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
2019-07-28 10:51:09	ga_s2am_ard_3-2-1_53LPE_2019-07-28_final.stac-item
2019-07-28 10:51:12	ga_s2am_ard_3-2-1_53LNE_2019-07-28_final.stac-item
2019-07-28 10:51:15	ga_s2am_ard_3-2-1_53LME_2019-07-28_final.stac-item
2019-07-28 10:51:18	ga_s2am_ard_3-2-1_53LLE_2019-07-28_final.stac-item
2019-08-07 10:51:08	ga_s2am_ard_3-2-1_53LPE_2019-08-07_final.stac-item
2019-08-07 10:51:11	ga_s2am_ard_3-2-1_53LNE_2019-08-07_final.stac-item
2019-08-07 10:51:15	ga_s2am_ard_3-2-1_53LME_2019-08-07_final.stac-item
2019-08-07 10:51:18	ga_s2am_ard_3-2-1_53LLE_2019-08-07_final.stac-item
2020-02-01 11:51:19	ga_s2am_ard_3-2-1_51LUE_2020-02-01_final.stac-item
2020-02-01 11:51:31	ga_s2am_ard_3-2-1_51LUD_2020-02-01_final
2020-11-14 11:41:29	ga_s2am_ard_3-2-1_51LWE_2020-11-14_final.stac-item
2020-11-14 11:41:33	ga_s2am_ard_3-2-1_51LVE_2020-11-14_final.stac-item
2020-11-14 11:41:36	ga_s2am_ard_3-2-1_51LUE_2020-11-14_final.stac-item
2020-11-14 11:41:51	ga_s2am_ard_3-2-1_51LUD_2020-11-14_final
2020-11-17 10:11:08	ga_s2am_ard_3-2-1_55LCD_2020-11-17_final.stac-item
2020-11-17 10:11:11	ga_s2am_ard_3-2-1_55LBD_2020-11-17_final.stac-item
2020-11-17 10:11:12	ga_s2am_ard_3-2-1_54LZJ_2020-11-17_final.stac-item
2020-11-17 10:11:16	ga_s2am_ard_3-2-1_54LYJ_2020-11-17_final.stac-item
2020-11-19 10:51:07	ga_s2am_ard_3-2-1_53LPE_2020-11-19_final.stac-item
2020-11-19 10:51:10	ga_s2am_ard_3-2-1_53LNE_2020-11-19_final.stac-item
2020-11-19 10:51:13	ga_s2am_ard_3-2-1_53LME_2020-11-19_final.stac-item
2020-11-19 10:51:16	ga_s2am_ard_3-2-1_53LLE_2020-11-19_final.stac-item
2020-11-20 10:20:52	ga_s2am_ard_3-2-1_54LZK_2020-11-20_final.stac-item
2020-11-20 10:20:56	ga_s2am_ard_3-2-1_54LYK_2020-11-20_final.stac-item
2020-11-20 10:20:59	ga_s2am_ard_3-2-1_54LXK_2020-11-20_final.stac-item
2020-11-20 10:21:03	ga_s2am_ard_3-2-1_54LWK_2020-11-20_final.stac-item
2020-11-20 10:21:03	ga_s2am_ard_3-2-1_54LZJ_2020-11-20_final.stac-item
2020-11-20 10:21:10	ga_s2am_ard_3-2-1_54LYJ_2020-11-20_final.stac-item
2020-11-20 10:21:14	ga_s2am_ard_3-2-1_54LXJ_2020-11-20_final.stac-item
2020-11-24 11:41:27	ga_s2am_ard_3-2-1_51LWE_2020-11-24_final.stac-item
2020-11-24 11:41:31	ga_s2am_ard_3-2-1_51LVE_2020-11-24_final.stac-item
2020-11-24 11:41:34	ga_s2am_ard_3-2-1_51LUE_2020-11-24_final.stac-item
2020-11-24 11:41:49	ga_s2am_ard_3-2-1_51LUD_2020-11-24_final
2020-11-27 11:51:28	ga_s2am_ard_3-2-1_51LUE_2020-11-27_final.stac-item
2020-11-27 11:51:39	ga_s2am_ard_3-2-1_51LUD_2020-11-27_final
2020-12-01 11:31:18	ga_s2am_ard_3-2-1_52LBK_2020-12-01_final.stac-item
2020-12-01 11:31:20	ga_s2am_ard_3-2-1_51LZE_2020-12-01_final.stac-item
2020-12-01 11:31:24	ga_s2am_ard_3-2-1_51LYE_2020-12-01_final.stac-item
2020-12-01 11:31:29	ga_s2am_ard_3-2-1_52LBJ_2020-12-01_final.stac-item
2020-12-01 11:31:30	ga_s2am_ard_3-2-1_51LWE_2020-12-01_final.stac-item
2020-12-01 11:31:35	ga_s2am_ard_3-2-1_51LZD_2020-12-01_final.stac-item
2020-12-01 11:31:38	ga_s2am_ard_3-2-1_51LYD_2020-12-01_final.stac-item
2020-12-01 11:31:42	ga_s2am_ard_3-2-1_51LXD_2020-12-01_final.stac-item
2020-12-06 10:41:03	ga_s2am_ard_3-2-1_53LQE_2020-12-06_final
2020-12-06 10:41:07	ga_s2am_ard_3-2-1_53LPE_2020-12-06_final.stac-item
2020-12-06 10:41:18	ga_s2am_ard_3-2-1_53LQD_2020-12-06_final.stac-item
2020-12-06 10:41:21	ga_s2am_ard_3-2-1_53LPD_2020-12-06_final
2020-12-07 10:11:10	ga_s2am_ard_3-2-1_54LYJ_2020-12-07_final.stac-item
2020-12-09 10:51:01	ga_s2am_ard_3-2-1_53LPE_2020-12-09_final.stac-item
2020-12-09 10:51:04	ga_s2am_ard_3-2-1_53LNE_2020-12-09_final.stac-item
2020-12-09 10:51:08	ga_s2am_ard_3-2-1_53LME_2020-12-09_final.stac-item
2020-12-09 10:51:10	ga_s2am_ard_3-2-1_53LLE_2020-12-09_final.stac-item
2023-09-30 11:41:28	ga_s2am_ard_3-2-1_51LWE_2023-09-30_final
2023-09-30 11:41:32	ga_s2am_ard_3-2-1_51LVE_2023-09-30_final
2023-09-30 11:41:35	ga_s2am_ard_3-2-1_51LUE_2023-09-30_final
2023-09-30 11:41:39	ga_s2am_ard_3-2-1_51LTE_2023-09-30_nrt
2023-09-30 11:41:49	ga_s2am_ard_3-2-1_51LUD_2023-09-30_final
2023-09-30 11:41:52	ga_s2am_ard_3-2-1_51LTD_2023-09-30_nrt
2023-10-17 11:31:17	ga_s2am_ard_3-2-1_52LBK_2023-10-17_final
2023-10-17 11:31:20	ga_s2am_ard_3-2-1_51LZE_2023-10-17_final
2023-10-17 11:31:23	ga_s2am_ard_3-2-1_51LYE_2023-10-17_final
2023-10-17 11:31:27	ga_s2am_ard_3-2-1_51LXE_2023-10-17_final
2023-10-17 11:31:28	ga_s2am_ard_3-2-1_52LBJ_2023-10-17_final
2023-10-17 11:31:30	ga_s2am_ard_3-2-1_51LWE_2023-10-17_final
2023-10-17 11:31:34	ga_s2am_ard_3-2-1_51LZD_2023-10-17_final
2023-10-17 11:31:38	ga_s2am_ard_3-2-1_51LYD_2023-10-17_final
2023-10-17 11:31:41	ga_s2am_ard_3-2-1_51LXD_2023-10-17_final
2023-10-18 11:01:08	ga_s2am_ard_3-2-1_53LLE_2023-10-18_final
2023-10-18 11:01:12	ga_s2am_ard_3-2-1_53LKE_2023-10-18_final
2023-10-18 11:01:13	ga_s2am_ard_3-2-1_52LHK_2023-10-18_final
2023-10-18 11:01:16	ga_s2am_ard_3-2-1_52LGK_2023-10-18_final
2023-10-18 11:01:23	ga_s2am_ard_3-2-1_53LLD_2023-10-18_final
2023-10-18 11:01:26	ga_s2am_ard_3-2-1_53LKD_2023-10-18_final
2023-10-18 11:01:28	ga_s2am_ard_3-2-1_52LHJ_2023-10-18_final
2023-10-18 11:01:31	ga_s2am_ard_3-2-1_52LGJ_2023-10-18_final
2023-10-19 10:30:55	ga_s2am_ard_3-2-1_54LWK_2023-10-19_final
2023-10-19 10:30:58	ga_s2am_ard_3-2-1_54LVK_2023-10-19_final
2023-10-19 10:31:02	ga_s2am_ard_3-2-1_54LUK_2023-10-19_nrt
2023-10-19 10:31:04	ga_s2am_ard_3-2-1_54LTK_2023-10-19_nrt
2023-10-19 10:31:09	ga_s2am_ard_3-2-1_53LRE_2023-10-19_nrt
2023-10-19 10:31:16	ga_s2am_ard_3-2-1_54LUJ_2023-10-19_final
2023-10-19 10:31:19	ga_s2am_ard_3-2-1_54LTJ_2023-10-19_nrt
2023-10-19 10:31:21	ga_s2am_ard_3-2-1_53LRD_2023-10-19_nrt
2023-10-20 10:00:40	ga_s2am_ard_3-2-1_55LGE_2023-10-20_nrt
2023-10-20 10:00:45	ga_s2am_ard_3-2-1_55LFE_2023-10-20_nrt
2023-10-20 10:00:48	ga_s2am_ard_3-2-1_55LEE_2023-10-20_nrt
2023-10-20 10:00:51	ga_s2am_ard_3-2-1_55LGD_2023-10-20_nrt
2023-10-20 10:00:52	ga_s2am_ard_3-2-1_55LDE_2023-10-20_nrt
2023-10-20 10:00:59	ga_s2am_ard_3-2-1_55LFD_2023-10-20_nrt
2023-10-20 11:41:26	ga_s2am_ard_3-2-1_51LWE_2023-10-20_final
2023-10-20 11:41:29	ga_s2am_ard_3-2-1_51LVE_2023-10-20_final
2023-10-20 11:41:33	ga_s2am_ard_3-2-1_51LUE_2023-10-20_final
2023-10-20 11:41:37	ga_s2am_ard_3-2-1_51LTE_2023-10-20_nrt
2023-10-20 11:41:47	ga_s2am_ard_3-2-1_51LUD_2023-10-20_final
2023-10-20 11:41:50	ga_s2am_ard_3-2-1_51LTD_2023-10-20_nrt
2023-10-21 11:11:12	ga_s2am_ard_3-2-1_52LGK_2023-10-21_final
2023-10-21 11:11:16	ga_s2am_ard_3-2-1_52LFK_2023-10-21_final
2023-10-21 11:11:19	ga_s2am_ard_3-2-1_52LEK_2023-10-21_final
2023-10-21 11:11:22	ga_s2am_ard_3-2-1_52LDK_2023-10-21_nrt
2023-10-21 11:11:27	ga_s2am_ard_3-2-1_52LGJ_2023-10-21_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