Search ga_ls7e_ard_3 whole collection

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
2022-02-16 08:44:47	ga_ls7e_ard_3-2-1_097080_2022-02-15_final
2022-03-11 08:43:33	ga_ls7e_ard_3-2-1_098067_2022-03-10_final
2022-03-11 08:44:21	ga_ls7e_ard_3-2-1_098069_2022-03-10_final
2022-03-13 08:34:11	ga_ls7e_ard_3-2-1_096075_2022-03-12_final
2022-03-13 08:34:35	ga_ls7e_ard_3-2-1_096076_2022-03-12_final
2022-03-13 08:34:59	ga_ls7e_ard_3-2-1_096077_2022-03-12_final
2022-03-13 08:35:23	ga_ls7e_ard_3-2-1_096078_2022-03-12_final
2022-03-13 08:35:47	ga_ls7e_ard_3-2-1_096079_2022-03-12_final
2022-03-13 08:36:11	ga_ls7e_ard_3-2-1_096080_2022-03-12_final
2022-03-13 08:36:35	ga_ls7e_ard_3-2-1_096081_2022-03-12_final
2022-03-13 08:36:59	ga_ls7e_ard_3-2-1_096082_2022-03-12_final
2022-03-13 08:37:23	ga_ls7e_ard_3-2-1_096083_2022-03-12_final
2022-03-13 08:37:46	ga_ls7e_ard_3-2-1_096084_2022-03-12_final
2022-03-13 08:38:10	ga_ls7e_ard_3-2-1_096085_2022-03-12_final
2022-03-13 08:38:34	ga_ls7e_ard_3-2-1_096086_2022-03-12_final
2022-03-15 08:25:13	ga_ls7e_ard_3-2-1_094084_2022-03-14_final
2022-03-15 08:26:01	ga_ls7e_ard_3-2-1_094086_2022-03-14_final
2022-03-15 08:26:24	ga_ls7e_ard_3-2-1_094087_2022-03-14_final
2022-03-18 08:49:25	ga_ls7e_ard_3-2-1_099068_2022-03-17_final
2022-03-18 08:49:49	ga_ls7e_ard_3-2-1_099069_2022-03-17_final
2022-03-18 08:50:37	ga_ls7e_ard_3-2-1_099071_2022-03-17_final
2022-03-18 08:51:01	ga_ls7e_ard_3-2-1_099072_2022-03-17_final
2022-03-18 08:51:25	ga_ls7e_ard_3-2-1_099073_2022-03-17_final
2022-03-18 08:51:48	ga_ls7e_ard_3-2-1_099074_2022-03-17_final
2022-03-18 08:52:12	ga_ls7e_ard_3-2-1_099075_2022-03-17_final
2022-03-18 08:52:36	ga_ls7e_ard_3-2-1_099076_2022-03-17_final
2022-03-18 08:53:00	ga_ls7e_ard_3-2-1_099077_2022-03-17_final
2022-03-18 08:53:24	ga_ls7e_ard_3-2-1_099078_2022-03-17_final
2022-03-20 08:37:40	ga_ls7e_ard_3-2-1_097070_2022-03-19_final
2022-03-20 08:38:04	ga_ls7e_ard_3-2-1_097071_2022-03-19_final
2022-03-20 08:38:28	ga_ls7e_ard_3-2-1_097072_2022-03-19_final
2022-03-20 08:38:52	ga_ls7e_ard_3-2-1_097073_2022-03-19_final
2022-03-20 08:39:16	ga_ls7e_ard_3-2-1_097074_2022-03-19_final
2022-03-20 08:39:40	ga_ls7e_ard_3-2-1_097075_2022-03-19_final
2022-03-20 08:40:04	ga_ls7e_ard_3-2-1_097076_2022-03-19_final
2022-03-20 08:40:28	ga_ls7e_ard_3-2-1_097077_2022-03-19_final
2022-03-20 08:40:52	ga_ls7e_ard_3-2-1_097078_2022-03-19_final
2022-03-20 08:41:16	ga_ls7e_ard_3-2-1_097079_2022-03-19_final
2022-03-20 08:41:40	ga_ls7e_ard_3-2-1_097080_2022-03-19_final
2022-03-20 08:42:04	ga_ls7e_ard_3-2-1_097081_2022-03-19_final
2022-03-20 08:42:27	ga_ls7e_ard_3-2-1_097082_2022-03-19_final
2022-03-20 08:42:51	ga_ls7e_ard_3-2-1_097083_2022-03-19_final
2022-03-20 08:43:15	ga_ls7e_ard_3-2-1_097084_2022-03-19_final
2022-03-20 08:43:39	ga_ls7e_ard_3-2-1_097085_2022-03-19_final
2022-03-22 08:29:31	ga_ls7e_ard_3-2-1_095081_2022-03-21_final
2022-03-22 08:29:55	ga_ls7e_ard_3-2-1_095082_2022-03-21_final
2022-03-22 08:30:19	ga_ls7e_ard_3-2-1_095083_2022-03-21_final
2022-03-22 08:30:43	ga_ls7e_ard_3-2-1_095084_2022-03-21_final
2022-03-22 08:31:07	ga_ls7e_ard_3-2-1_095085_2022-03-21_final
2022-03-25 08:56:32	ga_ls7e_ard_3-2-1_100072_2022-03-24_final
2022-03-27 08:42:48	ga_ls7e_ard_3-2-1_098069_2022-03-26_final
2022-03-27 08:44:24	ga_ls7e_ard_3-2-1_098073_2022-03-26_final
2022-03-27 08:45:35	ga_ls7e_ard_3-2-1_098076_2022-03-26_final
2022-03-27 08:45:59	ga_ls7e_ard_3-2-1_098077_2022-03-26_final
2022-03-27 08:46:23	ga_ls7e_ard_3-2-1_098078_2022-03-26_final
2022-03-27 08:46:47	ga_ls7e_ard_3-2-1_098079_2022-03-26_final
2022-03-27 08:47:11	ga_ls7e_ard_3-2-1_098080_2022-03-26_final
2022-03-29 08:32:38	ga_ls7e_ard_3-2-1_096075_2022-03-28_final
2022-03-29 08:33:02	ga_ls7e_ard_3-2-1_096076_2022-03-28_final
2022-03-29 08:33:26	ga_ls7e_ard_3-2-1_096077_2022-03-28_final
2022-03-29 08:33:50	ga_ls7e_ard_3-2-1_096078_2022-03-28_final
2022-03-29 08:34:14	ga_ls7e_ard_3-2-1_096079_2022-03-28_final
2022-03-29 08:34:38	ga_ls7e_ard_3-2-1_096080_2022-03-28_final
2022-03-29 08:35:02	ga_ls7e_ard_3-2-1_096081_2022-03-28_final
2022-03-29 08:35:26	ga_ls7e_ard_3-2-1_096082_2022-03-28_final
2022-03-29 08:35:50	ga_ls7e_ard_3-2-1_096083_2022-03-28_final
2022-03-29 08:36:14	ga_ls7e_ard_3-2-1_096084_2022-03-28_final
2022-03-29 08:36:38	ga_ls7e_ard_3-2-1_096085_2022-03-28_final
2022-03-29 08:37:02	ga_ls7e_ard_3-2-1_096086_2022-03-28_final
2022-03-31 08:23:41	ga_ls7e_ard_3-2-1_094084_2022-03-30_final
2022-03-31 08:24:05	ga_ls7e_ard_3-2-1_094085_2022-03-30_final
2022-03-31 08:24:29	ga_ls7e_ard_3-2-1_094086_2022-03-30_final
2022-03-31 08:24:53	ga_ls7e_ard_3-2-1_094087_2022-03-30_final
2022-04-03 08:47:30	ga_ls7e_ard_3-2-1_099067_2022-04-02_final
2022-04-03 08:47:54	ga_ls7e_ard_3-2-1_099068_2022-04-02_final
2022-04-03 08:48:18	ga_ls7e_ard_3-2-1_099069_2022-04-02_final
2022-04-03 08:48:42	ga_ls7e_ard_3-2-1_099070_2022-04-02_final
2022-04-03 08:49:06	ga_ls7e_ard_3-2-1_099071_2022-04-02_final
2022-04-03 08:49:30	ga_ls7e_ard_3-2-1_099072_2022-04-02_final
2022-04-03 08:49:54	ga_ls7e_ard_3-2-1_099073_2022-04-02_final
2022-04-03 08:50:18	ga_ls7e_ard_3-2-1_099074_2022-04-02_final
2022-04-03 08:50:41	ga_ls7e_ard_3-2-1_099075_2022-04-02_final
2022-04-03 08:51:05	ga_ls7e_ard_3-2-1_099076_2022-04-02_final
2022-04-03 08:51:29	ga_ls7e_ard_3-2-1_099077_2022-04-02_final
2022-04-03 08:51:53	ga_ls7e_ard_3-2-1_099078_2022-04-02_final
2022-04-05 08:36:08	ga_ls7e_ard_3-2-1_097070_2022-04-04_final
2022-04-05 08:36:32	ga_ls7e_ard_3-2-1_097071_2022-04-04_final
2022-04-05 08:36:56	ga_ls7e_ard_3-2-1_097072_2022-04-04_final
2022-04-05 08:37:20	ga_ls7e_ard_3-2-1_097073_2022-04-04_final
2022-04-05 08:37:44	ga_ls7e_ard_3-2-1_097074_2022-04-04_final
2022-04-05 08:38:08	ga_ls7e_ard_3-2-1_097075_2022-04-04_final
2022-04-05 08:38:32	ga_ls7e_ard_3-2-1_097076_2022-04-04_final
2022-04-05 08:38:56	ga_ls7e_ard_3-2-1_097077_2022-04-04_final
2022-04-05 08:39:20	ga_ls7e_ard_3-2-1_097078_2022-04-04_final
2022-04-05 08:39:44	ga_ls7e_ard_3-2-1_097079_2022-04-04_final
2022-04-05 08:40:08	ga_ls7e_ard_3-2-1_097080_2022-04-04_final
2022-04-05 08:40:31	ga_ls7e_ard_3-2-1_097081_2022-04-04_final
2022-04-05 08:40:55	ga_ls7e_ard_3-2-1_097082_2022-04-04_final
2022-04-05 08:41:19	ga_ls7e_ard_3-2-1_097083_2022-04-04_final
2022-04-05 08:41:43	ga_ls7e_ard_3-2-1_097084_2022-04-04_final

Return to ga_ls7e_ard_3 whole collection overview

ga_ls7e_ard_3: Geoscience Australia Landsat 7 Enhanced Thematic Mapper Plus Analysis Ready Data Collection 3

Baseline satellite data

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

All years