Search ga_ls7e_ard_3 across August 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
2019-08-14 09:27:53	ga_ls7e_ard_3-0-0_094073_2019-08-13_final
2019-08-14 09:28:17	ga_ls7e_ard_3-0-0_094074_2019-08-13_final
2019-08-14 09:28:41	ga_ls7e_ard_3-0-0_094075_2019-08-13_final
2019-08-14 09:29:05	ga_ls7e_ard_3-0-0_094076_2019-08-13_final
2019-08-14 09:29:29	ga_ls7e_ard_3-0-0_094077_2019-08-13_final
2019-08-14 09:29:53	ga_ls7e_ard_3-1-0_094078_2019-08-13_final
2019-08-14 09:30:17	ga_ls7e_ard_3-0-0_094079_2019-08-14_final
2019-08-14 09:30:41	ga_ls7e_ard_3-0-0_094080_2019-08-14_final
2019-08-14 09:31:05	ga_ls7e_ard_3-0-0_094081_2019-08-14_final
2019-08-14 09:31:29	ga_ls7e_ard_3-0-0_094082_2019-08-14_final
2019-08-14 09:31:53	ga_ls7e_ard_3-1-0_094083_2019-08-14_final
2019-08-14 09:32:17	ga_ls7e_ard_3-0-0_094084_2019-08-14_final
2019-08-14 09:32:41	ga_ls7e_ard_3-0-0_094085_2019-08-14_final
2019-08-14 09:33:04	ga_ls7e_ard_3-0-0_094086_2019-08-14_final
2019-08-14 09:33:28	ga_ls7e_ard_3-0-0_094087_2019-08-14_final
2019-08-14 11:04:22	ga_ls7e_ard_3-0-0_110067_2019-08-14_final
2019-08-14 11:05:34	ga_ls7e_ard_3-0-0_110070_2019-08-14_final
2019-08-14 11:05:58	ga_ls7e_ard_3-0-0_110071_2019-08-14_final
2019-08-14 11:06:22	ga_ls7e_ard_3-1-0_110072_2019-08-14_final
2019-08-14 11:06:46	ga_ls7e_ard_3-0-0_110073_2019-08-14_final
2019-08-14 11:07:10	ga_ls7e_ard_3-0-0_110074_2019-08-14_final
2019-08-14 11:07:34	ga_ls7e_ard_3-1-0_110075_2019-08-14_final
2019-08-14 11:07:58	ga_ls7e_ard_3-0-0_110076_2019-08-14_final
2019-08-14 11:08:22	ga_ls7e_ard_3-0-0_110077_2019-08-14_final
2019-08-14 11:08:46	ga_ls7e_ard_3-0-0_110078_2019-08-14_final
2019-08-14 11:09:58	ga_ls7e_ard_3-0-0_110081_2019-08-14_final
2019-08-14 11:10:45	ga_ls7e_ard_3-0-0_110083_2019-08-14_final
2019-08-14 11:11:09	ga_ls7e_ard_3-0-0_110084_2019-08-14_final
2019-08-15 10:10:18	ga_ls7e_ard_3-0-0_101071_2019-08-15_final
2019-08-15 10:10:42	ga_ls7e_ard_3-0-0_101072_2019-08-15_final
2019-08-15 10:11:06	ga_ls7e_ard_3-0-0_101073_2019-08-15_final
2019-08-15 10:11:30	ga_ls7e_ard_3-0-0_101074_2019-08-15_final
2019-08-15 10:11:54	ga_ls7e_ard_3-1-0_101075_2019-08-15_final
2019-08-15 10:12:18	ga_ls7e_ard_3-0-0_101076_2019-08-15_final
2019-08-15 10:12:42	ga_ls7e_ard_3-0-0_101077_2019-08-15_final
2019-08-15 10:13:06	ga_ls7e_ard_3-0-0_101078_2019-08-15_final
2019-08-15 10:13:30	ga_ls7e_ard_3-0-0_101079_2019-08-15_final
2019-08-15 10:13:54	ga_ls7e_ard_3-0-0_101080_2019-08-15_final
2019-08-15 10:14:18	ga_ls7e_ard_3-0-0_101081_2019-08-15_final
2019-08-15 10:14:42	ga_ls7e_ard_3-0-0_101082_2019-08-15_final
2019-08-15 10:15:06	ga_ls7e_ard_3-0-0_101083_2019-08-15_final
2019-08-16 09:16:14	ga_ls7e_ard_3-0-0_092075_2019-08-15_final
2019-08-16 09:16:38	ga_ls7e_ard_3-0-0_092076_2019-08-15_final
2019-08-16 09:17:02	ga_ls7e_ard_3-0-0_092077_2019-08-15_final
2019-08-16 09:17:26	ga_ls7e_ard_3-0-0_092078_2019-08-15_final
2019-08-16 09:17:50	ga_ls7e_ard_3-0-0_092079_2019-08-15_final
2019-08-16 09:18:14	ga_ls7e_ard_3-0-0_092080_2019-08-15_final
2019-08-16 09:18:38	ga_ls7e_ard_3-0-0_092081_2019-08-15_final
2019-08-16 09:19:02	ga_ls7e_ard_3-0-0_092082_2019-08-15_final
2019-08-16 09:19:26	ga_ls7e_ard_3-0-0_092083_2019-08-15_final
2019-08-16 09:19:50	ga_ls7e_ard_3-0-0_092084_2019-08-15_final
2019-08-16 09:21:02	ga_ls7e_ard_3-0-0_092087_2019-08-15_final
2019-08-16 10:52:44	ga_ls7e_ard_3-0-0_108069_2019-08-16_final
2019-08-16 10:53:08	ga_ls7e_ard_3-0-0_108070_2019-08-16_final
2019-08-16 10:53:32	ga_ls7e_ard_3-1-0_108071_2019-08-16_final
2019-08-16 10:53:55	ga_ls7e_ard_3-0-0_108072_2019-08-16_final
2019-08-16 10:54:19	ga_ls7e_ard_3-0-0_108073_2019-08-16_final
2019-08-16 10:54:43	ga_ls7e_ard_3-0-0_108074_2019-08-16_final
2019-08-16 10:55:07	ga_ls7e_ard_3-1-0_108075_2019-08-16_final
2019-08-16 10:55:55	ga_ls7e_ard_3-0-0_108077_2019-08-16_final
2019-08-16 10:56:19	ga_ls7e_ard_3-0-0_108078_2019-08-16_final
2019-08-16 10:56:43	ga_ls7e_ard_3-0-0_108079_2019-08-16_final
2019-08-16 10:57:07	ga_ls7e_ard_3-0-0_108080_2019-08-16_final
2019-08-16 10:57:31	ga_ls7e_ard_3-0-0_108081_2019-08-16_final
2019-08-16 10:57:55	ga_ls7e_ard_3-0-0_108082_2019-08-16_final
2019-08-16 10:58:19	ga_ls7e_ard_3-0-0_108083_2019-08-16_final
2019-08-16 10:58:43	ga_ls7e_ard_3-0-0_108084_2019-08-16_final
2019-08-17 09:56:40	ga_ls7e_ard_3-0-0_099068_2019-08-17_final
2019-08-17 09:57:04	ga_ls7e_ard_3-0-0_099069_2019-08-17_final
2019-08-17 09:57:28	ga_ls7e_ard_3-0-0_099070_2019-08-17_final
2019-08-17 09:57:52	ga_ls7e_ard_3-0-0_099071_2019-08-17_final
2019-08-17 09:58:16	ga_ls7e_ard_3-0-0_099072_2019-08-17_final
2019-08-17 09:58:40	ga_ls7e_ard_3-0-0_099073_2019-08-17_final
2019-08-17 09:59:04	ga_ls7e_ard_3-0-0_099074_2019-08-17_final
2019-08-17 09:59:28	ga_ls7e_ard_3-0-0_099075_2019-08-17_final
2019-08-17 09:59:51	ga_ls7e_ard_3-0-0_099076_2019-08-17_final
2019-08-17 10:00:15	ga_ls7e_ard_3-0-0_099077_2019-08-17_final
2019-08-17 10:00:39	ga_ls7e_ard_3-0-0_099078_2019-08-17_final
2019-08-17 10:01:03	ga_ls7e_ard_3-0-0_099079_2019-08-17_final
2019-08-17 10:01:27	ga_ls7e_ard_3-0-0_099080_2019-08-17_final
2019-08-17 10:01:51	ga_ls7e_ard_3-0-0_099081_2019-08-17_final
2019-08-17 10:02:15	ga_ls7e_ard_3-0-0_099082_2019-08-17_final
2019-08-17 10:02:39	ga_ls7e_ard_3-0-0_099083_2019-08-17_final
2019-08-17 10:03:03	ga_ls7e_ard_3-0-0_099084_2019-08-17_final
2019-08-17 11:37:56	ga_ls7e_ard_3-0-0_115074_2019-08-17_final
2019-08-17 11:38:20	ga_ls7e_ard_3-0-0_115075_2019-08-17_final
2019-08-17 11:38:44	ga_ls7e_ard_3-0-0_115076_2019-08-17_final
2019-08-17 11:39:08	ga_ls7e_ard_3-1-0_115077_2019-08-17_final
2019-08-17 11:39:32	ga_ls7e_ard_3-0-0_115078_2019-08-17_final
2019-08-25 09:10:05	ga_ls7e_ard_3-1-0_091076_2019-08-24_final
2019-08-25 09:10:29	ga_ls7e_ard_3-1-0_091077_2019-08-24_final
2019-08-25 09:10:53	ga_ls7e_ard_3-1-0_091078_2019-08-24_final
2019-08-25 09:11:17	ga_ls7e_ard_3-1-0_091079_2019-08-24_final
2019-08-25 09:11:41	ga_ls7e_ard_3-1-0_091080_2019-08-24_final
2019-08-25 09:12:04	ga_ls7e_ard_3-1-0_091081_2019-08-24_final
2019-08-25 09:12:28	ga_ls7e_ard_3-1-0_091082_2019-08-24_final
2019-08-25 09:12:52	ga_ls7e_ard_3-1-0_091083_2019-08-24_final
2019-08-25 09:13:16	ga_ls7e_ard_3-1-0_091084_2019-08-24_final
2019-08-25 09:13:40	ga_ls7e_ard_3-1-0_091085_2019-08-24_final
2019-08-25 09:14:04	ga_ls7e_ard_3-1-0_091086_2019-08-24_final

Return to ga_ls7e_ard_3 across August 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

August

All days