01 11:37:42	ga_ls9c_ard_3-2-1_113072_2023-10-01_final.stac-item
2023-10-02 09:07:12	ga_ls9c_ard_3-2-1_088082_2023-10-01_final.stac-item
2023-10-02 10:41:42	ga_ls9c_ard_3-2-1_104071_2023-10-02_final.stac-item
2023-10-02 10:42:06	ga_ls9c_ard_3-2-1_104072_2023-10-02_final.stac-item
2023-10-03 09:52:28	ga_ls9c_ard_3-2-1_095087_2023-10-03_final.stac-item
2023-10-03 11:24:58	ga_ls9c_ard_3-2-1_111071_2023-10-03_final.stac-item
2023-10-04 10:28:10	ga_ls9c_ard_3-2-1_102068_2023-10-04_final.stac-item
2023-10-04 10:29:22	ga_ls9c_ard_3-2-1_102071_2023-10-04_final.stac-item
2023-10-04 10:29:46	ga_ls9c_ard_3-2-1_102072_2023-10-04_final.stac-item
2023-10-04 10:33:45	ga_ls9c_ard_3-2-1_102082_2023-10-04_final.stac-item
2023-10-05 09:33:45	ga_ls9c_ard_3-2-1_093071_2023-10-05_final.stac-item
2023-10-05 09:34:09	ga_ls9c_ard_3-2-1_093072_2023-10-05_final.stac-item
2023-10-05 09:39:44	ga_ls9c_ard_3-2-1_093086_2023-10-05_final.stac-item
2023-10-05 11:12:38	ga_ls9c_ard_3-2-1_109071_2023-10-05_final.stac-item
2023-10-05 11:13:02	ga_ls9c_ard_3-2-1_109072_2023-10-05_final.stac-item
2023-10-06 08:42:32	ga_ls9c_ard_3-2-1_084082_2023-10-05_final.stac-item
2023-10-06 10:17:01	ga_ls9c_ard_3-2-1_100071_2023-10-06_final.stac-item
2023-10-06 10:17:25	ga_ls9c_ard_3-2-1_100072_2023-10-06_final.stac-item
2023-10-07 09:21:48	ga_ls9c_ard_3-2-1_091072_2023-10-06_final.stac-item
2023-10-07 09:25:48	ga_ls9c_ard_3-2-1_091082_2023-10-06_final.stac-item
2023-10-07 11:00:41	ga_ls9c_ard_3-2-1_107072_2023-10-07_final.stac-item
2023-10-07 12:37:35	ga_ls9c_ard_3-2-1_123067_2023-10-07_final.stac-item
2023-10-08 10:04:40	ga_ls9c_ard_3-2-1_098071_2023-10-08_final.stac-item
2023-10-08 10:05:04	ga_ls9c_ard_3-2-1_098072_2023-10-08_final.stac-item
2023-10-09 09:13:27	ga_ls9c_ard_3-2-1_089082_2023-10-08_final.stac-item
2023-10-09 09:14:39	ga_ls9c_ard_3-2-1_089085_2023-10-08_final.stac-item
2023-10-09 09:15:51	ga_ls9c_ard_3-2-1_089088_2023-10-08_final.stac-item
2023-10-09 10:47:57	ga_ls9c_ard_3-2-1_105071_2023-10-09_final.stac-item
2023-10-09 10:48:21	ga_ls9c_ard_3-2-1_105072_2023-10-09_final.stac-item
2023-10-10 09:52:21	ga_ls9c_ard_3-2-1_096071_2023-10-10_final.stac-item
2023-10-11 10:35:37	ga_ls9c_ard_3-2-1_103071_2023-10-11_final.stac-item
2023-10-11 10:36:01	ga_ls9c_ard_3-2-1_103072_2023-10-11_final.stac-item
2023-10-12 09:40:01	ga_ls9c_ard_3-2-1_094071_2023-10-12_final.stac-item
2023-10-12 09:46:00	ga_ls9c_ard_3-2-1_094086_2023-10-12_final.stac-item
2023-10-12 11:18:54	ga_ls9c_ard_3-2-1_110071_2023-10-12_final.stac-item
2023-10-13 08:48:48	ga_ls9c_ard_3-2-1_085082_2023-10-12_final.stac-item
2023-10-13 10:23:18	ga_ls9c_ard_3-2-1_101071_2023-10-13_final.stac-item
2023-10-13 10:23:41	ga_ls9c_ard_3-2-1_101072_2023-10-13_final.stac-item
2023-10-14 09:27:41	ga_ls9c_ard_3-2-1_092071_2023-10-13_final.stac-item
2023-10-14 09:33:40	ga_ls9c_ard_3-2-1_092086_2023-10-14_final.stac-item
2023-10-14 11:06:34	ga_ls9c_ard_3-2-1_108071_2023-10-14_final.stac-item
2023-10-14 11:06:58	ga_ls9c_ard_3-2-1_108072_2023-10-14_final.stac-item
2023-10-14 11:10:33	ga_ls9c_ard_3-2-1_108081_2023-10-14_final.stac-item
2023-10-15 10:10:57	ga_ls9c_ard_3-2-1_099071_2023-10-15_final.stac-item
2023-10-15 10:11:21	ga_ls9c_ard_3-2-1_099072_2023-10-15_final.stac-item
2023-10-15 10:15:21	ga_ls9c_ard_3-2-1_099082_2023-10-15_final.stac-item
2023-10-16 09:19:44	ga_ls9c_ard_3-2-1_090082_2023-10-15_final.stac-item
2023-10-16 09:20:56	ga_ls9c_ard_3-2-1_090085_2023-10-15_final.stac-item
2023-10-16 09:22:07	ga_ls9c_ard_3-2-1_090088_2023-10-15_final.stac-item
2023-10-16 10:54:13	ga_ls9c_ard_3-2-1_106071_2023-10-16_final.stac-item
2023-10-16 10:54:37	ga_ls9c_ard_3-2-1_106072_2023-10-16_final.stac-item
2023-10-17 09:58:36	ga_ls9c_ard_3-2-1_097071_2023-10-17_final.stac-item
2023-10-17 09:59:00	ga_ls9c_ard_3-2-1_097072_2023-10-17_final.stac-item
2023-10-17 11:37:53	ga_ls9c_ard_3-2-1_113072_2023-10-17_final.stac-item
2023-10-18 09:07:23	ga_ls9c_ard_3-2-1_088082_2023-10-17_final.stac-item
2023-10-18 10:41:52	ga_ls9c_ard_3-2-1_104071_2023-10-18_final.stac-item
2023-10-18 10:42:16	ga_ls9c_ard_3-2-1_104072_2023-10-18_final.stac-item
2023-10-19 09:52:38	ga_ls9c_ard_3-2-1_095087_2023-10-19_final.stac-item
2023-10-19 11:25:08	ga_ls9c_ard_3-2-1_111071_2023-10-19_final.stac-item
2023-10-20 10:29:31	ga_ls9c_ard_3-2-1_102071_2023-10-20_final.stac-item
2023-10-20 10:29:55	ga_ls9c_ard_3-2-1_102072_2023-10-20_final.stac-item
2023-10-21 09:33:54	ga_ls9c_ard_3-2-1_093071_2023-10-21_final.stac-item
2023-10-21 09:34:18	ga_ls9c_ard_3-2-1_093072_2023-10-21_final.stac-item
2023-10-21 09:39:53	ga_ls9c_ard_3-2-1_093086_2023-10-21_final.stac-item
2023-10-21 11:12:47	ga_ls9c_ard_3-2-1_109071_2023-10-21_final.stac-item
2023-10-21 11:13:11	ga_ls9c_ard_3-2-1_109072_2023-10-21_final.stac-item
2023-10-22 08:42:40	ga_ls9c_ard_3-2-1_084082_2023-10-21_final.stac-item
2023-10-22 10:17:09	ga_ls9c_ard_3-2-1_100071_2023-10-22_final.stac-item
2023-10-22 10:17:33	ga_ls9c_ard_3-2-1_100072_2023-10-22_final.stac-item
2023-10-22 10:21:33	ga_ls9c_ard_3-2-1_100082_2023-10-22_final.stac-item
2023-10-23 09:21:56	ga_ls9c_ard_3-2-1_091072_2023-10-22_final.stac-item
2023-10-23 09:25:55	ga_ls9c_ard_3-2-1_091082_2023-10-22_final.stac-item
2023-10-23 11:00:49	ga_ls9c_ard_3-2-1_107072_2023-10-23_final.stac-item
2023-10-24 10:04:47	ga_ls9c_ard_3-2-1_098071_2023-10-24_final.stac-item
2023-10-24 10:05:11	ga_ls9c_ard_3-2-1_098072_2023-10-24_final.stac-item
2023-10-24 10:09:10	ga_ls9c_ard_3-2-1_098082_2023-10-24_final.stac-item
2023-10-25 09:13:33	ga_ls9c_ard_3-2-1_089082_2023-10-24_final.stac-item
2023-10-25 09:14:45	ga_ls9c_ard_3-2-1_089085_2023-10-24_final.stac-item
2023-10-25 09:15:56	ga_ls9c_ard_3-2-1_089088_2023-10-24_final.stac-item
2023-10-25 10:48:02	ga_ls9c_ard_3-2-1_105071_2023-10-25_final.stac-item
2023-10-25 10:48:26	ga_ls9c_ard_3-2-1_105072_2023-10-25_final.stac-item
2023-10-26 09:52:25	ga_ls9c_ard_3-2-1_096071_2023-10-26_final.stac-item
2023-10-26 09:52:48	ga_ls9c_ard_3-2-1_096072_2023-10-26_final.stac-item
2023-10-27 10:35:40	ga_ls9c_ard_3-2-1_103071_2023-10-27_final.stac-item
2023-10-27 10:36:04	ga_ls9c_ard_3-2-1_103072_2023-10-27_final.stac-item
2023-10-28 09:40:02	ga_ls9c_ard_3-2-1_094071_2023-10-28_final.stac-item
2023-10-28 09:46:01	ga_ls9c_ard_3-2-1_094086_2023-10-28_final.stac-item
2023-10-28 11:18:55	ga_ls9c_ard_3-2-1_110071_2023-10-28_final.stac-item
2023-10-29 08:48:47	ga_ls9c_ard_3-2-1_085082_2023-10-28_final.stac-item
2023-10-29 10:23:17	ga_ls9c_ard_3-2-1_101071_2023-10-29_final.stac-item
2023-10-29 10:23:41	ga_ls9c_ard_3-2-1_101072_2023-10-29_final.stac-item
2023-10-29 10:27:40	ga_ls9c_ard_3-2-1_101082_2023-10-29_final.stac-item
2023-10-30 09:27:39	ga_ls9c_ard_3-2-1_092071_2023-10-29_final.stac-item
2023-10-30 09:33:38	ga_ls9c_ard_3-2-1_092086_2023-10-30_final.stac-item
2023-10-30 11:06:32	ga_ls9c_ard_3-2-1_108071_2023-10-30_final.stac-item
2023-10-30 11:06:55	ga_ls9c_ard_3-2-1_108072_2023-10-30_final.stac-item
2023-10-31 10:10:53	ga_ls9c_ard_3-2-1_099071_2023-10-31_final.stac-item
2023-10-31 10:11:17	ga_ls9c_ard_3-2-1_099072_2023-10-31_final.stac-item

Return to ga_ls9c_ard_3 across October 2023 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

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

2023

October

All days