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
2019-06-01 11:03:44	ga_s2am_ard_3-2-1_52KGV_2019-06-01_final.stac-item
2019-06-01 11:03:48	ga_s2am_ard_3-2-1_52KFV_2019-06-01_final.stac-item
2019-06-01 11:03:51	ga_s2am_ard_3-2-1_52KEV_2019-06-01_final.stac-item
2019-06-01 11:03:56	ga_s2am_ard_3-2-1_52KDV_2019-06-01_final.stac-item
2019-06-03 10:03:19	ga_s2am_ard_3-2-1_55KDQ_2019-06-03_final.stac-item
2019-06-03 10:03:23	ga_s2am_ard_3-2-1_55KCQ_2019-06-03_final.stac-item
2019-06-03 10:03:26	ga_s2am_ard_3-2-1_55KBQ_2019-06-03_final.stac-item
2019-06-03 10:03:26	ga_s2am_ard_3-2-1_54KZV_2019-06-03_final.stac-item
2019-06-03 10:03:40	ga_s2am_ard_3-2-1_54KZU_2019-06-03_final.stac-item
2019-06-08 10:53:38	ga_s2am_ard_3-2-1_53KMQ_2019-06-08_final.stac-item
2019-06-08 10:53:42	ga_s2am_ard_3-2-1_53KLQ_2019-06-08_final.stac-item
2019-06-08 10:53:45	ga_s2am_ard_3-2-1_53KKQ_2019-06-08_final.stac-item
2019-06-08 10:53:45	ga_s2am_ard_3-2-1_52KHV_2019-06-08_final.stac-item
2019-06-08 10:53:48	ga_s2am_ard_3-2-1_52KGV_2019-06-08_final.stac-item
2019-06-11 11:03:44	ga_s2am_ard_3-2-1_52KGV_2019-06-11_final.stac-item
2019-06-11 11:03:48	ga_s2am_ard_3-2-1_52KFV_2019-06-11_final.stac-item
2019-06-11 11:03:52	ga_s2am_ard_3-2-1_52KEV_2019-06-11_final.stac-item
2019-06-11 11:03:56	ga_s2am_ard_3-2-1_52KDV_2019-06-11_final.stac-item
2019-07-03 11:44:03	ga_s2am_ard_3-2-1_51KUQ_2019-07-03_final.stac-item
2019-07-03 11:44:06	ga_s2am_ard_3-2-1_51KTQ_2019-07-03_final.stac-item
2019-07-03 11:44:07	ga_s2am_ard_3-2-1_50KRV_2019-07-03_final.stac-item
2019-07-03 11:44:10	ga_s2am_ard_3-2-1_50KQV_2019-07-03_final.stac-item
2019-07-03 11:44:15	ga_s2am_ard_3-2-1_50KPV_2019-07-03_final.stac-item
2019-07-03 11:44:21	ga_s2am_ard_3-2-1_50KRU_2019-07-03_final.stac-item
2019-07-16 11:54:06	ga_s2am_ard_3-2-1_50KQV_2019-07-16_final.stac-item
2019-07-16 11:54:10	ga_s2am_ard_3-2-1_50KPV_2019-07-16_final.stac-item
2019-07-16 11:54:13	ga_s2am_ard_3-2-1_50KNV_2019-07-16_final.stac-item
2019-07-16 11:54:16	ga_s2am_ard_3-2-1_50KMV_2019-07-16_final.stac-item
2023-09-24 09:43:10	ga_s2am_ard_3-2-1_56KLV_2023-09-24_final
2023-09-24 09:43:14	ga_s2am_ard_3-2-1_56KKV_2023-09-24_final
2023-09-24 09:43:14	ga_s2am_ard_3-2-1_55KHQ_2023-09-24_final
2023-09-24 09:43:18	ga_s2am_ard_3-2-1_55KGQ_2023-09-24_final
2023-09-24 09:43:29	ga_s2am_ard_3-2-1_55KHP_2023-09-24_final
2023-09-24 11:23:51	ga_s2am_ard_3-2-1_52KBV_2023-09-24_final
2023-09-24 11:23:52	ga_s2am_ard_3-2-1_51KZQ_2023-09-24_final
2023-09-24 11:23:56	ga_s2am_ard_3-2-1_51KYQ_2023-09-24_final
2023-09-24 11:23:59	ga_s2am_ard_3-2-1_51KXQ_2023-09-24_final
2023-09-24 11:24:04	ga_s2am_ard_3-2-1_51KWQ_2023-09-24_final
2023-09-26 10:23:27	ga_s2am_ard_3-2-1_54KWV_2023-09-26_final
2023-09-26 10:23:31	ga_s2am_ard_3-2-1_54KVV_2023-09-26_final
2023-09-26 10:23:34	ga_s2am_ard_3-2-1_54KUV_2023-09-26_final
2023-09-26 10:23:39	ga_s2am_ard_3-2-1_54KTV_2023-09-26_final
2023-09-26 10:23:42	ga_s2am_ard_3-2-1_53KRQ_2023-09-26_final
2023-09-26 10:23:53	ga_s2am_ard_3-2-1_53KRP_2023-09-26_final
2023-09-27 11:33:55	ga_s2am_ard_3-2-1_51KXQ_2023-09-27_final
2023-09-27 11:33:58	ga_s2am_ard_3-2-1_51KWQ_2023-09-27_final
2023-09-27 11:34:02	ga_s2am_ard_3-2-1_51KVQ_2023-09-27_final
2023-09-27 11:34:05	ga_s2am_ard_3-2-1_51KUQ_2023-09-27_final
2023-09-28 09:23:10	ga_s2am_ard_3-2-1_56KPV_2023-09-27_nrt
2023-09-29 10:33:37	ga_s2am_ard_3-2-1_53KQQ_2023-09-29_final
2023-09-29 10:33:40	ga_s2am_ard_3-2-1_53KPQ_2023-09-29_final
2023-09-29 10:33:48	ga_s2am_ard_3-2-1_53KRP_2023-09-29_final
2023-09-29 12:14:10	ga_s2am_ard_3-2-1_50KKV_2023-09-29_final
2023-09-29 12:14:11	ga_s2am_ard_3-2-1_49KHQ_2023-09-29_final
2023-09-29 12:14:15	ga_s2am_ard_3-2-1_49KGQ_2023-09-29_final
2023-09-29 12:14:18	ga_s2am_ard_3-2-1_49KFQ_2023-09-29_nrt
2023-09-30 11:44:00	ga_s2am_ard_3-2-1_51KUQ_2023-09-30_final
2023-09-30 11:44:04	ga_s2am_ard_3-2-1_51KTQ_2023-09-30_final
2023-09-30 11:44:04	ga_s2am_ard_3-2-1_50KRV_2023-09-30_final
2023-09-30 11:44:08	ga_s2am_ard_3-2-1_50KQV_2023-09-30_final
2023-09-30 11:44:12	ga_s2am_ard_3-2-1_50KPV_2023-09-30_final
2023-09-30 11:44:18	ga_s2am_ard_3-2-1_50KRU_2023-09-30_final
2023-10-03 10:13:21	ga_s2am_ard_3-2-1_55KBQ_2023-10-03_final
2023-10-03 10:13:21	ga_s2am_ard_3-2-1_54KZV_2023-10-03_final
2023-10-03 10:13:25	ga_s2am_ard_3-2-1_54KYV_2023-10-03_final
2023-10-03 10:13:28	ga_s2am_ard_3-2-1_54KXV_2023-10-03_final
2023-10-03 10:13:31	ga_s2am_ard_3-2-1_54KWV_2023-10-03_final
2023-10-04 11:23:51	ga_s2am_ard_3-2-1_52KBV_2023-10-04_final
2023-10-04 11:23:59	ga_s2am_ard_3-2-1_51KXQ_2023-10-04_final
2023-10-07 09:53:12	ga_s2am_ard_3-2-1_55KGQ_2023-10-07_final
2023-10-07 09:53:15	ga_s2am_ard_3-2-1_55KFQ_2023-10-07_final
2023-10-07 09:53:19	ga_s2am_ard_3-2-1_55KEQ_2023-10-07_final
2023-10-07 09:53:22	ga_s2am_ard_3-2-1_55KDQ_2023-10-07_final
2023-10-11 11:13:43	ga_s2am_ard_3-2-1_52KEV_2023-10-11_final
2023-10-11 11:13:46	ga_s2am_ard_3-2-1_52KDV_2023-10-11_final
2023-10-11 11:13:50	ga_s2am_ard_3-2-1_52KCV_2023-10-11_final
2023-10-11 11:13:53	ga_s2am_ard_3-2-1_51KZQ_2023-10-11_final
2023-10-11 11:13:53	ga_s2am_ard_3-2-1_52KBV_2023-10-11_final
2023-10-11 11:14:07	ga_s2am_ard_3-2-1_51KZP_2023-10-11_final
2023-10-12 10:43:31	ga_s2am_ard_3-2-1_53KPQ_2023-10-12_final
2023-10-12 10:43:35	ga_s2am_ard_3-2-1_53KNQ_2023-10-12_final
2023-10-12 10:43:39	ga_s2am_ard_3-2-1_53KMQ_2023-10-12_final
2023-10-12 10:43:43	ga_s2am_ard_3-2-1_53KLQ_2023-10-12_final
2023-11-06 11:33:52	ga_s2am_ard_3-2-1_51KXQ_2023-11-06_final
2023-11-06 11:33:56	ga_s2am_ard_3-2-1_51KWQ_2023-11-06_final
2023-11-06 11:33:59	ga_s2am_ard_3-2-1_51KVQ_2023-11-06_final
2023-11-06 11:34:02	ga_s2am_ard_3-2-1_51KUQ_2023-11-06_final
2023-11-09 10:03:16	ga_s2am_ard_3-2-1_55KDQ_2023-11-09_final
2023-11-09 10:03:20	ga_s2am_ard_3-2-1_55KCQ_2023-11-09_final
2023-11-09 10:03:23	ga_s2am_ard_3-2-1_55KBQ_2023-11-09_final
2023-11-09 10:03:23	ga_s2am_ard_3-2-1_54KZV_2023-11-09_final
2023-11-09 10:03:31	ga_s2am_ard_3-2-1_54KYV_2023-11-09_final
2023-11-09 10:03:38	ga_s2am_ard_3-2-1_54KZU_2023-11-09_final
2023-11-16 11:33:51	ga_s2am_ard_3-2-1_51KXQ_2023-11-16_final
2023-11-16 11:33:55	ga_s2am_ard_3-2-1_51KWQ_2023-11-16_final
2023-11-16 11:33:58	ga_s2am_ard_3-2-1_51KVQ_2023-11-16_final
2023-11-16 11:34:01	ga_s2am_ard_3-2-1_51KUQ_2023-11-16_final
2023-11-18 12:14:05	ga_s2am_ard_3-2-1_50KKV_2023-11-18_final
2023-11-18 12:14:06	ga_s2am_ard_3-2-1_49KHQ_2023-11-18_final
2023-11-18 12:14:10	ga_s2am_ard_3-2-1_49KGQ_2023-11-18_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