Search ga_ls7e_ard_3 across February 2022

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
2022-02-01 09:24:48	ga_ls7e_ard_3-2-1_104068_2022-01-31_final
2022-02-01 09:25:12	ga_ls7e_ard_3-2-1_104069_2022-01-31_final
2022-02-01 09:25:36	ga_ls7e_ard_3-2-1_104070_2022-01-31_final
2022-02-01 09:26:00	ga_ls7e_ard_3-2-1_104071_2022-01-31_final
2022-02-01 09:26:24	ga_ls7e_ard_3-2-1_104072_2022-01-31_final
2022-02-01 09:26:48	ga_ls7e_ard_3-2-1_104073_2022-01-31_final
2022-02-01 09:29:35	ga_ls7e_ard_3-2-1_104080_2022-01-31_final
2022-02-01 09:29:59	ga_ls7e_ard_3-2-1_104081_2022-01-31_final
2022-02-02 08:30:41	ga_ls7e_ard_3-2-1_095072_2022-02-01_final
2022-02-02 08:31:05	ga_ls7e_ard_3-2-1_095073_2022-02-01_final
2022-02-02 08:31:29	ga_ls7e_ard_3-2-1_095074_2022-02-01_final
2022-02-02 08:31:53	ga_ls7e_ard_3-2-1_095075_2022-02-01_final
2022-02-02 08:32:17	ga_ls7e_ard_3-2-1_095076_2022-02-01_final
2022-02-02 08:32:40	ga_ls7e_ard_3-2-1_095077_2022-02-01_final
2022-02-02 08:33:04	ga_ls7e_ard_3-2-1_095078_2022-02-01_final
2022-02-02 08:34:16	ga_ls7e_ard_3-2-1_095081_2022-02-01_final
2022-02-02 08:34:40	ga_ls7e_ard_3-2-1_095082_2022-02-01_final
2022-02-02 08:35:04	ga_ls7e_ard_3-2-1_095083_2022-02-01_final
2022-02-02 08:35:28	ga_ls7e_ard_3-2-1_095084_2022-02-01_final
2022-02-02 08:35:52	ga_ls7e_ard_3-2-1_095085_2022-02-01_final
2022-02-02 08:36:16	ga_ls7e_ard_3-2-1_095086_2022-02-01_final
2022-02-02 08:36:40	ga_ls7e_ard_3-2-1_095087_2022-02-01_final
2022-02-02 10:07:58	ga_ls7e_ard_3-2-1_111068_2022-02-02_final
2022-02-02 10:08:22	ga_ls7e_ard_3-2-1_111069_2022-02-02_final
2022-02-02 10:08:46	ga_ls7e_ard_3-2-1_111070_2022-02-02_final
2022-02-02 10:10:45	ga_ls7e_ard_3-2-1_111075_2022-02-02_final
2022-02-02 10:11:09	ga_ls7e_ard_3-2-1_111076_2022-02-02_final
2022-02-02 10:11:33	ga_ls7e_ard_3-2-1_111077_2022-02-02_final
2022-02-02 10:11:57	ga_ls7e_ard_3-2-1_111078_2022-02-02_final
2022-02-02 10:12:21	ga_ls7e_ard_3-2-1_111079_2022-02-02_final
2022-02-02 10:12:45	ga_ls7e_ard_3-2-1_111080_2022-02-02_final
2022-02-02 10:13:09	ga_ls7e_ard_3-2-1_111081_2022-02-02_final
2022-02-02 10:13:33	ga_ls7e_ard_3-2-1_111082_2022-02-02_final
2022-02-02 10:13:57	ga_ls7e_ard_3-2-1_111083_2022-02-02_final
2022-02-02 10:14:21	ga_ls7e_ard_3-2-1_111084_2022-02-02_final
2022-02-03 09:12:15	ga_ls7e_ard_3-2-1_102068_2022-02-02_final
2022-02-03 09:12:38	ga_ls7e_ard_3-2-1_102069_2022-02-02_final
2022-02-03 09:13:02	ga_ls7e_ard_3-2-1_102070_2022-02-02_final
2022-02-03 09:13:26	ga_ls7e_ard_3-2-1_102071_2022-02-02_final
2022-02-03 09:13:50	ga_ls7e_ard_3-2-1_102072_2022-02-02_final
2022-02-03 09:14:38	ga_ls7e_ard_3-2-1_102074_2022-02-02_final
2022-02-03 09:15:02	ga_ls7e_ard_3-2-1_102075_2022-02-02_final
2022-02-03 09:15:26	ga_ls7e_ard_3-2-1_102076_2022-02-02_final
2022-02-03 09:15:50	ga_ls7e_ard_3-2-1_102077_2022-02-02_final
2022-02-03 09:16:14	ga_ls7e_ard_3-2-1_102078_2022-02-02_final
2022-02-03 09:16:38	ga_ls7e_ard_3-2-1_102079_2022-02-02_final
2022-02-03 09:17:02	ga_ls7e_ard_3-2-1_102080_2022-02-02_final
2022-02-03 09:17:26	ga_ls7e_ard_3-2-1_102081_2022-02-02_final
2022-02-03 09:17:50	ga_ls7e_ard_3-2-1_102082_2022-02-02_final
2022-02-04 08:19:43	ga_ls7e_ard_3-2-1_093076_2022-02-03_final
2022-02-04 08:20:07	ga_ls7e_ard_3-2-1_093077_2022-02-03_final
2022-02-04 08:20:31	ga_ls7e_ard_3-2-1_093078_2022-02-03_final
2022-02-04 08:20:55	ga_ls7e_ard_3-2-1_093079_2022-02-03_final
2022-02-04 08:21:19	ga_ls7e_ard_3-2-1_093080_2022-02-03_final
2022-02-04 08:21:43	ga_ls7e_ard_3-2-1_093081_2022-02-03_final
2022-02-04 08:22:06	ga_ls7e_ard_3-2-1_093082_2022-02-03_final
2022-02-04 08:22:30	ga_ls7e_ard_3-2-1_093083_2022-02-03_final
2022-02-04 08:22:54	ga_ls7e_ard_3-2-1_093084_2022-02-03_final
2022-02-04 08:23:18	ga_ls7e_ard_3-2-1_093085_2022-02-03_final
2022-02-04 08:23:42	ga_ls7e_ard_3-2-1_093086_2022-02-03_final
2022-02-04 08:24:06	ga_ls7e_ard_3-2-1_093087_2022-02-03_final
2022-02-04 08:24:30	ga_ls7e_ard_3-2-1_093088_2022-02-03_final
2022-02-04 09:56:12	ga_ls7e_ard_3-2-1_109070_2022-02-04_final
2022-02-04 09:56:36	ga_ls7e_ard_3-2-1_109071_2022-02-04_final
2022-02-04 09:57:00	ga_ls7e_ard_3-2-1_109072_2022-02-04_final
2022-02-04 09:57:24	ga_ls7e_ard_3-2-1_109073_2022-02-04_final
2022-02-04 09:57:48	ga_ls7e_ard_3-2-1_109074_2022-02-04_final
2022-02-04 09:58:11	ga_ls7e_ard_3-2-1_109075_2022-02-04_final
2022-02-04 09:58:35	ga_ls7e_ard_3-2-1_109076_2022-02-04_final
2022-02-04 09:58:59	ga_ls7e_ard_3-2-1_109077_2022-02-04_final
2022-02-04 09:59:23	ga_ls7e_ard_3-2-1_109078_2022-02-04_final
2022-02-04 09:59:47	ga_ls7e_ard_3-2-1_109079_2022-02-04_final
2022-02-04 10:00:11	ga_ls7e_ard_3-2-1_109080_2022-02-04_final
2022-02-04 10:00:35	ga_ls7e_ard_3-2-1_109081_2022-02-04_final
2022-02-04 10:00:59	ga_ls7e_ard_3-2-1_109082_2022-02-04_final
2022-02-04 10:01:23	ga_ls7e_ard_3-2-1_109083_2022-02-04_final
2022-02-04 10:01:47	ga_ls7e_ard_3-2-1_109084_2022-02-04_final
2022-02-05 09:00:53	ga_ls7e_ard_3-2-1_100071_2022-02-04_final
2022-02-05 09:01:16	ga_ls7e_ard_3-2-1_100072_2022-02-04_final
2022-02-05 09:01:40	ga_ls7e_ard_3-2-1_100073_2022-02-04_final
2022-02-05 09:02:04	ga_ls7e_ard_3-2-1_100074_2022-02-04_final
2022-02-05 09:02:28	ga_ls7e_ard_3-2-1_100075_2022-02-04_final
2022-02-05 09:02:52	ga_ls7e_ard_3-2-1_100076_2022-02-04_final
2022-02-05 09:03:16	ga_ls7e_ard_3-2-1_100077_2022-02-04_final
2022-02-05 09:03:40	ga_ls7e_ard_3-2-1_100078_2022-02-04_final
2022-02-05 09:04:04	ga_ls7e_ard_3-2-1_100079_2022-02-04_final
2022-02-05 09:04:28	ga_ls7e_ard_3-2-1_100080_2022-02-04_final
2022-02-05 09:04:52	ga_ls7e_ard_3-2-1_100081_2022-02-04_final
2022-02-05 09:05:16	ga_ls7e_ard_3-2-1_100082_2022-02-04_final
2022-02-05 09:05:40	ga_ls7e_ard_3-2-1_100083_2022-02-04_final
2022-02-05 09:06:04	ga_ls7e_ard_3-2-1_100084_2022-02-04_final
2022-02-07 10:28:23	ga_ls7e_ard_3-2-1_114074_2022-02-07_final
2022-02-07 10:28:47	ga_ls7e_ard_3-2-1_114075_2022-02-07_final
2022-02-07 10:29:11	ga_ls7e_ard_3-2-1_114076_2022-02-07_final
2022-02-07 10:29:35	ga_ls7e_ard_3-2-1_114077_2022-02-07_final
2022-02-07 10:29:59	ga_ls7e_ard_3-2-1_114078_2022-02-07_final
2022-02-07 10:30:23	ga_ls7e_ard_3-2-1_114079_2022-02-07_final
2022-02-07 10:30:47	ga_ls7e_ard_3-2-1_114080_2022-02-07_final
2022-02-07 10:31:11	ga_ls7e_ard_3-2-1_114081_2022-02-07_final
2022-02-08 09:32:40	ga_ls7e_ard_3-2-1_105074_2022-02-08_final

Return to ga_ls7e_ard_3 across February 2022 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

2022

February

All days