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
2019-06-25 10:42:51	ga_s2am_ard_3-2-1_53KQT_2019-06-25_final.stac-item
2019-06-25 10:42:55	ga_s2am_ard_3-2-1_53KPT_2019-06-25_final.stac-item
2019-06-25 10:42:58	ga_s2am_ard_3-2-1_53KNT_2019-06-25_final.stac-item
2019-06-25 10:43:01	ga_s2am_ard_3-2-1_53KMT_2019-06-25_final.stac-item
2019-06-26 10:12:39	ga_s2am_ard_3-2-1_55KBT_2019-06-26_final.stac-item
2019-06-26 10:12:40	ga_s2am_ard_3-2-1_54KZC_2019-06-26_final.stac-item
2019-07-05 10:42:52	ga_s2am_ard_3-2-1_53KQT_2019-07-05_final.stac-item
2019-07-05 10:42:55	ga_s2am_ard_3-2-1_53KPT_2019-07-05_final.stac-item
2019-07-05 10:42:59	ga_s2am_ard_3-2-1_53KNT_2019-07-05_final.stac-item
2019-07-05 10:43:02	ga_s2am_ard_3-2-1_53KMT_2019-07-05_final.stac-item
2019-07-06 10:12:40	ga_s2am_ard_3-2-1_55KBT_2019-07-06_final.stac-item
2019-07-06 10:12:41	ga_s2am_ard_3-2-1_54KZC_2019-07-06_final.stac-item
2019-07-06 10:12:45	ga_s2am_ard_3-2-1_54KYC_2019-07-06_final.stac-item
2019-07-06 10:12:48	ga_s2am_ard_3-2-1_54KXC_2019-07-06_final.stac-item
2019-07-06 10:12:55	ga_s2am_ard_3-2-1_54KZB_2019-07-06_final.stac-item
2019-07-11 11:02:59	ga_s2am_ard_3-2-1_53KKT_2019-07-11_final.stac-item
2019-07-11 11:03:00	ga_s2am_ard_3-2-1_52KHC_2019-07-11_final.stac-item
2019-07-11 11:03:04	ga_s2am_ard_3-2-1_52KGC_2019-07-11_final.stac-item
2019-07-11 11:03:07	ga_s2am_ard_3-2-1_52KFC_2019-07-11_final.stac-item
2019-07-11 11:03:10	ga_s2am_ard_3-2-1_52KEC_2019-07-11_final.stac-item
2019-07-27 09:42:38	ga_s2am_ard_3-2-1_55KGT_2019-07-27_final.stac-item
2019-07-27 09:42:48	ga_s2am_ard_3-2-1_55KHS_2019-07-27_final.stac-item
2019-07-30 09:52:32	ga_s2am_ard_3-2-1_55KGT_2019-07-30_final.stac-item
2019-07-30 09:52:36	ga_s2am_ard_3-2-1_55KFT_2019-07-30_final.stac-item
2019-07-30 09:52:39	ga_s2am_ard_3-2-1_55KET_2019-07-30_final.stac-item
2019-08-06 09:42:37	ga_s2am_ard_3-2-1_55KGT_2019-08-06_final.stac-item
2019-08-06 09:42:47	ga_s2am_ard_3-2-1_55KHS_2019-08-06_final.stac-item
2019-08-07 10:52:56	ga_s2am_ard_3-2-1_53KMT_2019-08-07_final.stac-item
2019-08-07 10:53:00	ga_s2am_ard_3-2-1_53KLT_2019-08-07_final.stac-item
2019-08-07 10:53:03	ga_s2am_ard_3-2-1_53KKT_2019-08-07_final.stac-item
2019-08-07 10:53:04	ga_s2am_ard_3-2-1_52KHC_2019-08-07_final.stac-item
2019-08-07 10:53:18	ga_s2am_ard_3-2-1_52KHB_2019-08-07_final.stac-item
2019-08-09 09:52:32	ga_s2am_ard_3-2-1_55KGT_2019-08-09_final.stac-item
2019-08-09 09:52:35	ga_s2am_ard_3-2-1_55KFT_2019-08-09_final.stac-item
2019-08-09 09:52:39	ga_s2am_ard_3-2-1_55KET_2019-08-09_final.stac-item
2023-09-24 11:23:09	ga_s2am_ard_3-2-1_51KZT_2023-09-24_final
2023-09-24 11:23:12	ga_s2am_ard_3-2-1_51KYT_2023-09-24_final
2023-09-24 11:23:15	ga_s2am_ard_3-2-1_51KXT_2023-09-24_final
2023-09-24 11:23:23	ga_s2am_ard_3-2-1_51KZS_2023-09-24_final
2023-09-26 10:22:41	ga_s2am_ard_3-2-1_54KXC_2023-09-26_final
2023-09-26 10:22:44	ga_s2am_ard_3-2-1_54KWC_2023-09-26_final
2023-09-26 10:22:47	ga_s2am_ard_3-2-1_54KVC_2023-09-26_final
2023-09-26 10:22:50	ga_s2am_ard_3-2-1_54KUC_2023-09-26_final
2023-09-27 09:52:26	ga_s2am_ard_3-2-1_55KHT_2023-09-27_final
2023-09-27 09:52:30	ga_s2am_ard_3-2-1_55KGT_2023-09-27_final
2023-09-27 09:52:34	ga_s2am_ard_3-2-1_55KFT_2023-09-27_final
2023-09-27 09:52:37	ga_s2am_ard_3-2-1_55KET_2023-09-27_final
2023-09-27 11:33:11	ga_s2am_ard_3-2-1_51KXT_2023-09-27_final
2023-09-27 11:33:15	ga_s2am_ard_3-2-1_51KWT_2023-09-27_final
2023-09-27 11:33:18	ga_s2am_ard_3-2-1_51KVT_2023-09-27_final
2023-09-27 11:33:24	ga_s2am_ard_3-2-1_51KUT_2023-09-27_final
2023-09-28 11:02:56	ga_s2am_ard_3-2-1_53KKT_2023-09-28_final
2023-09-28 11:02:57	ga_s2am_ard_3-2-1_52KHC_2023-09-28_final
2023-09-28 11:03:01	ga_s2am_ard_3-2-1_52KGC_2023-09-28_final
2023-09-28 11:03:04	ga_s2am_ard_3-2-1_52KFC_2023-09-28_final
2023-09-28 11:03:07	ga_s2am_ard_3-2-1_52KEC_2023-09-28_final
2023-09-29 10:32:46	ga_s2am_ard_3-2-1_54KUC_2023-09-29_final
2023-09-29 10:32:49	ga_s2am_ard_3-2-1_54KTC_2023-09-29_final
2023-09-29 10:32:50	ga_s2am_ard_3-2-1_53KRT_2023-09-29_final
2023-09-29 10:32:54	ga_s2am_ard_3-2-1_53KQT_2023-09-29_final
2023-09-29 10:33:04	ga_s2am_ard_3-2-1_53KRS_2023-09-29_final
2023-09-30 10:02:32	ga_s2am_ard_3-2-1_55KET_2023-09-30_final
2023-09-30 10:02:36	ga_s2am_ard_3-2-1_55KDT_2023-09-30_final
2023-09-30 10:02:39	ga_s2am_ard_3-2-1_55KCT_2023-09-30_final
2023-09-30 10:02:42	ga_s2am_ard_3-2-1_55KBT_2023-09-30_final
2023-09-30 10:02:44	ga_s2am_ard_3-2-1_54KZC_2023-09-30_final
2023-09-30 10:02:57	ga_s2am_ard_3-2-1_54KZB_2023-09-30_final
2023-10-01 09:32:28	ga_s2am_ard_3-2-1_56KPC_2023-10-01_final
2023-10-01 09:32:30	ga_s2am_ard_3-2-1_56KNC_2023-10-01_nrt
2023-10-01 09:32:32	ga_s2am_ard_3-2-1_56KMC_2023-10-01_nrt
2023-10-01 11:13:03	ga_s2am_ard_3-2-1_52KEC_2023-10-01_final
2023-10-01 11:13:06	ga_s2am_ard_3-2-1_52KDC_2023-10-01_final
2023-10-01 11:13:10	ga_s2am_ard_3-2-1_52KCC_2023-10-01_final
2023-10-01 11:13:15	ga_s2am_ard_3-2-1_52KBC_2023-10-01_final
2023-10-03 10:12:37	ga_s2am_ard_3-2-1_55KBT_2023-10-03_final
2023-10-03 10:12:38	ga_s2am_ard_3-2-1_54KZC_2023-10-03_final
2023-10-03 10:12:41	ga_s2am_ard_3-2-1_54KYC_2023-10-03_final
2023-10-03 10:12:45	ga_s2am_ard_3-2-1_54KXC_2023-10-03_final
2023-10-03 10:12:52	ga_s2am_ard_3-2-1_54KZB_2023-10-03_final
2023-10-03 11:53:19	ga_s2am_ard_3-2-1_50KQC_2023-10-03_final
2023-10-03 11:53:23	ga_s2am_ard_3-2-1_50KPC_2023-10-03_final
2023-10-03 11:53:26	ga_s2am_ard_3-2-1_50KNC_2023-10-03_final
2023-10-03 11:53:32	ga_s2am_ard_3-2-1_50KMC_2023-10-03_final
2023-10-06 12:03:21	ga_s2am_ard_3-2-1_50KNC_2023-10-06_final
2023-10-06 12:03:24	ga_s2am_ard_3-2-1_50KMC_2023-10-06_final
2023-10-06 12:03:27	ga_s2am_ard_3-2-1_50KLC_2023-10-06_final
2023-10-06 12:03:30	ga_s2am_ard_3-2-1_50KKC_2023-10-06_final
2023-10-06 12:03:31	ga_s2am_ard_3-2-1_49KHT_2023-10-06_nrt
2023-10-06 12:03:45	ga_s2am_ard_3-2-1_49KHS_2023-10-06_final
2023-10-20 10:02:30	ga_s2am_ard_3-2-1_55KET_2023-10-20_final
2023-10-20 10:02:33	ga_s2am_ard_3-2-1_55KDT_2023-10-20_final
2023-10-20 10:02:36	ga_s2am_ard_3-2-1_55KCT_2023-10-20_final
2023-10-20 10:02:39	ga_s2am_ard_3-2-1_55KBT_2023-10-20_final
2023-10-20 10:02:41	ga_s2am_ard_3-2-1_54KZC_2023-10-20_final
2023-10-20 10:02:54	ga_s2am_ard_3-2-1_54KZB_2023-10-20_final
2023-10-20 11:43:11	ga_s2am_ard_3-2-1_51KVT_2023-10-20_final
2023-10-20 11:43:15	ga_s2am_ard_3-2-1_51KUT_2023-10-20_final
2023-10-20 11:43:18	ga_s2am_ard_3-2-1_51KTT_2023-10-20_final
2023-10-20 11:43:18	ga_s2am_ard_3-2-1_50KRC_2023-10-20_final
2023-10-20 11:43:21	ga_s2am_ard_3-2-1_50KQC_2023-10-20_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