Search ga_ls7e_ard_3 across July 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
2018-07-04 10:16:15	ga_ls7e_ard_3-0-0_100071_2018-07-04_final
2018-07-04 10:16:38	ga_ls7e_ard_3-0-0_100072_2018-07-04_final
2018-07-04 10:17:02	ga_ls7e_ard_3-0-0_100073_2018-07-04_final
2018-07-04 10:17:26	ga_ls7e_ard_3-0-0_100074_2018-07-04_final
2018-07-04 10:17:50	ga_ls7e_ard_3-0-0_100075_2018-07-04_final
2018-07-04 10:18:14	ga_ls7e_ard_3-0-0_100076_2018-07-04_final
2018-07-04 10:18:38	ga_ls7e_ard_3-0-0_100077_2018-07-04_final
2018-07-04 10:19:02	ga_ls7e_ard_3-0-0_100078_2018-07-04_final
2018-07-04 10:19:26	ga_ls7e_ard_3-0-0_100079_2018-07-04_final
2018-07-04 10:19:50	ga_ls7e_ard_3-0-0_100080_2018-07-04_final
2018-07-04 10:20:14	ga_ls7e_ard_3-1-0_100081_2018-07-04_final
2018-07-04 10:20:38	ga_ls7e_ard_3-0-0_100082_2018-07-04_final
2018-07-04 10:21:02	ga_ls7e_ard_3-0-0_100083_2018-07-04_final
2018-07-05 09:22:36	ga_ls7e_ard_3-0-0_091076_2018-07-04_final
2018-07-05 09:23:00	ga_ls7e_ard_3-0-0_091077_2018-07-04_final
2018-07-05 09:23:24	ga_ls7e_ard_3-0-0_091078_2018-07-04_final
2018-07-05 09:23:48	ga_ls7e_ard_3-0-0_091079_2018-07-04_final
2018-07-05 09:24:11	ga_ls7e_ard_3-0-0_091080_2018-07-04_final
2018-07-05 09:24:35	ga_ls7e_ard_3-0-0_091081_2018-07-04_final
2018-07-05 09:24:59	ga_ls7e_ard_3-0-0_091082_2018-07-04_final
2018-07-05 09:25:23	ga_ls7e_ard_3-0-0_091083_2018-07-04_final
2018-07-05 09:25:47	ga_ls7e_ard_3-0-0_091084_2018-07-04_final
2018-07-05 09:26:11	ga_ls7e_ard_3-0-0_091085_2018-07-04_final
2018-07-05 09:26:35	ga_ls7e_ard_3-1-0_091086_2018-07-04_final
2018-07-05 09:26:59	ga_ls7e_ard_3-0-0_091087_2018-07-04_final
2018-07-05 09:27:23	ga_ls7e_ard_3-0-0_091088_2018-07-04_final
2018-07-05 09:27:47	ga_ls7e_ard_3-0-0_091089_2018-07-04_final
2018-07-05 09:28:11	ga_ls7e_ard_3-0-0_091090_2018-07-04_final
2018-07-05 10:59:05	ga_ls7e_ard_3-0-0_107070_2018-07-05_final
2018-07-05 10:59:29	ga_ls7e_ard_3-0-0_107071_2018-07-05_final
2018-07-05 10:59:53	ga_ls7e_ard_3-0-0_107072_2018-07-05_final
2018-07-05 11:00:17	ga_ls7e_ard_3-0-0_107073_2018-07-05_final
2018-07-05 11:00:41	ga_ls7e_ard_3-0-0_107074_2018-07-05_final
2018-07-05 11:01:05	ga_ls7e_ard_3-0-0_107075_2018-07-05_final
2018-07-05 11:01:29	ga_ls7e_ard_3-0-0_107076_2018-07-05_final
2018-07-05 11:01:53	ga_ls7e_ard_3-0-0_107077_2018-07-05_final
2018-07-05 11:02:16	ga_ls7e_ard_3-0-0_107078_2018-07-05_final
2018-07-05 11:02:40	ga_ls7e_ard_3-0-0_107079_2018-07-05_final
2018-07-05 11:03:04	ga_ls7e_ard_3-0-0_107080_2018-07-05_final
2018-07-05 11:03:28	ga_ls7e_ard_3-0-0_107081_2018-07-05_final
2018-07-05 11:03:52	ga_ls7e_ard_3-0-0_107082_2018-07-05_final
2018-07-05 11:04:16	ga_ls7e_ard_3-0-0_107083_2018-07-05_final
2018-07-05 11:04:40	ga_ls7e_ard_3-0-0_107084_2018-07-05_final
2018-07-06 10:02:39	ga_ls7e_ard_3-0-0_098068_2018-07-06_final
2018-07-06 10:03:03	ga_ls7e_ard_3-0-0_098069_2018-07-06_final
2018-07-06 10:03:26	ga_ls7e_ard_3-0-0_098070_2018-07-06_final
2018-07-06 10:03:50	ga_ls7e_ard_3-0-0_098071_2018-07-06_final
2018-07-06 10:04:14	ga_ls7e_ard_3-1-0_098072_2018-07-06_final
2018-07-06 10:04:38	ga_ls7e_ard_3-1-0_098073_2018-07-06_final
2018-07-06 10:05:02	ga_ls7e_ard_3-0-0_098074_2018-07-06_final
2018-07-06 10:05:26	ga_ls7e_ard_3-1-0_098075_2018-07-06_final
2018-07-06 10:05:50	ga_ls7e_ard_3-1-0_098076_2018-07-06_final
2018-07-06 10:06:14	ga_ls7e_ard_3-1-0_098077_2018-07-06_final
2018-07-06 10:06:38	ga_ls7e_ard_3-1-0_098078_2018-07-06_final
2018-07-06 10:07:02	ga_ls7e_ard_3-1-0_098079_2018-07-06_final
2018-07-06 10:07:26	ga_ls7e_ard_3-1-0_098080_2018-07-06_final
2018-07-06 10:07:50	ga_ls7e_ard_3-0-0_098081_2018-07-06_final
2018-07-06 10:08:14	ga_ls7e_ard_3-1-0_098082_2018-07-06_final
2018-07-06 10:08:38	ga_ls7e_ard_3-0-0_098083_2018-07-06_final
2018-07-06 10:09:02	ga_ls7e_ard_3-0-0_098084_2018-07-06_final
2018-07-06 10:09:26	ga_ls7e_ard_3-0-0_098085_2018-07-06_final
2018-07-06 11:41:08	ga_ls7e_ard_3-0-0_114067_2018-07-06_final
2018-07-06 11:43:55	ga_ls7e_ard_3-0-0_114074_2018-07-06_final
2018-07-06 11:44:19	ga_ls7e_ard_3-0-0_114075_2018-07-06_final
2018-07-06 11:44:43	ga_ls7e_ard_3-0-0_114076_2018-07-06_final
2018-07-06 11:45:07	ga_ls7e_ard_3-0-0_114077_2018-07-06_final
2018-07-06 11:45:31	ga_ls7e_ard_3-0-0_114078_2018-07-06_final
2018-07-06 11:45:55	ga_ls7e_ard_3-0-0_114079_2018-07-06_final
2018-07-06 11:46:19	ga_ls7e_ard_3-0-0_114080_2018-07-06_final
2018-07-06 11:46:43	ga_ls7e_ard_3-0-0_114081_2018-07-06_final
2018-07-29 10:08:01	ga_ls7e_ard_3-0-0_099067_2018-07-29_final
2018-07-30 09:16:22	ga_ls7e_ard_3-0-0_090077_2018-07-29_final
2018-07-30 09:16:46	ga_ls7e_ard_3-0-0_090078_2018-07-29_final
2018-07-30 09:17:10	ga_ls7e_ard_3-0-0_090079_2018-07-29_final
2018-07-30 09:17:34	ga_ls7e_ard_3-0-0_090080_2018-07-29_final
2018-07-30 09:17:58	ga_ls7e_ard_3-0-0_090081_2018-07-29_final
2018-07-30 09:18:22	ga_ls7e_ard_3-0-0_090082_2018-07-29_final
2018-07-30 09:18:46	ga_ls7e_ard_3-0-0_090083_2018-07-29_final
2018-07-30 09:19:10	ga_ls7e_ard_3-0-0_090084_2018-07-29_final
2018-07-30 09:19:34	ga_ls7e_ard_3-1-0_090085_2018-07-29_final
2018-07-30 09:19:58	ga_ls7e_ard_3-0-0_090086_2018-07-29_final
2018-07-30 09:20:45	ga_ls7e_ard_3-0-0_090088_2018-07-29_final
2018-07-30 09:21:09	ga_ls7e_ard_3-0-0_090089_2018-07-29_final
2018-07-30 09:21:33	ga_ls7e_ard_3-0-0_090090_2018-07-29_final
2018-07-31 09:56:25	ga_ls7e_ard_3-0-0_097069_2018-07-31_final
2018-07-31 09:56:49	ga_ls7e_ard_3-0-0_097070_2018-07-31_final
2018-07-31 09:57:13	ga_ls7e_ard_3-0-0_097071_2018-07-31_final
2018-07-31 09:57:37	ga_ls7e_ard_3-0-0_097072_2018-07-31_final
2018-07-31 09:58:01	ga_ls7e_ard_3-0-0_097073_2018-07-31_final
2018-07-31 09:58:25	ga_ls7e_ard_3-0-0_097074_2018-07-31_final
2018-07-31 09:58:49	ga_ls7e_ard_3-0-0_097075_2018-07-31_final
2018-07-31 09:59:13	ga_ls7e_ard_3-0-0_097076_2018-07-31_final
2018-07-31 09:59:37	ga_ls7e_ard_3-0-0_097077_2018-07-31_final
2018-07-31 10:00:01	ga_ls7e_ard_3-0-0_097078_2018-07-31_final
2018-07-31 10:00:24	ga_ls7e_ard_3-0-0_097079_2018-07-31_final
2018-07-31 10:00:48	ga_ls7e_ard_3-0-0_097080_2018-07-31_final
2018-07-31 10:01:12	ga_ls7e_ard_3-0-0_097081_2018-07-31_final
2018-07-31 10:01:36	ga_ls7e_ard_3-0-0_097082_2018-07-31_final
2018-07-31 10:02:00	ga_ls7e_ard_3-0-0_097083_2018-07-31_final
2018-07-31 10:02:24	ga_ls7e_ard_3-0-0_097084_2018-07-31_final

Return to ga_ls7e_ard_3 across July 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

July

All days