Search ga_ls7e_ard_3 across July 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
2019-07-01 09:06:31	ga_ls7e_ard_3-0-0_090077_2019-06-30_final
2019-07-01 09:06:55	ga_ls7e_ard_3-0-0_090078_2019-06-30_final
2019-07-01 09:07:19	ga_ls7e_ard_3-0-0_090079_2019-06-30_final
2019-07-01 09:07:43	ga_ls7e_ard_3-0-0_090080_2019-06-30_final
2019-07-01 09:08:07	ga_ls7e_ard_3-0-0_090081_2019-06-30_final
2019-07-01 09:08:31	ga_ls7e_ard_3-0-0_090082_2019-06-30_final
2019-07-01 09:08:55	ga_ls7e_ard_3-0-0_090083_2019-06-30_final
2019-07-01 09:09:19	ga_ls7e_ard_3-0-0_090084_2019-06-30_final
2019-07-01 09:09:43	ga_ls7e_ard_3-1-0_090085_2019-06-30_final
2019-07-01 09:10:07	ga_ls7e_ard_3-0-0_090086_2019-06-30_final
2019-07-08 09:12:02	ga_ls7e_ard_3-0-0_091076_2019-07-07_final
2019-07-10 09:00:00	ga_ls7e_ard_3-0-0_089077_2019-07-09_final
2019-07-10 09:00:24	ga_ls7e_ard_3-0-0_089078_2019-07-09_final
2019-07-10 09:00:48	ga_ls7e_ard_3-0-0_089079_2019-07-09_final
2019-07-10 09:01:11	ga_ls7e_ard_3-0-0_089080_2019-07-09_final
2019-07-10 09:01:35	ga_ls7e_ard_3-1-0_089081_2019-07-09_final
2019-07-10 09:01:59	ga_ls7e_ard_3-0-0_089082_2019-07-09_final
2019-07-10 09:02:23	ga_ls7e_ard_3-0-0_089083_2019-07-09_final
2019-07-10 09:02:47	ga_ls7e_ard_3-0-0_089084_2019-07-09_final
2019-07-10 09:03:11	ga_ls7e_ard_3-0-0_089085_2019-07-09_final
2019-07-10 09:03:35	ga_ls7e_ard_3-0-0_089086_2019-07-09_final
2019-07-16 09:57:34	ga_ls7e_ard_3-0-0_099067_2019-07-16_final
2019-07-16 09:57:58	ga_ls7e_ard_3-0-0_099068_2019-07-16_final
2019-07-16 09:58:22	ga_ls7e_ard_3-0-0_099069_2019-07-16_final
2019-07-16 09:58:46	ga_ls7e_ard_3-0-0_099070_2019-07-16_final
2019-07-16 09:59:10	ga_ls7e_ard_3-0-0_099071_2019-07-16_final
2019-07-16 09:59:34	ga_ls7e_ard_3-0-0_099072_2019-07-16_final
2019-07-16 09:59:58	ga_ls7e_ard_3-0-0_099073_2019-07-16_final
2019-07-16 10:00:22	ga_ls7e_ard_3-0-0_099074_2019-07-16_final
2019-07-16 10:00:46	ga_ls7e_ard_3-0-0_099075_2019-07-16_final
2019-07-16 10:01:10	ga_ls7e_ard_3-0-0_099076_2019-07-16_final
2019-07-16 10:01:34	ga_ls7e_ard_3-0-0_099077_2019-07-16_final
2019-07-16 10:01:57	ga_ls7e_ard_3-0-0_099078_2019-07-16_final
2019-07-16 10:02:21	ga_ls7e_ard_3-0-0_099079_2019-07-16_final
2019-07-16 10:02:45	ga_ls7e_ard_3-0-0_099080_2019-07-16_final
2019-07-16 10:03:09	ga_ls7e_ard_3-0-0_099081_2019-07-16_final
2019-07-16 10:03:33	ga_ls7e_ard_3-0-0_099082_2019-07-16_final
2019-07-16 10:03:57	ga_ls7e_ard_3-0-0_099083_2019-07-16_final
2019-07-16 10:04:21	ga_ls7e_ard_3-0-0_099084_2019-07-16_final
2019-07-16 11:39:15	ga_ls7e_ard_3-0-0_115074_2019-07-16_final
2019-07-16 11:39:39	ga_ls7e_ard_3-0-0_115075_2019-07-16_final
2019-07-16 11:40:03	ga_ls7e_ard_3-0-0_115076_2019-07-16_final
2019-07-16 11:40:26	ga_ls7e_ard_3-1-0_115077_2019-07-16_final
2019-07-16 11:40:50	ga_ls7e_ard_3-0-0_115078_2019-07-16_final
2019-07-16 11:41:14	ga_ls7e_ard_3-0-0_115079_2019-07-16_final
2019-07-17 09:05:54	ga_ls7e_ard_3-0-0_090077_2019-07-16_final
2019-07-17 09:06:18	ga_ls7e_ard_3-0-0_090078_2019-07-16_final
2019-07-17 09:06:42	ga_ls7e_ard_3-0-0_090079_2019-07-16_final
2019-07-17 09:07:06	ga_ls7e_ard_3-0-0_090080_2019-07-16_final
2019-07-17 09:07:30	ga_ls7e_ard_3-0-0_090081_2019-07-16_final
2019-07-17 09:07:54	ga_ls7e_ard_3-1-0_090082_2019-07-16_final
2019-07-17 09:08:18	ga_ls7e_ard_3-0-0_090083_2019-07-16_final
2019-07-17 09:08:42	ga_ls7e_ard_3-0-0_090084_2019-07-16_final
2019-07-17 09:09:06	ga_ls7e_ard_3-1-0_090085_2019-07-16_final
2019-07-17 09:09:29	ga_ls7e_ard_3-0-0_090086_2019-07-16_final
2019-07-17 09:10:41	ga_ls7e_ard_3-0-0_090089_2019-07-16_final
2019-07-17 10:41:11	ga_ls7e_ard_3-0-0_106068_2019-07-17_final
2019-07-17 10:41:35	ga_ls7e_ard_3-0-0_106069_2019-07-17_final
2019-07-17 10:41:59	ga_ls7e_ard_3-0-0_106070_2019-07-17_final
2019-07-17 10:42:23	ga_ls7e_ard_3-0-0_106071_2019-07-17_final
2019-07-17 10:42:47	ga_ls7e_ard_3-0-0_106072_2019-07-17_final
2019-07-17 10:43:11	ga_ls7e_ard_3-0-0_106073_2019-07-17_final
2019-07-17 10:43:35	ga_ls7e_ard_3-0-0_106074_2019-07-17_final
2019-07-17 10:43:59	ga_ls7e_ard_3-1-0_106075_2019-07-17_final
2019-07-17 10:44:23	ga_ls7e_ard_3-0-0_106076_2019-07-17_final
2019-07-17 10:44:47	ga_ls7e_ard_3-0-0_106077_2019-07-17_final
2019-07-17 10:45:11	ga_ls7e_ard_3-0-0_106078_2019-07-17_final
2019-07-17 10:45:35	ga_ls7e_ard_3-0-0_106079_2019-07-17_final
2019-07-17 10:45:59	ga_ls7e_ard_3-0-0_106080_2019-07-17_final
2019-07-17 10:46:23	ga_ls7e_ard_3-0-0_106081_2019-07-17_final
2019-07-17 10:46:47	ga_ls7e_ard_3-0-0_106082_2019-07-17_final
2019-07-17 10:47:10	ga_ls7e_ard_3-0-0_106083_2019-07-17_final
2019-07-18 09:45:56	ga_ls7e_ard_3-0-0_097069_2019-07-18_final
2019-07-18 09:46:20	ga_ls7e_ard_3-0-0_097070_2019-07-18_final
2019-07-18 09:46:44	ga_ls7e_ard_3-0-0_097071_2019-07-18_final
2019-07-18 09:47:08	ga_ls7e_ard_3-0-0_097072_2019-07-18_final
2019-07-18 09:47:31	ga_ls7e_ard_3-0-0_097073_2019-07-18_final
2019-07-18 09:47:55	ga_ls7e_ard_3-0-0_097074_2019-07-18_final
2019-07-18 09:48:19	ga_ls7e_ard_3-0-0_097075_2019-07-18_final
2019-07-18 09:48:43	ga_ls7e_ard_3-1-0_097076_2019-07-18_final
2019-07-18 09:49:07	ga_ls7e_ard_3-0-0_097077_2019-07-18_final
2019-07-18 09:49:31	ga_ls7e_ard_3-0-0_097078_2019-07-18_final
2019-07-18 09:49:55	ga_ls7e_ard_3-0-0_097079_2019-07-18_final
2019-07-18 09:50:19	ga_ls7e_ard_3-0-0_097080_2019-07-18_final
2019-07-18 09:50:43	ga_ls7e_ard_3-0-0_097081_2019-07-18_final
2019-07-18 09:51:07	ga_ls7e_ard_3-0-0_097082_2019-07-18_final
2019-07-18 09:51:31	ga_ls7e_ard_3-0-0_097083_2019-07-18_final
2019-07-18 09:51:55	ga_ls7e_ard_3-0-0_097084_2019-07-18_final
2019-07-18 09:52:19	ga_ls7e_ard_3-0-0_097085_2019-07-18_final
2019-07-18 11:24:01	ga_ls7e_ard_3-0-0_113067_2019-07-18_final
2019-07-18 11:26:48	ga_ls7e_ard_3-0-0_113074_2019-07-18_final
2019-07-18 11:27:12	ga_ls7e_ard_3-0-0_113075_2019-07-18_final
2019-07-18 11:27:36	ga_ls7e_ard_3-0-0_113076_2019-07-18_final
2019-07-18 11:28:00	ga_ls7e_ard_3-0-0_113077_2019-07-18_final
2019-07-18 11:28:24	ga_ls7e_ard_3-0-0_113078_2019-07-18_final
2019-07-18 11:28:48	ga_ls7e_ard_3-0-0_113079_2019-07-18_final
2019-07-18 11:29:12	ga_ls7e_ard_3-0-0_113080_2019-07-18_final
2019-07-18 11:29:36	ga_ls7e_ard_3-0-0_113081_2019-07-18_final
2019-07-18 11:30:00	ga_ls7e_ard_3-0-0_113082_2019-07-18_final
2019-07-18 11:30:24	ga_ls7e_ard_3-0-0_113083_2019-07-18_final

Return to ga_ls7e_ard_3 across July 2019 overview

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

Baseline satellite data

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

2019

July

All days