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
2021-08-27 12:04:26	ga_s2am_ard_3-2-1_50KLU_2021-08-27_final.stac-item
2021-09-06 12:04:25	ga_s2am_ard_3-2-1_50KLU_2021-09-06_final.stac-item
2021-09-23 11:54:36	ga_s2am_ard_3-2-1_50KLU_2021-09-23_final.stac-item
2021-09-26 12:04:28	ga_s2am_ard_3-2-1_50KLU_2021-09-26_final.stac-item
2021-10-06 12:04:30	ga_s2am_ard_3-2-1_50KLU_2021-10-06_final.stac-item
2021-10-13 11:54:38	ga_s2am_ard_3-2-1_50KLU_2021-10-13_final.stac-item
2021-10-23 11:54:38	ga_s2am_ard_3-2-1_50KLU_2021-10-23_final.stac-item
2021-11-02 11:54:37	ga_s2am_ard_3-2-1_50KLU_2021-11-02_final.stac-item
2021-11-12 11:54:35	ga_s2am_ard_3-2-1_50KLU_2021-11-12_final.stac-item
2021-11-15 12:04:26	ga_s2am_ard_3-2-1_50KLU_2021-11-15_final.stac-item
2021-11-22 11:54:33	ga_s2am_ard_3-2-1_50KLU_2021-11-22_final.stac-item
2021-11-25 12:04:25	ga_s2am_ard_3-2-1_50KLU_2021-11-25_final.stac-item
2021-12-05 12:04:24	ga_s2am_ard_3-2-1_50KLU_2021-12-05_final.stac-item
2021-12-12 11:54:32	ga_s2am_ard_3-2-1_50KLU_2021-12-12_final.stac-item
2021-12-15 12:04:24	ga_s2am_ard_3-2-1_50KLU_2021-12-15_final.stac-item
2021-12-25 12:04:26	ga_s2am_ard_3-2-1_50KLU_2021-12-25_final.stac-item
2022-01-01 11:54:35	ga_s2am_ard_3-2-1_50KLU_2022-01-01_final.stac-item
2022-01-04 12:04:26	ga_s2am_ard_3-2-1_50KLU_2022-01-04_final.stac-item
2022-01-31 11:54:33	ga_s2am_ard_3-2-1_50KLU_2022-01-31_final.stac-item
2022-02-20 11:54:35	ga_s2am_ard_3-2-1_50KLU_2022-02-20_final.stac-item
2022-02-23 12:04:22	ga_s2am_ard_3-2-1_50KLU_2022-02-23_final.stac-item
2022-02-23 12:04:29	ga_s2am_ard_3-2-1_50KLU_2022-02-23_final.stac-item
2022-03-02 11:54:37	ga_s2am_ard_3-2-1_50KLU_2022-03-02_final.stac-item
2022-03-05 12:04:28	ga_s2am_ard_3-2-1_50KLU_2022-03-05_final.stac-item
2022-03-12 11:54:37	ga_s2am_ard_3-2-1_50KLU_2022-03-12_final.stac-item
2022-03-22 11:54:36	ga_s2am_ard_3-2-1_50KLU_2022-03-22_final.stac-item
2022-04-01 11:54:35	ga_s2am_ard_3-2-1_50KLU_2022-04-01_final.stac-item
2022-04-04 12:04:26	ga_s2am_ard_3-2-1_50KLU_2022-04-04_final.stac-item
2022-04-11 11:54:35	ga_s2am_ard_3-2-1_50KLU_2022-04-11_final.stac-item
2022-04-14 12:04:28	ga_s2am_ard_3-2-1_50KLU_2022-04-14_final.stac-item
2022-04-21 11:54:38	ga_s2am_ard_3-2-1_50KLU_2022-04-21_final.stac-item
2022-04-24 12:04:30	ga_s2am_ard_3-2-1_50KLU_2022-04-24_final.stac-item
2022-05-01 11:54:38	ga_s2am_ard_3-2-1_50KLU_2022-05-01_final.stac-item
2022-05-04 12:04:30	ga_s2am_ard_3-2-1_50KLU_2022-05-04_final.stac-item
2022-05-11 11:54:37	ga_s2am_ard_3-2-1_50KLU_2022-05-11_final.stac-item
2022-05-14 12:04:28	ga_s2am_ard_3-2-1_50KLU_2022-05-14_final.stac-item
2022-05-14 12:04:36	ga_s2am_ard_3-2-1_50KLU_2022-05-14_final.stac-item
2022-05-21 11:54:40	ga_s2am_ard_3-2-1_50KLU_2022-05-21_final.stac-item
2022-05-24 12:04:32	ga_s2am_ard_3-2-1_50KLU_2022-05-24_final.stac-item
2022-05-24 12:04:39	ga_s2am_ard_3-2-1_50KLU_2022-05-24_final.stac-item
2022-05-31 11:54:41	ga_s2am_ard_3-2-1_50KLU_2022-05-31_final.stac-item
2022-06-03 12:04:33	ga_s2am_ard_3-2-1_50KLU_2022-06-03_final.stac-item
2022-06-10 11:54:42	ga_s2am_ard_3-2-1_50KLU_2022-06-10_final.stac-item
2022-06-13 12:04:35	ga_s2am_ard_3-2-1_50KLU_2022-06-13_final.stac-item
2022-06-13 12:04:43	ga_s2am_ard_3-2-1_50KLU_2022-06-13_final.stac-item
2022-06-20 11:54:45	ga_s2am_ard_3-2-1_50KLU_2022-06-20_final.stac-item
2022-06-23 12:04:37	ga_s2am_ard_3-2-1_50KLU_2022-06-23_final.stac-item
2022-06-23 12:04:45	ga_s2am_ard_3-2-1_50KLU_2022-06-23_final.stac-item
2022-06-30 11:54:46	ga_s2am_ard_3-2-1_50KLU_2022-06-30_final.stac-item
2022-07-03 12:04:38	ga_s2am_ard_3-2-1_50KLU_2022-07-03_final.stac-item
2022-07-10 11:54:46	ga_s2am_ard_3-2-1_50KLU_2022-07-10_final.stac-item
2022-07-13 12:04:37	ga_s2am_ard_3-2-1_50KLU_2022-07-13_final.stac-item
2022-07-13 12:04:45	ga_s2am_ard_3-2-1_50KLU_2022-07-13_final.stac-item
2022-07-20 11:54:45	ga_s2am_ard_3-2-1_50KLU_2022-07-20_final.stac-item
2022-07-23 12:04:36	ga_s2am_ard_3-2-1_50KLU_2022-07-23_final.stac-item
2022-07-30 11:54:43	ga_s2am_ard_3-2-1_50KLU_2022-07-30_final.stac-item
2022-08-02 12:04:34	ga_s2am_ard_3-2-1_50KLU_2022-08-02_final.stac-item
2022-08-02 12:04:41	ga_s2am_ard_3-2-1_50KLU_2022-08-02_final.stac-item
2022-08-09 11:54:45	ga_s2am_ard_3-2-1_50KLU_2022-08-09_final.stac-item
2022-08-12 12:04:37	ga_s2am_ard_3-2-1_50KLU_2022-08-12_final.stac-item
2022-08-19 11:54:46	ga_s2am_ard_3-2-1_50KLU_2022-08-19_final.stac-item
2022-08-22 12:04:37	ga_s2am_ard_3-2-1_50KLU_2022-08-22_final.stac-item
2022-09-01 12:04:37	ga_s2am_ard_3-2-1_50KLU_2022-09-01_final.stac-item
2022-09-08 11:54:44	ga_s2am_ard_3-2-1_50KLU_2022-09-08_final.stac-item
2022-09-11 12:04:35	ga_s2am_ard_3-2-1_50KLU_2022-09-11_final.stac-item
2022-09-18 11:54:42	ga_s2am_ard_3-2-1_50KLU_2022-09-18_final.stac-item
2022-09-21 12:04:32	ga_s2am_ard_3-2-1_50KLU_2022-09-21_final.stac-item
2022-09-21 12:04:40	ga_s2am_ard_3-2-1_50KLU_2022-09-21_final.stac-item
2022-09-28 11:54:40	ga_s2am_ard_3-2-1_50KLU_2022-09-28_final.stac-item
2022-10-01 12:04:32	ga_s2am_ard_3-2-1_50KLU_2022-10-01_final.stac-item
2022-10-01 12:04:40	ga_s2am_ard_3-2-1_50KLU_2022-10-01_final.stac-item
2022-10-08 11:54:40	ga_s2am_ard_3-2-1_50KLU_2022-10-08_final.stac-item
2022-10-11 12:04:30	ga_s2am_ard_3-2-1_50KLU_2022-10-11_final.stac-item
2022-10-11 12:04:38	ga_s2am_ard_3-2-1_50KLU_2022-10-11_final.stac-item
2022-10-21 12:04:26	ga_s2am_ard_3-2-1_50KLU_2022-10-21_final.stac-item
2022-10-28 11:54:37	ga_s2am_ard_3-2-1_50KLU_2022-10-28_final.stac-item
2022-10-31 12:04:29	ga_s2am_ard_3-2-1_50KLU_2022-10-31_final.stac-item
2022-11-07 11:54:37	ga_s2am_ard_3-2-1_50KLU_2022-11-07_final.stac-item
2022-11-10 12:04:28	ga_s2am_ard_3-2-1_50KLU_2022-11-10_final.stac-item
2022-11-17 11:54:35	ga_s2am_ard_3-2-1_50KLU_2022-11-17_final.stac-item
2022-11-20 12:04:26	ga_s2am_ard_3-2-1_50KLU_2022-11-20_final.stac-item
2022-11-27 11:54:33	ga_s2am_ard_3-2-1_50KLU_2022-11-27_final.stac-item
2022-11-30 12:04:25	ga_s2am_ard_3-2-1_50KLU_2022-11-30_final.stac-item
2022-12-07 11:54:34	ga_s2am_ard_3-2-1_50KLU_2022-12-07_final.stac-item
2022-12-10 12:04:25	ga_s2am_ard_3-2-1_50KLU_2022-12-10_final.stac-item
2022-12-17 11:54:33	ga_s2am_ard_3-2-1_50KLU_2022-12-17_final.stac-item
2022-12-20 12:04:23	ga_s2am_ard_3-2-1_50KLU_2022-12-20_final.stac-item
2022-12-27 11:54:32	ga_s2am_ard_3-2-1_50KLU_2022-12-27_final.stac-item
2022-12-30 12:04:24	ga_s2am_ard_3-2-1_50KLU_2022-12-30_final.stac-item
2023-01-06 11:54:32	ga_s2am_ard_3-2-1_50KLU_2023-01-06_final.stac-item
2023-01-09 12:04:23	ga_s2am_ard_3-2-1_50KLU_2023-01-09_final.stac-item
2023-01-16 11:54:30	ga_s2am_ard_3-2-1_50KLU_2023-01-16_final.stac-item
2023-01-19 12:04:20	ga_s2am_ard_3-2-1_50KLU_2023-01-19_final.stac-item
2023-01-26 11:54:30	ga_s2am_ard_3-2-1_50KLU_2023-01-26_final.stac-item
2023-01-29 12:04:23	ga_s2am_ard_3-2-1_50KLU_2023-01-29_final.stac-item
2023-02-05 11:54:32	ga_s2am_ard_3-2-1_50KLU_2023-02-05_final
2023-02-08 12:04:24	ga_s2am_ard_3-2-1_50KLU_2023-02-08_final
2023-02-15 11:54:30	ga_s2am_ard_3-2-1_50KLU_2023-02-15_nrt.stac-item
2023-02-18 12:04:21	ga_s2am_ard_3-2-1_50KLU_2023-02-18_nrt.stac-item
2023-08-04 11:54:38	ga_s2am_ard_3-2-1_50KLU_2023-08-04_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

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

All years