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
2018-11-27 10:41:37	ga_s2am_ard_3-2-1_53KRB_2018-11-27_final
2018-11-27 10:41:51	ga_s2am_ard_3-2-1_53KRA_2018-11-27_final.stac-item
2018-11-27 10:42:04	ga_s2am_ard_3-2-1_53KRV_2018-11-27_final.stac-item
2018-12-04 10:31:41	ga_s2am_ard_3-2-1_53KRB_2018-12-04_final
2018-12-04 10:31:56	ga_s2am_ard_3-2-1_53KRA_2018-12-04_final.stac-item
2018-12-04 10:32:10	ga_s2am_ard_3-2-1_53KRV_2018-12-04_final.stac-item
2019-04-23 10:31:53	ga_s2am_ard_3-2-1_53KRB_2019-04-23_final
2019-04-23 10:32:08	ga_s2am_ard_3-2-1_53KRA_2019-04-23_final.stac-item
2019-04-23 10:32:22	ga_s2am_ard_3-2-1_53KRV_2019-04-23_final.stac-item
2019-04-23 10:32:36	ga_s2am_ard_3-2-1_53KRU_2019-04-23_final.stac-item
2019-04-23 10:32:51	ga_s2am_ard_3-2-1_53KRT_2019-04-23_final.stac-item
2019-04-23 10:33:05	ga_s2am_ard_3-2-1_53KRS_2019-04-23_final.stac-item
2019-04-30 10:24:37	ga_s2am_ard_3-2-1_54JTR_2019-04-30_final.stac-item
2019-04-30 10:24:43	ga_s2am_ard_3-2-1_53JQL_2019-04-30_final.stac-item
2019-04-30 10:24:52	ga_s2am_ard_3-2-1_54JTQ_2019-04-30_final.stac-item
2019-04-30 10:24:55	ga_s2am_ard_3-2-1_53JQK_2019-04-30_final.stac-item
2019-04-30 10:25:06	ga_s2am_ard_3-2-1_54JTP_2019-04-30_final.stac-item
2019-04-30 10:25:09	ga_s2am_ard_3-2-1_53JQJ_2019-04-30_final.stac-item
2019-04-30 10:25:21	ga_s2am_ard_3-2-1_54JTN_2019-04-30_final.stac-item
2019-04-30 10:25:24	ga_s2am_ard_3-2-1_53JQH_2019-04-30_final.stac-item
2019-05-03 10:31:54	ga_s2am_ard_3-2-1_53KRB_2019-05-03_final
2019-05-03 10:32:08	ga_s2am_ard_3-2-1_53KRA_2019-05-03_final.stac-item
2019-05-03 10:32:22	ga_s2am_ard_3-2-1_53KRV_2019-05-03_final.stac-item
2019-05-03 10:32:37	ga_s2am_ard_3-2-1_53KRU_2019-05-03_final.stac-item
2019-05-03 10:32:51	ga_s2am_ard_3-2-1_53KRT_2019-05-03_final.stac-item
2019-05-10 10:24:37	ga_s2am_ard_3-2-1_54JTR_2019-05-10_final.stac-item
2019-05-10 10:24:52	ga_s2am_ard_3-2-1_54JTQ_2019-05-10_final.stac-item
2019-05-10 10:25:06	ga_s2am_ard_3-2-1_54JTP_2019-05-10_final.stac-item
2019-05-10 10:25:21	ga_s2am_ard_3-2-1_54JTN_2019-05-10_final.stac-item
2019-05-10 10:25:24	ga_s2am_ard_3-2-1_53JQH_2019-05-10_final.stac-item
2019-05-10 10:25:36	ga_s2am_ard_3-2-1_54JTM_2019-05-10_final.stac-item
2019-05-10 10:25:38	ga_s2am_ard_3-2-1_53JQG_2019-05-10_final.stac-item
2019-05-10 10:25:50	ga_s2am_ard_3-2-1_54JTL_2019-05-10_final.stac-item
2019-05-10 10:25:53	ga_s2am_ard_3-2-1_53JQF_2019-05-10_final.stac-item
2019-05-10 10:26:05	ga_s2am_ard_3-2-1_54HTK_2019-05-10_final.stac-item
2019-05-10 10:26:07	ga_s2am_ard_3-2-1_53HQE_2019-05-10_final.stac-item
2019-05-10 10:26:19	ga_s2am_ard_3-2-1_54HTJ_2019-05-10_final.stac-item
2019-05-10 10:26:22	ga_s2am_ard_3-2-1_53HQD_2019-05-10_final.stac-item
2019-05-10 10:26:34	ga_s2am_ard_3-2-1_54HTH_2019-05-10_final.stac-item
2019-05-10 10:26:36	ga_s2am_ard_3-2-1_53HQC_2019-05-10_final.stac-item
2019-05-10 10:26:49	ga_s2am_ard_3-2-1_54HTG_2019-05-10_final.stac-item
2019-05-10 10:26:50	ga_s2am_ard_3-2-1_53HQB_2019-05-10_final.stac-item
2019-05-10 10:27:03	ga_s2am_ard_3-2-1_54HTF_2019-05-10_final.stac-item
2019-05-10 10:27:05	ga_s2am_ard_3-2-1_53HQA_2019-05-10_final.stac-item
2019-05-16 10:41:49	ga_s2am_ard_3-2-1_53KRB_2019-05-16_final
2019-05-16 10:42:03	ga_s2am_ard_3-2-1_53KRA_2019-05-16_final.stac-item
2019-05-16 10:42:16	ga_s2am_ard_3-2-1_53KRV_2019-05-16_final.stac-item
2019-05-17 10:16:52	ga_s2am_ard_3-2-1_54HTG_2019-05-17_final.stac-item
2019-05-17 10:16:55	ga_s2am_ard_3-2-1_53HQB_2019-05-17_final.stac-item
2019-05-17 10:17:07	ga_s2am_ard_3-2-1_54HTF_2019-05-17_final.stac-item
2019-05-17 10:17:08	ga_s2am_ard_3-2-1_53HQA_2019-05-17_final.stac-item
2019-05-23 10:31:53	ga_s2am_ard_3-2-1_53KRB_2019-05-23_final
2019-05-23 10:32:08	ga_s2am_ard_3-2-1_53KRA_2019-05-23_final.stac-item
2019-05-23 10:32:22	ga_s2am_ard_3-2-1_53KRV_2019-05-23_final.stac-item
2019-05-27 10:16:52	ga_s2am_ard_3-2-1_54HTG_2019-05-27_final.stac-item
2019-05-27 10:16:55	ga_s2am_ard_3-2-1_53HQB_2019-05-27_final.stac-item
2019-05-27 10:17:06	ga_s2am_ard_3-2-1_54HTF_2019-05-27_final.stac-item
2019-05-27 10:17:08	ga_s2am_ard_3-2-1_53HQA_2019-05-27_final.stac-item
2019-06-25 10:42:04	ga_s2am_ard_3-2-1_53KRA_2019-06-25_final.stac-item
2019-06-25 10:42:17	ga_s2am_ard_3-2-1_53KRV_2019-06-25_final.stac-item
2019-07-15 10:41:50	ga_s2am_ard_3-2-1_53KRB_2019-07-15_final
2019-07-15 10:42:05	ga_s2am_ard_3-2-1_53KRA_2019-07-15_final.stac-item
2019-07-15 10:42:17	ga_s2am_ard_3-2-1_53KRV_2019-07-15_final.stac-item
2023-06-08 10:24:37	ga_s2am_ard_3-2-1_54JTR_2023-06-08_final
2023-06-08 10:24:43	ga_s2am_ard_3-2-1_53JQL_2023-06-08_final
2023-06-08 10:24:52	ga_s2am_ard_3-2-1_54JTQ_2023-06-08_final
2023-06-08 10:24:55	ga_s2am_ard_3-2-1_53JQK_2023-06-08_final
2023-06-08 10:25:06	ga_s2am_ard_3-2-1_54JTP_2023-06-08_final
2023-06-08 10:25:09	ga_s2am_ard_3-2-1_53JQJ_2023-06-08_final
2023-06-08 10:25:21	ga_s2am_ard_3-2-1_54JTN_2023-06-08_final
2023-06-08 10:25:24	ga_s2am_ard_3-2-1_53JQH_2023-06-08_final
2023-06-08 10:25:35	ga_s2am_ard_3-2-1_54JTM_2023-06-08_final
2023-06-08 10:25:38	ga_s2am_ard_3-2-1_53JQG_2023-06-08_final
2023-06-08 10:25:50	ga_s2am_ard_3-2-1_54JTL_2023-06-08_final
2023-06-08 10:25:52	ga_s2am_ard_3-2-1_53JQF_2023-06-08_final
2023-06-08 10:26:05	ga_s2am_ard_3-2-1_54HTK_2023-06-08_final
2023-06-08 10:26:07	ga_s2am_ard_3-2-1_53HQE_2023-06-08_final
2023-06-11 10:31:54	ga_s2am_ard_3-2-1_53KRB_2023-06-11_final
2023-06-11 10:32:08	ga_s2am_ard_3-2-1_53KRA_2023-06-11_final
2023-06-11 10:32:23	ga_s2am_ard_3-2-1_53KRV_2023-06-11_final
2023-06-11 10:32:37	ga_s2am_ard_3-2-1_53KRU_2023-06-11_final
2023-06-11 10:32:46	ga_s2am_ard_3-2-1_53KRT_2023-06-11_final
2023-06-11 10:32:53	ga_s2am_ard_3-2-1_53KRT_2023-06-11_final
2023-06-11 10:33:06	ga_s2am_ard_3-2-1_53KRS_2023-06-11_final
2023-06-11 10:33:21	ga_s2am_ard_3-2-1_53KRR_2023-06-11_final
2023-06-11 10:33:35	ga_s2am_ard_3-2-1_54KTV_2023-06-11_final
2023-06-11 10:33:35	ga_s2am_ard_3-2-1_53KRQ_2023-06-11_final
2023-06-11 10:33:49	ga_s2am_ard_3-2-1_54KTU_2023-06-11_final
2023-06-11 10:33:49	ga_s2am_ard_3-2-1_53KRP_2023-06-11_final
2023-06-11 10:34:04	ga_s2am_ard_3-2-1_53JRN_2023-06-11_final
2023-06-11 10:34:04	ga_s2am_ard_3-2-1_54JTT_2023-06-11_final
2023-06-11 10:34:08	ga_s2am_ard_3-2-1_53JQN_2023-06-11_final
2023-06-11 10:34:18	ga_s2am_ard_3-2-1_53JRM_2023-06-11_final
2023-06-11 10:34:18	ga_s2am_ard_3-2-1_54JTS_2023-06-11_final
2023-06-11 10:34:22	ga_s2am_ard_3-2-1_53JQM_2023-06-11_final
2023-06-11 10:34:33	ga_s2am_ard_3-2-1_54JTR_2023-06-11_final
2023-06-11 10:34:36	ga_s2am_ard_3-2-1_53JQL_2023-06-11_final
2023-06-11 10:34:48	ga_s2am_ard_3-2-1_54JTQ_2023-06-11_final
2023-06-11 10:34:51	ga_s2am_ard_3-2-1_53JQK_2023-06-11_final
2023-06-11 10:35:01	ga_s2am_ard_3-2-1_54JTP_2023-06-11_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

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