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
2019-07-01 11:05:06	ga_s2am_ard_3-2-1_52JEQ_2019-07-01_final.stac-item
2019-07-01 11:05:21	ga_s2am_ard_3-2-1_52JEP_2019-07-01_final.stac-item
2019-07-01 11:05:35	ga_s2am_ard_3-2-1_52JEN_2019-07-01_final.stac-item
2019-07-01 11:05:49	ga_s2am_ard_3-2-1_52JEM_2019-07-01_final.stac-item
2019-07-01 11:06:04	ga_s2am_ard_3-2-1_52JEL_2019-07-01_final.stac-item
2019-07-11 11:04:52	ga_s2am_ard_3-2-1_52JER_2019-07-11_final.stac-item
2019-07-11 11:05:07	ga_s2am_ard_3-2-1_52JEQ_2019-07-11_final.stac-item
2019-07-11 11:05:21	ga_s2am_ard_3-2-1_52JEP_2019-07-11_final.stac-item
2019-07-11 11:05:36	ga_s2am_ard_3-2-1_52JEN_2019-07-11_final.stac-item
2019-07-11 11:05:50	ga_s2am_ard_3-2-1_52JEM_2019-07-11_final.stac-item
2019-07-11 11:06:04	ga_s2am_ard_3-2-1_52JEL_2019-07-11_final.stac-item
2022-12-09 10:55:49	ga_s2am_ard_3-2-1_52JEM_2022-12-09_final.stac-item
2022-12-09 10:56:04	ga_s2am_ard_3-2-1_52JEL_2022-12-09_final.stac-item
2022-12-09 10:56:18	ga_s2am_ard_3-2-1_52HEK_2022-12-09_final
2022-12-12 11:03:20	ga_s2am_ard_3-2-1_52KEB_2022-12-12_final.stac-item
2022-12-12 11:03:35	ga_s2am_ard_3-2-1_52KEA_2022-12-12_final.stac-item
2022-12-12 11:03:49	ga_s2am_ard_3-2-1_52KEV_2022-12-12_final.stac-item
2022-12-12 11:04:04	ga_s2am_ard_3-2-1_52KEU_2022-12-12_final.stac-item
2022-12-12 11:04:18	ga_s2am_ard_3-2-1_52JET_2022-12-12_final.stac-item
2022-12-12 11:04:33	ga_s2am_ard_3-2-1_52JES_2022-12-12_final.stac-item
2022-12-12 11:06:14	ga_s2am_ard_3-2-1_52HEK_2022-12-12_final
2022-12-15 11:10:35	ga_s2am_ard_3-2-1_52LEN_2022-12-15_nrt.stac-item
2022-12-15 11:10:50	ga_s2am_ard_3-2-1_52LEM_2022-12-15_nrt.stac-item
2022-12-15 11:11:34	ga_s2am_ard_3-2-1_52LEJ_2022-12-15_final.stac-item
2022-12-15 11:11:48	ga_s2am_ard_3-2-1_52LEH_2022-12-15_final.stac-item
2022-12-15 11:12:03	ga_s2am_ard_3-2-1_52KEG_2022-12-15_final.stac-item
2022-12-15 11:12:17	ga_s2am_ard_3-2-1_52KEF_2022-12-15_final.stac-item
2022-12-15 11:12:32	ga_s2am_ard_3-2-1_52KEE_2022-12-15_final.stac-item
2022-12-15 11:12:46	ga_s2am_ard_3-2-1_52KED_2022-12-15_final.stac-item
2022-12-15 11:13:00	ga_s2am_ard_3-2-1_52KEC_2022-12-15_final.stac-item
2022-12-15 11:13:15	ga_s2am_ard_3-2-1_52KEB_2022-12-15_final.stac-item
2022-12-15 11:13:30	ga_s2am_ard_3-2-1_52KEA_2022-12-15_final.stac-item
2022-12-15 11:13:44	ga_s2am_ard_3-2-1_52KEV_2022-12-15_final.stac-item
2022-12-18 11:20:15	ga_s2am_ard_3-2-1_52LEP_2022-12-18_nrt.stac-item
2022-12-18 11:20:30	ga_s2am_ard_3-2-1_52LEN_2022-12-18_nrt.stac-item
2022-12-18 11:20:44	ga_s2am_ard_3-2-1_52LEM_2022-12-18_nrt.stac-item
2023-01-21 11:03:15	ga_s2am_ard_3-2-1_52KEB_2023-01-21_final.stac-item
2023-01-21 11:03:30	ga_s2am_ard_3-2-1_52KEA_2023-01-21_final.stac-item
2023-01-21 11:03:44	ga_s2am_ard_3-2-1_52KEV_2023-01-21_final.stac-item
2023-01-21 11:03:59	ga_s2am_ard_3-2-1_52KEU_2023-01-21_final.stac-item
2023-01-21 11:04:13	ga_s2am_ard_3-2-1_52JET_2023-01-21_final.stac-item
2023-01-21 11:04:28	ga_s2am_ard_3-2-1_52JES_2023-01-21_final.stac-item
2023-01-21 11:04:42	ga_s2am_ard_3-2-1_52JER_2023-01-21_final.stac-item
2023-01-21 11:04:57	ga_s2am_ard_3-2-1_52JEQ_2023-01-21_final.stac-item
2023-01-21 11:05:09	ga_s2am_ard_3-2-1_52JEP_2023-01-21_final.stac-item
2023-01-21 11:05:15	ga_s2am_ard_3-2-1_52JEP_2023-01-21_final.stac-item
2023-01-21 11:05:26	ga_s2am_ard_3-2-1_52JEN_2023-01-21_final.stac-item
2023-01-21 11:05:40	ga_s2am_ard_3-2-1_52JEM_2023-01-21_final.stac-item
2023-01-21 11:05:54	ga_s2am_ard_3-2-1_52JEL_2023-01-21_final.stac-item
2023-01-21 11:06:09	ga_s2am_ard_3-2-1_52HEK_2023-01-21_final
2023-01-21 11:06:24	ga_s2am_ard_3-2-1_52HEJ_2023-01-21_nrt.stac-item
2023-01-24 11:10:32	ga_s2am_ard_3-2-1_52LEN_2023-01-24_nrt.stac-item
2023-01-24 11:10:47	ga_s2am_ard_3-2-1_52LEM_2023-01-24_nrt.stac-item
2023-01-24 11:11:01	ga_s2am_ard_3-2-1_52LEL_2023-01-24_final
2023-01-24 11:11:16	ga_s2am_ard_3-2-1_52LEK_2023-01-24_final
2023-01-24 11:11:30	ga_s2am_ard_3-2-1_52LEJ_2023-01-24_final.stac-item
2023-01-24 11:11:45	ga_s2am_ard_3-2-1_52LEH_2023-01-24_final.stac-item
2023-01-24 11:11:59	ga_s2am_ard_3-2-1_52KEG_2023-01-24_final.stac-item
2023-01-24 11:12:14	ga_s2am_ard_3-2-1_52KEF_2023-01-24_final.stac-item
2023-01-24 11:12:28	ga_s2am_ard_3-2-1_52KEE_2023-01-24_final.stac-item
2023-01-24 11:12:43	ga_s2am_ard_3-2-1_52KED_2023-01-24_final.stac-item
2023-01-24 11:12:57	ga_s2am_ard_3-2-1_52KEC_2023-01-24_final.stac-item
2023-01-24 11:13:12	ga_s2am_ard_3-2-1_52KEB_2023-01-24_final.stac-item
2023-01-24 11:13:24	ga_s2am_ard_3-2-1_52KEA_2023-01-24_final.stac-item
2023-01-24 11:13:31	ga_s2am_ard_3-2-1_52KEA_2023-01-24_final.stac-item
2023-01-24 11:13:41	ga_s2am_ard_3-2-1_52KEV_2023-01-24_final.stac-item
2023-01-28 10:55:47	ga_s2am_ard_3-2-1_52JEM_2023-01-28_final.stac-item
2023-01-28 10:55:56	ga_s2am_ard_3-2-1_52JEL_2023-01-28_final.stac-item
2023-01-28 10:56:03	ga_s2am_ard_3-2-1_52JEL_2023-01-28_final.stac-item
2023-01-28 10:56:16	ga_s2am_ard_3-2-1_52HEK_2023-01-28_final
2023-01-28 10:56:23	ga_s2am_ard_3-2-1_52HEJ_2023-01-28_nrt.stac-item
2023-01-31 11:05:00	ga_s2am_ard_3-2-1_52JEQ_2023-01-31_final.stac-item
2023-01-31 11:05:14	ga_s2am_ard_3-2-1_52JEP_2023-01-31_final.stac-item
2023-01-31 11:05:29	ga_s2am_ard_3-2-1_52JEN_2023-01-31_final.stac-item
2023-01-31 11:05:43	ga_s2am_ard_3-2-1_52JEM_2023-01-31_final.stac-item
2023-01-31 11:05:58	ga_s2am_ard_3-2-1_52JEL_2023-01-31_final.stac-item
2023-01-31 11:06:12	ga_s2am_ard_3-2-1_52HEK_2023-01-31_final
2023-01-31 11:06:27	ga_s2am_ard_3-2-1_52HEJ_2023-01-31_nrt.stac-item
2023-02-03 11:10:34	ga_s2am_ard_3-2-1_52LEN_2023-02-03_nrt.stac-item
2023-02-03 11:10:49	ga_s2am_ard_3-2-1_52LEM_2023-02-03_nrt.stac-item
2023-02-03 11:11:03	ga_s2am_ard_3-2-1_52LEL_2023-02-03_final
2023-02-03 11:11:18	ga_s2am_ard_3-2-1_52LEK_2023-02-03_final
2023-02-03 11:11:32	ga_s2am_ard_3-2-1_52LEJ_2023-02-03_final.stac-item
2023-02-03 11:11:47	ga_s2am_ard_3-2-1_52LEH_2023-02-03_final.stac-item
2023-02-03 11:12:02	ga_s2am_ard_3-2-1_52KEG_2023-02-03_final.stac-item
2023-02-03 11:12:16	ga_s2am_ard_3-2-1_52KEF_2023-02-03_final.stac-item
2023-02-03 11:12:31	ga_s2am_ard_3-2-1_52KEE_2023-02-03_final.stac-item
2023-02-03 11:12:41	ga_s2am_ard_3-2-1_52KED_2023-02-03_final.stac-item
2023-02-03 11:12:48	ga_s2am_ard_3-2-1_52KED_2023-02-03_final.stac-item
2023-02-03 11:12:59	ga_s2am_ard_3-2-1_52KEC_2023-02-03_final.stac-item
2023-02-03 11:13:14	ga_s2am_ard_3-2-1_52KEB_2023-02-03_final.stac-item
2023-02-03 11:13:29	ga_s2am_ard_3-2-1_52KEA_2023-02-03_final
2023-02-03 11:13:43	ga_s2am_ard_3-2-1_52KEV_2023-02-03_final.stac-item
2023-02-06 11:20:15	ga_s2am_ard_3-2-1_52LEP_2023-02-06_nrt.stac-item
2023-02-06 11:20:30	ga_s2am_ard_3-2-1_52LEN_2023-02-06_nrt.stac-item
2023-02-06 11:20:44	ga_s2am_ard_3-2-1_52LEM_2023-02-06_nrt.stac-item
2023-02-07 10:56:17	ga_s2am_ard_3-2-1_52HEK_2023-02-07_final
2023-02-07 10:56:24	ga_s2am_ard_3-2-1_52HEJ_2023-02-07_nrt.stac-item
2023-07-26 11:20:21	ga_s2am_ard_3-2-1_52LEP_2023-07-26_nrt
2023-07-26 11:20:36	ga_s2am_ard_3-2-1_52LEN_2023-07-26_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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