Search ga_ls7e_ard_3 across May 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
2019-05-03 09:29:39	ga_ls7e_ard_3-0-0_093083_2019-05-02_final
2019-05-03 09:30:03	ga_ls7e_ard_3-1-0_093084_2019-05-03_final
2019-05-03 09:30:27	ga_ls7e_ard_3-0-0_093085_2019-05-03_final
2019-05-03 09:30:51	ga_ls7e_ard_3-0-0_093086_2019-05-03_final
2019-05-03 09:31:15	ga_ls7e_ard_3-0-0_093087_2019-05-03_final
2019-05-03 09:31:39	ga_ls7e_ard_3-0-0_093088_2019-05-03_final
2019-05-03 11:03:20	ga_ls7e_ard_3-0-0_109070_2019-05-03_final
2019-05-03 11:03:44	ga_ls7e_ard_3-0-0_109071_2019-05-03_final
2019-05-03 11:04:08	ga_ls7e_ard_3-0-0_109072_2019-05-03_final
2019-05-03 11:04:32	ga_ls7e_ard_3-0-0_109073_2019-05-03_final
2019-05-03 11:04:56	ga_ls7e_ard_3-0-0_109074_2019-05-03_final
2019-05-03 11:05:20	ga_ls7e_ard_3-0-0_109075_2019-05-03_final
2019-05-03 11:05:44	ga_ls7e_ard_3-0-0_109076_2019-05-03_final
2019-05-03 11:06:08	ga_ls7e_ard_3-0-0_109077_2019-05-03_final
2019-05-03 11:06:32	ga_ls7e_ard_3-0-0_109078_2019-05-03_final
2019-05-03 11:06:56	ga_ls7e_ard_3-0-0_109079_2019-05-03_final
2019-05-03 11:07:20	ga_ls7e_ard_3-0-0_109080_2019-05-03_final
2019-05-03 11:07:44	ga_ls7e_ard_3-1-0_109081_2019-05-03_final
2019-05-03 11:08:08	ga_ls7e_ard_3-0-0_109082_2019-05-03_final
2019-05-03 11:08:32	ga_ls7e_ard_3-0-0_109083_2019-05-03_final
2019-05-03 11:08:56	ga_ls7e_ard_3-0-0_109084_2019-05-03_final
2019-05-04 10:08:05	ga_ls7e_ard_3-0-0_100071_2019-05-04_final
2019-05-04 10:08:29	ga_ls7e_ard_3-0-0_100072_2019-05-04_final
2019-05-04 10:08:53	ga_ls7e_ard_3-0-0_100073_2019-05-04_final
2019-05-04 10:09:17	ga_ls7e_ard_3-0-0_100074_2019-05-04_final
2019-05-04 10:09:41	ga_ls7e_ard_3-0-0_100075_2019-05-04_final
2019-05-04 10:10:05	ga_ls7e_ard_3-0-0_100076_2019-05-04_final
2019-05-04 10:10:29	ga_ls7e_ard_3-0-0_100077_2019-05-04_final
2019-05-04 10:10:52	ga_ls7e_ard_3-0-0_100078_2019-05-04_final
2019-05-04 10:11:16	ga_ls7e_ard_3-0-0_100079_2019-05-04_final
2019-05-04 10:11:40	ga_ls7e_ard_3-0-0_100080_2019-05-04_final
2019-05-04 10:12:04	ga_ls7e_ard_3-0-0_100081_2019-05-04_final
2019-05-04 10:12:28	ga_ls7e_ard_3-0-0_100082_2019-05-04_final
2019-05-04 10:12:52	ga_ls7e_ard_3-0-0_100083_2019-05-04_final
2019-05-05 09:14:25	ga_ls7e_ard_3-0-0_091076_2019-05-04_final
2019-05-05 09:14:49	ga_ls7e_ard_3-0-0_091077_2019-05-04_final
2019-05-05 09:15:13	ga_ls7e_ard_3-0-0_091078_2019-05-04_final
2019-05-05 09:15:37	ga_ls7e_ard_3-0-0_091079_2019-05-04_final
2019-05-05 09:16:01	ga_ls7e_ard_3-0-0_091080_2019-05-04_final
2019-05-05 09:16:25	ga_ls7e_ard_3-0-0_091081_2019-05-04_final
2019-05-05 09:16:49	ga_ls7e_ard_3-0-0_091082_2019-05-04_final
2019-05-05 09:17:13	ga_ls7e_ard_3-0-0_091083_2019-05-04_final
2019-05-05 09:17:37	ga_ls7e_ard_3-0-0_091084_2019-05-04_final
2019-05-05 09:18:01	ga_ls7e_ard_3-0-0_091085_2019-05-04_final
2019-05-05 09:18:25	ga_ls7e_ard_3-1-0_091086_2019-05-04_final
2019-05-05 09:18:48	ga_ls7e_ard_3-0-0_091087_2019-05-04_final
2019-05-05 09:19:12	ga_ls7e_ard_3-0-0_091088_2019-05-04_final
2019-05-05 09:19:36	ga_ls7e_ard_3-0-0_091089_2019-05-04_final
2019-05-05 09:20:00	ga_ls7e_ard_3-0-0_091090_2019-05-04_final
2019-05-05 10:50:54	ga_ls7e_ard_3-0-0_107070_2019-05-05_final
2019-05-05 10:51:18	ga_ls7e_ard_3-0-0_107071_2019-05-05_final
2019-05-05 10:51:42	ga_ls7e_ard_3-0-0_107072_2019-05-05_final
2019-05-05 10:52:06	ga_ls7e_ard_3-0-0_107073_2019-05-05_final
2019-05-05 10:52:30	ga_ls7e_ard_3-0-0_107074_2019-05-05_final
2019-05-05 10:52:54	ga_ls7e_ard_3-0-0_107075_2019-05-05_final
2019-05-05 10:53:18	ga_ls7e_ard_3-0-0_107076_2019-05-05_final
2019-05-05 10:53:42	ga_ls7e_ard_3-0-0_107077_2019-05-05_final
2019-05-05 10:54:06	ga_ls7e_ard_3-0-0_107078_2019-05-05_final
2019-05-05 10:54:30	ga_ls7e_ard_3-0-0_107079_2019-05-05_final
2019-05-05 10:54:54	ga_ls7e_ard_3-0-0_107080_2019-05-05_final
2019-05-05 10:55:18	ga_ls7e_ard_3-0-0_107081_2019-05-05_final
2019-05-05 10:55:42	ga_ls7e_ard_3-0-0_107082_2019-05-05_final
2019-05-05 10:56:06	ga_ls7e_ard_3-0-0_107083_2019-05-05_final
2019-05-05 10:56:29	ga_ls7e_ard_3-0-0_107084_2019-05-05_final
2019-05-19 09:25:26	ga_ls7e_ard_3-0-0_093074_2019-05-18_final
2019-05-19 09:25:50	ga_ls7e_ard_3-0-0_093075_2019-05-18_final
2019-05-19 09:26:14	ga_ls7e_ard_3-0-0_093076_2019-05-18_final
2019-05-19 09:26:38	ga_ls7e_ard_3-0-0_093077_2019-05-18_final
2019-05-19 09:27:01	ga_ls7e_ard_3-0-0_093078_2019-05-18_final
2019-05-19 09:27:25	ga_ls7e_ard_3-0-0_093079_2019-05-18_final
2019-05-19 09:27:49	ga_ls7e_ard_3-0-0_093080_2019-05-18_final
2019-05-19 09:28:13	ga_ls7e_ard_3-0-0_093081_2019-05-18_final
2019-05-19 09:28:37	ga_ls7e_ard_3-0-0_093082_2019-05-18_final
2019-05-19 09:29:01	ga_ls7e_ard_3-1-0_093083_2019-05-18_final
2019-05-19 09:29:25	ga_ls7e_ard_3-0-0_093084_2019-05-18_final
2019-05-19 09:30:13	ga_ls7e_ard_3-0-0_093086_2019-05-19_final
2019-05-19 09:30:37	ga_ls7e_ard_3-0-0_093087_2019-05-19_final
2019-05-19 09:31:01	ga_ls7e_ard_3-0-0_093088_2019-05-19_final
2019-05-19 11:07:06	ga_ls7e_ard_3-1-0_109081_2019-05-19_final
2019-05-20 10:07:27	ga_ls7e_ard_3-0-0_100071_2019-05-20_final
2019-05-20 10:08:15	ga_ls7e_ard_3-0-0_100073_2019-05-20_final
2019-05-20 10:08:39	ga_ls7e_ard_3-0-0_100074_2019-05-20_final
2019-05-20 10:09:03	ga_ls7e_ard_3-0-0_100075_2019-05-20_final
2019-05-20 10:09:51	ga_ls7e_ard_3-0-0_100077_2019-05-20_final
2019-05-20 10:10:15	ga_ls7e_ard_3-0-0_100078_2019-05-20_final
2019-05-20 10:10:39	ga_ls7e_ard_3-0-0_100079_2019-05-20_final
2019-05-20 10:11:03	ga_ls7e_ard_3-0-0_100080_2019-05-20_final
2019-05-20 10:11:27	ga_ls7e_ard_3-0-0_100081_2019-05-20_final
2019-05-20 10:11:50	ga_ls7e_ard_3-0-0_100082_2019-05-20_final
2019-05-20 10:12:14	ga_ls7e_ard_3-0-0_100083_2019-05-20_final
2019-05-20 10:12:38	ga_ls7e_ard_3-0-0_100084_2019-05-20_final
2019-05-31 11:28:36	ga_ls7e_ard_3-0-0_113074_2019-05-31_final
2019-05-31 11:29:00	ga_ls7e_ard_3-0-0_113075_2019-05-31_final
2019-05-31 11:29:24	ga_ls7e_ard_3-0-0_113076_2019-05-31_final
2019-05-31 11:29:48	ga_ls7e_ard_3-0-0_113077_2019-05-31_final
2019-05-31 11:30:12	ga_ls7e_ard_3-0-0_113078_2019-05-31_final
2019-05-31 11:30:36	ga_ls7e_ard_3-0-0_113079_2019-05-31_final
2019-05-31 11:31:00	ga_ls7e_ard_3-0-0_113080_2019-05-31_final
2019-05-31 11:31:24	ga_ls7e_ard_3-0-0_113081_2019-05-31_final
2019-05-31 11:31:48	ga_ls7e_ard_3-0-0_113082_2019-05-31_final

Return to ga_ls7e_ard_3 across May 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

May

All days