Search ga_ls9c_ard_3 across February 2022

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
2022-02-01 09:06:37	ga_ls9c_ard_3-2-1_088080_2022-01-31_final
2022-02-01 09:07:00	ga_ls9c_ard_3-2-1_088081_2022-01-31_final
2022-02-01 09:07:24	ga_ls9c_ard_3-2-1_088082_2022-01-31_final
2022-02-01 09:07:48	ga_ls9c_ard_3-2-1_088083_2022-01-31_final
2022-02-01 10:40:42	ga_ls9c_ard_3-2-1_104068_2022-02-01_final
2022-02-01 10:41:06	ga_ls9c_ard_3-2-1_104069_2022-02-01_final
2022-02-01 10:41:30	ga_ls9c_ard_3-2-1_104070_2022-02-01_final
2022-02-01 10:41:54	ga_ls9c_ard_3-2-1_104071_2022-02-01_final
2022-02-01 10:42:18	ga_ls9c_ard_3-2-1_104072_2022-02-01_final
2022-02-01 10:42:42	ga_ls9c_ard_3-2-1_104073_2022-02-01_final
2022-02-01 10:43:06	ga_ls9c_ard_3-2-1_104074_2022-02-01_final
2022-02-01 10:43:30	ga_ls9c_ard_3-2-1_104075_2022-02-01_final
2022-02-01 10:43:54	ga_ls9c_ard_3-2-1_104076_2022-02-01_final
2022-02-01 10:44:18	ga_ls9c_ard_3-2-1_104077_2022-02-01_final
2022-02-01 10:44:42	ga_ls9c_ard_3-2-1_104078_2022-02-01_final
2022-02-01 10:45:06	ga_ls9c_ard_3-2-1_104079_2022-02-01_final
2022-02-01 10:45:29	ga_ls9c_ard_3-2-1_104080_2022-02-01_final
2022-02-01 10:45:53	ga_ls9c_ard_3-2-1_104081_2022-02-01_final
2022-02-01 10:46:17	ga_ls9c_ard_3-2-1_104082_2022-02-01_final
2022-02-01 14:10:03	ga_ls9c_ard_3-2-1_136097_2022-02-01_final
2022-02-02 11:23:58	ga_ls9c_ard_3-2-1_111068_2022-02-02_final
2022-02-02 11:24:22	ga_ls9c_ard_3-2-1_111069_2022-02-02_final
2022-02-02 11:24:46	ga_ls9c_ard_3-2-1_111070_2022-02-02_final
2022-02-02 11:25:10	ga_ls9c_ard_3-2-1_111071_2022-02-02_final
2022-02-02 11:25:34	ga_ls9c_ard_3-2-1_111072_2022-02-02_final
2022-02-02 11:25:58	ga_ls9c_ard_3-2-1_111073_2022-02-02_final
2022-02-02 11:26:21	ga_ls9c_ard_3-2-1_111074_2022-02-02_final
2022-02-02 11:26:45	ga_ls9c_ard_3-2-1_111075_2022-02-02_final
2022-02-02 11:27:09	ga_ls9c_ard_3-2-1_111076_2022-02-02_final
2022-02-02 11:27:33	ga_ls9c_ard_3-2-1_111077_2022-02-02_final
2022-02-02 11:27:57	ga_ls9c_ard_3-2-1_111078_2022-02-02_final
2022-02-02 11:28:21	ga_ls9c_ard_3-2-1_111079_2022-02-02_final
2022-02-02 11:28:45	ga_ls9c_ard_3-2-1_111080_2022-02-02_final
2022-02-02 11:29:09	ga_ls9c_ard_3-2-1_111081_2022-02-02_final
2022-02-02 11:29:33	ga_ls9c_ard_3-2-1_111082_2022-02-02_final
2022-02-02 11:29:57	ga_ls9c_ard_3-2-1_111083_2022-02-02_final
2022-02-02 11:30:21	ga_ls9c_ard_3-2-1_111084_2022-02-02_final
2022-02-03 10:28:20	ga_ls9c_ard_3-2-1_102068_2022-02-03_final
2022-02-03 10:28:44	ga_ls9c_ard_3-2-1_102069_2022-02-03_final
2022-02-03 10:29:08	ga_ls9c_ard_3-2-1_102070_2022-02-03_final
2022-02-03 10:29:32	ga_ls9c_ard_3-2-1_102071_2022-02-03_final
2022-02-03 10:29:56	ga_ls9c_ard_3-2-1_102072_2022-02-03_final
2022-02-03 10:30:20	ga_ls9c_ard_3-2-1_102073_2022-02-03_final
2022-02-03 10:30:44	ga_ls9c_ard_3-2-1_102074_2022-02-03_final
2022-02-03 10:31:08	ga_ls9c_ard_3-2-1_102075_2022-02-03_final
2022-02-03 10:31:32	ga_ls9c_ard_3-2-1_102076_2022-02-03_final
2022-02-03 10:31:56	ga_ls9c_ard_3-2-1_102077_2022-02-03_final
2022-02-03 10:32:20	ga_ls9c_ard_3-2-1_102078_2022-02-03_final
2022-02-03 10:32:44	ga_ls9c_ard_3-2-1_102079_2022-02-03_final
2022-02-03 10:33:08	ga_ls9c_ard_3-2-1_102080_2022-02-03_final
2022-02-03 10:33:32	ga_ls9c_ard_3-2-1_102081_2022-02-03_final
2022-02-03 10:33:56	ga_ls9c_ard_3-2-1_102082_2022-02-03_final
2022-02-05 11:58:03	ga_ls9c_ard_3-2-1_116076_2022-02-05_final
2022-02-05 11:58:27	ga_ls9c_ard_3-2-1_116077_2022-02-05_final
2022-02-05 11:58:51	ga_ls9c_ard_3-2-1_116078_2022-02-05_final
2022-02-06 09:21:57	ga_ls9c_ard_3-2-1_091072_2022-02-05_final
2022-02-06 09:22:20	ga_ls9c_ard_3-2-1_091073_2022-02-05_final
2022-02-06 09:22:44	ga_ls9c_ard_3-2-1_091074_2022-02-05_final
2022-02-06 09:23:08	ga_ls9c_ard_3-2-1_091075_2022-02-05_final
2022-02-06 09:23:32	ga_ls9c_ard_3-2-1_091076_2022-02-05_final
2022-02-06 09:23:56	ga_ls9c_ard_3-2-1_091077_2022-02-05_final
2022-02-06 09:24:20	ga_ls9c_ard_3-2-1_091078_2022-02-05_final
2022-02-06 09:24:44	ga_ls9c_ard_3-2-1_091079_2022-02-05_final
2022-02-06 09:25:08	ga_ls9c_ard_3-2-1_091080_2022-02-05_final
2022-02-06 09:25:32	ga_ls9c_ard_3-2-1_091081_2022-02-05_final
2022-02-06 09:25:56	ga_ls9c_ard_3-2-1_091082_2022-02-05_final
2022-02-06 09:26:20	ga_ls9c_ard_3-2-1_091083_2022-02-05_final
2022-02-06 09:26:44	ga_ls9c_ard_3-2-1_091084_2022-02-05_final
2022-02-06 09:27:08	ga_ls9c_ard_3-2-1_091085_2022-02-05_final
2022-02-06 09:27:32	ga_ls9c_ard_3-2-1_091086_2022-02-05_final
2022-02-06 09:27:56	ga_ls9c_ard_3-2-1_091087_2022-02-05_final
2022-02-06 09:28:20	ga_ls9c_ard_3-2-1_091088_2022-02-05_final
2022-02-06 09:28:44	ga_ls9c_ard_3-2-1_091089_2022-02-05_final
2022-02-06 09:29:08	ga_ls9c_ard_3-2-1_091090_2022-02-05_final
2022-02-06 11:00:02	ga_ls9c_ard_3-2-1_107070_2022-02-06_final
2022-02-06 11:00:26	ga_ls9c_ard_3-2-1_107071_2022-02-06_final
2022-02-06 11:00:50	ga_ls9c_ard_3-2-1_107072_2022-02-06_final
2022-02-06 11:03:13	ga_ls9c_ard_3-2-1_107078_2022-02-06_final
2022-02-06 11:03:37	ga_ls9c_ard_3-2-1_107079_2022-02-06_final
2022-02-06 11:04:01	ga_ls9c_ard_3-2-1_107080_2022-02-06_final
2022-02-06 11:04:25	ga_ls9c_ard_3-2-1_107081_2022-02-06_final
2022-02-06 11:04:49	ga_ls9c_ard_3-2-1_107082_2022-02-06_final
2022-02-06 11:05:13	ga_ls9c_ard_3-2-1_107083_2022-02-06_final
2022-02-06 11:05:37	ga_ls9c_ard_3-2-1_107084_2022-02-06_final
2022-02-06 12:37:43	ga_ls9c_ard_3-2-1_123067_2022-02-06_final
2022-02-07 10:02:48	ga_ls9c_ard_3-2-1_098066_2022-02-07_final
2022-02-07 10:03:12	ga_ls9c_ard_3-2-1_098067_2022-02-07_final
2022-02-07 10:03:36	ga_ls9c_ard_3-2-1_098068_2022-02-07_final
2022-02-07 10:04:00	ga_ls9c_ard_3-2-1_098069_2022-02-07_final
2022-02-07 10:07:36	ga_ls9c_ard_3-2-1_098078_2022-02-07_final
2022-02-07 10:08:00	ga_ls9c_ard_3-2-1_098079_2022-02-07_final
2022-02-07 10:08:24	ga_ls9c_ard_3-2-1_098080_2022-02-07_final
2022-02-07 10:08:47	ga_ls9c_ard_3-2-1_098081_2022-02-07_final
2022-02-07 10:09:11	ga_ls9c_ard_3-2-1_098082_2022-02-07_final
2022-02-07 10:09:35	ga_ls9c_ard_3-2-1_098083_2022-02-07_final
2022-02-07 10:09:59	ga_ls9c_ard_3-2-1_098084_2022-02-07_final
2022-02-07 10:10:23	ga_ls9c_ard_3-2-1_098085_2022-02-07_final
2022-02-09 09:55:13	ga_ls9c_ard_3-2-1_096078_2022-02-09_final
2022-02-09 09:58:01	ga_ls9c_ard_3-2-1_096085_2022-02-09_final
2022-02-09 11:31:43	ga_ls9c_ard_3-2-1_112072_2022-02-09_final

Return to ga_ls9c_ard_3 across February 2022 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

2022

February

All days