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
2022-12-21 09:58:11	ga_s2am_ard_3-2-1_54GWA_2022-12-21_nrt.stac-item
2022-12-21 09:58:30	ga_s2am_ard_3-2-1_54GVV_2022-12-21_nrt.stac-item
2022-12-21 09:58:31	ga_s2am_ard_3-2-1_54GWU_2022-12-21_nrt.stac-item
2022-12-31 09:58:12	ga_s2am_ard_3-2-1_54GWA_2022-12-31_nrt.stac-item
2023-01-10 09:58:11	ga_s2am_ard_3-2-1_54GWA_2023-01-10_nrt.stac-item
2023-01-10 09:58:30	ga_s2am_ard_3-2-1_54GVV_2023-01-10_nrt.stac-item
2023-01-10 09:58:32	ga_s2am_ard_3-2-1_54GWU_2023-01-10_nrt.stac-item
2023-01-20 09:58:08	ga_s2am_ard_3-2-1_54GWA_2023-01-20_nrt.stac-item
2023-01-30 09:58:11	ga_s2am_ard_3-2-1_54GWA_2023-01-30_nrt.stac-item
2023-02-09 09:58:12	ga_s2am_ard_3-2-1_54GWA_2023-02-09_nrt.stac-item
2023-02-09 09:58:30	ga_s2am_ard_3-2-1_54GVV_2023-02-09_nrt.stac-item
2023-02-19 09:58:10	ga_s2am_ard_3-2-1_54GWA_2023-02-19_nrt.stac-item
2023-03-01 09:58:11	ga_s2am_ard_3-2-1_54GWA_2023-03-01_nrt
2023-03-01 09:58:29	ga_s2am_ard_3-2-1_54GVV_2023-03-01_nrt
2023-03-01 09:58:31	ga_s2am_ard_3-2-1_54GWU_2023-03-01_nrt
2023-03-11 09:58:08	ga_s2am_ard_3-2-1_54GWA_2023-03-11_nrt
2023-03-11 09:58:27	ga_s2am_ard_3-2-1_54GVV_2023-03-11_nrt
2023-03-11 09:58:29	ga_s2am_ard_3-2-1_54GWU_2023-03-11_nrt
2023-03-21 09:58:12	ga_s2am_ard_3-2-1_54GWA_2023-03-21_nrt
2023-03-21 09:58:30	ga_s2am_ard_3-2-1_54GVV_2023-03-21_nrt
2023-03-31 09:58:11	ga_s2am_ard_3-2-1_54GWA_2023-03-31_nrt
2023-03-31 09:58:30	ga_s2am_ard_3-2-1_54GVV_2023-03-31_nrt
2023-03-31 09:58:32	ga_s2am_ard_3-2-1_54GWU_2023-03-31_nrt
2023-04-10 09:58:13	ga_s2am_ard_3-2-1_54GWA_2023-04-10_nrt
2023-04-10 09:58:32	ga_s2am_ard_3-2-1_54GVV_2023-04-10_nrt
2023-04-10 09:58:33	ga_s2am_ard_3-2-1_54GWU_2023-04-10_nrt
2023-04-20 09:58:12	ga_s2am_ard_3-2-1_54GWA_2023-04-20_nrt
2023-04-20 09:58:30	ga_s2am_ard_3-2-1_54GVV_2023-04-20_nrt
2023-04-30 09:58:13	ga_s2am_ard_3-2-1_54GWA_2023-04-30_nrt
2023-04-30 09:58:32	ga_s2am_ard_3-2-1_54GVV_2023-04-30_nrt
2023-04-30 09:58:34	ga_s2am_ard_3-2-1_54GWU_2023-04-30_nrt
2023-05-10 09:58:13	ga_s2am_ard_3-2-1_54GWA_2023-05-10_nrt
2023-05-10 09:58:31	ga_s2am_ard_3-2-1_54GVV_2023-05-10_nrt
2023-05-20 09:58:15	ga_s2am_ard_3-2-1_54GWA_2023-05-20_nrt
2023-05-20 09:58:34	ga_s2am_ard_3-2-1_54GVV_2023-05-20_nrt
2023-05-20 09:58:36	ga_s2am_ard_3-2-1_54GWU_2023-05-20_nrt
2023-05-30 09:58:17	ga_s2am_ard_3-2-1_54GWA_2023-05-30_nrt
2023-05-30 09:58:35	ga_s2am_ard_3-2-1_54GVV_2023-05-30_nrt
2023-06-09 09:58:17	ga_s2am_ard_3-2-1_54GWA_2023-06-09_nrt
2023-06-09 09:58:35	ga_s2am_ard_3-2-1_54GVV_2023-06-09_nrt
2023-06-19 09:58:17	ga_s2am_ard_3-2-1_54GWA_2023-06-19_nrt
2023-06-19 09:58:35	ga_s2am_ard_3-2-1_54GVV_2023-06-19_nrt
2023-06-29 09:58:17	ga_s2am_ard_3-2-1_54GWA_2023-06-29_nrt
2023-07-09 09:58:18	ga_s2am_ard_3-2-1_54GWA_2023-07-09_nrt
2023-07-09 09:58:37	ga_s2am_ard_3-2-1_54GVV_2023-07-09_nrt
2023-07-09 09:58:39	ga_s2am_ard_3-2-1_54GWU_2023-07-09_nrt
2023-07-19 09:58:17	ga_s2am_ard_3-2-1_54GWA_2023-07-19_nrt
2023-07-19 09:58:36	ga_s2am_ard_3-2-1_54GVV_2023-07-19_nrt
2023-07-19 09:58:38	ga_s2am_ard_3-2-1_54GWU_2023-07-19_nrt
2023-07-29 09:58:18	ga_s2am_ard_3-2-1_54GWA_2023-07-29_nrt
2023-07-29 09:58:37	ga_s2am_ard_3-2-1_54GVV_2023-07-29_nrt
2023-07-29 09:58:39	ga_s2am_ard_3-2-1_54GWU_2023-07-29_nrt
2023-08-08 09:58:18	ga_s2am_ard_3-2-1_54GWA_2023-08-08_nrt
2023-08-08 09:58:36	ga_s2am_ard_3-2-1_54GVV_2023-08-08_nrt
2023-08-18 09:58:19	ga_s2am_ard_3-2-1_54GWA_2023-08-18_nrt
2023-08-18 09:58:37	ga_s2am_ard_3-2-1_54GVV_2023-08-18_nrt
2023-08-28 09:58:19	ga_s2am_ard_3-2-1_54GWA_2023-08-28_nrt
2023-08-28 09:58:37	ga_s2am_ard_3-2-1_54GVV_2023-08-28_nrt
2023-09-07 09:58:18	ga_s2am_ard_3-2-1_54GWA_2023-09-07_nrt
2023-09-07 09:58:37	ga_s2am_ard_3-2-1_54GVV_2023-09-07_nrt
2023-09-07 09:58:39	ga_s2am_ard_3-2-1_54GWU_2023-09-07_nrt
2023-09-17 09:58:17	ga_s2am_ard_3-2-1_54GWA_2023-09-17_nrt
2023-09-17 09:58:35	ga_s2am_ard_3-2-1_54GVV_2023-09-17_nrt
2023-09-27 09:58:15	ga_s2am_ard_3-2-1_54GWA_2023-09-27_nrt
2023-09-27 09:58:34	ga_s2am_ard_3-2-1_54GVV_2023-09-27_nrt
2023-09-27 09:58:36	ga_s2am_ard_3-2-1_54GWU_2023-09-27_nrt
2023-10-07 09:58:14	ga_s2am_ard_3-2-1_54GWA_2023-10-07_nrt
2023-10-07 09:58:32	ga_s2am_ard_3-2-1_54GVV_2023-10-07_nrt
2023-10-17 09:58:12	ga_s2am_ard_3-2-1_54GWA_2023-10-17_nrt
2023-10-27 09:58:13	ga_s2am_ard_3-2-1_54GWA_2023-10-27_nrt
2023-11-06 09:58:31	ga_s2am_ard_3-2-1_54GVV_2023-11-06_nrt
2023-11-16 09:58:12	ga_s2am_ard_3-2-1_54GWA_2023-11-16_nrt
2023-11-16 09:58:30	ga_s2am_ard_3-2-1_54GVV_2023-11-16_nrt
2023-11-26 09:58:07	ga_s2am_ard_3-2-1_54GWA_2023-11-26_nrt
2023-11-26 09:58:27	ga_s2am_ard_3-2-1_54GWU_2023-11-26_nrt
2023-12-06 09:58:07	ga_s2am_ard_3-2-1_54GWA_2023-12-06_nrt
2023-12-16 09:58:09	ga_s2am_ard_3-2-1_54GWA_2023-12-16_nrt
2023-12-26 09:58:10	ga_s2am_ard_3-2-1_54GWA_2023-12-26_nrt
2024-01-05 09:58:10	ga_s2am_ard_3-2-1_54GWA_2024-01-05_nrt
2024-01-05 09:58:28	ga_s2am_ard_3-2-1_54GVV_2024-01-05_nrt
2024-01-15 09:58:06	ga_s2am_ard_3-2-1_54GWA_2024-01-15_nrt
2024-01-25 09:58:07	ga_s2am_ard_3-2-1_54GWA_2024-01-25_nrt
2024-02-04 09:58:09	ga_s2am_ard_3-2-1_54GWA_2024-02-04_nrt
2024-02-14 09:58:11	ga_s2am_ard_3-2-1_54GWA_2024-02-14_nrt
2024-02-14 09:58:29	ga_s2am_ard_3-2-1_54GVV_2024-02-14_nrt
2024-02-24 09:58:11	ga_s2am_ard_3-2-1_54GWA_2024-02-24_nrt
2024-02-24 09:58:30	ga_s2am_ard_3-2-1_54GVV_2024-02-24_nrt
2024-03-05 09:58:14	ga_s2am_ard_3-2-1_54GWA_2024-03-05_nrt
2024-03-05 09:58:32	ga_s2am_ard_3-2-1_54GVV_2024-03-05_nrt
2024-03-15 09:58:13	ga_s2am_ard_3-2-1_54GWA_2024-03-15_nrt
2024-03-15 09:58:31	ga_s2am_ard_3-2-1_54GVV_2024-03-15_nrt
2024-03-25 09:58:14	ga_s2am_ard_3-2-1_54GWA_2024-03-25_nrt
2024-03-25 09:58:32	ga_s2am_ard_3-2-1_54GVV_2024-03-25_nrt
2024-04-04 09:58:10	ga_s2am_ard_3-2-1_54GWA_2024-04-04_nrt
2024-04-14 09:58:15	ga_s2am_ard_3-2-1_54GWA_2024-04-14_nrt
2024-04-24 09:58:16	ga_s2am_ard_3-2-1_54GWA_2024-04-24_nrt
2024-04-24 09:58:34	ga_s2am_ard_3-2-1_54GVV_2024-04-24_nrt
2024-05-04 09:58:16	ga_s2am_ard_3-2-1_54GWA_2024-05-04_nrt
2024-05-04 09:58:34	ga_s2am_ard_3-2-1_54GVV_2024-05-04_nrt
2024-05-14 09:58:16	ga_s2am_ard_3-2-1_54GWA_2024-05-14_nrt

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

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