Search ga_ls7e_ard_3 across October 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
2019-10-06 09:42:33	ga_ls7e_ard_3-1-0_097069_2019-10-06_final
2019-10-06 09:42:57	ga_ls7e_ard_3-1-0_097070_2019-10-06_final
2019-10-06 09:43:21	ga_ls7e_ard_3-1-0_097071_2019-10-06_final
2019-10-06 09:43:45	ga_ls7e_ard_3-1-0_097072_2019-10-06_final
2019-10-06 09:44:09	ga_ls7e_ard_3-1-0_097073_2019-10-06_final
2019-10-06 09:44:33	ga_ls7e_ard_3-1-0_097074_2019-10-06_final
2019-10-06 09:44:57	ga_ls7e_ard_3-1-0_097075_2019-10-06_final
2019-10-06 09:45:45	ga_ls7e_ard_3-1-0_097077_2019-10-06_final
2019-10-06 09:46:09	ga_ls7e_ard_3-1-0_097078_2019-10-06_final
2019-10-06 09:46:33	ga_ls7e_ard_3-1-0_097079_2019-10-06_final
2019-10-06 09:46:57	ga_ls7e_ard_3-1-0_097080_2019-10-06_final
2019-10-06 09:48:09	ga_ls7e_ard_3-1-0_097083_2019-10-06_final
2019-10-06 09:48:32	ga_ls7e_ard_3-1-0_097084_2019-10-06_final
2019-10-06 11:20:38	ga_ls7e_ard_3-1-0_113067_2019-10-06_final
2019-10-06 11:23:26	ga_ls7e_ard_3-1-0_113074_2019-10-06_final
2019-10-06 11:23:50	ga_ls7e_ard_3-1-0_113075_2019-10-06_final
2019-10-06 11:24:14	ga_ls7e_ard_3-1-0_113076_2019-10-06_final
2019-10-06 11:24:38	ga_ls7e_ard_3-1-0_113077_2019-10-06_final
2019-10-06 11:25:02	ga_ls7e_ard_3-1-0_113078_2019-10-06_final
2019-10-06 11:25:26	ga_ls7e_ard_3-1-0_113079_2019-10-06_final
2019-10-06 11:25:50	ga_ls7e_ard_3-1-0_113080_2019-10-06_final
2019-10-06 11:26:14	ga_ls7e_ard_3-1-0_113081_2019-10-06_final
2019-10-06 11:26:37	ga_ls7e_ard_3-1-0_113082_2019-10-06_final
2019-10-06 11:27:25	ga_ls7e_ard_3-1-0_113084_2019-10-06_final
2019-10-07 10:25:23	ga_ls7e_ard_3-1-0_104068_2019-10-07_final
2019-10-07 10:25:47	ga_ls7e_ard_3-1-0_104069_2019-10-07_final
2019-10-07 10:26:10	ga_ls7e_ard_3-1-0_104070_2019-10-07_final
2019-10-07 10:26:34	ga_ls7e_ard_3-1-0_104071_2019-10-07_final
2019-10-07 10:26:58	ga_ls7e_ard_3-1-0_104072_2019-10-07_final
2019-10-07 10:27:22	ga_ls7e_ard_3-1-0_104073_2019-10-07_final
2019-10-07 10:27:46	ga_ls7e_ard_3-1-0_104074_2019-10-07_final
2019-10-07 10:28:10	ga_ls7e_ard_3-1-0_104075_2019-10-07_final
2019-10-07 10:28:34	ga_ls7e_ard_3-1-0_104076_2019-10-07_final
2019-10-07 10:28:58	ga_ls7e_ard_3-1-0_104077_2019-10-07_final
2019-10-07 10:29:22	ga_ls7e_ard_3-1-0_104078_2019-10-07_final
2019-10-07 10:29:46	ga_ls7e_ard_3-1-0_104079_2019-10-07_final
2019-10-07 10:30:10	ga_ls7e_ard_3-1-0_104080_2019-10-07_final
2019-10-07 10:30:34	ga_ls7e_ard_3-1-0_104081_2019-10-07_final
2019-10-07 10:30:58	ga_ls7e_ard_3-1-0_104082_2019-10-07_final
2019-10-26 09:18:59	ga_ls7e_ard_3-1-0_093074_2019-10-25_final
2019-10-26 09:19:23	ga_ls7e_ard_3-1-0_093075_2019-10-25_final
2019-10-26 09:19:47	ga_ls7e_ard_3-1-0_093076_2019-10-25_final
2019-10-26 09:20:11	ga_ls7e_ard_3-1-0_093077_2019-10-25_final
2019-10-26 09:20:35	ga_ls7e_ard_3-1-0_093078_2019-10-25_final
2019-10-26 09:20:59	ga_ls7e_ard_3-1-0_093079_2019-10-25_final
2019-10-26 09:21:23	ga_ls7e_ard_3-1-0_093080_2019-10-25_final
2019-10-26 09:21:47	ga_ls7e_ard_3-1-0_093081_2019-10-25_final
2019-10-26 09:22:10	ga_ls7e_ard_3-1-0_093082_2019-10-25_final
2019-10-26 09:22:34	ga_ls7e_ard_3-1-0_093083_2019-10-25_final
2019-10-26 09:22:58	ga_ls7e_ard_3-1-0_093084_2019-10-25_final
2019-10-26 09:23:22	ga_ls7e_ard_3-1-0_093085_2019-10-25_final
2019-10-26 09:23:46	ga_ls7e_ard_3-1-0_093086_2019-10-25_final
2019-10-26 10:56:16	ga_ls7e_ard_3-1-0_109070_2019-10-26_final
2019-10-26 10:56:40	ga_ls7e_ard_3-1-0_109071_2019-10-26_final
2019-10-26 10:57:04	ga_ls7e_ard_3-1-0_109072_2019-10-26_final
2019-10-26 10:57:28	ga_ls7e_ard_3-1-0_109073_2019-10-26_final
2019-10-26 10:57:52	ga_ls7e_ard_3-1-0_109074_2019-10-26_final
2019-10-26 10:58:16	ga_ls7e_ard_3-1-0_109075_2019-10-26_final
2019-10-26 10:58:40	ga_ls7e_ard_3-1-0_109076_2019-10-26_final
2019-10-26 10:59:04	ga_ls7e_ard_3-1-0_109077_2019-10-26_final
2019-10-26 10:59:28	ga_ls7e_ard_3-1-0_109078_2019-10-26_final
2019-10-26 10:59:51	ga_ls7e_ard_3-1-0_109079_2019-10-26_final
2019-10-26 11:00:15	ga_ls7e_ard_3-1-0_109080_2019-10-26_final
2019-10-26 11:00:39	ga_ls7e_ard_3-1-0_109081_2019-10-26_final
2019-10-26 11:01:03	ga_ls7e_ard_3-1-0_109082_2019-10-26_final
2019-10-26 11:01:27	ga_ls7e_ard_3-1-0_109083_2019-10-26_final
2019-10-27 10:01:00	ga_ls7e_ard_3-1-0_100071_2019-10-27_final
2019-10-27 10:01:24	ga_ls7e_ard_3-1-0_100072_2019-10-27_final
2019-10-27 10:01:48	ga_ls7e_ard_3-1-0_100073_2019-10-27_final
2019-10-27 10:02:12	ga_ls7e_ard_3-1-0_100074_2019-10-27_final
2019-10-27 10:03:00	ga_ls7e_ard_3-1-0_100076_2019-10-27_final
2019-10-27 10:03:48	ga_ls7e_ard_3-1-0_100078_2019-10-27_final
2019-10-27 10:04:12	ga_ls7e_ard_3-1-0_100079_2019-10-27_final
2019-10-27 10:04:36	ga_ls7e_ard_3-1-0_100080_2019-10-27_final
2019-10-27 10:05:00	ga_ls7e_ard_3-1-0_100081_2019-10-27_final
2019-10-27 10:05:23	ga_ls7e_ard_3-1-0_100082_2019-10-27_final
2019-10-27 10:05:47	ga_ls7e_ard_3-1-0_100083_2019-10-27_final
2019-10-27 10:06:11	ga_ls7e_ard_3-1-0_100084_2019-10-27_final
2019-10-28 09:07:20	ga_ls7e_ard_3-1-0_091076_2019-10-27_final
2019-10-28 09:07:44	ga_ls7e_ard_3-1-0_091077_2019-10-27_final
2019-10-28 09:08:08	ga_ls7e_ard_3-1-0_091078_2019-10-27_final
2019-10-28 09:08:32	ga_ls7e_ard_3-1-0_091079_2019-10-27_final
2019-10-28 09:08:56	ga_ls7e_ard_3-1-0_091080_2019-10-27_final
2019-10-28 09:09:20	ga_ls7e_ard_3-1-0_091081_2019-10-27_final
2019-10-28 09:09:44	ga_ls7e_ard_3-1-0_091082_2019-10-27_final
2019-10-28 09:10:08	ga_ls7e_ard_3-1-0_091083_2019-10-27_final
2019-10-28 09:10:32	ga_ls7e_ard_3-1-0_091084_2019-10-27_final
2019-10-28 09:10:55	ga_ls7e_ard_3-1-0_091085_2019-10-27_final
2019-10-28 09:11:19	ga_ls7e_ard_3-1-0_091086_2019-10-27_final
2019-10-28 09:11:43	ga_ls7e_ard_3-1-0_091087_2019-10-27_final
2019-10-28 09:12:07	ga_ls7e_ard_3-1-0_091088_2019-10-27_final
2019-10-28 09:12:31	ga_ls7e_ard_3-1-0_091089_2019-10-27_final
2019-10-28 09:12:55	ga_ls7e_ard_3-1-0_091090_2019-10-27_final
2019-10-28 10:43:49	ga_ls7e_ard_3-1-0_107070_2019-10-28_final
2019-10-28 10:44:13	ga_ls7e_ard_3-1-0_107071_2019-10-28_final
2019-10-28 10:44:37	ga_ls7e_ard_3-1-0_107072_2019-10-28_final
2019-10-28 10:45:01	ga_ls7e_ard_3-1-0_107073_2019-10-28_final
2019-10-28 10:45:25	ga_ls7e_ard_3-1-0_107074_2019-10-28_final
2019-10-28 10:45:49	ga_ls7e_ard_3-1-0_107075_2019-10-28_final
2019-10-28 10:46:13	ga_ls7e_ard_3-1-0_107076_2019-10-28_final

Return to ga_ls7e_ard_3 across October 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

October

All days