Search ga_s2am_ard_3 whole collection

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 (e.g. ‘epsg:32654’) :

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):

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:

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 Sentinel it is MGRS code. :

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless to (exclusive):

Unique identifier for a datastrip relative to a given Datatake. (e.g. ‘S2A_OPER_MSI_L1C_DS_SGS__20161214T040601_S20161214T005840_N02.04’) :

ESA product URI, with the '.SAFE' ending removed. (e.g. 'S2A_MSIL1C_20220303T000731_N0400_R073_T56LNM_20220303T012845') :

Granule ID according to the ESA naming convention (e.g. ‘S2A_OPER_MSI_L1C_TL_SGS__20161214T040601_A007721_T53KRB_N02.04’) :

Acquisition time range From

Acquisition time range to (exclusive)

Dataset URI:

Add field

First 100 results (query limit)

Time (local)	Label
2015-10-22 11:38:10	ga_s2am_ard_3-2-1_50JNP_2015-10-22_final.stac-item
2015-11-14 11:54:02	ga_s2am_ard_3-2-1_50JNP_2015-11-14_final.stac-item
2015-11-21 11:41:49	ga_s2am_ard_3-2-1_50JNP_2015-11-21_final.stac-item
2015-12-04 11:50:12	ga_s2am_ard_3-2-1_50JNP_2015-12-04_final.stac-item
2015-12-21 11:47:51	ga_s2am_ard_3-2-1_50JNP_2015-12-21_final.stac-item
2016-01-03 11:57:46	ga_s2am_ard_3-2-1_50JNP_2016-01-03_final.stac-item
2016-01-20 11:47:47	ga_s2am_ard_3-2-1_50JNP_2016-01-20_final.stac-item
2016-02-02 11:48:17	ga_s2am_ard_3-2-1_50JNP_2016-02-02_final.stac-item
2016-02-19 11:45:19	ga_s2am_ard_3-2-1_50JNP_2016-02-19_final.stac-item
2016-04-02 11:49:05	ga_s2am_ard_3-2-1_50JNP_2016-04-02_final.stac-item
2016-04-19 11:37:58	ga_s2am_ard_3-2-1_50JNP_2016-04-19_final.stac-item
2016-04-29 11:42:00	ga_s2am_ard_3-2-1_50JNP_2016-04-29_final.stac-item
2016-05-09 11:40:18	ga_s2am_ard_3-2-1_50JNP_2016-05-09_final.stac-item
2016-05-22 11:48:35	ga_s2am_ard_3-2-1_50JNP_2016-05-22_final.stac-item
2016-05-29 11:40:18	ga_s2am_ard_3-2-1_50JNP_2016-05-29_final.stac-item
2016-09-06 11:40:12	ga_s2am_ard_3-2-1_50JNP_2016-09-06_final.stac-item
2016-09-19 11:48:24	ga_s2am_ard_3-2-1_50JNP_2016-09-19_final.stac-item
2016-09-26 11:40:08	ga_s2am_ard_3-2-1_50JNP_2016-09-26_final.stac-item
2016-10-09 11:48:21	ga_s2am_ard_3-2-1_50JNP_2016-10-09_final.stac-item
2016-10-16 11:40:08	ga_s2am_ard_3-2-1_50JNP_2016-10-16_final.stac-item
2016-10-26 11:39:44	ga_s2am_ard_3-2-1_50JNP_2016-10-26_final.stac-item
2016-10-29 11:54:20	ga_s2am_ard_3-2-1_50JNP_2016-10-29_final.stac-item
2017-05-04 11:45:11	ga_s2am_ard_3-2-1_50JNP_2017-05-04_final
2017-05-24 11:45:07	ga_s2am_ard_3-2-1_50JNP_2017-05-24_final
2017-06-06 11:51:13	ga_s2am_ard_3-2-1_50JNP_2017-06-06_final
2017-06-26 11:49:44	ga_s2am_ard_3-2-1_50JNP_2017-06-26_final
2017-07-16 11:49:46	ga_s2am_ard_3-2-1_50JNP_2017-07-16_final
2017-09-14 11:51:53	ga_s2am_ard_3-2-1_50JNP_2017-09-14_final
2017-09-21 11:40:44	ga_s2am_ard_3-2-1_50JNP_2017-09-21_final
2017-10-11 11:40:47	ga_s2am_ard_3-2-1_50JNP_2017-10-11_final
2017-10-24 11:52:38	ga_s2am_ard_3-2-1_50JNP_2017-10-24_final
2017-12-30 11:42:34	ga_s2am_ard_3-2-1_50JNP_2017-12-30_final
2018-01-12 11:50:28	ga_s2am_ard_3-2-1_50JNP_2018-01-12_final
2018-02-01 11:49:16	ga_s2am_ard_3-2-1_50JNP_2018-02-01_final
2018-02-18 11:45:05	ga_s2am_ard_3-2-1_50JNP_2018-02-18_final
2018-02-21 11:54:34	ga_s2am_ard_3-2-1_50JNP_2018-02-21_final
2018-03-03 11:52:20	ga_s2am_ard_3-2-1_50JNP_2018-03-03_final
2018-03-10 11:39:48	ga_s2am_ard_3-2-1_50JNP_2018-03-10_final
2018-03-13 11:52:23	ga_s2am_ard_3-2-1_50JNP_2018-03-13_final
2018-03-20 11:44:19	ga_s2am_ard_3-2-1_50JNP_2018-03-20_final
2018-03-23 11:53:37	ga_s2am_ard_3-2-1_50JNP_2018-03-23_final
2018-04-09 11:40:31	ga_s2am_ard_3-2-1_50JNP_2018-04-09_final
2018-04-19 11:40:43	ga_s2am_ard_3-2-1_50JNP_2018-04-19_final
2018-09-26 11:42:55	ga_s2am_ard_3-2-1_50JNP_2018-09-26_final
2018-10-06 11:40:38	ga_s2am_ard_3-2-1_50JNP_2018-10-06_final
2018-10-09 11:51:12	ga_s2am_ard_3-2-1_50JNP_2018-10-09_final
2018-11-05 11:45:30	ga_s2am_ard_3-2-1_50JNP_2018-11-05_final
2018-11-15 11:45:33	ga_s2am_ard_3-2-1_50JNP_2018-11-15_final
2018-11-25 11:45:31	ga_s2am_ard_3-2-1_50JNP_2018-11-25_final
2019-05-27 11:54:22	ga_s2am_ard_3-2-1_50KPU_2019-05-27_final
2019-06-06 11:54:21	ga_s2am_ard_3-2-1_50KPU_2019-06-06_final
2019-06-16 11:54:22	ga_s2am_ard_3-2-1_50KPU_2019-06-16_final
2019-06-26 11:54:23	ga_s2am_ard_3-2-1_50KPU_2019-06-26_final
2019-07-06 11:54:24	ga_s2am_ard_3-2-1_50KPU_2019-07-06_final
2019-07-06 11:54:33	ga_s2am_ard_3-2-1_50KPU_2019-07-06_final
2019-07-16 11:54:24	ga_s2am_ard_3-2-1_50KPU_2019-07-16_final
2019-07-26 11:54:24	ga_s2am_ard_3-2-1_50KPU_2019-07-26_final
2019-08-05 11:54:23	ga_s2am_ard_3-2-1_50KPU_2019-08-05_final
2019-08-05 11:54:33	ga_s2am_ard_3-2-1_50KPU_2019-08-05_final
2019-08-15 11:54:22	ga_s2am_ard_3-2-1_50KPU_2019-08-15_final
2019-08-25 11:54:21	ga_s2am_ard_3-2-1_50KPU_2019-08-25_final
2019-09-04 11:54:19	ga_s2am_ard_3-2-1_50KPU_2019-09-04_final
2019-09-14 11:54:17	ga_s2am_ard_3-2-1_50KPU_2019-09-14_final
2019-10-04 11:54:21	ga_s2am_ard_3-2-1_50KPU_2019-10-04_final
2019-10-14 11:54:22	ga_s2am_ard_3-2-1_50KPU_2019-10-14_final
2019-10-24 11:54:22	ga_s2am_ard_3-2-1_50KPU_2019-10-24_final
2019-10-24 11:54:32	ga_s2am_ard_3-2-1_50KPU_2019-10-24_final
2019-11-03 11:54:22	ga_s2am_ard_3-2-1_50KPU_2019-11-03_final
2019-11-13 11:54:21	ga_s2am_ard_3-2-1_50KPU_2019-11-13_final
2019-11-23 11:54:19	ga_s2am_ard_3-2-1_50KPU_2019-11-23_final
2019-12-03 11:54:17	ga_s2am_ard_3-2-1_50KPU_2019-12-03_final
2019-12-13 11:54:14	ga_s2am_ard_3-2-1_50KPU_2019-12-13_final
2019-12-23 11:54:13	ga_s2am_ard_3-2-1_50KPU_2019-12-23_final
2020-01-02 11:54:13	ga_s2am_ard_3-2-1_50KPU_2020-01-02_final
2020-01-12 11:54:13	ga_s2am_ard_3-2-1_50KPU_2020-01-12_final
2020-01-22 11:54:12	ga_s2am_ard_3-2-1_50KPU_2020-01-22_final
2020-02-01 11:54:11	ga_s2am_ard_3-2-1_50KPU_2020-02-01_final
2020-02-11 11:54:11	ga_s2am_ard_3-2-1_50KPU_2020-02-11_final
2020-02-21 11:54:13	ga_s2am_ard_3-2-1_50KPU_2020-02-21_final
2020-03-02 11:54:14	ga_s2am_ard_3-2-1_50KPU_2020-03-02_final
2020-03-12 11:54:15	ga_s2am_ard_3-2-1_50KPU_2020-03-12_final
2020-03-22 11:54:15	ga_s2am_ard_3-2-1_50KPU_2020-03-22_final
2020-04-01 11:54:14	ga_s2am_ard_3-2-1_50KPU_2020-04-01_final
2020-04-01 11:54:21	ga_s2am_ard_3-2-1_50KPU_2020-04-01_final
2020-04-11 11:54:17	ga_s2am_ard_3-2-1_50KPU_2020-04-11_final
2020-04-21 11:54:20	ga_s2am_ard_3-2-1_50KPU_2020-04-21_final
2020-05-01 11:54:21	ga_s2am_ard_3-2-1_50KPU_2020-05-01_final
2020-05-01 11:54:28	ga_s2am_ard_3-2-1_50KPU_2020-05-01_final
2020-05-11 11:54:23	ga_s2am_ard_3-2-1_50KPU_2020-05-11_final
2020-05-21 11:54:24	ga_s2am_ard_3-2-1_50KPU_2020-05-21_final
2020-05-31 11:54:25	ga_s2am_ard_3-2-1_50KPU_2020-05-31_final
2020-05-31 11:54:35	ga_s2am_ard_3-2-1_50KPU_2020-05-31_final
2020-06-10 11:54:25	ga_s2am_ard_3-2-1_50KPU_2020-06-10_final
2020-06-20 11:54:25	ga_s2am_ard_3-2-1_50KPU_2020-06-20_final
2020-06-30 11:54:24	ga_s2am_ard_3-2-1_50KPU_2020-06-30_final
2020-06-30 11:54:31	ga_s2am_ard_3-2-1_50KPU_2020-06-30_final
2020-07-10 11:54:23	ga_s2am_ard_3-2-1_50KPU_2020-07-10_final
2020-07-20 11:54:24	ga_s2am_ard_3-2-1_50KPU_2020-07-20_final
2020-07-30 11:54:24	ga_s2am_ard_3-2-1_50KPU_2020-07-30_final
2020-08-09 11:54:25	ga_s2am_ard_3-2-1_50KPU_2020-08-09_final

Return to ga_s2am_ard_3 whole collection overview

ga_s2am_ard_3: Geoscience Australia Sentinel 2A MSI Analysis Ready Data Collection 3

Baseline satellite data

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

All years