Search ga_s2am_ard_3 whole collection

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels.\n\nFor these ARD products, this value comes from the Fmask algorithm.\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels.\n\nFor these ARD products, this value comes from the Fmask algorithm.\n 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\n\n(e.g. ‘epsg:32654’)\n:

One of - final|interim|nrt (near real time):

ID of a dataset type:

Ground sampling distance of the sensor’s best resolution band\nin metres; represents the size (or spatial resolution) of one pixel.\n from:

Ground sampling distance of the sensor’s best resolution band\nin metres; represents the size (or spatial resolution) of one pixel.\n to (exclusive):

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north\n from:

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north\n to (exclusive):

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon.\n from:

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon.\n 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\n from:

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

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm\n 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\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm\n 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\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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.\nFor Sentinel it is MGRS code.\n:

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

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

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

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

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

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

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

Acquisition time range From

Acquisition time range to (exclusive)

Dataset URI:

Add field

First 100 results (query limit)

Time (local)	Label
2021-08-24 11:55:51	ga_s2am_ard_3-2-1_50JNN_2021-08-24_final.stac-item
2021-08-31 11:45:55	ga_s2am_ard_3-2-1_50JNN_2021-08-31_final.stac-item
2021-09-03 11:55:50	ga_s2am_ard_3-2-1_50JNN_2021-09-03_final.stac-item
2021-09-13 11:55:50	ga_s2am_ard_3-2-1_50JNN_2021-09-13_final.stac-item
2021-09-20 11:45:56	ga_s2am_ard_3-2-1_50JNN_2021-09-20_final.stac-item
2021-09-23 11:55:52	ga_s2am_ard_3-2-1_50JNN_2021-09-23_final.stac-item
2021-10-10 11:45:59	ga_s2am_ard_3-2-1_50JNN_2021-10-10_final.stac-item
2021-10-13 11:55:55	ga_s2am_ard_3-2-1_50JNN_2021-10-13_final.stac-item
2021-10-20 11:45:59	ga_s2am_ard_3-2-1_50JNN_2021-10-20_final.stac-item
2021-10-23 11:55:55	ga_s2am_ard_3-2-1_50JNN_2021-10-23_final.stac-item
2021-10-30 11:45:58	ga_s2am_ard_3-2-1_50JNN_2021-10-30_final.stac-item
2021-11-02 11:55:54	ga_s2am_ard_3-2-1_50JNN_2021-11-02_final.stac-item
2021-11-12 11:55:51	ga_s2am_ard_3-2-1_50JNN_2021-11-12_final.stac-item
2021-11-19 11:45:54	ga_s2am_ard_3-2-1_50JNN_2021-11-19_final.stac-item
2021-11-22 11:55:49	ga_s2am_ard_3-2-1_50JNN_2021-11-22_final.stac-item
2021-11-29 11:45:54	ga_s2am_ard_3-2-1_50JNN_2021-11-29_final.stac-item
2021-12-02 11:55:49	ga_s2am_ard_3-2-1_50JNN_2021-12-02_final.stac-item
2021-12-12 11:55:48	ga_s2am_ard_3-2-1_50JNN_2021-12-12_final.stac-item
2021-12-29 11:45:55	ga_s2am_ard_3-2-1_50JNN_2021-12-29_final.stac-item
2022-01-01 11:55:51	ga_s2am_ard_3-2-1_50JNN_2022-01-01_final.stac-item
2022-01-08 11:45:55	ga_s2am_ard_3-2-1_50JNN_2022-01-08_final.stac-item
2022-01-11 11:55:51	ga_s2am_ard_3-2-1_50JNN_2022-01-11_final.stac-item
2022-01-18 11:45:45	ga_s2am_ard_3-2-1_50JNN_2022-01-18_final.stac-item
2022-01-18 11:45:54	ga_s2am_ard_3-2-1_50JNN_2022-01-18_final.stac-item
2022-01-21 11:55:49	ga_s2am_ard_3-2-1_50JNN_2022-01-21_final.stac-item
2022-01-31 11:55:49	ga_s2am_ard_3-2-1_50JNN_2022-01-31_final.stac-item
2022-02-07 11:45:53	ga_s2am_ard_3-2-1_50JNN_2022-02-07_final.stac-item
2022-02-10 11:55:48	ga_s2am_ard_3-2-1_50JNN_2022-02-10_final.stac-item
2022-02-17 11:45:55	ga_s2am_ard_3-2-1_50JNN_2022-02-17_final.stac-item
2022-02-20 11:55:51	ga_s2am_ard_3-2-1_50JNN_2022-02-20_final.stac-item
2022-03-02 11:55:53	ga_s2am_ard_3-2-1_50JNN_2022-03-02_final.stac-item
2022-03-09 11:45:58	ga_s2am_ard_3-2-1_50JNN_2022-03-09_final.stac-item
2022-03-12 11:55:53	ga_s2am_ard_3-2-1_50JNN_2022-03-12_final.stac-item
2022-03-22 11:55:53	ga_s2am_ard_3-2-1_50JNN_2022-03-22_final.stac-item
2022-03-29 11:45:56	ga_s2am_ard_3-2-1_50JNN_2022-03-29_final.stac-item
2022-04-01 11:55:54	ga_s2am_ard_3-2-1_50JNN_2022-04-01_final.stac-item
2022-04-08 11:45:55	ga_s2am_ard_3-2-1_50JNN_2022-04-08_final.stac-item
2022-04-11 11:55:51	ga_s2am_ard_3-2-1_50JNN_2022-04-11_final.stac-item
2022-04-18 11:45:58	ga_s2am_ard_3-2-1_50JNN_2022-04-18_final.stac-item
2022-04-21 11:55:54	ga_s2am_ard_3-2-1_50JNN_2022-04-21_final.stac-item
2022-04-28 11:45:59	ga_s2am_ard_3-2-1_50JNN_2022-04-28_final.stac-item
2022-05-01 11:55:54	ga_s2am_ard_3-2-1_50JNN_2022-05-01_final.stac-item
2022-05-08 11:45:58	ga_s2am_ard_3-2-1_50JNN_2022-05-08_final.stac-item
2022-05-11 11:55:53	ga_s2am_ard_3-2-1_50JNN_2022-05-11_final.stac-item
2022-05-18 11:45:59	ga_s2am_ard_3-2-1_50JNN_2022-05-18_final.stac-item
2022-05-21 11:55:56	ga_s2am_ard_3-2-1_50JNN_2022-05-21_final.stac-item
2022-05-21 11:56:04	ga_s2am_ard_3-2-1_50JNN_2022-05-21_final.stac-item
2022-05-28 11:46:01	ga_s2am_ard_3-2-1_50JNN_2022-05-28_final.stac-item
2022-05-31 11:55:57	ga_s2am_ard_3-2-1_50JNN_2022-05-31_final.stac-item
2022-06-07 11:46:02	ga_s2am_ard_3-2-1_50JNN_2022-06-07_final.stac-item
2022-06-10 11:55:58	ga_s2am_ard_3-2-1_50JNN_2022-06-10_final.stac-item
2022-06-17 11:46:05	ga_s2am_ard_3-2-1_50JNN_2022-06-17_final.stac-item
2022-06-20 11:56:01	ga_s2am_ard_3-2-1_50JNN_2022-06-20_final.stac-item
2022-06-27 11:46:07	ga_s2am_ard_3-2-1_50JNN_2022-06-27_final.stac-item
2022-06-30 11:56:02	ga_s2am_ard_3-2-1_50JNN_2022-06-30_final.stac-item
2022-07-07 11:46:07	ga_s2am_ard_3-2-1_50JNN_2022-07-07_final.stac-item
2022-07-10 11:56:02	ga_s2am_ard_3-2-1_50JNN_2022-07-10_final.stac-item
2022-07-17 11:46:06	ga_s2am_ard_3-2-1_50JNN_2022-07-17_final.stac-item
2022-07-20 11:56:01	ga_s2am_ard_3-2-1_50JNN_2022-07-20_final.stac-item
2022-07-27 11:46:04	ga_s2am_ard_3-2-1_50JNN_2022-07-27_final.stac-item
2022-07-30 11:55:59	ga_s2am_ard_3-2-1_50JNN_2022-07-30_final.stac-item
2022-08-06 11:46:05	ga_s2am_ard_3-2-1_50JNN_2022-08-06_final.stac-item
2022-08-09 11:56:01	ga_s2am_ard_3-2-1_50JNN_2022-08-09_final.stac-item
2022-08-16 11:46:06	ga_s2am_ard_3-2-1_50JNN_2022-08-16_final.stac-item
2022-08-19 11:56:02	ga_s2am_ard_3-2-1_50JNN_2022-08-19_final.stac-item
2022-09-05 11:46:06	ga_s2am_ard_3-2-1_50JNN_2022-09-05_final.stac-item
2022-09-08 11:56:01	ga_s2am_ard_3-2-1_50JNN_2022-09-08_final.stac-item
2022-09-15 11:46:03	ga_s2am_ard_3-2-1_50JNN_2022-09-15_final.stac-item
2022-09-18 11:55:58	ga_s2am_ard_3-2-1_50JNN_2022-09-18_final.stac-item
2022-09-25 11:46:01	ga_s2am_ard_3-2-1_50JNN_2022-09-25_final.stac-item
2022-09-28 11:55:57	ga_s2am_ard_3-2-1_50JNN_2022-09-28_final.stac-item
2022-10-05 11:46:01	ga_s2am_ard_3-2-1_50JNN_2022-10-05_final.stac-item
2022-10-08 11:55:56	ga_s2am_ard_3-2-1_50JNN_2022-10-08_final.stac-item
2022-10-15 11:45:58	ga_s2am_ard_3-2-1_50JNN_2022-10-15_final.stac-item
2022-10-18 11:55:52	ga_s2am_ard_3-2-1_50JNN_2022-10-18_final.stac-item
2022-10-25 11:45:48	ga_s2am_ard_3-2-1_50JNN_2022-10-25_final.stac-item
2022-10-25 11:45:57	ga_s2am_ard_3-2-1_50JNN_2022-10-25_final.stac-item
2022-10-28 11:55:53	ga_s2am_ard_3-2-1_50JNN_2022-10-28_final.stac-item
2022-11-04 11:45:58	ga_s2am_ard_3-2-1_50JNN_2022-11-04_final.stac-item
2022-11-07 11:55:53	ga_s2am_ard_3-2-1_50JNN_2022-11-07_final.stac-item
2022-11-14 11:45:57	ga_s2am_ard_3-2-1_50JNN_2022-11-14_final.stac-item
2022-11-17 11:55:51	ga_s2am_ard_3-2-1_50JNN_2022-11-17_final.stac-item
2022-11-24 11:45:53	ga_s2am_ard_3-2-1_50JNN_2022-11-24_final.stac-item
2022-11-27 11:55:49	ga_s2am_ard_3-2-1_50JNN_2022-11-27_final.stac-item
2022-12-04 11:45:54	ga_s2am_ard_3-2-1_50JNN_2022-12-04_final.stac-item
2022-12-07 11:55:50	ga_s2am_ard_3-2-1_50JNN_2022-12-07_final.stac-item
2022-12-17 11:55:49	ga_s2am_ard_3-2-1_50JNN_2022-12-17_final.stac-item
2022-12-24 11:45:52	ga_s2am_ard_3-2-1_50JNN_2022-12-24_final.stac-item
2022-12-27 11:55:48	ga_s2am_ard_3-2-1_50JNN_2022-12-27_final.stac-item
2023-01-03 11:45:53	ga_s2am_ard_3-2-1_50JNN_2023-01-03_final.stac-item
2023-01-06 11:55:48	ga_s2am_ard_3-2-1_50JNN_2023-01-06_final.stac-item
2023-01-13 11:45:51	ga_s2am_ard_3-2-1_50JNN_2023-01-13_final.stac-item
2023-01-16 11:55:46	ga_s2am_ard_3-2-1_50JNN_2023-01-16_final.stac-item
2023-01-23 11:45:50	ga_s2am_ard_3-2-1_50JNN_2023-01-23_final.stac-item
2023-01-26 11:55:46	ga_s2am_ard_3-2-1_50JNN_2023-01-26_final.stac-item
2023-02-02 11:45:52	ga_s2am_ard_3-2-1_50JNN_2023-02-02_final
2023-02-05 11:55:46	ga_s2am_ard_3-2-1_50JNN_2023-02-05_final
2023-02-05 11:55:52	ga_s2am_ard_3-2-1_50JNN_2023-02-05_final
2023-02-12 11:45:52	ga_s2am_ard_3-2-1_50JNN_2023-02-12_nrt.stac-item
2023-02-15 11:55:46	ga_s2am_ard_3-2-1_50JNN_2023-02-15_nrt.stac-item

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

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

All years