Search ga_ls7e_ard_3 across January 2019

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
2019-01-04 09:24:12	ga_ls7e_ard_3-0-0_092075_2019-01-03_final
2019-01-04 09:24:36	ga_ls7e_ard_3-0-0_092076_2019-01-03_final
2019-01-04 09:25:00	ga_ls7e_ard_3-0-0_092077_2019-01-03_final
2019-01-04 09:25:24	ga_ls7e_ard_3-0-0_092078_2019-01-03_final
2019-01-04 09:25:48	ga_ls7e_ard_3-0-0_092079_2019-01-03_final
2019-01-04 09:26:12	ga_ls7e_ard_3-0-0_092080_2019-01-03_final
2019-01-04 09:26:36	ga_ls7e_ard_3-0-0_092081_2019-01-03_final
2019-01-04 09:27:00	ga_ls7e_ard_3-0-0_092082_2019-01-03_final
2019-01-04 09:27:24	ga_ls7e_ard_3-0-0_092083_2019-01-03_final
2019-01-04 09:27:47	ga_ls7e_ard_3-0-0_092084_2019-01-03_final
2019-01-04 09:28:11	ga_ls7e_ard_3-0-0_092085_2019-01-03_final
2019-01-04 09:28:35	ga_ls7e_ard_3-0-0_092086_2019-01-03_final
2019-01-04 09:28:59	ga_ls7e_ard_3-0-0_092087_2019-01-03_final
2019-01-04 09:29:23	ga_ls7e_ard_3-0-0_092088_2019-01-03_final
2019-01-04 09:29:47	ga_ls7e_ard_3-1-0_092089_2019-01-03_final
2019-01-04 11:00:41	ga_ls7e_ard_3-0-0_108069_2019-01-04_final
2019-01-04 11:01:05	ga_ls7e_ard_3-0-0_108070_2019-01-04_final
2019-01-04 11:02:41	ga_ls7e_ard_3-0-0_108074_2019-01-04_final
2019-01-04 11:03:05	ga_ls7e_ard_3-0-0_108075_2019-01-04_final
2019-01-04 11:03:29	ga_ls7e_ard_3-0-0_108076_2019-01-04_final
2019-01-04 11:03:53	ga_ls7e_ard_3-0-0_108077_2019-01-04_final
2019-01-04 11:04:17	ga_ls7e_ard_3-0-0_108078_2019-01-04_final
2019-01-04 11:04:41	ga_ls7e_ard_3-0-0_108079_2019-01-04_final
2019-01-04 11:05:05	ga_ls7e_ard_3-0-0_108080_2019-01-04_final
2019-01-04 11:05:52	ga_ls7e_ard_3-0-0_108082_2019-01-04_final
2019-01-04 11:06:16	ga_ls7e_ard_3-0-0_108083_2019-01-04_final
2019-01-04 11:06:40	ga_ls7e_ard_3-0-0_108084_2019-01-04_final
2019-01-05 10:04:14	ga_ls7e_ard_3-0-0_099067_2019-01-05_final
2019-01-05 10:04:38	ga_ls7e_ard_3-0-0_099068_2019-01-05_final
2019-01-05 10:05:02	ga_ls7e_ard_3-0-0_099069_2019-01-05_final
2019-01-05 10:05:26	ga_ls7e_ard_3-0-0_099070_2019-01-05_final
2019-01-05 10:05:50	ga_ls7e_ard_3-0-0_099071_2019-01-05_final
2019-01-05 10:06:14	ga_ls7e_ard_3-0-0_099072_2019-01-05_final
2019-01-05 10:06:38	ga_ls7e_ard_3-0-0_099073_2019-01-05_final
2019-01-05 10:07:02	ga_ls7e_ard_3-0-0_099074_2019-01-05_final
2019-01-05 10:07:26	ga_ls7e_ard_3-0-0_099075_2019-01-05_final
2019-01-05 10:07:50	ga_ls7e_ard_3-0-0_099076_2019-01-05_final
2019-01-05 10:08:14	ga_ls7e_ard_3-0-0_099077_2019-01-05_final
2019-01-05 10:08:38	ga_ls7e_ard_3-0-0_099078_2019-01-05_final
2019-01-05 10:09:01	ga_ls7e_ard_3-0-0_099079_2019-01-05_final
2019-01-05 10:09:25	ga_ls7e_ard_3-0-0_099080_2019-01-05_final
2019-01-05 10:09:49	ga_ls7e_ard_3-0-0_099081_2019-01-05_final
2019-01-05 10:10:13	ga_ls7e_ard_3-0-0_099082_2019-01-05_final
2019-01-05 10:10:37	ga_ls7e_ard_3-0-0_099083_2019-01-05_final
2019-01-05 10:11:01	ga_ls7e_ard_3-0-0_099084_2019-01-05_final
2019-01-05 11:45:55	ga_ls7e_ard_3-0-0_115074_2019-01-05_final
2019-01-05 11:46:19	ga_ls7e_ard_3-0-0_115075_2019-01-05_final
2019-01-05 11:46:43	ga_ls7e_ard_3-0-0_115076_2019-01-05_final
2019-01-05 11:47:07	ga_ls7e_ard_3-1-0_115077_2019-01-05_final
2019-01-05 11:47:54	ga_ls7e_ard_3-0-0_115079_2019-01-05_final
2019-01-06 09:12:35	ga_ls7e_ard_3-0-0_090077_2019-01-05_final
2019-01-06 09:12:58	ga_ls7e_ard_3-0-0_090078_2019-01-05_final
2019-01-06 09:13:22	ga_ls7e_ard_3-0-0_090079_2019-01-05_final
2019-01-06 09:13:46	ga_ls7e_ard_3-0-0_090080_2019-01-05_final
2019-01-06 09:14:10	ga_ls7e_ard_3-0-0_090081_2019-01-05_final
2019-01-06 09:14:34	ga_ls7e_ard_3-1-0_090082_2019-01-05_final
2019-01-06 09:15:22	ga_ls7e_ard_3-0-0_090084_2019-01-05_final
2019-01-06 09:15:46	ga_ls7e_ard_3-1-0_090085_2019-01-05_final
2019-01-06 09:16:10	ga_ls7e_ard_3-0-0_090086_2019-01-05_final
2019-01-06 09:16:58	ga_ls7e_ard_3-0-0_090088_2019-01-05_final
2019-01-06 09:17:22	ga_ls7e_ard_3-0-0_090089_2019-01-05_final
2019-01-06 09:17:46	ga_ls7e_ard_3-0-0_090090_2019-01-05_final
2019-01-06 10:48:16	ga_ls7e_ard_3-0-0_106069_2019-01-06_final
2019-01-06 10:48:40	ga_ls7e_ard_3-0-0_106070_2019-01-06_final
2019-01-06 10:49:04	ga_ls7e_ard_3-0-0_106071_2019-01-06_final
2019-01-06 10:49:28	ga_ls7e_ard_3-0-0_106072_2019-01-06_final
2019-01-06 10:49:52	ga_ls7e_ard_3-0-0_106073_2019-01-06_final
2019-01-06 10:50:16	ga_ls7e_ard_3-0-0_106074_2019-01-06_final
2019-01-06 10:50:39	ga_ls7e_ard_3-1-0_106075_2019-01-06_final
2019-01-06 10:51:03	ga_ls7e_ard_3-0-0_106076_2019-01-06_final
2019-01-06 10:51:27	ga_ls7e_ard_3-0-0_106077_2019-01-06_final
2019-01-06 10:51:51	ga_ls7e_ard_3-0-0_106078_2019-01-06_final
2019-01-06 10:52:15	ga_ls7e_ard_3-0-0_106079_2019-01-06_final
2019-01-06 10:52:39	ga_ls7e_ard_3-0-0_106080_2019-01-06_final
2019-01-06 10:53:03	ga_ls7e_ard_3-0-0_106081_2019-01-06_final
2019-01-06 10:53:27	ga_ls7e_ard_3-0-0_106082_2019-01-06_final
2019-01-06 10:53:51	ga_ls7e_ard_3-0-0_106083_2019-01-06_final
2019-01-07 09:52:37	ga_ls7e_ard_3-0-0_097069_2019-01-07_final
2019-01-07 09:53:01	ga_ls7e_ard_3-0-0_097070_2019-01-07_final
2019-01-07 09:53:25	ga_ls7e_ard_3-0-0_097071_2019-01-07_final
2019-01-07 09:53:49	ga_ls7e_ard_3-0-0_097072_2019-01-07_final
2019-01-07 09:54:13	ga_ls7e_ard_3-0-0_097073_2019-01-07_final
2019-01-07 09:54:36	ga_ls7e_ard_3-0-0_097074_2019-01-07_final
2019-01-07 09:55:00	ga_ls7e_ard_3-0-0_097075_2019-01-07_final
2019-01-07 09:55:24	ga_ls7e_ard_3-1-0_097076_2019-01-07_final
2019-01-07 09:55:48	ga_ls7e_ard_3-0-0_097077_2019-01-07_final
2019-01-07 09:56:12	ga_ls7e_ard_3-0-0_097078_2019-01-07_final
2019-01-07 09:56:36	ga_ls7e_ard_3-0-0_097079_2019-01-07_final
2019-01-07 09:57:00	ga_ls7e_ard_3-0-0_097080_2019-01-07_final
2019-01-07 09:57:24	ga_ls7e_ard_3-0-0_097081_2019-01-07_final
2019-01-07 09:57:48	ga_ls7e_ard_3-0-0_097082_2019-01-07_final
2019-01-07 09:58:12	ga_ls7e_ard_3-0-0_097083_2019-01-07_final
2019-01-07 09:58:36	ga_ls7e_ard_3-0-0_097084_2019-01-07_final
2019-01-07 09:59:00	ga_ls7e_ard_3-0-0_097085_2019-01-07_final
2019-01-22 09:15:15	ga_ls7e_ard_3-1-0_090085_2019-01-21_final
2019-01-22 09:15:39	ga_ls7e_ard_3-0-0_090086_2019-01-21_final
2019-01-22 09:16:27	ga_ls7e_ard_3-0-0_090088_2019-01-21_final
2019-01-22 10:50:09	ga_ls7e_ard_3-1-0_106075_2019-01-22_final
2019-01-22 10:50:57	ga_ls7e_ard_3-1-0_106077_2019-01-22_final
2019-01-23 09:54:30	ga_ls7e_ard_3-1-0_097075_2019-01-23_final

Return to ga_ls7e_ard_3 across January 2019 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

2019

January

All days