Search ga_ls7e_ard_3 across April 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
2022-04-01 09:01:15	ga_ls7e_ard_3-2-1_101070_2022-03-31_final
2022-04-01 09:01:39	ga_ls7e_ard_3-2-1_101071_2022-03-31_final
2022-04-01 09:02:03	ga_ls7e_ard_3-2-1_101072_2022-03-31_final
2022-04-01 09:02:27	ga_ls7e_ard_3-2-1_101073_2022-03-31_final
2022-04-01 09:02:51	ga_ls7e_ard_3-2-1_101074_2022-03-31_final
2022-04-01 09:03:15	ga_ls7e_ard_3-2-1_101075_2022-03-31_final
2022-04-01 09:03:39	ga_ls7e_ard_3-2-1_101076_2022-03-31_final
2022-04-01 09:04:03	ga_ls7e_ard_3-2-1_101077_2022-03-31_final
2022-04-01 09:04:27	ga_ls7e_ard_3-2-1_101078_2022-03-31_final
2022-04-01 09:04:51	ga_ls7e_ard_3-2-1_101079_2022-03-31_final
2022-04-01 09:05:15	ga_ls7e_ard_3-2-1_101080_2022-03-31_final
2022-04-01 09:05:39	ga_ls7e_ard_3-2-1_101081_2022-03-31_final
2022-04-01 09:06:03	ga_ls7e_ard_3-2-1_101082_2022-03-31_final
2022-04-01 09:06:27	ga_ls7e_ard_3-2-1_101083_2022-03-31_final
2022-04-02 08:07:32	ga_ls7e_ard_3-2-1_092075_2022-04-01_final
2022-04-02 08:07:56	ga_ls7e_ard_3-2-1_092076_2022-04-01_final
2022-04-02 08:08:20	ga_ls7e_ard_3-2-1_092077_2022-04-01_final
2022-04-02 08:08:44	ga_ls7e_ard_3-2-1_092078_2022-04-01_final
2022-04-02 08:09:08	ga_ls7e_ard_3-2-1_092079_2022-04-01_final
2022-04-02 08:09:32	ga_ls7e_ard_3-2-1_092080_2022-04-01_final
2022-04-02 08:09:56	ga_ls7e_ard_3-2-1_092081_2022-04-01_final
2022-04-02 08:10:20	ga_ls7e_ard_3-2-1_092082_2022-04-01_final
2022-04-02 08:10:44	ga_ls7e_ard_3-2-1_092083_2022-04-01_final
2022-04-02 08:11:07	ga_ls7e_ard_3-2-1_092084_2022-04-01_final
2022-04-02 08:12:43	ga_ls7e_ard_3-2-1_092088_2022-04-01_final
2022-04-02 08:13:07	ga_ls7e_ard_3-2-1_092089_2022-04-01_final
2022-04-02 09:44:01	ga_ls7e_ard_3-2-1_108069_2022-04-02_final
2022-04-02 09:44:25	ga_ls7e_ard_3-2-1_108070_2022-04-02_final
2022-04-02 09:44:49	ga_ls7e_ard_3-2-1_108071_2022-04-02_final
2022-04-02 09:45:13	ga_ls7e_ard_3-2-1_108072_2022-04-02_final
2022-04-04 07:54:58	ga_ls7e_ard_3-2-1_090075_2022-04-03_final
2022-04-04 07:55:46	ga_ls7e_ard_3-2-1_090077_2022-04-03_final
2022-04-04 07:56:10	ga_ls7e_ard_3-2-1_090078_2022-04-03_final
2022-04-04 07:56:34	ga_ls7e_ard_3-2-1_090079_2022-04-03_final
2022-04-04 07:56:58	ga_ls7e_ard_3-2-1_090080_2022-04-03_final
2022-04-04 07:57:22	ga_ls7e_ard_3-2-1_090081_2022-04-03_final
2022-04-04 07:57:46	ga_ls7e_ard_3-2-1_090082_2022-04-03_final
2022-04-04 07:58:10	ga_ls7e_ard_3-2-1_090083_2022-04-03_final
2022-04-04 07:58:34	ga_ls7e_ard_3-2-1_090084_2022-04-03_final
2022-04-04 07:58:58	ga_ls7e_ard_3-2-1_090085_2022-04-03_final
2022-04-04 08:00:10	ga_ls7e_ard_3-2-1_090088_2022-04-03_final
2022-04-04 08:00:34	ga_ls7e_ard_3-2-1_090089_2022-04-03_final
2022-04-04 08:00:58	ga_ls7e_ard_3-2-1_090090_2022-04-03_final
2022-04-04 09:31:03	ga_ls7e_ard_3-2-1_106068_2022-04-04_final
2022-04-04 09:31:27	ga_ls7e_ard_3-2-1_106069_2022-04-04_final
2022-04-04 09:31:51	ga_ls7e_ard_3-2-1_106070_2022-04-04_final
2022-04-04 09:32:15	ga_ls7e_ard_3-2-1_106071_2022-04-04_final
2022-04-04 09:32:39	ga_ls7e_ard_3-2-1_106072_2022-04-04_final
2022-04-04 09:33:03	ga_ls7e_ard_3-2-1_106073_2022-04-04_final
2022-04-04 09:33:27	ga_ls7e_ard_3-2-1_106074_2022-04-04_final
2022-04-04 09:33:51	ga_ls7e_ard_3-2-1_106075_2022-04-04_final
2022-04-04 09:34:15	ga_ls7e_ard_3-2-1_106076_2022-04-04_final
2022-04-04 09:34:39	ga_ls7e_ard_3-2-1_106077_2022-04-04_final
2022-04-04 09:35:03	ga_ls7e_ard_3-2-1_106078_2022-04-04_final
2022-04-04 09:35:27	ga_ls7e_ard_3-2-1_106079_2022-04-04_final
2022-04-04 09:35:51	ga_ls7e_ard_3-2-1_106080_2022-04-04_final
2022-04-04 09:36:15	ga_ls7e_ard_3-2-1_106081_2022-04-04_final
2022-04-04 09:36:39	ga_ls7e_ard_3-2-1_106082_2022-04-04_final
2022-04-05 08:35:44	ga_ls7e_ard_3-2-1_097069_2022-04-04_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
2022-04-05 08:42:07	ga_ls7e_ard_3-2-1_097085_2022-04-04_final
2022-04-05 10:15:49	ga_ls7e_ard_3-2-1_113072_2022-04-05_final
2022-04-05 10:16:37	ga_ls7e_ard_3-2-1_113074_2022-04-05_final
2022-04-05 10:17:00	ga_ls7e_ard_3-2-1_113075_2022-04-05_final
2022-04-05 10:17:24	ga_ls7e_ard_3-2-1_113076_2022-04-05_final
2022-04-05 10:17:48	ga_ls7e_ard_3-2-1_113077_2022-04-05_final
2022-04-05 10:18:12	ga_ls7e_ard_3-2-1_113078_2022-04-05_final
2022-04-05 10:18:36	ga_ls7e_ard_3-2-1_113079_2022-04-05_final
2022-04-05 10:19:00	ga_ls7e_ard_3-2-1_113080_2022-04-05_final
2022-04-05 10:19:24	ga_ls7e_ard_3-2-1_113081_2022-04-05_final
2022-04-05 10:19:48	ga_ls7e_ard_3-2-1_113082_2022-04-05_final
2022-04-05 10:20:12	ga_ls7e_ard_3-2-1_113083_2022-04-05_final
2022-04-06 09:18:30	ga_ls7e_ard_3-2-1_104068_2022-04-05_final
2022-04-06 09:18:54	ga_ls7e_ard_3-2-1_104069_2022-04-05_final
2022-04-06 09:19:18	ga_ls7e_ard_3-2-1_104070_2022-04-05_final
2022-04-06 09:19:41	ga_ls7e_ard_3-2-1_104071_2022-04-05_final
2022-04-06 09:20:05	ga_ls7e_ard_3-2-1_104072_2022-04-05_final
2022-04-06 09:20:29	ga_ls7e_ard_3-2-1_104073_2022-04-05_final
2022-04-06 09:20:53	ga_ls7e_ard_3-2-1_104074_2022-04-05_final
2022-04-06 09:21:17	ga_ls7e_ard_3-2-1_104075_2022-04-05_final
2022-04-06 09:21:41	ga_ls7e_ard_3-2-1_104076_2022-04-05_final
2022-04-06 09:22:05	ga_ls7e_ard_3-2-1_104077_2022-04-05_final
2022-04-06 09:22:29	ga_ls7e_ard_3-2-1_104078_2022-04-05_final
2022-04-06 09:22:53	ga_ls7e_ard_3-2-1_104079_2022-04-05_final
2022-04-06 09:23:17	ga_ls7e_ard_3-2-1_104080_2022-04-05_final
2022-04-06 09:23:41	ga_ls7e_ard_3-2-1_104081_2022-04-05_final

Return to ga_ls7e_ard_3 across April 2022 overview

ga_ls7e_ard_3: Geoscience Australia Landsat 7 Enhanced Thematic Mapper Plus 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

April

All days