Search ga_ls7e_ard_3 across 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
2019-08-28 11:18:56	ga_ls7e_ard_3-1-0_112074_2019-08-28_final
2019-08-28 11:19:20	ga_ls7e_ard_3-1-0_112075_2019-08-28_final
2019-08-28 11:19:44	ga_ls7e_ard_3-1-0_112076_2019-08-28_final
2019-08-28 11:20:08	ga_ls7e_ard_3-1-0_112077_2019-08-28_final
2019-08-28 11:20:32	ga_ls7e_ard_3-1-0_112078_2019-08-28_final
2019-08-28 11:20:56	ga_ls7e_ard_3-1-0_112079_2019-08-28_final
2019-08-28 11:21:20	ga_ls7e_ard_3-1-0_112080_2019-08-28_final
2019-08-28 11:21:44	ga_ls7e_ard_3-1-0_112081_2019-08-28_final
2019-08-28 11:22:08	ga_ls7e_ard_3-1-0_112082_2019-08-28_final
2019-08-28 11:22:32	ga_ls7e_ard_3-1-0_112083_2019-08-28_final
2019-08-28 11:22:56	ga_ls7e_ard_3-1-0_112084_2019-08-28_final
2019-08-29 10:20:53	ga_ls7e_ard_3-1-0_103068_2019-08-29_final
2019-08-29 10:21:17	ga_ls7e_ard_3-1-0_103069_2019-08-29_final
2019-08-29 10:21:40	ga_ls7e_ard_3-1-0_103070_2019-08-29_final
2019-08-29 10:22:04	ga_ls7e_ard_3-1-0_103071_2019-08-29_final
2019-08-29 10:22:28	ga_ls7e_ard_3-1-0_103072_2019-08-29_final
2019-08-29 10:22:52	ga_ls7e_ard_3-1-0_103073_2019-08-29_final
2019-08-29 10:23:16	ga_ls7e_ard_3-1-0_103074_2019-08-29_final
2019-08-29 10:23:40	ga_ls7e_ard_3-1-0_103075_2019-08-29_final
2019-08-29 10:24:04	ga_ls7e_ard_3-1-0_103076_2019-08-29_final
2019-08-29 10:24:28	ga_ls7e_ard_3-1-0_103077_2019-08-29_final
2019-08-29 10:24:52	ga_ls7e_ard_3-1-0_103078_2019-08-29_final
2019-08-29 10:25:16	ga_ls7e_ard_3-1-0_103079_2019-08-29_final
2019-08-29 10:25:40	ga_ls7e_ard_3-1-0_103080_2019-08-29_final
2019-08-29 10:26:04	ga_ls7e_ard_3-1-0_103081_2019-08-29_final
2019-08-29 10:26:28	ga_ls7e_ard_3-1-0_103082_2019-08-29_final
2019-08-30 09:27:12	ga_ls7e_ard_3-1-0_094073_2019-08-29_final
2019-08-30 09:27:36	ga_ls7e_ard_3-1-0_094074_2019-08-29_final
2019-08-30 09:28:00	ga_ls7e_ard_3-1-0_094075_2019-08-29_final
2019-08-30 09:28:24	ga_ls7e_ard_3-1-0_094076_2019-08-29_final
2019-08-30 09:28:48	ga_ls7e_ard_3-1-0_094077_2019-08-29_final
2019-08-30 09:29:12	ga_ls7e_ard_3-1-0_094078_2019-08-29_final
2019-08-30 09:29:36	ga_ls7e_ard_3-1-0_094079_2019-08-29_final
2019-08-30 09:30:24	ga_ls7e_ard_3-1-0_094081_2019-08-30_final
2019-08-30 09:30:48	ga_ls7e_ard_3-1-0_094082_2019-08-30_final
2019-08-30 09:31:12	ga_ls7e_ard_3-1-0_094083_2019-08-30_final
2019-08-30 09:31:36	ga_ls7e_ard_3-1-0_094084_2019-08-30_final
2019-08-30 09:32:00	ga_ls7e_ard_3-1-0_094085_2019-08-30_final
2019-08-30 09:32:24	ga_ls7e_ard_3-1-0_094086_2019-08-30_final
2019-08-30 09:32:48	ga_ls7e_ard_3-1-0_094087_2019-08-30_final
2019-08-30 11:03:42	ga_ls7e_ard_3-1-0_110067_2019-08-30_final
2019-08-30 11:04:54	ga_ls7e_ard_3-1-0_110070_2019-08-30_final
2019-08-30 11:05:17	ga_ls7e_ard_3-1-0_110071_2019-08-30_final
2019-08-30 11:05:41	ga_ls7e_ard_3-1-0_110072_2019-08-30_final
2019-08-30 11:06:05	ga_ls7e_ard_3-1-0_110073_2019-08-30_final
2019-08-30 11:06:29	ga_ls7e_ard_3-1-0_110074_2019-08-30_final
2019-08-30 11:06:53	ga_ls7e_ard_3-1-0_110075_2019-08-30_final
2019-08-30 11:07:17	ga_ls7e_ard_3-1-0_110076_2019-08-30_final
2019-08-30 11:08:05	ga_ls7e_ard_3-1-0_110078_2019-08-30_final
2019-08-30 11:08:29	ga_ls7e_ard_3-1-0_110079_2019-08-30_final
2019-08-30 11:08:53	ga_ls7e_ard_3-1-0_110080_2019-08-30_final
2019-08-30 11:09:17	ga_ls7e_ard_3-1-0_110081_2019-08-30_final
2019-08-30 11:10:29	ga_ls7e_ard_3-1-0_110084_2019-08-30_final
2019-08-31 10:09:38	ga_ls7e_ard_3-1-0_101071_2019-08-31_final
2019-08-31 10:10:02	ga_ls7e_ard_3-1-0_101072_2019-08-31_final
2019-08-31 10:10:26	ga_ls7e_ard_3-1-0_101073_2019-08-31_final
2019-08-31 10:10:49	ga_ls7e_ard_3-1-0_101074_2019-08-31_final
2019-08-31 10:11:13	ga_ls7e_ard_3-1-0_101075_2019-08-31_final
2019-08-31 10:11:37	ga_ls7e_ard_3-1-0_101076_2019-08-31_final
2019-08-31 10:12:01	ga_ls7e_ard_3-1-0_101077_2019-08-31_final
2019-08-31 10:12:25	ga_ls7e_ard_3-1-0_101078_2019-08-31_final
2019-08-31 10:12:49	ga_ls7e_ard_3-1-0_101079_2019-08-31_final
2019-08-31 10:13:13	ga_ls7e_ard_3-1-0_101080_2019-08-31_final
2019-08-31 10:13:37	ga_ls7e_ard_3-1-0_101081_2019-08-31_final
2019-09-01 09:15:34	ga_ls7e_ard_3-1-0_092075_2019-08-31_final
2019-09-01 09:15:58	ga_ls7e_ard_3-1-0_092076_2019-08-31_final
2019-09-01 09:16:21	ga_ls7e_ard_3-1-0_092077_2019-08-31_final
2019-09-01 09:16:45	ga_ls7e_ard_3-1-0_092078_2019-08-31_final
2019-09-01 09:17:09	ga_ls7e_ard_3-1-0_092079_2019-08-31_final
2019-09-01 09:17:33	ga_ls7e_ard_3-1-0_092080_2019-08-31_final
2019-09-01 09:17:57	ga_ls7e_ard_3-1-0_092081_2019-08-31_final
2019-09-01 09:18:21	ga_ls7e_ard_3-1-0_092082_2019-08-31_final
2019-09-01 09:18:45	ga_ls7e_ard_3-1-0_092083_2019-08-31_final
2019-09-01 09:19:09	ga_ls7e_ard_3-1-0_092084_2019-08-31_final
2019-09-01 09:19:33	ga_ls7e_ard_3-1-0_092085_2019-08-31_final
2019-09-01 09:19:57	ga_ls7e_ard_3-1-0_092086_2019-08-31_final
2019-09-01 10:52:03	ga_ls7e_ard_3-1-0_108069_2019-09-01_final
2019-09-01 10:52:27	ga_ls7e_ard_3-1-0_108070_2019-09-01_final
2019-09-01 10:52:51	ga_ls7e_ard_3-1-0_108071_2019-09-01_final
2019-09-01 10:53:15	ga_ls7e_ard_3-1-0_108072_2019-09-01_final
2019-09-01 10:53:39	ga_ls7e_ard_3-1-0_108073_2019-09-01_final
2019-09-01 10:54:02	ga_ls7e_ard_3-1-0_108074_2019-09-01_final
2019-09-01 10:54:26	ga_ls7e_ard_3-1-0_108075_2019-09-01_final
2019-09-01 10:54:50	ga_ls7e_ard_3-1-0_108076_2019-09-01_final
2019-09-01 10:55:14	ga_ls7e_ard_3-1-0_108077_2019-09-01_final
2019-09-01 10:55:38	ga_ls7e_ard_3-1-0_108078_2019-09-01_final
2019-09-01 10:56:02	ga_ls7e_ard_3-1-0_108079_2019-09-01_final
2019-09-01 10:56:26	ga_ls7e_ard_3-1-0_108080_2019-09-01_final
2019-09-01 10:56:50	ga_ls7e_ard_3-1-0_108081_2019-09-01_final
2019-09-01 10:57:14	ga_ls7e_ard_3-1-0_108082_2019-09-01_final
2019-09-01 10:57:38	ga_ls7e_ard_3-1-0_108083_2019-09-01_final
2019-09-01 10:58:02	ga_ls7e_ard_3-1-0_108084_2019-09-01_final
2019-12-23 09:50:30	ga_ls7e_ard_3-1-0_099067_2019-12-23_final
2019-12-23 09:51:42	ga_ls7e_ard_3-1-0_099070_2019-12-23_final
2019-12-23 09:52:53	ga_ls7e_ard_3-1-0_099073_2019-12-23_final
2019-12-23 09:53:17	ga_ls7e_ard_3-1-0_099074_2019-12-23_final
2019-12-23 09:54:29	ga_ls7e_ard_3-1-0_099077_2019-12-23_final
2019-12-23 09:54:53	ga_ls7e_ard_3-1-0_099078_2019-12-23_final
2019-12-23 09:55:17	ga_ls7e_ard_3-1-0_099079_2019-12-23_final
2019-12-23 09:56:53	ga_ls7e_ard_3-1-0_099083_2019-12-23_final

Return to ga_ls7e_ard_3 across 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

All months