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
2022-10-27 10:46:37	ga_s2am_ard_3-2-1_53HKC_2022-10-27_nrt.stac-item
2022-12-16 10:46:27	ga_s2am_ard_3-2-1_53HKD_2022-12-16_nrt.stac-item
2022-12-16 10:46:34	ga_s2am_ard_3-2-1_53HKC_2022-12-16_nrt.stac-item
2022-12-26 10:46:25	ga_s2am_ard_3-2-1_53HKD_2022-12-26_nrt.stac-item
2023-01-05 10:46:26	ga_s2am_ard_3-2-1_53HKD_2023-01-05_nrt.stac-item
2023-01-05 10:46:33	ga_s2am_ard_3-2-1_53HKC_2023-01-05_nrt.stac-item
2023-01-15 10:46:24	ga_s2am_ard_3-2-1_53HKD_2023-01-15_nrt.stac-item
2023-01-15 10:46:31	ga_s2am_ard_3-2-1_53HKC_2023-01-15_nrt.stac-item
2023-01-18 10:56:15	ga_s2am_ard_3-2-1_53HKD_2023-01-18_final
2023-01-25 10:46:24	ga_s2am_ard_3-2-1_53HKD_2023-01-25_nrt.stac-item
2023-01-25 10:46:31	ga_s2am_ard_3-2-1_53HKC_2023-01-25_nrt.stac-item
2023-01-28 10:56:17	ga_s2am_ard_3-2-1_53HKD_2023-01-28_final
2023-02-04 10:46:26	ga_s2am_ard_3-2-1_53HKD_2023-02-04_nrt.stac-item
2023-02-04 10:46:33	ga_s2am_ard_3-2-1_53HKC_2023-02-04_nrt.stac-item
2023-02-07 10:56:18	ga_s2am_ard_3-2-1_53HKD_2023-02-07_final
2023-02-14 10:46:25	ga_s2am_ard_3-2-1_53HKD_2023-02-14_nrt.stac-item
2023-02-14 10:46:32	ga_s2am_ard_3-2-1_53HKC_2023-02-14_nrt.stac-item
2023-02-17 10:56:16	ga_s2am_ard_3-2-1_53HKD_2023-02-17_final
2023-02-24 10:46:25	ga_s2am_ard_3-2-1_53HKD_2023-02-24_nrt
2023-02-24 10:46:32	ga_s2am_ard_3-2-1_53HKC_2023-02-24_nrt
2023-02-27 10:56:17	ga_s2am_ard_3-2-1_53HKD_2023-02-27_final
2023-03-06 10:46:23	ga_s2am_ard_3-2-1_53HKD_2023-03-06_nrt
2023-03-06 10:46:31	ga_s2am_ard_3-2-1_53HKC_2023-03-06_nrt
2023-03-09 10:56:14	ga_s2am_ard_3-2-1_53HKD_2023-03-09_final
2023-03-16 10:46:24	ga_s2am_ard_3-2-1_53HKD_2023-03-16_nrt
2023-03-16 10:46:32	ga_s2am_ard_3-2-1_53HKC_2023-03-16_nrt
2023-03-19 10:56:17	ga_s2am_ard_3-2-1_53HKD_2023-03-19_final
2023-03-26 10:46:25	ga_s2am_ard_3-2-1_53HKD_2023-03-26_nrt
2023-03-26 10:46:33	ga_s2am_ard_3-2-1_53HKC_2023-03-26_nrt
2023-04-05 10:46:27	ga_s2am_ard_3-2-1_53HKD_2023-04-05_nrt
2023-04-05 10:46:34	ga_s2am_ard_3-2-1_53HKC_2023-04-05_nrt
2023-04-08 10:56:19	ga_s2am_ard_3-2-1_53HKD_2023-04-08_final
2023-04-15 10:46:26	ga_s2am_ard_3-2-1_53HKD_2023-04-15_nrt
2023-04-15 10:46:33	ga_s2am_ard_3-2-1_53HKC_2023-04-15_nrt
2023-04-25 10:46:26	ga_s2am_ard_3-2-1_53HKD_2023-04-25_nrt
2023-04-25 10:46:33	ga_s2am_ard_3-2-1_53HKC_2023-04-25_nrt
2023-05-05 10:46:27	ga_s2am_ard_3-2-1_53HKD_2023-05-05_nrt
2023-05-05 10:46:34	ga_s2am_ard_3-2-1_53HKC_2023-05-05_nrt
2023-05-08 10:56:19	ga_s2am_ard_3-2-1_53HKD_2023-05-08_nrt
2023-05-15 10:46:27	ga_s2am_ard_3-2-1_53HKD_2023-05-15_nrt
2023-05-15 10:46:34	ga_s2am_ard_3-2-1_53HKC_2023-05-15_nrt
2023-05-25 10:46:30	ga_s2am_ard_3-2-1_53HKD_2023-05-25_nrt
2023-05-25 10:46:37	ga_s2am_ard_3-2-1_53HKC_2023-05-25_nrt
2023-06-04 10:46:30	ga_s2am_ard_3-2-1_53HKD_2023-06-04_nrt
2023-06-04 10:46:37	ga_s2am_ard_3-2-1_53HKC_2023-06-04_nrt
2023-06-14 10:46:31	ga_s2am_ard_3-2-1_53HKD_2023-06-14_nrt
2023-06-14 10:46:39	ga_s2am_ard_3-2-1_53HKC_2023-06-14_nrt
2023-06-24 10:46:30	ga_s2am_ard_3-2-1_53HKD_2023-06-24_nrt
2023-06-24 10:46:38	ga_s2am_ard_3-2-1_53HKC_2023-06-24_nrt
2023-07-04 10:46:31	ga_s2am_ard_3-2-1_53HKD_2023-07-04_nrt
2023-07-04 10:46:38	ga_s2am_ard_3-2-1_53HKC_2023-07-04_nrt
2023-07-14 10:46:32	ga_s2am_ard_3-2-1_53HKD_2023-07-14_nrt
2023-07-14 10:46:40	ga_s2am_ard_3-2-1_53HKC_2023-07-14_nrt
2023-07-24 10:46:31	ga_s2am_ard_3-2-1_53HKD_2023-07-24_nrt
2023-07-24 10:46:38	ga_s2am_ard_3-2-1_53HKC_2023-07-24_nrt
2023-08-03 10:46:32	ga_s2am_ard_3-2-1_53HKD_2023-08-03_nrt
2023-08-03 10:46:40	ga_s2am_ard_3-2-1_53HKC_2023-08-03_nrt
2023-08-13 10:46:32	ga_s2am_ard_3-2-1_53HKD_2023-08-13_nrt
2023-08-13 10:46:40	ga_s2am_ard_3-2-1_53HKC_2023-08-13_nrt
2023-08-16 10:56:24	ga_s2am_ard_3-2-1_53HKD_2023-08-16_nrt
2023-08-23 10:46:33	ga_s2am_ard_3-2-1_53HKD_2023-08-23_nrt
2023-08-23 10:46:41	ga_s2am_ard_3-2-1_53HKC_2023-08-23_nrt
2023-09-02 10:46:32	ga_s2am_ard_3-2-1_53HKD_2023-09-02_nrt
2023-09-02 10:46:39	ga_s2am_ard_3-2-1_53HKC_2023-09-02_nrt
2023-09-12 10:46:31	ga_s2am_ard_3-2-1_53HKD_2023-09-12_nrt
2023-09-12 10:46:38	ga_s2am_ard_3-2-1_53HKC_2023-09-12_nrt
2023-09-22 10:46:28	ga_s2am_ard_3-2-1_53HKD_2023-09-22_nrt
2023-09-22 10:46:35	ga_s2am_ard_3-2-1_53HKC_2023-09-22_nrt
2023-10-02 10:46:28	ga_s2am_ard_3-2-1_53HKD_2023-10-02_nrt
2023-10-02 10:46:35	ga_s2am_ard_3-2-1_53HKC_2023-10-02_nrt
2023-10-05 10:56:24	ga_s2am_ard_3-2-1_53HKD_2023-10-05_nrt
2023-10-12 10:46:24	ga_s2am_ard_3-2-1_53HKD_2023-10-12_nrt
2023-10-12 10:46:31	ga_s2am_ard_3-2-1_53HKC_2023-10-12_nrt
2023-10-22 10:46:26	ga_s2am_ard_3-2-1_53HKD_2023-10-22_nrt
2023-10-22 10:46:34	ga_s2am_ard_3-2-1_53HKC_2023-10-22_nrt
2023-11-01 10:46:25	ga_s2am_ard_3-2-1_53HKD_2023-11-01_nrt
2023-11-01 10:46:33	ga_s2am_ard_3-2-1_53HKC_2023-11-01_nrt
2023-11-11 10:46:26	ga_s2am_ard_3-2-1_53HKD_2023-11-11_nrt
2023-11-11 10:46:33	ga_s2am_ard_3-2-1_53HKC_2023-11-11_nrt
2023-11-21 10:46:23	ga_s2am_ard_3-2-1_53HKD_2023-11-21_nrt
2023-11-21 10:46:30	ga_s2am_ard_3-2-1_53HKC_2023-11-21_nrt
2023-12-01 10:46:25	ga_s2am_ard_3-2-1_53HKC_2023-12-01_nrt
2023-12-11 10:46:29	ga_s2am_ard_3-2-1_53HKC_2023-12-11_nrt
2023-12-21 10:46:31	ga_s2am_ard_3-2-1_53HKC_2023-12-21_nrt
2023-12-31 10:46:31	ga_s2am_ard_3-2-1_53HKC_2023-12-31_nrt
2024-01-10 10:46:30	ga_s2am_ard_3-2-1_53HKC_2024-01-10_nrt
2024-01-20 10:46:25	ga_s2am_ard_3-2-1_53HKC_2024-01-20_nrt
2024-01-30 10:46:30	ga_s2am_ard_3-2-1_53HKC_2024-01-30_nrt
2024-02-09 10:46:29	ga_s2am_ard_3-2-1_53HKC_2024-02-09_nrt
2024-02-19 10:46:31	ga_s2am_ard_3-2-1_53HKC_2024-02-19_nrt
2024-02-29 10:46:33	ga_s2am_ard_3-2-1_53HKC_2024-02-29_nrt
2024-03-10 10:46:34	ga_s2am_ard_3-2-1_53HKC_2024-03-10_nrt
2024-03-20 10:46:34	ga_s2am_ard_3-2-1_53HKC_2024-03-20_nrt
2024-03-30 10:46:33	ga_s2am_ard_3-2-1_53HKC_2024-03-30_nrt
2024-04-09 10:46:26	ga_s2am_ard_3-2-1_53HKD_2024-04-09_interim
2024-04-09 10:46:33	ga_s2am_ard_3-2-1_53HKC_2024-04-09_interim
2024-04-12 10:56:21	ga_s2am_ard_3-2-1_53HKD_2024-04-12_nrt
2024-04-19 10:46:37	ga_s2am_ard_3-2-1_53HKC_2024-04-19_nrt
2024-04-29 10:46:36	ga_s2am_ard_3-2-1_53HKC_2024-04-29_nrt
2024-05-09 10:46:36	ga_s2am_ard_3-2-1_53HKC_2024-05-09_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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