Search ga_ls7e_ard_3 across 2018

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
2018-09-30 09:26:38	ga_ls7e_ard_3-0-0_092075_2018-09-29_final
2018-09-30 09:27:02	ga_ls7e_ard_3-0-0_092076_2018-09-29_final
2018-09-30 09:27:26	ga_ls7e_ard_3-0-0_092077_2018-09-29_final
2018-09-30 09:27:50	ga_ls7e_ard_3-0-0_092078_2018-09-29_final
2018-09-30 09:28:14	ga_ls7e_ard_3-0-0_092079_2018-09-29_final
2018-09-30 09:28:38	ga_ls7e_ard_3-0-0_092080_2018-09-29_final
2018-09-30 09:29:02	ga_ls7e_ard_3-0-0_092081_2018-09-29_final
2018-09-30 09:29:26	ga_ls7e_ard_3-0-0_092082_2018-09-29_final
2018-09-30 09:30:14	ga_ls7e_ard_3-0-0_092084_2018-09-30_final
2018-09-30 09:30:37	ga_ls7e_ard_3-0-0_092085_2018-09-30_final
2018-09-30 09:31:01	ga_ls7e_ard_3-0-0_092086_2018-09-30_final
2018-09-30 09:31:25	ga_ls7e_ard_3-0-0_092087_2018-09-30_final
2018-09-30 11:03:07	ga_ls7e_ard_3-0-0_108069_2018-09-30_final
2018-09-30 11:03:31	ga_ls7e_ard_3-0-0_108070_2018-09-30_final
2018-09-30 11:03:55	ga_ls7e_ard_3-1-0_108071_2018-09-30_final
2018-09-30 11:04:19	ga_ls7e_ard_3-0-0_108072_2018-09-30_final
2018-09-30 11:04:43	ga_ls7e_ard_3-0-0_108073_2018-09-30_final
2018-09-30 11:05:07	ga_ls7e_ard_3-0-0_108074_2018-09-30_final
2018-09-30 11:05:31	ga_ls7e_ard_3-0-0_108075_2018-09-30_final
2018-09-30 11:06:19	ga_ls7e_ard_3-0-0_108077_2018-09-30_final
2018-09-30 11:06:43	ga_ls7e_ard_3-0-0_108078_2018-09-30_final
2018-09-30 11:07:07	ga_ls7e_ard_3-0-0_108079_2018-09-30_final
2018-09-30 11:09:06	ga_ls7e_ard_3-0-0_108084_2018-09-30_final
2018-10-01 10:06:41	ga_ls7e_ard_3-0-0_099067_2018-10-01_final
2018-10-01 10:07:05	ga_ls7e_ard_3-0-0_099068_2018-10-01_final
2018-10-01 10:07:29	ga_ls7e_ard_3-0-0_099069_2018-10-01_final
2018-10-01 10:07:52	ga_ls7e_ard_3-0-0_099070_2018-10-01_final
2018-10-01 10:08:16	ga_ls7e_ard_3-0-0_099071_2018-10-01_final
2018-10-01 10:08:40	ga_ls7e_ard_3-0-0_099072_2018-10-01_final
2018-10-01 10:09:04	ga_ls7e_ard_3-0-0_099073_2018-10-01_final
2018-10-01 10:09:28	ga_ls7e_ard_3-0-0_099074_2018-10-01_final
2018-10-01 10:09:52	ga_ls7e_ard_3-0-0_099075_2018-10-01_final
2018-10-01 10:10:16	ga_ls7e_ard_3-0-0_099076_2018-10-01_final
2018-10-01 10:10:40	ga_ls7e_ard_3-0-0_099077_2018-10-01_final
2018-10-01 10:11:04	ga_ls7e_ard_3-0-0_099078_2018-10-01_final
2018-10-01 10:11:28	ga_ls7e_ard_3-0-0_099079_2018-10-01_final
2018-10-01 10:11:52	ga_ls7e_ard_3-0-0_099080_2018-10-01_final
2018-10-01 10:12:16	ga_ls7e_ard_3-0-0_099081_2018-10-01_final
2018-10-01 10:12:40	ga_ls7e_ard_3-0-0_099082_2018-10-01_final
2018-10-01 11:48:21	ga_ls7e_ard_3-0-0_115074_2018-10-01_final
2018-10-01 11:48:45	ga_ls7e_ard_3-0-0_115075_2018-10-01_final
2018-10-01 11:49:09	ga_ls7e_ard_3-0-0_115076_2018-10-01_final
2018-10-01 11:49:33	ga_ls7e_ard_3-0-0_115077_2018-10-01_final
2018-10-01 11:49:57	ga_ls7e_ard_3-0-0_115078_2018-10-01_final
2018-10-02 09:15:01	ga_ls7e_ard_3-0-0_090077_2018-10-01_final
2018-10-02 09:15:25	ga_ls7e_ard_3-0-0_090078_2018-10-01_final
2018-10-02 09:15:49	ga_ls7e_ard_3-0-0_090079_2018-10-01_final
2018-10-02 09:16:13	ga_ls7e_ard_3-0-0_090080_2018-10-01_final
2018-10-02 09:16:37	ga_ls7e_ard_3-0-0_090081_2018-10-01_final
2018-10-02 09:17:01	ga_ls7e_ard_3-0-0_090082_2018-10-01_final
2018-10-02 09:17:25	ga_ls7e_ard_3-0-0_090083_2018-10-01_final
2018-10-02 09:17:49	ga_ls7e_ard_3-0-0_090084_2018-10-01_final
2018-10-02 09:18:13	ga_ls7e_ard_3-0-0_090085_2018-10-01_final
2018-10-02 09:18:37	ga_ls7e_ard_3-0-0_090086_2018-10-01_final
2018-10-02 09:19:25	ga_ls7e_ard_3-0-0_090088_2018-10-01_final
2018-10-02 09:19:49	ga_ls7e_ard_3-0-0_090089_2018-10-01_final
2018-10-02 09:20:13	ga_ls7e_ard_3-0-0_090090_2018-10-01_final
2018-10-02 10:50:19	ga_ls7e_ard_3-0-0_106068_2018-10-02_final
2018-10-02 10:50:43	ga_ls7e_ard_3-0-0_106069_2018-10-02_final
2018-10-02 10:51:07	ga_ls7e_ard_3-0-0_106070_2018-10-02_final
2018-10-02 10:51:31	ga_ls7e_ard_3-0-0_106071_2018-10-02_final
2018-10-02 10:51:55	ga_ls7e_ard_3-0-0_106072_2018-10-02_final
2018-10-02 10:52:18	ga_ls7e_ard_3-0-0_106073_2018-10-02_final
2018-10-02 10:52:42	ga_ls7e_ard_3-0-0_106074_2018-10-02_final
2018-10-02 10:53:06	ga_ls7e_ard_3-1-0_106075_2018-10-02_final
2018-10-02 10:53:30	ga_ls7e_ard_3-0-0_106076_2018-10-02_final
2018-10-02 10:53:54	ga_ls7e_ard_3-0-0_106077_2018-10-02_final
2018-10-02 10:54:18	ga_ls7e_ard_3-0-0_106078_2018-10-02_final
2018-10-02 10:54:42	ga_ls7e_ard_3-0-0_106079_2018-10-02_final
2018-10-02 10:55:06	ga_ls7e_ard_3-0-0_106080_2018-10-02_final
2018-10-03 09:55:04	ga_ls7e_ard_3-0-0_097069_2018-10-03_final
2018-10-03 09:55:28	ga_ls7e_ard_3-0-0_097070_2018-10-03_final
2018-10-03 09:55:52	ga_ls7e_ard_3-0-0_097071_2018-10-03_final
2018-10-03 09:56:16	ga_ls7e_ard_3-0-0_097072_2018-10-03_final
2018-10-03 09:56:40	ga_ls7e_ard_3-0-0_097073_2018-10-03_final
2018-10-03 09:57:04	ga_ls7e_ard_3-0-0_097074_2018-10-03_final
2018-10-03 09:57:28	ga_ls7e_ard_3-1-0_097075_2018-10-03_final
2018-10-03 09:57:52	ga_ls7e_ard_3-0-0_097076_2018-10-03_final
2018-10-03 09:58:16	ga_ls7e_ard_3-0-0_097077_2018-10-03_final
2018-10-03 09:58:39	ga_ls7e_ard_3-0-0_097078_2018-10-03_final
2018-10-03 09:59:03	ga_ls7e_ard_3-0-0_097079_2018-10-03_final
2018-10-03 09:59:27	ga_ls7e_ard_3-0-0_097080_2018-10-03_final
2018-10-03 09:59:51	ga_ls7e_ard_3-0-0_097081_2018-10-03_final
2018-10-03 10:00:15	ga_ls7e_ard_3-0-0_097082_2018-10-03_final
2018-10-03 10:00:39	ga_ls7e_ard_3-0-0_097083_2018-10-03_final
2018-10-03 11:33:09	ga_ls7e_ard_3-0-0_113067_2018-10-03_final
2018-11-24 09:31:08	ga_ls7e_ard_3-0-0_093074_2018-11-24_final
2018-11-24 09:31:56	ga_ls7e_ard_3-0-0_093076_2018-11-24_final
2018-11-24 09:32:20	ga_ls7e_ard_3-0-0_093077_2018-11-24_final
2018-11-24 09:32:44	ga_ls7e_ard_3-0-0_093078_2018-11-24_final
2018-11-24 09:33:08	ga_ls7e_ard_3-0-0_093079_2018-11-24_final
2018-11-24 09:33:32	ga_ls7e_ard_3-0-0_093080_2018-11-24_final
2018-11-24 09:33:56	ga_ls7e_ard_3-0-0_093081_2018-11-24_final
2018-11-24 09:34:19	ga_ls7e_ard_3-0-0_093082_2018-11-24_final
2018-11-24 09:34:43	ga_ls7e_ard_3-1-0_093083_2018-11-24_final
2018-11-24 09:35:07	ga_ls7e_ard_3-0-0_093084_2018-11-24_final
2018-11-24 09:35:31	ga_ls7e_ard_3-0-0_093085_2018-11-24_final
2018-11-24 09:35:55	ga_ls7e_ard_3-0-0_093086_2018-11-24_final
2018-11-24 11:12:48	ga_ls7e_ard_3-1-0_109081_2018-11-24_final
2018-11-26 09:23:31	ga_ls7e_ard_3-1-0_091086_2018-11-25_final

Return to ga_ls7e_ard_3 across 2018 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

2018

All months