Search ga_ls9c_ard_3 across 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
2023-01-04 09:47:39	ga_ls9c_ard_3-2-1_095074_2023-01-04_final
2023-01-07 10:18:09	ga_ls9c_ard_3-2-1_100073_2023-01-07_final
2023-01-09 11:45:04	ga_ls9c_ard_3-2-1_114074_2023-01-09_final
2023-01-13 09:41:26	ga_ls9c_ard_3-2-1_094074_2023-01-13_final
2023-01-14 10:24:17	ga_ls9c_ard_3-2-1_101073_2023-01-14_final
2023-01-16 11:51:11	ga_ls9c_ard_3-2-1_115074_2023-01-16_final
2023-01-20 09:47:29	ga_ls9c_ard_3-2-1_095074_2023-01-20_final
2023-01-25 11:45:00	ga_ls9c_ard_3-2-1_114074_2023-01-25_final
2023-01-30 10:24:20	ga_ls9c_ard_3-2-1_101073_2023-01-30_final
2023-02-01 11:51:16	ga_ls9c_ard_3-2-1_115074_2023-02-01_final
2023-02-05 09:47:42	ga_ls9c_ard_3-2-1_095074_2023-02-05_final
2023-02-06 10:30:34	ga_ls9c_ard_3-2-1_102073_2023-02-06_final
2023-02-08 10:18:13	ga_ls9c_ard_3-2-1_100073_2023-02-08_final
2023-02-10 11:45:08	ga_ls9c_ard_3-2-1_114074_2023-02-10_final
2023-02-14 09:41:30	ga_ls9c_ard_3-2-1_094074_2023-02-14_final
2023-02-17 10:14:45	ga_ls9c_ard_3-2-1_099080_2023-02-17_final
2023-02-17 11:51:14	ga_ls9c_ard_3-2-1_115074_2023-02-17_final
2023-02-21 09:47:32	ga_ls9c_ard_3-2-1_095074_2023-02-21_final
2023-02-22 10:30:22	ga_ls9c_ard_3-2-1_102073_2023-02-22_final
2023-02-26 11:44:54	ga_ls9c_ard_3-2-1_114074_2023-02-26_final
2023-03-02 09:41:16	ga_ls9c_ard_3-2-1_094074_2023-03-02_final
2023-03-05 11:51:02	ga_ls9c_ard_3-2-1_115074_2023-03-05_final
2023-03-09 09:47:21	ga_ls9c_ard_3-2-1_095074_2023-03-09_final
2023-03-10 10:30:12	ga_ls9c_ard_3-2-1_102073_2023-03-10_final
2023-03-12 10:17:51	ga_ls9c_ard_3-2-1_100073_2023-03-12_final
2023-03-14 11:44:47	ga_ls9c_ard_3-2-1_114074_2023-03-14_final
2023-03-16 09:53:32	ga_ls9c_ard_3-2-1_096074_2023-03-16_final
2023-03-18 09:41:10	ga_ls9c_ard_3-2-1_094074_2023-03-18_final
2023-03-19 10:24:01	ga_ls9c_ard_3-2-1_101073_2023-03-19_final
2023-03-21 11:50:55	ga_ls9c_ard_3-2-1_115074_2023-03-21_final
2023-03-25 09:47:15	ga_ls9c_ard_3-2-1_095074_2023-03-25_final
2023-03-26 10:30:06	ga_ls9c_ard_3-2-1_102073_2023-03-26_final
2023-03-28 10:17:41	ga_ls9c_ard_3-2-1_100073_2023-03-28_final
2023-03-28 10:20:29	ga_ls9c_ard_3-2-1_100080_2023-03-28_final
2023-03-30 11:44:34	ga_ls9c_ard_3-2-1_114074_2023-03-30_final
2023-04-01 09:53:20	ga_ls9c_ard_3-2-1_096074_2023-04-01_final
2023-04-03 09:40:59	ga_ls9c_ard_3-2-1_094074_2023-04-03_final
2023-04-04 10:23:50	ga_ls9c_ard_3-2-1_101073_2023-04-04_final
2023-04-04 10:26:38	ga_ls9c_ard_3-2-1_101080_2023-04-04_final
2023-04-06 10:14:16	ga_ls9c_ard_3-2-1_099080_2023-04-06_final
2023-04-06 11:50:45	ga_ls9c_ard_3-2-1_115074_2023-04-06_final
2023-04-11 10:29:58	ga_ls9c_ard_3-2-1_102073_2023-04-11_final
2023-04-13 10:17:34	ga_ls9c_ard_3-2-1_100073_2023-04-13_final
2023-04-13 10:20:22	ga_ls9c_ard_3-2-1_100080_2023-04-13_final
2023-04-15 11:44:28	ga_ls9c_ard_3-2-1_114074_2023-04-15_final
2023-04-20 10:23:44	ga_ls9c_ard_3-2-1_101073_2023-04-20_final
2023-04-22 11:50:39	ga_ls9c_ard_3-2-1_115074_2023-04-22_final
2023-04-27 10:29:51	ga_ls9c_ard_3-2-1_102073_2023-04-27_final
2023-04-29 10:17:28	ga_ls9c_ard_3-2-1_100073_2023-04-29_final
2023-05-01 11:44:21	ga_ls9c_ard_3-2-1_114074_2023-05-01_final
2023-05-06 10:23:32	ga_ls9c_ard_3-2-1_101073_2023-05-06_final
2023-05-08 11:50:27	ga_ls9c_ard_3-2-1_115074_2023-05-08_final
2023-05-13 10:29:42	ga_ls9c_ard_3-2-1_102073_2023-05-13_final
2023-05-15 10:17:19	ga_ls9c_ard_3-2-1_100073_2023-05-15_final
2023-05-17 11:44:13	ga_ls9c_ard_3-2-1_114074_2023-05-17_final
2023-05-22 10:23:24	ga_ls9c_ard_3-2-1_101073_2023-05-22_final
2023-05-24 11:50:17	ga_ls9c_ard_3-2-1_115074_2023-05-24_final
2023-05-29 10:29:32	ga_ls9c_ard_3-2-1_102073_2023-05-29_final
2023-05-31 10:17:11	ga_ls9c_ard_3-2-1_100073_2023-05-31_final
2023-06-02 11:44:06	ga_ls9c_ard_3-2-1_114074_2023-06-02_final
2023-06-07 10:23:19	ga_ls9c_ard_3-2-1_101073_2023-06-07_final
2023-06-09 11:50:13	ga_ls9c_ard_3-2-1_115074_2023-06-09_final
2023-06-14 10:29:24	ga_ls9c_ard_3-2-1_102073_2023-06-14_final
2023-06-16 10:17:01	ga_ls9c_ard_3-2-1_100073_2023-06-16_final
2023-06-23 10:23:12	ga_ls9c_ard_3-2-1_101073_2023-06-23_final
2023-06-30 10:29:31	ga_ls9c_ard_3-2-1_102073_2023-06-30_final
2023-07-02 10:17:11	ga_ls9c_ard_3-2-1_100073_2023-07-02_final
2023-07-11 11:50:23	ga_ls9c_ard_3-2-1_115074_2023-07-11_final
2023-07-18 10:17:19	ga_ls9c_ard_3-2-1_100073_2023-07-18_final
2023-07-25 10:23:28	ga_ls9c_ard_3-2-1_101073_2023-07-25_final
2023-08-03 10:17:26	ga_ls9c_ard_3-2-1_100073_2023-08-03_final
2023-08-10 10:23:39	ga_ls9c_ard_3-2-1_101073_2023-08-10_final
2023-08-17 10:29:50	ga_ls9c_ard_3-2-1_102073_2023-08-17_final
2023-08-19 10:17:29	ga_ls9c_ard_3-2-1_100073_2023-08-19_final
2023-08-26 10:23:46	ga_ls9c_ard_3-2-1_101073_2023-08-26_final
2023-09-02 10:30:01	ga_ls9c_ard_3-2-1_102073_2023-09-02_final
2023-09-04 10:17:40	ga_ls9c_ard_3-2-1_100073_2023-09-04_final
2023-09-11 10:23:56	ga_ls9c_ard_3-2-1_101073_2023-09-11_final
2023-09-13 11:50:53	ga_ls9c_ard_3-2-1_115074_2023-09-13_final
2023-09-18 10:30:09	ga_ls9c_ard_3-2-1_102073_2023-09-18_final
2023-09-20 10:17:47	ga_ls9c_ard_3-2-1_100073_2023-09-20_final
2023-09-27 10:23:53	ga_ls9c_ard_3-2-1_101073_2023-09-27_final
2023-10-04 10:30:10	ga_ls9c_ard_3-2-1_102073_2023-10-04_final
2023-10-06 10:17:49	ga_ls9c_ard_3-2-1_100073_2023-10-06_final
2023-10-13 10:24:05	ga_ls9c_ard_3-2-1_101073_2023-10-13_final
2023-10-15 11:51:02	ga_ls9c_ard_3-2-1_115074_2023-10-15_final
2023-10-20 10:30:19	ga_ls9c_ard_3-2-1_102073_2023-10-20_final
2023-10-22 10:17:57	ga_ls9c_ard_3-2-1_100073_2023-10-22_final
2023-10-29 10:24:05	ga_ls9c_ard_3-2-1_101073_2023-10-29_final
2023-11-05 10:30:13	ga_ls9c_ard_3-2-1_102073_2023-11-05_final
2023-11-07 10:17:52	ga_ls9c_ard_3-2-1_100073_2023-11-07_final
2023-11-14 10:24:03	ga_ls9c_ard_3-2-1_101073_2023-11-14_final
2023-11-21 10:30:19	ga_ls9c_ard_3-2-1_102073_2023-11-21_final
2023-11-23 10:17:59	ga_ls9c_ard_3-2-1_100073_2023-11-23_final
2023-11-30 10:24:12	ga_ls9c_ard_3-2-1_101073_2023-11-30_final
2023-12-07 10:30:17	ga_ls9c_ard_3-2-1_102073_2023-12-07_final
2023-12-09 10:17:56	ga_ls9c_ard_3-2-1_100073_2023-12-09_final
2023-12-16 10:24:12	ga_ls9c_ard_3-2-1_101073_2023-12-16_final
2023-12-18 11:51:08	ga_ls9c_ard_3-2-1_115074_2023-12-18_final
2023-12-23 10:30:25	ga_ls9c_ard_3-2-1_102073_2023-12-23_final

Return to ga_ls9c_ard_3 across 2023 overview

ga_ls9c_ard_3: Geoscience Australia Landsat 9 Operational Land Imager and Thermal Infra-Red Scanner Analysis Ready Data Collection 3

GA Collection 3

USGS Products

Collection 2 NBAR

Collection 2 NBAR Tiled

Pixel Quality

Collection 2 Level1

Tidal

Water Observations

Fractional Cover

Sentinel 2

Ingested Pixel Quality

Geomedians

TMAD

Telemetry data

Summary

Other Products

2023

All months