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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

40 results

Time (local)	Label
2022-09-10 11:51:02	ga_ls9c_ard_3-2-1_115074_2022-09-10_final.stac-item
2022-09-10 11:51:26	ga_ls9c_ard_3-2-1_115075_2022-09-10_final.stac-item
2022-09-10 11:51:50	ga_ls9c_ard_3-2-1_115076_2022-09-10_final.stac-item
2022-09-10 11:52:14	ga_ls9c_ard_3-2-1_115077_2022-09-10_final.stac-item
2022-09-10 11:52:38	ga_ls9c_ard_3-2-1_115078_2022-09-10_final.stac-item
2022-09-10 11:53:02	ga_ls9c_ard_3-2-1_115079_2022-09-10_final.stac-item
2022-09-10 11:53:26	ga_ls9c_ard_3-2-1_115080_2022-09-10_final.stac-item
2022-09-11 09:16:33	ga_ls9c_ard_3-2-1_090074_2022-09-10_final.stac-item
2022-09-11 09:16:57	ga_ls9c_ard_3-2-1_090075_2022-09-10_final.stac-item
2022-09-11 09:17:21	ga_ls9c_ard_3-2-1_090076_2022-09-10_final.stac-item
2022-09-11 09:17:45	ga_ls9c_ard_3-2-1_090077_2022-09-10_final.stac-item
2022-09-11 09:18:08	ga_ls9c_ard_3-2-1_090078_2022-09-10_final.stac-item
2022-09-11 09:18:32	ga_ls9c_ard_3-2-1_090079_2022-09-10_final.stac-item
2022-09-11 09:18:56	ga_ls9c_ard_3-2-1_090080_2022-09-10_final.stac-item
2022-09-11 09:19:20	ga_ls9c_ard_3-2-1_090081_2022-09-10_final.stac-item
2022-09-11 09:19:44	ga_ls9c_ard_3-2-1_090082_2022-09-10_final.stac-item
2022-09-11 09:20:08	ga_ls9c_ard_3-2-1_090083_2022-09-10_final.stac-item
2022-09-11 09:20:32	ga_ls9c_ard_3-2-1_090084_2022-09-10_final.stac-item
2022-09-11 09:20:56	ga_ls9c_ard_3-2-1_090085_2022-09-10_final.stac-item
2022-09-11 09:21:20	ga_ls9c_ard_3-2-1_090086_2022-09-10_final.stac-item
2022-09-11 09:21:44	ga_ls9c_ard_3-2-1_090087_2022-09-10_final.stac-item
2022-09-11 09:22:08	ga_ls9c_ard_3-2-1_090088_2022-09-10_final.stac-item
2022-09-11 09:22:32	ga_ls9c_ard_3-2-1_090089_2022-09-10_final.stac-item
2022-09-11 09:22:56	ga_ls9c_ard_3-2-1_090090_2022-09-10_final.stac-item
2022-09-11 10:53:02	ga_ls9c_ard_3-2-1_106068_2022-09-11_final.stac-item
2022-09-11 10:53:26	ga_ls9c_ard_3-2-1_106069_2022-09-11_final.stac-item
2022-09-11 10:53:50	ga_ls9c_ard_3-2-1_106070_2022-09-11_final.stac-item
2022-09-11 10:54:14	ga_ls9c_ard_3-2-1_106071_2022-09-11_final.stac-item
2022-09-11 10:54:38	ga_ls9c_ard_3-2-1_106072_2022-09-11_final.stac-item
2022-09-11 10:55:02	ga_ls9c_ard_3-2-1_106073_2022-09-11_final.stac-item
2022-09-11 10:55:26	ga_ls9c_ard_3-2-1_106074_2022-09-11_final.stac-item
2022-09-11 10:55:50	ga_ls9c_ard_3-2-1_106075_2022-09-11_final.stac-item
2022-09-11 10:56:14	ga_ls9c_ard_3-2-1_106076_2022-09-11_final.stac-item
2022-09-11 10:56:38	ga_ls9c_ard_3-2-1_106077_2022-09-11_final.stac-item
2022-09-11 10:57:02	ga_ls9c_ard_3-2-1_106078_2022-09-11_final.stac-item
2022-09-11 10:57:25	ga_ls9c_ard_3-2-1_106079_2022-09-11_final.stac-item
2022-09-11 10:57:49	ga_ls9c_ard_3-2-1_106080_2022-09-11_final.stac-item
2022-09-11 10:58:13	ga_ls9c_ard_3-2-1_106081_2022-09-11_final.stac-item
2022-09-11 10:58:37	ga_ls9c_ard_3-2-1_106082_2022-09-11_final.stac-item
2022-09-11 10:59:01	ga_ls9c_ard_3-2-1_106083_2022-09-11_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