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
2016-12-08 11:44:46	ga_s2am_ard_3-2-1_50JNR_2016-12-08_final
2016-12-15 11:38:49	ga_s2am_ard_3-2-1_50JNR_2016-12-15_final
2016-12-28 11:45:05	ga_s2am_ard_3-2-1_50JNR_2016-12-28_final
2017-01-14 11:34:53	ga_s2am_ard_3-2-1_50JNR_2017-01-14_final
2017-01-24 11:44:18	ga_s2am_ard_3-2-1_50JNR_2017-01-24_final
2017-02-03 11:41:40	ga_s2am_ard_3-2-1_50JNR_2017-02-03_final
2017-02-13 11:44:16	ga_s2am_ard_3-2-1_50JNR_2017-02-13_final
2017-02-16 11:52:25	ga_s2am_ard_3-2-1_50JNR_2017-02-16_final
2017-02-26 11:52:43	ga_s2am_ard_3-2-1_50JNR_2017-02-26_final
2017-03-05 11:44:15	ga_s2am_ard_3-2-1_50JNR_2017-03-05_final
2017-03-18 11:51:11	ga_s2am_ard_3-2-1_50JNR_2017-03-18_final
2017-03-25 11:38:25	ga_s2am_ard_3-2-1_50JNR_2017-03-25_final
2017-05-04 11:38:37	ga_s2am_ard_3-2-1_50JNR_2017-05-04_final
2017-05-17 11:49:48	ga_s2am_ard_3-2-1_50JNR_2017-05-17_final
2017-05-24 11:38:34	ga_s2am_ard_3-2-1_50JNR_2017-05-24_final
2017-06-06 11:51:13	ga_s2am_ard_3-2-1_50JNR_2017-06-06_final
2017-06-26 11:49:44	ga_s2am_ard_3-2-1_50JNR_2017-06-26_final
2017-07-03 11:43:16	ga_s2am_ard_3-2-1_50JNR_2017-07-03_final
2017-07-16 11:49:46	ga_s2am_ard_3-2-1_50JNR_2017-07-16_final
2017-08-05 11:51:57	ga_s2am_ard_3-2-1_50JNR_2017-08-05_final
2017-08-12 11:40:50	ga_s2am_ard_3-2-1_50JNR_2017-08-12_final
2017-09-01 11:40:44	ga_s2am_ard_3-2-1_50JNR_2017-09-01_final
2017-09-21 11:40:44	ga_s2am_ard_3-2-1_50JNR_2017-09-21_final
2017-10-24 11:52:38	ga_s2am_ard_3-2-1_50JNR_2017-10-24_final
2017-10-31 11:42:00	ga_s2am_ard_3-2-1_50JNR_2017-10-31_final
2017-11-13 11:54:29	ga_s2am_ard_3-2-1_50JNR_2017-11-13_final
2017-11-20 11:41:28	ga_s2am_ard_3-2-1_50JNR_2017-11-20_final
2021-12-29 11:45:13	ga_s2am_ard_3-2-1_50JNR_2021-12-29_final
2022-01-01 11:55:08	ga_s2am_ard_3-2-1_50JNR_2022-01-01_final
2022-01-02 11:25:00	ga_s2am_ard_3-2-1_51JWL_2022-01-02_final
2022-01-08 11:45:13	ga_s2am_ard_3-2-1_50JNR_2022-01-08_final
2022-01-11 11:55:07	ga_s2am_ard_3-2-1_50JNR_2022-01-11_final
2022-01-12 11:25:00	ga_s2am_ard_3-2-1_51JWL_2022-01-12_final
2022-01-18 11:45:12	ga_s2am_ard_3-2-1_50JNR_2022-01-18_final
2022-01-21 11:55:06	ga_s2am_ard_3-2-1_50JNR_2022-01-21_final
2022-01-22 11:24:59	ga_s2am_ard_3-2-1_51JWL_2022-01-22_final
2022-01-28 11:45:11	ga_s2am_ard_3-2-1_50JNR_2022-01-28_final
2022-01-31 11:55:06	ga_s2am_ard_3-2-1_50JNR_2022-01-31_final
2022-02-01 11:24:59	ga_s2am_ard_3-2-1_51JWL_2022-02-01_final
2022-02-07 11:45:11	ga_s2am_ard_3-2-1_50JNR_2022-02-07_final
2022-02-10 11:55:05	ga_s2am_ard_3-2-1_50JNR_2022-02-10_final
2022-02-11 11:24:58	ga_s2am_ard_3-2-1_51JWL_2022-02-11_final
2022-02-17 11:45:12	ga_s2am_ard_3-2-1_50JNR_2022-02-17_final
2022-02-20 11:55:08	ga_s2am_ard_3-2-1_50JNR_2022-02-20_final
2022-02-21 11:25:00	ga_s2am_ard_3-2-1_51JWL_2022-02-21_final
2022-02-27 11:45:14	ga_s2am_ard_3-2-1_50JNR_2022-02-27_final
2022-03-02 11:55:10	ga_s2am_ard_3-2-1_50JNR_2022-03-02_final
2022-03-03 11:25:02	ga_s2am_ard_3-2-1_51JWL_2022-03-03_final
2022-03-09 11:45:15	ga_s2am_ard_3-2-1_50JNR_2022-03-09_final
2022-03-12 11:55:10	ga_s2am_ard_3-2-1_50JNR_2022-03-12_final
2022-03-13 11:25:03	ga_s2am_ard_3-2-1_51JWL_2022-03-13_final
2022-03-19 11:45:15	ga_s2am_ard_3-2-1_50JNR_2022-03-19_final
2022-03-22 11:55:09	ga_s2am_ard_3-2-1_50JNR_2022-03-22_final
2022-03-23 11:25:02	ga_s2am_ard_3-2-1_51JWL_2022-03-23_final
2022-03-29 11:45:14	ga_s2am_ard_3-2-1_50JNR_2022-03-29_final
2022-04-01 11:55:08	ga_s2am_ard_3-2-1_50JNR_2022-04-01_final
2022-04-02 11:25:01	ga_s2am_ard_3-2-1_51JWL_2022-04-02_final
2022-04-08 11:45:12	ga_s2am_ard_3-2-1_50JNR_2022-04-08_final
2022-04-11 11:55:08	ga_s2am_ard_3-2-1_50JNR_2022-04-11_final
2022-04-12 11:25:01	ga_s2am_ard_3-2-1_51JWL_2022-04-12_final
2022-04-18 11:45:15	ga_s2am_ard_3-2-1_50JNR_2022-04-18_final
2022-04-21 11:55:11	ga_s2am_ard_3-2-1_50JNR_2022-04-21_final
2022-04-22 11:25:03	ga_s2am_ard_3-2-1_51JWL_2022-04-22_final
2022-04-28 11:45:16	ga_s2am_ard_3-2-1_50JNR_2022-04-28_final
2022-05-01 11:55:11	ga_s2am_ard_3-2-1_50JNR_2022-05-01_final
2022-05-02 11:25:04	ga_s2am_ard_3-2-1_51JWL_2022-05-02_final
2022-05-08 11:45:16	ga_s2am_ard_3-2-1_50JNR_2022-05-08_final
2022-05-11 11:55:10	ga_s2am_ard_3-2-1_50JNR_2022-05-11_final
2022-05-12 11:25:02	ga_s2am_ard_3-2-1_51JWL_2022-05-12_final
2022-05-18 11:45:17	ga_s2am_ard_3-2-1_50JNR_2022-05-18_final
2022-05-21 11:55:12	ga_s2am_ard_3-2-1_50JNR_2022-05-21_final
2022-05-22 11:25:05	ga_s2am_ard_3-2-1_51JWL_2022-05-22_final
2022-05-28 11:45:19	ga_s2am_ard_3-2-1_50JNR_2022-05-28_final
2022-05-31 11:55:14	ga_s2am_ard_3-2-1_50JNR_2022-05-31_final
2022-06-01 11:25:06	ga_s2am_ard_3-2-1_51JWL_2022-06-01_final
2022-06-07 11:45:19	ga_s2am_ard_3-2-1_50JNR_2022-06-07_final
2022-06-10 11:55:15	ga_s2am_ard_3-2-1_50JNR_2022-06-10_final
2022-06-11 11:25:08	ga_s2am_ard_3-2-1_51JWL_2022-06-11_final
2022-06-17 11:45:22	ga_s2am_ard_3-2-1_50JNR_2022-06-17_final
2022-06-20 11:55:18	ga_s2am_ard_3-2-1_50JNR_2022-06-20_final
2022-06-21 11:25:11	ga_s2am_ard_3-2-1_51JWL_2022-06-21_final
2022-06-27 11:45:15	ga_s2am_ard_3-2-1_50JNR_2022-06-27_final
2022-06-27 11:45:24	ga_s2am_ard_3-2-1_50JNR_2022-06-27_final
2022-06-30 11:55:19	ga_s2am_ard_3-2-1_50JNR_2022-06-30_final
2022-07-01 11:25:12	ga_s2am_ard_3-2-1_51JWL_2022-07-01_final
2022-07-07 11:45:24	ga_s2am_ard_3-2-1_50JNR_2022-07-07_final
2022-07-10 11:55:19	ga_s2am_ard_3-2-1_50JNR_2022-07-10_final
2022-07-11 11:25:11	ga_s2am_ard_3-2-1_51JWL_2022-07-11_final
2022-07-17 11:45:23	ga_s2am_ard_3-2-1_50JNR_2022-07-17_final
2022-07-20 11:55:18	ga_s2am_ard_3-2-1_50JNR_2022-07-20_final
2022-07-21 11:25:10	ga_s2am_ard_3-2-1_51JWL_2022-07-21_final
2022-07-27 11:45:21	ga_s2am_ard_3-2-1_50JNR_2022-07-27_final
2022-07-30 11:55:16	ga_s2am_ard_3-2-1_50JNR_2022-07-30_final
2022-07-31 11:25:09	ga_s2am_ard_3-2-1_51JWL_2022-07-31_final
2022-08-06 11:45:22	ga_s2am_ard_3-2-1_50JNR_2022-08-06_final
2022-08-09 11:55:18	ga_s2am_ard_3-2-1_50JNR_2022-08-09_final.stac-item
2022-08-10 11:25:10	ga_s2am_ard_3-2-1_51JWL_2022-08-10_final
2022-08-16 11:45:24	ga_s2am_ard_3-2-1_50JNR_2022-08-16_final
2022-08-19 11:55:19	ga_s2am_ard_3-2-1_50JNR_2022-08-19_final
2022-08-20 11:25:11	ga_s2am_ard_3-2-1_51JWL_2022-08-20_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