Search ga_ls7e_ard_3 across 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
2022-01-01 08:33:47	ga_ls7e_ard_3-2-1_095072_2021-12-31_final
2022-01-01 08:34:10	ga_ls7e_ard_3-2-1_095073_2021-12-31_final
2022-01-01 08:34:34	ga_ls7e_ard_3-2-1_095074_2021-12-31_final
2022-01-01 08:34:58	ga_ls7e_ard_3-2-1_095075_2021-12-31_final
2022-01-01 08:35:22	ga_ls7e_ard_3-2-1_095076_2021-12-31_final
2022-01-01 08:35:46	ga_ls7e_ard_3-2-1_095077_2021-12-31_final
2022-01-01 08:36:10	ga_ls7e_ard_3-2-1_095078_2021-12-31_final
2022-01-01 08:36:34	ga_ls7e_ard_3-2-1_095079_2021-12-31_final
2022-01-01 08:36:58	ga_ls7e_ard_3-2-1_095080_2021-12-31_final
2022-01-01 08:37:22	ga_ls7e_ard_3-2-1_095081_2021-12-31_final
2022-01-01 08:37:46	ga_ls7e_ard_3-2-1_095082_2021-12-31_final
2022-01-01 08:38:10	ga_ls7e_ard_3-2-1_095083_2021-12-31_final
2022-01-01 08:38:34	ga_ls7e_ard_3-2-1_095084_2021-12-31_final
2022-01-01 08:38:58	ga_ls7e_ard_3-2-1_095085_2021-12-31_final
2022-01-01 08:39:22	ga_ls7e_ard_3-2-1_095086_2021-12-31_final
2022-01-01 10:12:15	ga_ls7e_ard_3-2-1_111071_2022-01-01_final
2022-01-01 10:12:39	ga_ls7e_ard_3-2-1_111072_2022-01-01_final
2022-01-01 10:13:03	ga_ls7e_ard_3-2-1_111073_2022-01-01_final
2022-01-01 10:13:27	ga_ls7e_ard_3-2-1_111074_2022-01-01_final
2022-01-01 10:13:51	ga_ls7e_ard_3-2-1_111075_2022-01-01_final
2022-01-01 10:14:15	ga_ls7e_ard_3-2-1_111076_2022-01-01_final
2022-01-01 10:14:39	ga_ls7e_ard_3-2-1_111077_2022-01-01_final
2022-01-01 10:15:03	ga_ls7e_ard_3-2-1_111078_2022-01-01_final
2022-01-01 10:15:27	ga_ls7e_ard_3-2-1_111079_2022-01-01_final
2022-01-01 10:15:51	ga_ls7e_ard_3-2-1_111080_2022-01-01_final
2022-01-01 10:16:15	ga_ls7e_ard_3-2-1_111081_2022-01-01_final
2022-01-01 10:16:39	ga_ls7e_ard_3-2-1_111082_2022-01-01_final
2022-01-01 10:17:03	ga_ls7e_ard_3-2-1_111083_2022-01-01_final
2022-01-01 10:17:26	ga_ls7e_ard_3-2-1_111084_2022-01-01_final
2022-01-02 09:15:20	ga_ls7e_ard_3-2-1_102068_2022-01-01_final
2022-01-02 09:15:44	ga_ls7e_ard_3-2-1_102069_2022-01-01_final
2022-01-02 09:16:08	ga_ls7e_ard_3-2-1_102070_2022-01-01_final
2022-01-02 09:16:32	ga_ls7e_ard_3-2-1_102071_2022-01-01_final
2022-01-02 09:16:56	ga_ls7e_ard_3-2-1_102072_2022-01-01_final
2022-01-02 09:17:20	ga_ls7e_ard_3-2-1_102073_2022-01-01_final
2022-01-02 09:17:44	ga_ls7e_ard_3-2-1_102074_2022-01-01_final
2022-01-02 09:18:08	ga_ls7e_ard_3-2-1_102075_2022-01-01_final
2022-01-02 09:18:32	ga_ls7e_ard_3-2-1_102076_2022-01-01_final
2022-01-02 09:18:56	ga_ls7e_ard_3-2-1_102077_2022-01-01_final
2022-01-02 09:19:20	ga_ls7e_ard_3-2-1_102078_2022-01-01_final
2022-01-02 09:19:43	ga_ls7e_ard_3-2-1_102079_2022-01-01_final
2022-01-02 09:20:07	ga_ls7e_ard_3-2-1_102080_2022-01-01_final
2022-01-02 09:20:31	ga_ls7e_ard_3-2-1_102081_2022-01-01_final
2022-01-03 08:22:01	ga_ls7e_ard_3-2-1_093074_2022-01-02_final
2022-01-03 08:22:24	ga_ls7e_ard_3-2-1_093075_2022-01-02_final
2022-01-03 08:22:48	ga_ls7e_ard_3-2-1_093076_2022-01-02_final
2022-01-03 08:23:12	ga_ls7e_ard_3-2-1_093077_2022-01-02_final
2022-01-03 08:23:36	ga_ls7e_ard_3-2-1_093078_2022-01-02_final
2022-01-03 08:24:00	ga_ls7e_ard_3-2-1_093079_2022-01-02_final
2022-01-03 08:24:24	ga_ls7e_ard_3-2-1_093080_2022-01-02_final
2022-01-03 08:24:48	ga_ls7e_ard_3-2-1_093081_2022-01-02_final
2022-01-03 08:25:12	ga_ls7e_ard_3-2-1_093082_2022-01-02_final
2022-01-03 08:25:36	ga_ls7e_ard_3-2-1_093083_2022-01-02_final
2022-01-03 08:26:00	ga_ls7e_ard_3-2-1_093084_2022-01-02_final
2022-01-03 08:26:24	ga_ls7e_ard_3-2-1_093085_2022-01-02_final
2022-01-03 08:26:48	ga_ls7e_ard_3-2-1_093086_2022-01-02_final
2022-01-03 08:27:12	ga_ls7e_ard_3-2-1_093087_2022-01-02_final
2022-01-03 08:27:36	ga_ls7e_ard_3-2-1_093088_2022-01-02_final
2022-01-05 09:49:55	ga_ls7e_ard_3-2-1_107078_2022-01-05_final
2022-01-05 09:50:19	ga_ls7e_ard_3-2-1_107079_2022-01-05_final
2022-01-05 09:50:43	ga_ls7e_ard_3-2-1_107080_2022-01-05_final
2022-01-05 09:51:07	ga_ls7e_ard_3-2-1_107081_2022-01-05_final
2022-01-05 09:51:31	ga_ls7e_ard_3-2-1_107082_2022-01-05_final
2022-01-05 09:51:55	ga_ls7e_ard_3-2-1_107083_2022-01-05_final
2022-01-06 08:50:37	ga_ls7e_ard_3-2-1_098069_2022-01-05_final
2022-01-06 08:51:01	ga_ls7e_ard_3-2-1_098070_2022-01-05_final
2022-01-06 08:51:25	ga_ls7e_ard_3-2-1_098071_2022-01-05_final
2022-01-06 08:51:49	ga_ls7e_ard_3-2-1_098072_2022-01-05_final
2022-01-06 08:52:13	ga_ls7e_ard_3-2-1_098073_2022-01-05_final
2022-01-06 08:52:37	ga_ls7e_ard_3-2-1_098074_2022-01-05_final
2022-01-06 08:53:01	ga_ls7e_ard_3-2-1_098075_2022-01-05_final
2022-01-06 08:53:25	ga_ls7e_ard_3-2-1_098076_2022-01-05_final
2022-01-06 08:53:49	ga_ls7e_ard_3-2-1_098077_2022-01-05_final
2022-01-06 08:54:12	ga_ls7e_ard_3-2-1_098078_2022-01-05_final
2022-01-06 08:54:36	ga_ls7e_ard_3-2-1_098079_2022-01-05_final
2022-01-06 08:55:00	ga_ls7e_ard_3-2-1_098080_2022-01-05_final
2022-01-06 08:55:24	ga_ls7e_ard_3-2-1_098081_2022-01-05_final
2022-01-06 08:55:48	ga_ls7e_ard_3-2-1_098082_2022-01-05_final
2022-01-06 10:31:29	ga_ls7e_ard_3-2-1_114074_2022-01-06_final
2022-01-06 10:31:53	ga_ls7e_ard_3-2-1_114075_2022-01-06_final
2022-01-06 10:32:17	ga_ls7e_ard_3-2-1_114076_2022-01-06_final
2022-01-06 10:32:41	ga_ls7e_ard_3-2-1_114077_2022-01-06_final
2022-01-06 10:33:05	ga_ls7e_ard_3-2-1_114078_2022-01-06_final
2022-01-06 10:33:29	ga_ls7e_ard_3-2-1_114079_2022-01-06_final
2022-01-06 10:33:53	ga_ls7e_ard_3-2-1_114080_2022-01-06_final
2022-01-06 10:34:17	ga_ls7e_ard_3-2-1_114081_2022-01-06_final
2022-01-07 09:33:23	ga_ls7e_ard_3-2-1_105068_2022-01-07_final
2022-01-07 09:33:47	ga_ls7e_ard_3-2-1_105069_2022-01-07_final
2022-01-07 09:34:11	ga_ls7e_ard_3-2-1_105070_2022-01-07_final
2022-01-07 09:34:35	ga_ls7e_ard_3-2-1_105071_2022-01-07_final
2022-01-07 09:34:59	ga_ls7e_ard_3-2-1_105072_2022-01-07_final
2022-01-07 09:35:23	ga_ls7e_ard_3-2-1_105073_2022-01-07_final
2022-01-07 09:35:47	ga_ls7e_ard_3-2-1_105074_2022-01-07_final
2022-01-07 09:36:11	ga_ls7e_ard_3-2-1_105075_2022-01-07_final
2022-01-07 09:36:35	ga_ls7e_ard_3-2-1_105076_2022-01-07_final
2022-01-07 09:36:58	ga_ls7e_ard_3-2-1_105077_2022-01-07_final
2022-01-07 09:37:22	ga_ls7e_ard_3-2-1_105078_2022-01-07_final
2022-01-07 09:37:46	ga_ls7e_ard_3-2-1_105079_2022-01-07_final
2022-01-07 09:38:10	ga_ls7e_ard_3-2-1_105080_2022-01-07_final
2022-01-07 09:38:34	ga_ls7e_ard_3-2-1_105081_2022-01-07_final

Return to ga_ls7e_ard_3 across 2022 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

2022

All months