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
2019-07-01 09:11:19	ga_ls7e_ard_3-0-0_090089_2019-06-30_final
2019-07-01 09:11:43	ga_ls7e_ard_3-0-0_090090_2019-06-30_final
2019-07-01 10:41:49	ga_ls7e_ard_3-0-0_106068_2019-07-01_final
2019-07-01 10:42:13	ga_ls7e_ard_3-0-0_106069_2019-07-01_final
2019-07-01 10:42:37	ga_ls7e_ard_3-0-0_106070_2019-07-01_final
2019-07-01 10:43:00	ga_ls7e_ard_3-0-0_106071_2019-07-01_final
2019-07-01 10:43:24	ga_ls7e_ard_3-0-0_106072_2019-07-01_final
2019-07-01 10:43:48	ga_ls7e_ard_3-0-0_106073_2019-07-01_final
2019-07-01 10:44:12	ga_ls7e_ard_3-0-0_106074_2019-07-01_final
2019-07-01 10:44:36	ga_ls7e_ard_3-1-0_106075_2019-07-01_final
2019-07-01 10:45:00	ga_ls7e_ard_3-0-0_106076_2019-07-01_final
2019-07-01 10:45:24	ga_ls7e_ard_3-0-0_106077_2019-07-01_final
2019-07-01 10:45:48	ga_ls7e_ard_3-0-0_106078_2019-07-01_final
2019-07-01 10:46:12	ga_ls7e_ard_3-0-0_106079_2019-07-01_final
2019-07-01 10:46:36	ga_ls7e_ard_3-0-0_106080_2019-07-01_final
2019-07-01 10:47:00	ga_ls7e_ard_3-0-0_106081_2019-07-01_final
2019-07-01 10:47:24	ga_ls7e_ard_3-0-0_106082_2019-07-01_final
2019-07-01 10:47:48	ga_ls7e_ard_3-0-0_106083_2019-07-01_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
2019-07-18 11:30:48	ga_ls7e_ard_3-0-0_113084_2019-07-18_final
2019-07-19 10:28:45	ga_ls7e_ard_3-0-0_104068_2019-07-19_final
2019-07-19 10:29:09	ga_ls7e_ard_3-0-0_104069_2019-07-19_final
2019-07-19 10:29:33	ga_ls7e_ard_3-1-0_104070_2019-07-19_final
2019-07-19 10:29:57	ga_ls7e_ard_3-0-0_104071_2019-07-19_final
2019-07-19 10:30:21	ga_ls7e_ard_3-0-0_104072_2019-07-19_final
2019-07-19 10:30:45	ga_ls7e_ard_3-0-0_104073_2019-07-19_final
2019-07-19 10:31:09	ga_ls7e_ard_3-0-0_104074_2019-07-19_final
2019-07-19 10:31:33	ga_ls7e_ard_3-0-0_104075_2019-07-19_final
2019-07-19 10:31:57	ga_ls7e_ard_3-0-0_104076_2019-07-19_final
2019-07-19 10:32:20	ga_ls7e_ard_3-0-0_104077_2019-07-19_final
2019-07-19 10:32:44	ga_ls7e_ard_3-0-0_104078_2019-07-19_final
2019-07-19 10:33:08	ga_ls7e_ard_3-0-0_104079_2019-07-19_final
2019-07-19 10:33:32	ga_ls7e_ard_3-0-0_104080_2019-07-19_final
2019-07-19 10:33:56	ga_ls7e_ard_3-0-0_104081_2019-07-19_final
2019-07-19 10:34:20	ga_ls7e_ard_3-0-0_104082_2019-07-19_final
2019-07-20 09:35:05	ga_ls7e_ard_3-1-0_095073_2019-07-20_final
2019-07-20 09:37:29	ga_ls7e_ard_3-1-0_095079_2019-07-20_final
2019-07-20 11:17:33	ga_ls7e_ard_3-1-0_111082_2019-07-20_final
2019-07-22 09:25:02	ga_ls7e_ard_3-0-0_093079_2019-07-21_final
2019-07-22 09:25:26	ga_ls7e_ard_3-0-0_093080_2019-07-21_final
2019-07-22 09:28:14	ga_ls7e_ard_3-1-0_093087_2019-07-21_final
2019-07-22 11:04:43	ga_ls7e_ard_3-1-0_109081_2019-07-22_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

GA Collection 3

USGS Products

Collection 2 NBAR

Collection 2 NBAR Tiled

Pixel Quality

Collection 2 Level1

Tidal

Water Observations

Fractional Cover

Sentinel 2

Ingested Pixel Quality

Geomedians

TMAD

Telemetry data

Summary

Other Products

2019

July

All days