Search ga_ls7e_ard_3 across April 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
2019-04-02 10:09:15	ga_ls7e_ard_3-0-0_100071_2019-04-02_final
2019-04-02 10:09:39	ga_ls7e_ard_3-0-0_100072_2019-04-02_final
2019-04-02 10:10:03	ga_ls7e_ard_3-0-0_100073_2019-04-02_final
2019-04-02 10:10:27	ga_ls7e_ard_3-0-0_100074_2019-04-02_final
2019-04-02 10:10:51	ga_ls7e_ard_3-0-0_100075_2019-04-02_final
2019-04-02 10:11:15	ga_ls7e_ard_3-0-0_100076_2019-04-02_final
2019-04-02 10:11:39	ga_ls7e_ard_3-0-0_100077_2019-04-02_final
2019-04-02 10:12:03	ga_ls7e_ard_3-0-0_100078_2019-04-02_final
2019-04-02 10:12:27	ga_ls7e_ard_3-0-0_100079_2019-04-02_final
2019-04-02 10:12:50	ga_ls7e_ard_3-0-0_100080_2019-04-02_final
2019-04-02 10:13:14	ga_ls7e_ard_3-1-0_100081_2019-04-02_final
2019-04-02 10:13:38	ga_ls7e_ard_3-0-0_100082_2019-04-02_final
2019-04-02 10:14:02	ga_ls7e_ard_3-0-0_100083_2019-04-02_final
2019-04-02 10:14:26	ga_ls7e_ard_3-0-0_100084_2019-04-02_final
2019-04-03 09:15:35	ga_ls7e_ard_3-0-0_091076_2019-04-02_final
2019-04-03 09:15:59	ga_ls7e_ard_3-0-0_091077_2019-04-02_final
2019-04-03 09:16:23	ga_ls7e_ard_3-0-0_091078_2019-04-02_final
2019-04-03 09:16:47	ga_ls7e_ard_3-0-0_091079_2019-04-02_final
2019-04-03 09:17:11	ga_ls7e_ard_3-0-0_091080_2019-04-02_final
2019-04-03 09:17:35	ga_ls7e_ard_3-0-0_091081_2019-04-02_final
2019-04-03 09:17:59	ga_ls7e_ard_3-0-0_091082_2019-04-02_final
2019-04-03 09:18:23	ga_ls7e_ard_3-0-0_091083_2019-04-02_final
2019-04-03 09:18:47	ga_ls7e_ard_3-0-0_091084_2019-04-02_final
2019-04-03 09:19:11	ga_ls7e_ard_3-0-0_091085_2019-04-02_final
2019-04-03 09:19:35	ga_ls7e_ard_3-1-0_091086_2019-04-02_final
2019-04-03 09:19:59	ga_ls7e_ard_3-0-0_091087_2019-04-02_final
2019-04-03 09:20:23	ga_ls7e_ard_3-0-0_091088_2019-04-02_final
2019-04-03 09:21:11	ga_ls7e_ard_3-0-0_091090_2019-04-02_final
2019-04-03 10:52:05	ga_ls7e_ard_3-0-0_107070_2019-04-03_final
2019-04-03 10:52:29	ga_ls7e_ard_3-0-0_107071_2019-04-03_final
2019-04-03 10:52:53	ga_ls7e_ard_3-0-0_107072_2019-04-03_final
2019-04-09 10:15:11	ga_ls7e_ard_3-0-0_101071_2019-04-09_final
2019-04-09 10:15:35	ga_ls7e_ard_3-0-0_101072_2019-04-09_final
2019-04-09 10:15:59	ga_ls7e_ard_3-0-0_101073_2019-04-09_final
2019-04-09 10:16:23	ga_ls7e_ard_3-0-0_101074_2019-04-09_final
2019-04-09 10:16:47	ga_ls7e_ard_3-1-0_101075_2019-04-09_final
2019-04-09 10:17:11	ga_ls7e_ard_3-0-0_101076_2019-04-09_final
2019-04-09 10:17:35	ga_ls7e_ard_3-0-0_101077_2019-04-09_final
2019-04-09 10:17:59	ga_ls7e_ard_3-0-0_101078_2019-04-09_final
2019-04-09 10:18:23	ga_ls7e_ard_3-0-0_101079_2019-04-09_final
2019-04-09 10:18:47	ga_ls7e_ard_3-0-0_101080_2019-04-09_final
2019-04-09 10:19:11	ga_ls7e_ard_3-0-0_101081_2019-04-09_final
2019-04-09 10:19:34	ga_ls7e_ard_3-0-0_101082_2019-04-09_final
2019-04-09 10:19:58	ga_ls7e_ard_3-0-0_101083_2019-04-09_final
2019-04-10 09:21:07	ga_ls7e_ard_3-0-0_092075_2019-04-09_final
2019-04-10 09:21:31	ga_ls7e_ard_3-0-0_092076_2019-04-09_final
2019-04-10 09:21:55	ga_ls7e_ard_3-0-0_092077_2019-04-09_final
2019-04-10 09:22:19	ga_ls7e_ard_3-0-0_092078_2019-04-09_final
2019-04-10 09:22:43	ga_ls7e_ard_3-0-0_092079_2019-04-09_final
2019-04-10 09:23:07	ga_ls7e_ard_3-0-0_092080_2019-04-09_final
2019-04-10 09:23:31	ga_ls7e_ard_3-0-0_092081_2019-04-09_final
2019-04-10 09:23:55	ga_ls7e_ard_3-0-0_092082_2019-04-09_final
2019-04-10 09:24:19	ga_ls7e_ard_3-0-0_092083_2019-04-09_final
2019-04-10 09:24:43	ga_ls7e_ard_3-0-0_092084_2019-04-09_final
2019-04-10 09:25:07	ga_ls7e_ard_3-0-0_092085_2019-04-09_final
2019-04-10 09:25:31	ga_ls7e_ard_3-0-0_092086_2019-04-09_final
2019-04-10 09:25:55	ga_ls7e_ard_3-0-0_092087_2019-04-09_final
2019-04-10 09:26:19	ga_ls7e_ard_3-0-0_092088_2019-04-09_final
2019-04-10 09:26:43	ga_ls7e_ard_3-0-0_092089_2019-04-09_final
2019-04-10 10:58:01	ga_ls7e_ard_3-0-0_108070_2019-04-10_final
2019-04-10 10:58:49	ga_ls7e_ard_3-0-0_108072_2019-04-10_final
2019-04-10 10:59:12	ga_ls7e_ard_3-0-0_108073_2019-04-10_final
2019-04-10 10:59:36	ga_ls7e_ard_3-0-0_108074_2019-04-10_final
2019-04-10 11:00:00	ga_ls7e_ard_3-0-0_108075_2019-04-10_final
2019-04-10 11:00:24	ga_ls7e_ard_3-0-0_108076_2019-04-10_final
2019-04-10 11:00:48	ga_ls7e_ard_3-0-0_108077_2019-04-10_final
2019-04-10 11:01:12	ga_ls7e_ard_3-0-0_108078_2019-04-10_final
2019-04-10 11:01:36	ga_ls7e_ard_3-0-0_108079_2019-04-10_final
2019-04-10 11:02:00	ga_ls7e_ard_3-0-0_108080_2019-04-10_final
2019-04-10 11:02:24	ga_ls7e_ard_3-0-0_108081_2019-04-10_final
2019-04-10 11:02:48	ga_ls7e_ard_3-0-0_108082_2019-04-10_final
2019-04-10 11:03:12	ga_ls7e_ard_3-0-0_108083_2019-04-10_final
2019-04-10 11:03:36	ga_ls7e_ard_3-0-0_108084_2019-04-10_final
2019-04-11 10:01:09	ga_ls7e_ard_3-0-0_099067_2019-04-11_final
2019-04-11 10:01:33	ga_ls7e_ard_3-0-0_099068_2019-04-11_final
2019-04-11 10:01:57	ga_ls7e_ard_3-0-0_099069_2019-04-11_final
2019-04-11 10:02:21	ga_ls7e_ard_3-0-0_099070_2019-04-11_final
2019-04-11 10:02:45	ga_ls7e_ard_3-0-0_099071_2019-04-11_final
2019-04-11 10:03:09	ga_ls7e_ard_3-0-0_099072_2019-04-11_final
2019-04-11 10:03:33	ga_ls7e_ard_3-0-0_099073_2019-04-11_final
2019-04-11 10:03:57	ga_ls7e_ard_3-0-0_099074_2019-04-11_final
2019-04-11 10:04:21	ga_ls7e_ard_3-0-0_099075_2019-04-11_final
2019-04-11 10:04:45	ga_ls7e_ard_3-0-0_099076_2019-04-11_final
2019-04-11 10:05:09	ga_ls7e_ard_3-0-0_099077_2019-04-11_final
2019-04-11 10:05:33	ga_ls7e_ard_3-0-0_099078_2019-04-11_final
2019-04-11 10:05:57	ga_ls7e_ard_3-0-0_099079_2019-04-11_final
2019-04-11 10:06:21	ga_ls7e_ard_3-0-0_099080_2019-04-11_final
2019-04-11 10:06:45	ga_ls7e_ard_3-0-0_099081_2019-04-11_final
2019-04-11 10:07:09	ga_ls7e_ard_3-0-0_099082_2019-04-11_final
2019-04-11 10:07:33	ga_ls7e_ard_3-0-0_099083_2019-04-11_final
2019-04-11 10:07:56	ga_ls7e_ard_3-0-0_099084_2019-04-11_final
2019-04-12 09:09:29	ga_ls7e_ard_3-0-0_090077_2019-04-11_final
2019-04-12 09:09:53	ga_ls7e_ard_3-0-0_090078_2019-04-11_final
2019-04-12 09:10:17	ga_ls7e_ard_3-0-0_090079_2019-04-11_final
2019-04-12 09:10:41	ga_ls7e_ard_3-0-0_090080_2019-04-11_final
2019-04-12 09:11:05	ga_ls7e_ard_3-0-0_090081_2019-04-11_final
2019-04-12 09:11:29	ga_ls7e_ard_3-0-0_090082_2019-04-11_final
2019-04-12 09:11:53	ga_ls7e_ard_3-0-0_090083_2019-04-11_final
2019-04-12 09:12:17	ga_ls7e_ard_3-0-0_090084_2019-04-11_final
2019-04-12 09:12:41	ga_ls7e_ard_3-0-0_090085_2019-04-11_final

Return to ga_ls7e_ard_3 across April 2019 overview

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

Baseline satellite data

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

2019

April

All days