Search ga_ls7e_ard_3 across February 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
2018-02-02 09:30:44	ga_ls7e_ard_3-0-0_092075_2018-02-02_final
2018-02-02 09:34:20	ga_ls7e_ard_3-0-0_092084_2018-02-02_final
2018-02-02 09:34:44	ga_ls7e_ard_3-0-0_092085_2018-02-02_final
2018-02-02 09:35:08	ga_ls7e_ard_3-0-0_092086_2018-02-02_final
2018-02-02 09:35:32	ga_ls7e_ard_3-0-0_092087_2018-02-02_final
2018-02-02 09:35:55	ga_ls7e_ard_3-0-0_092088_2018-02-02_final
2018-02-02 09:36:19	ga_ls7e_ard_3-0-0_092089_2018-02-02_final
2018-02-03 10:11:35	ga_ls7e_ard_3-0-0_099069_2018-02-03_final
2018-02-04 09:19:56	ga_ls7e_ard_3-0-0_090079_2018-02-03_final
2018-02-04 09:20:20	ga_ls7e_ard_3-0-0_090080_2018-02-03_final
2018-02-04 09:20:44	ga_ls7e_ard_3-0-0_090081_2018-02-03_final
2018-02-04 09:21:08	ga_ls7e_ard_3-0-0_090082_2018-02-03_final
2018-02-04 09:21:32	ga_ls7e_ard_3-0-0_090083_2018-02-03_final
2018-02-04 09:21:56	ga_ls7e_ard_3-0-0_090084_2018-02-03_final
2018-02-04 09:22:20	ga_ls7e_ard_3-1-0_090085_2018-02-03_final
2018-02-04 09:22:44	ga_ls7e_ard_3-0-0_090086_2018-02-03_final
2018-02-09 09:41:36	ga_ls7e_ard_3-0-0_093087_2018-02-09_final
2018-02-09 09:42:00	ga_ls7e_ard_3-0-0_093088_2018-02-09_final
2018-02-09 11:13:42	ga_ls7e_ard_3-0-0_109070_2018-02-09_final
2018-02-09 11:14:06	ga_ls7e_ard_3-0-0_109071_2018-02-09_final
2018-02-09 11:14:30	ga_ls7e_ard_3-0-0_109072_2018-02-09_final
2018-02-09 11:15:18	ga_ls7e_ard_3-0-0_109074_2018-02-09_final
2018-02-09 11:15:42	ga_ls7e_ard_3-0-0_109075_2018-02-09_final
2018-02-09 11:16:06	ga_ls7e_ard_3-0-0_109076_2018-02-09_final
2018-02-09 11:16:30	ga_ls7e_ard_3-0-0_109077_2018-02-09_final
2018-02-09 11:16:54	ga_ls7e_ard_3-0-0_109078_2018-02-09_final
2018-02-09 11:17:17	ga_ls7e_ard_3-0-0_109079_2018-02-09_final
2018-02-09 11:17:41	ga_ls7e_ard_3-0-0_109080_2018-02-09_final
2018-02-09 11:18:05	ga_ls7e_ard_3-1-0_109081_2018-02-09_final
2018-02-09 11:18:29	ga_ls7e_ard_3-0-0_109082_2018-02-09_final
2018-02-09 11:18:53	ga_ls7e_ard_3-0-0_109083_2018-02-09_final
2018-02-09 11:19:17	ga_ls7e_ard_3-0-0_109084_2018-02-09_final
2018-02-10 10:18:52	ga_ls7e_ard_3-0-0_100072_2018-02-10_final
2018-02-10 10:19:16	ga_ls7e_ard_3-0-0_100073_2018-02-10_final
2018-02-10 10:19:40	ga_ls7e_ard_3-0-0_100074_2018-02-10_final
2018-02-10 10:20:04	ga_ls7e_ard_3-0-0_100075_2018-02-10_final
2018-02-10 10:20:28	ga_ls7e_ard_3-0-0_100076_2018-02-10_final
2018-02-10 10:20:52	ga_ls7e_ard_3-0-0_100077_2018-02-10_final
2018-02-10 10:21:16	ga_ls7e_ard_3-0-0_100078_2018-02-10_final
2018-02-10 10:21:40	ga_ls7e_ard_3-0-0_100079_2018-02-10_final
2018-02-10 10:22:04	ga_ls7e_ard_3-0-0_100080_2018-02-10_final
2018-02-10 10:22:28	ga_ls7e_ard_3-0-0_100081_2018-02-10_final
2018-02-11 09:24:50	ga_ls7e_ard_3-0-0_091076_2018-02-10_final
2018-02-11 09:25:14	ga_ls7e_ard_3-0-0_091077_2018-02-10_final
2018-02-11 09:25:38	ga_ls7e_ard_3-0-0_091078_2018-02-10_final
2018-02-11 09:26:02	ga_ls7e_ard_3-0-0_091079_2018-02-10_final
2018-02-11 09:26:26	ga_ls7e_ard_3-0-0_091080_2018-02-10_final
2018-02-11 09:26:50	ga_ls7e_ard_3-0-0_091081_2018-02-10_final
2018-02-11 09:27:38	ga_ls7e_ard_3-0-0_091083_2018-02-10_final
2018-02-11 09:28:02	ga_ls7e_ard_3-0-0_091084_2018-02-10_final
2018-02-11 09:28:26	ga_ls7e_ard_3-0-0_091085_2018-02-10_final
2018-02-11 09:28:49	ga_ls7e_ard_3-1-0_091086_2018-02-10_final
2018-02-11 09:29:13	ga_ls7e_ard_3-0-0_091087_2018-02-10_final
2018-02-11 09:29:37	ga_ls7e_ard_3-0-0_091088_2018-02-10_final
2018-02-11 09:30:01	ga_ls7e_ard_3-1-0_091089_2018-02-11_final
2018-02-11 11:01:19	ga_ls7e_ard_3-0-0_107070_2018-02-11_final
2018-02-11 11:01:43	ga_ls7e_ard_3-0-0_107071_2018-02-11_final
2018-02-11 11:02:07	ga_ls7e_ard_3-0-0_107072_2018-02-11_final
2018-02-11 11:02:31	ga_ls7e_ard_3-0-0_107073_2018-02-11_final
2018-02-11 11:02:55	ga_ls7e_ard_3-0-0_107074_2018-02-11_final
2018-02-11 11:03:19	ga_ls7e_ard_3-0-0_107075_2018-02-11_final
2018-02-11 11:03:43	ga_ls7e_ard_3-0-0_107076_2018-02-11_final
2018-02-11 11:04:07	ga_ls7e_ard_3-0-0_107077_2018-02-11_final
2018-02-11 11:04:31	ga_ls7e_ard_3-0-0_107078_2018-02-11_final
2018-02-11 11:04:55	ga_ls7e_ard_3-0-0_107079_2018-02-11_final
2018-02-11 11:05:43	ga_ls7e_ard_3-0-0_107081_2018-02-11_final
2018-02-11 11:06:07	ga_ls7e_ard_3-0-0_107082_2018-02-11_final
2018-02-11 11:06:31	ga_ls7e_ard_3-0-0_107083_2018-02-11_final
2018-02-11 11:06:55	ga_ls7e_ard_3-0-0_107084_2018-02-11_final
2018-02-12 10:04:30	ga_ls7e_ard_3-0-0_098067_2018-02-12_final
2018-02-12 10:04:54	ga_ls7e_ard_3-0-0_098068_2018-02-12_final
2018-02-12 10:05:18	ga_ls7e_ard_3-0-0_098069_2018-02-12_final
2018-02-12 10:05:41	ga_ls7e_ard_3-0-0_098070_2018-02-12_final
2018-02-12 10:06:05	ga_ls7e_ard_3-0-0_098071_2018-02-12_final
2018-02-12 10:06:29	ga_ls7e_ard_3-0-0_098072_2018-02-12_final
2018-02-12 10:06:53	ga_ls7e_ard_3-0-0_098073_2018-02-12_final
2018-02-12 10:07:17	ga_ls7e_ard_3-0-0_098074_2018-02-12_final
2018-02-12 10:07:41	ga_ls7e_ard_3-0-0_098075_2018-02-12_final
2018-02-12 10:08:05	ga_ls7e_ard_3-0-0_098076_2018-02-12_final
2018-02-12 10:08:29	ga_ls7e_ard_3-0-0_098077_2018-02-12_final
2018-02-12 10:08:53	ga_ls7e_ard_3-0-0_098078_2018-02-12_final
2018-02-12 10:09:17	ga_ls7e_ard_3-0-0_098079_2018-02-12_final
2018-02-12 10:09:41	ga_ls7e_ard_3-0-0_098080_2018-02-12_final
2018-02-12 10:10:29	ga_ls7e_ard_3-0-0_098082_2018-02-12_final
2018-02-12 10:10:53	ga_ls7e_ard_3-0-0_098083_2018-02-12_final
2018-02-12 11:43:23	ga_ls7e_ard_3-0-0_114067_2018-02-12_final
2018-02-12 11:46:10	ga_ls7e_ard_3-0-0_114074_2018-02-12_final
2018-02-12 11:46:34	ga_ls7e_ard_3-0-0_114075_2018-02-12_final
2018-02-12 11:46:58	ga_ls7e_ard_3-0-0_114076_2018-02-12_final
2018-02-12 11:47:22	ga_ls7e_ard_3-0-0_114077_2018-02-12_final
2018-02-12 11:47:46	ga_ls7e_ard_3-0-0_114078_2018-02-12_final
2018-02-12 11:48:10	ga_ls7e_ard_3-0-0_114079_2018-02-12_final
2018-02-12 11:48:34	ga_ls7e_ard_3-0-0_114080_2018-02-12_final
2018-02-12 11:48:58	ga_ls7e_ard_3-0-0_114081_2018-02-12_final
2018-02-13 09:13:15	ga_ls7e_ard_3-0-0_089078_2018-02-12_final
2018-02-13 09:13:39	ga_ls7e_ard_3-0-0_089079_2018-02-12_final
2018-02-13 09:14:03	ga_ls7e_ard_3-0-0_089080_2018-02-12_final
2018-02-13 09:14:27	ga_ls7e_ard_3-1-0_089081_2018-02-12_final
2018-02-13 09:14:51	ga_ls7e_ard_3-0-0_089082_2018-02-12_final
2018-02-13 09:15:15	ga_ls7e_ard_3-0-0_089083_2018-02-12_final

Return to ga_ls7e_ard_3 across February 2018 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

2018

February

All days