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
2019-01-15 09:06:31	ga_ls7e_ard_3-0-0_089078_2019-01-14_final
2019-01-15 09:06:55	ga_ls7e_ard_3-0-0_089079_2019-01-14_final
2019-01-15 09:07:18	ga_ls7e_ard_3-0-0_089080_2019-01-14_final
2019-01-15 09:07:42	ga_ls7e_ard_3-1-0_089081_2019-01-14_final
2019-01-15 09:08:06	ga_ls7e_ard_3-0-0_089082_2019-01-14_final
2019-01-15 09:08:30	ga_ls7e_ard_3-0-0_089083_2019-01-14_final
2019-01-15 09:08:54	ga_ls7e_ard_3-0-0_089084_2019-01-14_final
2019-01-27 09:34:02	ga_ls7e_ard_3-0-0_093086_2019-01-27_final
2019-01-27 09:34:26	ga_ls7e_ard_3-1-0_093087_2019-01-27_final
2019-01-27 09:34:50	ga_ls7e_ard_3-0-0_093088_2019-01-27_final
2019-01-27 11:06:32	ga_ls7e_ard_3-0-0_109070_2019-01-27_final
2019-01-27 11:06:56	ga_ls7e_ard_3-0-0_109071_2019-01-27_final
2019-01-27 11:07:19	ga_ls7e_ard_3-0-0_109072_2019-01-27_final
2019-01-27 11:08:31	ga_ls7e_ard_3-0-0_109075_2019-01-27_final
2019-01-27 11:08:55	ga_ls7e_ard_3-0-0_109076_2019-01-27_final
2019-01-27 11:09:43	ga_ls7e_ard_3-0-0_109078_2019-01-27_final
2019-01-27 11:10:07	ga_ls7e_ard_3-0-0_109079_2019-01-27_final
2019-01-27 11:10:31	ga_ls7e_ard_3-0-0_109080_2019-01-27_final
2019-01-27 11:10:55	ga_ls7e_ard_3-1-0_109081_2019-01-27_final
2019-01-27 11:11:19	ga_ls7e_ard_3-0-0_109082_2019-01-27_final
2019-01-27 11:11:43	ga_ls7e_ard_3-0-0_109083_2019-01-27_final
2019-01-27 11:12:07	ga_ls7e_ard_3-0-0_109084_2019-01-27_final
2019-01-28 10:11:40	ga_ls7e_ard_3-0-0_100072_2019-01-28_final
2019-01-28 10:12:04	ga_ls7e_ard_3-0-0_100073_2019-01-28_final
2019-01-28 10:12:28	ga_ls7e_ard_3-0-0_100074_2019-01-28_final
2019-01-28 10:12:52	ga_ls7e_ard_3-0-0_100075_2019-01-28_final
2019-01-28 10:13:16	ga_ls7e_ard_3-0-0_100076_2019-01-28_final
2019-01-28 10:13:40	ga_ls7e_ard_3-0-0_100077_2019-01-28_final
2019-01-28 10:14:04	ga_ls7e_ard_3-0-0_100078_2019-01-28_final
2019-01-28 10:14:52	ga_ls7e_ard_3-0-0_100080_2019-01-28_final
2019-01-28 10:15:16	ga_ls7e_ard_3-1-0_100081_2019-01-28_final
2019-01-28 10:15:40	ga_ls7e_ard_3-0-0_100082_2019-01-28_final
2019-01-28 10:16:04	ga_ls7e_ard_3-0-0_100083_2019-01-28_final
2019-01-28 10:16:28	ga_ls7e_ard_3-0-0_100084_2019-01-28_final
2019-01-29 09:17:37	ga_ls7e_ard_3-0-0_091076_2019-01-28_final
2019-01-29 09:18:01	ga_ls7e_ard_3-0-0_091077_2019-01-28_final
2019-01-29 09:18:25	ga_ls7e_ard_3-0-0_091078_2019-01-28_final
2019-01-29 09:18:49	ga_ls7e_ard_3-0-0_091079_2019-01-28_final
2019-01-29 09:19:12	ga_ls7e_ard_3-0-0_091080_2019-01-28_final
2019-01-29 09:19:36	ga_ls7e_ard_3-0-0_091081_2019-01-28_final
2019-01-29 09:20:00	ga_ls7e_ard_3-0-0_091082_2019-01-28_final
2019-01-29 09:20:24	ga_ls7e_ard_3-0-0_091083_2019-01-28_final
2019-01-29 09:20:48	ga_ls7e_ard_3-0-0_091084_2019-01-28_final
2019-01-29 09:21:36	ga_ls7e_ard_3-1-0_091086_2019-01-28_final
2019-01-29 09:22:00	ga_ls7e_ard_3-0-0_091087_2019-01-28_final
2019-01-29 09:22:24	ga_ls7e_ard_3-0-0_091088_2019-01-28_final
2019-01-29 09:22:48	ga_ls7e_ard_3-0-0_091089_2019-01-28_final
2019-01-29 09:23:12	ga_ls7e_ard_3-0-0_091090_2019-01-28_final
2019-01-29 10:54:06	ga_ls7e_ard_3-0-0_107070_2019-01-29_final
2019-01-29 10:54:30	ga_ls7e_ard_3-0-0_107071_2019-01-29_final
2019-01-29 10:54:54	ga_ls7e_ard_3-0-0_107072_2019-01-29_final
2019-01-29 10:55:18	ga_ls7e_ard_3-0-0_107073_2019-01-29_final
2019-01-29 10:56:30	ga_ls7e_ard_3-0-0_107076_2019-01-29_final
2019-01-29 10:56:54	ga_ls7e_ard_3-0-0_107077_2019-01-29_final
2019-01-29 10:57:17	ga_ls7e_ard_3-0-0_107078_2019-01-29_final
2019-01-29 10:57:41	ga_ls7e_ard_3-0-0_107079_2019-01-29_final
2019-01-29 10:58:05	ga_ls7e_ard_3-0-0_107080_2019-01-29_final
2019-01-29 10:58:29	ga_ls7e_ard_3-0-0_107081_2019-01-29_final
2019-01-29 10:58:53	ga_ls7e_ard_3-0-0_107082_2019-01-29_final
2019-01-29 10:59:17	ga_ls7e_ard_3-0-0_107083_2019-01-29_final
2019-01-29 10:59:41	ga_ls7e_ard_3-0-0_107084_2019-01-29_final
2019-01-30 09:57:15	ga_ls7e_ard_3-0-0_098067_2019-01-30_final
2019-01-30 09:58:03	ga_ls7e_ard_3-0-0_098069_2019-01-30_final
2019-01-30 10:01:14	ga_ls7e_ard_3-0-0_098077_2019-01-30_final
2019-01-30 10:01:38	ga_ls7e_ard_3-0-0_098078_2019-01-30_final
2019-01-30 10:02:02	ga_ls7e_ard_3-0-0_098079_2019-01-30_final
2019-01-30 10:02:26	ga_ls7e_ard_3-0-0_098080_2019-01-30_final
2019-01-30 10:02:50	ga_ls7e_ard_3-0-0_098081_2019-01-30_final
2019-01-30 10:03:14	ga_ls7e_ard_3-0-0_098082_2019-01-30_final
2019-01-30 10:03:38	ga_ls7e_ard_3-0-0_098083_2019-01-30_final
2019-01-30 10:04:02	ga_ls7e_ard_3-0-0_098084_2019-01-30_final
2019-01-30 10:04:26	ga_ls7e_ard_3-0-0_098085_2019-01-30_final
2019-01-30 11:38:55	ga_ls7e_ard_3-0-0_114074_2019-01-30_final
2019-01-30 11:39:19	ga_ls7e_ard_3-0-0_114075_2019-01-30_final
2019-01-30 11:39:43	ga_ls7e_ard_3-0-0_114076_2019-01-30_final
2019-01-30 11:40:07	ga_ls7e_ard_3-0-0_114077_2019-01-30_final
2019-01-30 11:40:31	ga_ls7e_ard_3-0-0_114078_2019-01-30_final
2019-01-30 11:40:55	ga_ls7e_ard_3-0-0_114079_2019-01-30_final
2019-01-30 11:41:19	ga_ls7e_ard_3-0-0_114080_2019-01-30_final
2019-01-30 11:41:43	ga_ls7e_ard_3-0-0_114081_2019-01-30_final
2019-01-31 09:05:59	ga_ls7e_ard_3-0-0_089078_2019-01-30_final
2019-01-31 09:06:23	ga_ls7e_ard_3-0-0_089079_2019-01-30_final
2019-01-31 09:06:47	ga_ls7e_ard_3-0-0_089080_2019-01-30_final
2019-01-31 09:07:11	ga_ls7e_ard_3-1-0_089081_2019-01-30_final
2019-01-31 09:07:35	ga_ls7e_ard_3-0-0_089082_2019-01-30_final
2019-01-31 09:07:59	ga_ls7e_ard_3-0-0_089083_2019-01-30_final
2019-01-31 09:08:23	ga_ls7e_ard_3-0-0_089084_2019-01-30_final
2019-01-31 09:08:47	ga_ls7e_ard_3-0-0_089085_2019-01-30_final
2019-01-31 09:09:11	ga_ls7e_ard_3-0-0_089086_2019-01-30_final
2019-01-31 10:42:05	ga_ls7e_ard_3-0-0_105071_2019-01-31_final
2019-01-31 10:42:29	ga_ls7e_ard_3-0-0_105072_2019-01-31_final
2019-01-31 10:42:53	ga_ls7e_ard_3-0-0_105073_2019-01-31_final
2019-01-31 10:43:16	ga_ls7e_ard_3-0-0_105074_2019-01-31_final
2019-01-31 10:43:40	ga_ls7e_ard_3-0-0_105075_2019-01-31_final
2019-01-31 10:44:04	ga_ls7e_ard_3-0-0_105076_2019-01-31_final
2019-01-31 10:44:28	ga_ls7e_ard_3-0-0_105077_2019-01-31_final
2019-01-31 10:44:52	ga_ls7e_ard_3-0-0_105078_2019-01-31_final
2019-01-31 10:45:16	ga_ls7e_ard_3-0-0_105079_2019-01-31_final
2019-01-31 10:45:40	ga_ls7e_ard_3-1-0_105080_2019-01-31_final
2019-01-31 10:46:04	ga_ls7e_ard_3-0-0_105081_2019-01-31_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

Baseline satellite data

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

2019

January

All days