Search ga_ls7e_ard_3 across February 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
2019-02-13 10:10:48	ga_ls7e_ard_3-0-0_100071_2019-02-13_final
2019-02-13 10:11:12	ga_ls7e_ard_3-0-0_100072_2019-02-13_final
2019-02-13 10:11:36	ga_ls7e_ard_3-0-0_100073_2019-02-13_final
2019-02-13 10:12:00	ga_ls7e_ard_3-0-0_100074_2019-02-13_final
2019-02-13 10:12:24	ga_ls7e_ard_3-0-0_100075_2019-02-13_final
2019-02-13 10:12:48	ga_ls7e_ard_3-0-0_100076_2019-02-13_final
2019-02-13 10:13:12	ga_ls7e_ard_3-0-0_100077_2019-02-13_final
2019-02-13 10:13:36	ga_ls7e_ard_3-0-0_100078_2019-02-13_final
2019-02-13 10:13:59	ga_ls7e_ard_3-0-0_100079_2019-02-13_final
2019-02-13 10:14:23	ga_ls7e_ard_3-0-0_100080_2019-02-13_final
2019-02-13 10:14:47	ga_ls7e_ard_3-0-0_100081_2019-02-13_final
2019-02-13 10:15:11	ga_ls7e_ard_3-0-0_100082_2019-02-13_final
2019-02-13 10:15:35	ga_ls7e_ard_3-0-0_100083_2019-02-13_final
2019-02-14 09:17:09	ga_ls7e_ard_3-0-0_091076_2019-02-13_final
2019-02-14 09:17:33	ga_ls7e_ard_3-0-0_091077_2019-02-13_final
2019-02-14 09:17:56	ga_ls7e_ard_3-0-0_091078_2019-02-13_final
2019-02-14 09:18:20	ga_ls7e_ard_3-0-0_091079_2019-02-13_final
2019-02-14 09:18:44	ga_ls7e_ard_3-0-0_091080_2019-02-13_final
2019-02-14 09:19:08	ga_ls7e_ard_3-0-0_091081_2019-02-13_final
2019-02-14 09:19:32	ga_ls7e_ard_3-0-0_091082_2019-02-13_final
2019-02-14 09:19:56	ga_ls7e_ard_3-0-0_091083_2019-02-13_final
2019-02-14 09:20:20	ga_ls7e_ard_3-0-0_091084_2019-02-13_final
2019-02-14 09:20:44	ga_ls7e_ard_3-0-0_091085_2019-02-13_final
2019-02-14 09:21:08	ga_ls7e_ard_3-1-0_091086_2019-02-13_final
2019-02-14 09:21:32	ga_ls7e_ard_3-0-0_091087_2019-02-13_final
2019-02-14 09:21:56	ga_ls7e_ard_3-0-0_091088_2019-02-13_final
2019-02-14 09:22:20	ga_ls7e_ard_3-0-0_091089_2019-02-13_final
2019-02-14 10:54:02	ga_ls7e_ard_3-0-0_107071_2019-02-14_final
2019-02-14 10:54:26	ga_ls7e_ard_3-0-0_107072_2019-02-14_final
2019-02-14 10:54:50	ga_ls7e_ard_3-0-0_107073_2019-02-14_final
2019-02-14 10:55:14	ga_ls7e_ard_3-0-0_107074_2019-02-14_final
2019-02-14 10:55:38	ga_ls7e_ard_3-0-0_107075_2019-02-14_final
2019-02-14 10:56:01	ga_ls7e_ard_3-0-0_107076_2019-02-14_final
2019-02-14 10:56:25	ga_ls7e_ard_3-0-0_107077_2019-02-14_final
2019-02-14 10:56:49	ga_ls7e_ard_3-0-0_107078_2019-02-14_final
2019-02-14 10:57:13	ga_ls7e_ard_3-0-0_107079_2019-02-14_final
2019-02-14 10:57:37	ga_ls7e_ard_3-0-0_107080_2019-02-14_final
2019-02-14 10:58:01	ga_ls7e_ard_3-0-0_107081_2019-02-14_final
2019-02-14 10:58:25	ga_ls7e_ard_3-0-0_107082_2019-02-14_final
2019-02-14 10:58:49	ga_ls7e_ard_3-0-0_107083_2019-02-14_final
2019-02-14 10:59:13	ga_ls7e_ard_3-0-0_107084_2019-02-14_final
2019-02-15 09:56:47	ga_ls7e_ard_3-0-0_098067_2019-02-15_final
2019-02-15 09:57:11	ga_ls7e_ard_3-0-0_098068_2019-02-15_final
2019-02-15 09:57:35	ga_ls7e_ard_3-0-0_098069_2019-02-15_final
2019-02-15 09:57:59	ga_ls7e_ard_3-0-0_098070_2019-02-15_final
2019-02-15 09:58:23	ga_ls7e_ard_3-0-0_098071_2019-02-15_final
2019-02-15 09:58:47	ga_ls7e_ard_3-0-0_098072_2019-02-15_final
2019-02-15 09:59:11	ga_ls7e_ard_3-0-0_098073_2019-02-15_final
2019-02-15 09:59:35	ga_ls7e_ard_3-0-0_098074_2019-02-15_final
2019-02-15 09:59:58	ga_ls7e_ard_3-0-0_098075_2019-02-15_final
2019-02-15 10:00:22	ga_ls7e_ard_3-0-0_098076_2019-02-15_final
2019-02-15 10:00:46	ga_ls7e_ard_3-0-0_098077_2019-02-15_final
2019-02-15 10:01:10	ga_ls7e_ard_3-0-0_098078_2019-02-15_final
2019-02-15 10:01:34	ga_ls7e_ard_3-0-0_098079_2019-02-15_final
2019-02-15 10:01:58	ga_ls7e_ard_3-1-0_098080_2019-02-15_final
2019-02-15 10:02:22	ga_ls7e_ard_3-0-0_098081_2019-02-15_final
2019-02-15 10:02:46	ga_ls7e_ard_3-0-0_098082_2019-02-15_final
2019-02-15 10:03:10	ga_ls7e_ard_3-0-0_098083_2019-02-15_final
2019-02-15 10:03:34	ga_ls7e_ard_3-0-0_098084_2019-02-15_final
2019-02-15 10:03:58	ga_ls7e_ard_3-0-0_098085_2019-02-15_final
2019-02-15 11:35:40	ga_ls7e_ard_3-0-0_114067_2019-02-15_final
2019-02-15 11:38:28	ga_ls7e_ard_3-0-0_114074_2019-02-15_final
2019-02-15 11:38:51	ga_ls7e_ard_3-0-0_114075_2019-02-15_final
2019-02-15 11:39:15	ga_ls7e_ard_3-0-0_114076_2019-02-15_final
2019-02-15 11:39:39	ga_ls7e_ard_3-0-0_114077_2019-02-15_final
2019-02-15 11:40:03	ga_ls7e_ard_3-0-0_114078_2019-02-15_final
2019-02-15 11:40:27	ga_ls7e_ard_3-0-0_114079_2019-02-15_final
2019-02-15 11:40:51	ga_ls7e_ard_3-0-0_114080_2019-02-15_final
2019-02-15 11:41:15	ga_ls7e_ard_3-0-0_114081_2019-02-15_final
2019-02-16 09:05:31	ga_ls7e_ard_3-1-0_089078_2019-02-15_final
2019-02-16 09:05:55	ga_ls7e_ard_3-1-0_089079_2019-02-15_final
2019-02-16 09:06:43	ga_ls7e_ard_3-1-0_089081_2019-02-15_final
2019-02-16 09:07:07	ga_ls7e_ard_3-1-0_089082_2019-02-15_final
2019-02-16 09:07:31	ga_ls7e_ard_3-1-0_089083_2019-02-15_final
2019-02-16 09:07:55	ga_ls7e_ard_3-1-0_089084_2019-02-15_final
2019-02-16 09:08:19	ga_ls7e_ard_3-1-0_089085_2019-02-15_final
2019-02-16 09:08:43	ga_ls7e_ard_3-1-0_089086_2019-02-15_final
2019-02-16 10:40:25	ga_ls7e_ard_3-1-0_105068_2019-02-16_final
2019-02-16 10:40:49	ga_ls7e_ard_3-1-0_105069_2019-02-16_final
2019-02-16 10:41:37	ga_ls7e_ard_3-1-0_105071_2019-02-16_final
2019-02-16 10:42:01	ga_ls7e_ard_3-1-0_105072_2019-02-16_final
2019-02-16 10:42:24	ga_ls7e_ard_3-1-0_105073_2019-02-16_final
2019-02-16 10:42:48	ga_ls7e_ard_3-1-0_105074_2019-02-16_final
2019-02-16 10:43:12	ga_ls7e_ard_3-1-0_105075_2019-02-16_final
2019-02-16 10:43:36	ga_ls7e_ard_3-1-0_105076_2019-02-16_final
2019-02-16 10:44:00	ga_ls7e_ard_3-1-0_105077_2019-02-16_final
2019-02-16 10:44:24	ga_ls7e_ard_3-1-0_105078_2019-02-16_final
2019-02-16 10:44:48	ga_ls7e_ard_3-1-0_105079_2019-02-16_final
2019-02-16 10:45:12	ga_ls7e_ard_3-1-0_105080_2019-02-16_final
2019-02-16 10:45:36	ga_ls7e_ard_3-1-0_105081_2019-02-16_final
2019-02-16 10:46:00	ga_ls7e_ard_3-1-0_105082_2019-02-16_final
2019-02-16 10:46:24	ga_ls7e_ard_3-1-0_105083_2019-02-16_final
2019-02-17 09:44:22	ga_ls7e_ard_3-1-0_096067_2019-02-17_final
2019-02-23 09:11:05	ga_ls7e_ard_3-1-0_090077_2019-02-22_final
2019-02-23 09:11:29	ga_ls7e_ard_3-1-0_090078_2019-02-22_final
2019-02-23 09:11:53	ga_ls7e_ard_3-1-0_090079_2019-02-22_final
2019-02-23 09:12:17	ga_ls7e_ard_3-1-0_090080_2019-02-22_final
2019-02-23 09:12:41	ga_ls7e_ard_3-1-0_090081_2019-02-22_final
2019-02-23 09:13:05	ga_ls7e_ard_3-1-0_090082_2019-02-22_final
2019-02-23 09:13:29	ga_ls7e_ard_3-1-0_090083_2019-02-22_final

Return to ga_ls7e_ard_3 across February 2019 overview

ga_ls7e_ard_3: Geoscience Australia Landsat 7 Enhanced Thematic Mapper Plus Analysis Ready Data Collection 3

Baseline satellite data

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

2019

February

All days