Search ga_ls7e_ard_3 across January 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
2019-01-02 09:33:26	ga_ls7e_ard_3-1-0_094067_2019-01-02_final
2019-01-02 09:35:49	ga_ls7e_ard_3-0-0_094073_2019-01-02_final
2019-01-02 09:36:13	ga_ls7e_ard_3-0-0_094074_2019-01-02_final
2019-01-02 09:36:37	ga_ls7e_ard_3-0-0_094075_2019-01-02_final
2019-01-02 09:37:01	ga_ls7e_ard_3-0-0_094076_2019-01-02_final
2019-01-02 09:37:25	ga_ls7e_ard_3-0-0_094077_2019-01-02_final
2019-01-02 09:37:49	ga_ls7e_ard_3-0-0_094078_2019-01-02_final
2019-01-02 09:38:13	ga_ls7e_ard_3-0-0_094079_2019-01-02_final
2019-01-02 09:38:37	ga_ls7e_ard_3-0-0_094080_2019-01-02_final
2019-01-02 09:39:01	ga_ls7e_ard_3-0-0_094081_2019-01-02_final
2019-01-02 09:39:25	ga_ls7e_ard_3-0-0_094082_2019-01-02_final
2019-01-02 09:39:49	ga_ls7e_ard_3-0-0_094083_2019-01-02_final
2019-01-08 10:35:27	ga_ls7e_ard_3-0-0_104068_2019-01-08_final
2019-01-08 10:35:51	ga_ls7e_ard_3-0-0_104069_2019-01-08_final
2019-01-08 10:36:14	ga_ls7e_ard_3-1-0_104070_2019-01-08_final
2019-01-08 10:36:38	ga_ls7e_ard_3-0-0_104071_2019-01-08_final
2019-01-08 10:37:02	ga_ls7e_ard_3-0-0_104072_2019-01-08_final
2019-01-08 10:37:26	ga_ls7e_ard_3-0-0_104073_2019-01-08_final
2019-01-08 10:37:50	ga_ls7e_ard_3-0-0_104074_2019-01-08_final
2019-01-08 10:38:14	ga_ls7e_ard_3-0-0_104075_2019-01-08_final
2019-01-08 10:38:38	ga_ls7e_ard_3-0-0_104076_2019-01-08_final
2019-01-08 10:39:02	ga_ls7e_ard_3-0-0_104077_2019-01-08_final
2019-01-08 10:39:26	ga_ls7e_ard_3-0-0_104078_2019-01-08_final
2019-01-08 10:39:50	ga_ls7e_ard_3-0-0_104079_2019-01-08_final
2019-01-08 10:40:14	ga_ls7e_ard_3-0-0_104080_2019-01-08_final
2019-01-08 10:40:38	ga_ls7e_ard_3-0-0_104081_2019-01-08_final
2019-01-08 10:41:02	ga_ls7e_ard_3-0-0_104082_2019-01-08_final
2019-01-09 09:39:24	ga_ls7e_ard_3-0-0_095067_2019-01-09_final
2019-01-09 09:41:23	ga_ls7e_ard_3-0-0_095072_2019-01-09_final
2019-01-09 09:41:47	ga_ls7e_ard_3-1-0_095073_2019-01-09_final
2019-01-09 09:42:11	ga_ls7e_ard_3-0-0_095074_2019-01-09_final
2019-01-09 09:42:35	ga_ls7e_ard_3-0-0_095075_2019-01-09_final
2019-01-09 09:42:59	ga_ls7e_ard_3-0-0_095076_2019-01-09_final
2019-01-09 09:43:23	ga_ls7e_ard_3-0-0_095077_2019-01-09_final
2019-01-09 09:43:47	ga_ls7e_ard_3-0-0_095078_2019-01-09_final
2019-01-09 09:44:11	ga_ls7e_ard_3-1-0_095079_2019-01-09_final
2019-01-09 09:44:35	ga_ls7e_ard_3-0-0_095080_2019-01-09_final
2019-01-09 09:44:59	ga_ls7e_ard_3-0-0_095081_2019-01-09_final
2019-01-09 09:45:23	ga_ls7e_ard_3-0-0_095082_2019-01-09_final
2019-01-09 09:45:47	ga_ls7e_ard_3-0-0_095083_2019-01-09_final
2019-01-09 09:46:11	ga_ls7e_ard_3-0-0_095084_2019-01-09_final
2019-01-09 09:46:34	ga_ls7e_ard_3-0-0_095085_2019-01-09_final
2019-01-09 09:46:58	ga_ls7e_ard_3-0-0_095086_2019-01-09_final
2019-01-09 11:18:16	ga_ls7e_ard_3-0-0_111067_2019-01-09_final
2019-01-09 11:18:40	ga_ls7e_ard_3-0-0_111068_2019-01-09_final
2019-01-09 11:20:16	ga_ls7e_ard_3-0-0_111072_2019-01-09_final
2019-01-09 11:20:40	ga_ls7e_ard_3-0-0_111073_2019-01-09_final
2019-01-09 11:21:04	ga_ls7e_ard_3-0-0_111074_2019-01-09_final
2019-01-09 11:21:28	ga_ls7e_ard_3-0-0_111075_2019-01-09_final
2019-01-09 11:21:52	ga_ls7e_ard_3-0-0_111076_2019-01-09_final
2019-01-09 11:22:16	ga_ls7e_ard_3-0-0_111077_2019-01-09_final
2019-01-09 11:22:40	ga_ls7e_ard_3-0-0_111078_2019-01-09_final
2019-01-09 11:23:04	ga_ls7e_ard_3-0-0_111079_2019-01-09_final
2019-01-09 11:23:28	ga_ls7e_ard_3-0-0_111080_2019-01-09_final
2019-01-09 11:23:52	ga_ls7e_ard_3-0-0_111081_2019-01-09_final
2019-01-09 11:24:16	ga_ls7e_ard_3-1-0_111082_2019-01-09_final
2019-01-09 11:24:40	ga_ls7e_ard_3-0-0_111083_2019-01-09_final
2019-01-09 11:25:03	ga_ls7e_ard_3-0-0_111084_2019-01-09_final
2019-01-10 10:23:01	ga_ls7e_ard_3-0-0_102068_2019-01-10_final
2019-01-10 10:23:25	ga_ls7e_ard_3-0-0_102069_2019-01-10_final
2019-01-10 10:23:49	ga_ls7e_ard_3-0-0_102070_2019-01-10_final
2019-01-10 10:24:13	ga_ls7e_ard_3-0-0_102071_2019-01-10_final
2019-01-10 10:24:37	ga_ls7e_ard_3-0-0_102072_2019-01-10_final
2019-01-10 10:25:01	ga_ls7e_ard_3-0-0_102073_2019-01-10_final
2019-01-10 10:25:25	ga_ls7e_ard_3-0-0_102074_2019-01-10_final
2019-01-10 10:25:49	ga_ls7e_ard_3-0-0_102075_2019-01-10_final
2019-01-10 10:26:13	ga_ls7e_ard_3-0-0_102076_2019-01-10_final
2019-01-10 10:26:37	ga_ls7e_ard_3-0-0_102077_2019-01-10_final
2019-01-10 10:27:01	ga_ls7e_ard_3-0-0_102078_2019-01-10_final
2019-01-10 10:27:25	ga_ls7e_ard_3-0-0_102079_2019-01-10_final
2019-01-10 10:27:48	ga_ls7e_ard_3-0-0_102080_2019-01-10_final
2019-01-10 10:28:12	ga_ls7e_ard_3-0-0_102081_2019-01-10_final
2019-01-10 10:28:36	ga_ls7e_ard_3-0-0_102082_2019-01-10_final
2019-01-11 09:30:34	ga_ls7e_ard_3-0-0_093076_2019-01-11_final
2019-01-11 09:30:58	ga_ls7e_ard_3-0-0_093077_2019-01-11_final
2019-01-11 09:31:21	ga_ls7e_ard_3-0-0_093078_2019-01-11_final
2019-01-11 09:31:45	ga_ls7e_ard_3-0-0_093079_2019-01-11_final
2019-01-11 09:32:09	ga_ls7e_ard_3-0-0_093080_2019-01-11_final
2019-01-11 09:32:33	ga_ls7e_ard_3-0-0_093081_2019-01-11_final
2019-01-11 09:32:57	ga_ls7e_ard_3-0-0_093082_2019-01-11_final
2019-01-11 09:33:21	ga_ls7e_ard_3-0-0_093083_2019-01-11_final
2019-01-11 09:33:45	ga_ls7e_ard_3-0-0_093084_2019-01-11_final
2019-01-11 09:34:09	ga_ls7e_ard_3-0-0_093085_2019-01-11_final
2019-01-11 09:34:33	ga_ls7e_ard_3-0-0_093086_2019-01-11_final
2019-01-11 09:34:57	ga_ls7e_ard_3-1-0_093087_2019-01-11_final
2019-01-11 09:35:21	ga_ls7e_ard_3-0-0_093088_2019-01-11_final
2019-01-11 11:07:03	ga_ls7e_ard_3-0-0_109070_2019-01-11_final
2019-01-11 11:07:27	ga_ls7e_ard_3-0-0_109071_2019-01-11_final
2019-01-11 11:07:51	ga_ls7e_ard_3-0-0_109072_2019-01-11_final
2019-01-11 11:08:15	ga_ls7e_ard_3-0-0_109073_2019-01-11_final
2019-01-11 11:08:39	ga_ls7e_ard_3-0-0_109074_2019-01-11_final
2019-01-11 11:09:03	ga_ls7e_ard_3-0-0_109075_2019-01-11_final
2019-01-11 11:09:26	ga_ls7e_ard_3-0-0_109076_2019-01-11_final
2019-01-11 11:09:50	ga_ls7e_ard_3-0-0_109077_2019-01-11_final
2019-01-11 11:10:14	ga_ls7e_ard_3-0-0_109078_2019-01-11_final
2019-01-11 11:10:38	ga_ls7e_ard_3-0-0_109079_2019-01-11_final
2019-01-11 11:11:02	ga_ls7e_ard_3-0-0_109080_2019-01-11_final
2019-01-11 11:11:26	ga_ls7e_ard_3-1-0_109081_2019-01-11_final
2019-01-11 11:11:50	ga_ls7e_ard_3-0-0_109082_2019-01-11_final
2019-01-11 11:12:14	ga_ls7e_ard_3-0-0_109083_2019-01-11_final

Return to ga_ls7e_ard_3 across January 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

January

All days