Search ga_ls7e_ard_3 across July 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
2019-07-23 10:05:04	ga_ls7e_ard_3-0-0_100071_2019-07-23_final
2019-07-23 10:05:28	ga_ls7e_ard_3-0-0_100072_2019-07-23_final
2019-07-23 10:05:52	ga_ls7e_ard_3-0-0_100073_2019-07-23_final
2019-07-23 10:06:16	ga_ls7e_ard_3-0-0_100074_2019-07-23_final
2019-07-23 10:06:40	ga_ls7e_ard_3-0-0_100075_2019-07-23_final
2019-07-23 10:07:04	ga_ls7e_ard_3-0-0_100076_2019-07-23_final
2019-07-23 10:07:28	ga_ls7e_ard_3-0-0_100077_2019-07-23_final
2019-07-23 10:07:52	ga_ls7e_ard_3-0-0_100078_2019-07-23_final
2019-07-23 10:08:16	ga_ls7e_ard_3-0-0_100079_2019-07-23_final
2019-07-23 10:08:39	ga_ls7e_ard_3-0-0_100080_2019-07-23_final
2019-07-23 10:09:03	ga_ls7e_ard_3-1-0_100081_2019-07-23_final
2019-07-23 10:09:27	ga_ls7e_ard_3-0-0_100082_2019-07-23_final
2019-07-23 10:09:51	ga_ls7e_ard_3-0-0_100083_2019-07-23_final
2019-07-23 10:10:15	ga_ls7e_ard_3-0-0_100084_2019-07-23_final
2019-07-24 09:11:24	ga_ls7e_ard_3-0-0_091076_2019-07-23_final
2019-07-24 09:11:48	ga_ls7e_ard_3-0-0_091077_2019-07-23_final
2019-07-24 09:12:12	ga_ls7e_ard_3-0-0_091078_2019-07-23_final
2019-07-24 09:12:36	ga_ls7e_ard_3-0-0_091079_2019-07-23_final
2019-07-24 09:13:00	ga_ls7e_ard_3-0-0_091080_2019-07-23_final
2019-07-24 09:13:24	ga_ls7e_ard_3-0-0_091081_2019-07-23_final
2019-07-24 09:13:48	ga_ls7e_ard_3-0-0_091082_2019-07-23_final
2019-07-24 09:14:12	ga_ls7e_ard_3-0-0_091083_2019-07-23_final
2019-07-24 09:14:36	ga_ls7e_ard_3-0-0_091084_2019-07-23_final
2019-07-24 09:15:00	ga_ls7e_ard_3-0-0_091085_2019-07-23_final
2019-07-24 09:15:24	ga_ls7e_ard_3-1-0_091086_2019-07-23_final
2019-07-24 09:15:48	ga_ls7e_ard_3-0-0_091087_2019-07-23_final
2019-07-24 09:16:11	ga_ls7e_ard_3-0-0_091088_2019-07-23_final
2019-07-24 09:16:35	ga_ls7e_ard_3-0-0_091089_2019-07-23_final
2019-07-24 10:47:53	ga_ls7e_ard_3-0-0_107070_2019-07-24_final
2019-07-24 10:48:17	ga_ls7e_ard_3-0-0_107071_2019-07-24_final
2019-07-24 10:48:41	ga_ls7e_ard_3-0-0_107072_2019-07-24_final
2019-07-24 10:49:05	ga_ls7e_ard_3-0-0_107073_2019-07-24_final
2019-07-26 08:59:22	ga_ls7e_ard_3-0-0_089077_2019-07-25_final
2019-07-26 08:59:46	ga_ls7e_ard_3-0-0_089078_2019-07-25_final
2019-07-26 09:00:10	ga_ls7e_ard_3-0-0_089079_2019-07-25_final
2019-07-26 09:00:33	ga_ls7e_ard_3-0-0_089080_2019-07-25_final
2019-07-26 09:00:57	ga_ls7e_ard_3-1-0_089081_2019-07-25_final
2019-07-26 09:01:21	ga_ls7e_ard_3-0-0_089082_2019-07-25_final
2019-07-26 09:01:45	ga_ls7e_ard_3-0-0_089083_2019-07-25_final
2019-07-26 09:02:09	ga_ls7e_ard_3-0-0_089084_2019-07-25_final
2019-07-26 09:02:33	ga_ls7e_ard_3-0-0_089085_2019-07-25_final
2019-07-26 09:02:57	ga_ls7e_ard_3-0-0_089086_2019-07-25_final
2019-07-29 09:34:08	ga_ls7e_ard_3-0-0_094087_2019-07-29_final
2019-07-29 11:05:02	ga_ls7e_ard_3-0-0_110067_2019-07-29_final
2019-07-29 11:06:14	ga_ls7e_ard_3-0-0_110070_2019-07-29_final
2019-07-29 11:06:38	ga_ls7e_ard_3-0-0_110071_2019-07-29_final
2019-07-29 11:07:02	ga_ls7e_ard_3-1-0_110072_2019-07-29_final
2019-07-29 11:07:25	ga_ls7e_ard_3-0-0_110073_2019-07-29_final
2019-07-29 11:07:49	ga_ls7e_ard_3-0-0_110074_2019-07-29_final
2019-07-29 11:08:13	ga_ls7e_ard_3-0-0_110075_2019-07-29_final
2019-07-29 11:08:37	ga_ls7e_ard_3-0-0_110076_2019-07-29_final
2019-07-29 11:09:01	ga_ls7e_ard_3-0-0_110077_2019-07-29_final
2019-07-29 11:09:25	ga_ls7e_ard_3-0-0_110078_2019-07-29_final
2019-07-29 11:09:49	ga_ls7e_ard_3-0-0_110079_2019-07-29_final
2019-07-29 11:10:13	ga_ls7e_ard_3-0-0_110080_2019-07-29_final
2019-07-29 11:10:37	ga_ls7e_ard_3-0-0_110081_2019-07-29_final
2019-07-29 11:11:25	ga_ls7e_ard_3-0-0_110083_2019-07-29_final
2019-07-29 11:11:49	ga_ls7e_ard_3-0-0_110084_2019-07-29_final
2019-07-30 10:10:58	ga_ls7e_ard_3-0-0_101071_2019-07-30_final
2019-07-30 10:11:22	ga_ls7e_ard_3-0-0_101072_2019-07-30_final
2019-07-30 10:11:46	ga_ls7e_ard_3-0-0_101073_2019-07-30_final
2019-07-30 10:12:10	ga_ls7e_ard_3-0-0_101074_2019-07-30_final
2019-07-30 10:12:34	ga_ls7e_ard_3-1-0_101075_2019-07-30_final
2019-07-30 10:12:58	ga_ls7e_ard_3-0-0_101076_2019-07-30_final
2019-07-30 10:13:22	ga_ls7e_ard_3-0-0_101077_2019-07-30_final
2019-07-30 10:14:09	ga_ls7e_ard_3-0-0_101079_2019-07-30_final
2019-07-30 10:14:33	ga_ls7e_ard_3-0-0_101080_2019-07-30_final
2019-07-30 10:14:57	ga_ls7e_ard_3-0-0_101081_2019-07-30_final
2019-07-30 10:15:21	ga_ls7e_ard_3-0-0_101082_2019-07-30_final
2019-07-30 10:15:45	ga_ls7e_ard_3-0-0_101083_2019-07-30_final
2019-07-31 09:16:54	ga_ls7e_ard_3-0-0_092075_2019-07-30_final
2019-07-31 09:17:18	ga_ls7e_ard_3-0-0_092076_2019-07-30_final
2019-07-31 09:17:42	ga_ls7e_ard_3-0-0_092077_2019-07-30_final
2019-07-31 09:18:06	ga_ls7e_ard_3-0-0_092078_2019-07-30_final
2019-07-31 09:18:30	ga_ls7e_ard_3-0-0_092079_2019-07-30_final
2019-07-31 09:18:54	ga_ls7e_ard_3-0-0_092080_2019-07-30_final
2019-07-31 09:19:18	ga_ls7e_ard_3-0-0_092081_2019-07-30_final
2019-07-31 09:19:42	ga_ls7e_ard_3-0-0_092082_2019-07-30_final
2019-07-31 09:20:06	ga_ls7e_ard_3-0-0_092083_2019-07-30_final
2019-07-31 09:20:30	ga_ls7e_ard_3-0-0_092084_2019-07-30_final
2019-07-31 09:20:54	ga_ls7e_ard_3-0-0_092085_2019-07-30_final
2019-07-31 09:21:17	ga_ls7e_ard_3-0-0_092086_2019-07-30_final
2019-07-31 09:21:41	ga_ls7e_ard_3-0-0_092087_2019-07-30_final
2019-07-31 09:22:05	ga_ls7e_ard_3-0-0_092088_2019-07-30_final
2019-07-31 09:22:29	ga_ls7e_ard_3-0-0_092089_2019-07-30_final
2019-07-31 10:53:23	ga_ls7e_ard_3-0-0_108069_2019-07-31_final
2019-07-31 10:53:47	ga_ls7e_ard_3-0-0_108070_2019-07-31_final
2019-07-31 10:54:11	ga_ls7e_ard_3-0-0_108071_2019-07-31_final
2019-07-31 10:54:35	ga_ls7e_ard_3-0-0_108072_2019-07-31_final
2019-07-31 10:54:59	ga_ls7e_ard_3-0-0_108073_2019-07-31_final
2019-07-31 10:55:23	ga_ls7e_ard_3-0-0_108074_2019-07-31_final
2019-07-31 10:55:47	ga_ls7e_ard_3-1-0_108075_2019-07-31_final
2019-07-31 10:56:11	ga_ls7e_ard_3-0-0_108076_2019-07-31_final
2019-07-31 10:56:35	ga_ls7e_ard_3-0-0_108077_2019-07-31_final
2019-07-31 10:56:59	ga_ls7e_ard_3-0-0_108078_2019-07-31_final
2019-07-31 10:57:23	ga_ls7e_ard_3-0-0_108079_2019-07-31_final
2019-07-31 10:57:47	ga_ls7e_ard_3-0-0_108080_2019-07-31_final
2019-07-31 10:58:11	ga_ls7e_ard_3-0-0_108081_2019-07-31_final
2019-07-31 10:58:34	ga_ls7e_ard_3-1-0_108082_2019-07-31_final
2019-07-31 10:58:58	ga_ls7e_ard_3-0-0_108083_2019-07-31_final

Return to ga_ls7e_ard_3 across July 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

July

All days