Search ga_ls9c_ard_3 across September 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
2023-09-01 08:10:39	ga_ls9c_ard_3-2-1_079080_2023-08-31_final
2023-09-01 08:17:51	ga_ls9c_ard_3-2-1_079098_2023-08-31_final
2023-09-01 09:45:57	ga_ls9c_ard_3-2-1_095071_2023-09-01_final
2023-09-01 09:46:21	ga_ls9c_ard_3-2-1_095072_2023-09-01_final
2023-09-01 09:46:45	ga_ls9c_ard_3-2-1_095073_2023-09-01_final
2023-09-01 09:47:09	ga_ls9c_ard_3-2-1_095074_2023-09-01_final
2023-09-01 09:47:33	ga_ls9c_ard_3-2-1_095075_2023-09-01_final
2023-09-01 09:47:57	ga_ls9c_ard_3-2-1_095076_2023-09-01_final
2023-09-01 09:48:21	ga_ls9c_ard_3-2-1_095077_2023-09-01_final
2023-09-01 09:48:45	ga_ls9c_ard_3-2-1_095078_2023-09-01_final
2023-09-01 09:49:08	ga_ls9c_ard_3-2-1_095079_2023-09-01_final
2023-09-01 09:49:32	ga_ls9c_ard_3-2-1_095080_2023-09-01_final
2023-09-01 09:49:56	ga_ls9c_ard_3-2-1_095081_2023-09-01_final
2023-09-01 09:50:20	ga_ls9c_ard_3-2-1_095082_2023-09-01_final
2023-09-01 09:50:44	ga_ls9c_ard_3-2-1_095083_2023-09-01_final
2023-09-01 09:51:08	ga_ls9c_ard_3-2-1_095084_2023-09-01_final
2023-09-01 09:51:32	ga_ls9c_ard_3-2-1_095085_2023-09-01_final
2023-09-01 09:51:56	ga_ls9c_ard_3-2-1_095086_2023-09-01_final
2023-09-01 09:52:20	ga_ls9c_ard_3-2-1_095087_2023-09-01_final
2023-09-01 11:23:38	ga_ls9c_ard_3-2-1_111068_2023-09-01_final
2023-09-01 11:24:02	ga_ls9c_ard_3-2-1_111069_2023-09-01_final
2023-09-01 11:24:26	ga_ls9c_ard_3-2-1_111070_2023-09-01_final
2023-09-01 11:24:50	ga_ls9c_ard_3-2-1_111071_2023-09-01_final
2023-09-01 11:25:14	ga_ls9c_ard_3-2-1_111072_2023-09-01_final
2023-09-01 11:25:38	ga_ls9c_ard_3-2-1_111073_2023-09-01_final
2023-09-01 11:26:02	ga_ls9c_ard_3-2-1_111074_2023-09-01_final
2023-09-01 11:26:26	ga_ls9c_ard_3-2-1_111075_2023-09-01_final
2023-09-01 11:26:50	ga_ls9c_ard_3-2-1_111076_2023-09-01_final
2023-09-01 11:27:14	ga_ls9c_ard_3-2-1_111077_2023-09-01_final
2023-09-01 11:27:38	ga_ls9c_ard_3-2-1_111078_2023-09-01_final
2023-09-01 11:28:02	ga_ls9c_ard_3-2-1_111079_2023-09-01_final
2023-09-01 11:28:25	ga_ls9c_ard_3-2-1_111080_2023-09-01_final
2023-09-01 11:28:49	ga_ls9c_ard_3-2-1_111081_2023-09-01_final
2023-09-01 11:29:13	ga_ls9c_ard_3-2-1_111082_2023-09-01_final
2023-09-01 11:29:37	ga_ls9c_ard_3-2-1_111083_2023-09-01_final
2023-09-01 11:30:01	ga_ls9c_ard_3-2-1_111084_2023-09-01_final
2023-09-02 10:28:01	ga_ls9c_ard_3-2-1_102068_2023-09-02_final
2023-09-02 10:28:25	ga_ls9c_ard_3-2-1_102069_2023-09-02_final
2023-09-02 10:28:49	ga_ls9c_ard_3-2-1_102070_2023-09-02_final
2023-09-02 10:29:13	ga_ls9c_ard_3-2-1_102071_2023-09-02_final
2023-09-02 10:29:37	ga_ls9c_ard_3-2-1_102072_2023-09-02_final
2023-09-02 10:30:01	ga_ls9c_ard_3-2-1_102073_2023-09-02_final
2023-09-02 10:30:25	ga_ls9c_ard_3-2-1_102074_2023-09-02_final
2023-09-02 10:30:49	ga_ls9c_ard_3-2-1_102075_2023-09-02_final
2023-09-02 10:31:13	ga_ls9c_ard_3-2-1_102076_2023-09-02_final
2023-09-02 10:31:37	ga_ls9c_ard_3-2-1_102077_2023-09-02_final
2023-09-02 10:32:01	ga_ls9c_ard_3-2-1_102078_2023-09-02_final
2023-09-02 10:32:24	ga_ls9c_ard_3-2-1_102079_2023-09-02_final
2023-09-02 10:32:48	ga_ls9c_ard_3-2-1_102080_2023-09-02_final
2023-09-02 10:33:12	ga_ls9c_ard_3-2-1_102081_2023-09-02_final
2023-09-02 10:33:36	ga_ls9c_ard_3-2-1_102082_2023-09-02_final
2023-09-04 11:57:45	ga_ls9c_ard_3-2-1_116076_2023-09-04_final
2023-09-04 11:58:09	ga_ls9c_ard_3-2-1_116077_2023-09-04_final
2023-09-04 11:58:33	ga_ls9c_ard_3-2-1_116078_2023-09-04_final
2023-09-05 09:21:40	ga_ls9c_ard_3-2-1_091072_2023-09-04_final
2023-09-05 09:22:04	ga_ls9c_ard_3-2-1_091073_2023-09-04_final
2023-09-05 09:22:28	ga_ls9c_ard_3-2-1_091074_2023-09-04_final
2023-09-05 09:22:52	ga_ls9c_ard_3-2-1_091075_2023-09-04_final
2023-09-05 09:23:16	ga_ls9c_ard_3-2-1_091076_2023-09-04_final
2023-09-05 09:23:40	ga_ls9c_ard_3-2-1_091077_2023-09-04_final
2023-09-05 09:24:04	ga_ls9c_ard_3-2-1_091078_2023-09-04_final
2023-09-05 09:24:28	ga_ls9c_ard_3-2-1_091079_2023-09-04_final
2023-09-05 09:24:51	ga_ls9c_ard_3-2-1_091080_2023-09-04_final
2023-09-05 09:25:15	ga_ls9c_ard_3-2-1_091081_2023-09-04_final
2023-09-05 09:25:39	ga_ls9c_ard_3-2-1_091082_2023-09-04_final
2023-09-05 09:26:03	ga_ls9c_ard_3-2-1_091083_2023-09-04_final
2023-09-05 09:26:27	ga_ls9c_ard_3-2-1_091084_2023-09-04_final
2023-09-05 09:26:51	ga_ls9c_ard_3-2-1_091085_2023-09-04_final
2023-09-05 09:27:15	ga_ls9c_ard_3-2-1_091086_2023-09-04_final
2023-09-05 09:27:39	ga_ls9c_ard_3-2-1_091087_2023-09-04_final
2023-09-05 09:28:03	ga_ls9c_ard_3-2-1_091088_2023-09-04_final
2023-09-05 09:28:27	ga_ls9c_ard_3-2-1_091089_2023-09-04_final
2023-09-05 09:28:51	ga_ls9c_ard_3-2-1_091090_2023-09-04_final
2023-09-05 10:59:45	ga_ls9c_ard_3-2-1_107070_2023-09-05_final
2023-09-05 11:00:09	ga_ls9c_ard_3-2-1_107071_2023-09-05_final
2023-09-05 11:00:33	ga_ls9c_ard_3-2-1_107072_2023-09-05_final
2023-09-05 11:00:57	ga_ls9c_ard_3-2-1_107073_2023-09-05_final
2023-09-05 11:01:21	ga_ls9c_ard_3-2-1_107074_2023-09-05_final
2023-09-05 11:01:45	ga_ls9c_ard_3-2-1_107075_2023-09-05_final
2023-09-05 11:02:09	ga_ls9c_ard_3-2-1_107076_2023-09-05_final
2023-09-05 11:02:33	ga_ls9c_ard_3-2-1_107077_2023-09-05_final
2023-09-05 11:02:57	ga_ls9c_ard_3-2-1_107078_2023-09-05_final
2023-09-05 11:03:21	ga_ls9c_ard_3-2-1_107079_2023-09-05_final
2023-09-05 11:03:45	ga_ls9c_ard_3-2-1_107080_2023-09-05_final
2023-09-05 11:04:08	ga_ls9c_ard_3-2-1_107081_2023-09-05_final
2023-09-05 11:04:32	ga_ls9c_ard_3-2-1_107082_2023-09-05_final
2023-09-05 11:04:56	ga_ls9c_ard_3-2-1_107083_2023-09-05_final
2023-09-05 11:05:20	ga_ls9c_ard_3-2-1_107084_2023-09-05_final
2023-09-06 10:02:57	ga_ls9c_ard_3-2-1_098067_2023-09-06_final
2023-09-06 10:03:21	ga_ls9c_ard_3-2-1_098068_2023-09-06_final
2023-09-06 10:03:45	ga_ls9c_ard_3-2-1_098069_2023-09-06_final
2023-09-06 10:06:32	ga_ls9c_ard_3-2-1_098076_2023-09-06_final
2023-09-06 10:06:56	ga_ls9c_ard_3-2-1_098077_2023-09-06_final
2023-09-06 10:07:20	ga_ls9c_ard_3-2-1_098078_2023-09-06_final
2023-09-06 10:07:44	ga_ls9c_ard_3-2-1_098079_2023-09-06_final
2023-09-06 10:08:08	ga_ls9c_ard_3-2-1_098080_2023-09-06_final
2023-09-06 10:08:32	ga_ls9c_ard_3-2-1_098081_2023-09-06_final
2023-09-06 10:08:56	ga_ls9c_ard_3-2-1_098082_2023-09-06_final
2023-09-06 10:09:20	ga_ls9c_ard_3-2-1_098083_2023-09-06_final
2023-09-06 10:09:44	ga_ls9c_ard_3-2-1_098084_2023-09-06_final

Return to ga_ls9c_ard_3 across September 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

September

All days