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
2019-06-26 11:54:52	ga_s2am_ard_3-2-1_50JPS_2019-06-26_final.stac-item
2019-06-26 11:54:56	ga_s2am_ard_3-2-1_50JNS_2019-06-26_final.stac-item
2019-06-26 11:54:59	ga_s2am_ard_3-2-1_50JMS_2019-06-26_final.stac-item
2019-06-26 11:55:02	ga_s2am_ard_3-2-1_50JLS_2019-06-26_final.stac-item
2019-06-26 11:55:07	ga_s2am_ard_3-2-1_50JPR_2019-06-26_final.stac-item
2019-06-26 11:55:17	ga_s2am_ard_3-2-1_50JLR_2019-06-26_final.stac-item
2019-06-28 10:54:27	ga_s2am_ard_3-2-1_53JLM_2019-06-28_final.stac-item
2019-06-28 10:54:31	ga_s2am_ard_3-2-1_52JHS_2019-06-28_final.stac-item
2019-06-28 10:54:31	ga_s2am_ard_3-2-1_53JKM_2019-06-28_final.stac-item
2019-06-28 10:54:34	ga_s2am_ard_3-2-1_52JGS_2019-06-28_final.stac-item
2019-06-28 10:54:38	ga_s2am_ard_3-2-1_52JFS_2019-06-28_final.stac-item
2019-06-28 10:54:42	ga_s2am_ard_3-2-1_53JLL_2019-06-28_final.stac-item
2019-06-28 10:54:45	ga_s2am_ard_3-2-1_53JKL_2019-06-28_final.stac-item
2019-06-28 10:54:49	ga_s2am_ard_3-2-1_52JGR_2019-06-28_final.stac-item
2019-06-28 10:54:52	ga_s2am_ard_3-2-1_52JFR_2019-06-28_final.stac-item
2019-07-16 11:54:53	ga_s2am_ard_3-2-1_50JPS_2019-07-16_final.stac-item
2019-07-16 11:54:57	ga_s2am_ard_3-2-1_50JNS_2019-07-16_final.stac-item
2019-07-16 11:55:00	ga_s2am_ard_3-2-1_50JMS_2019-07-16_final.stac-item
2019-07-16 11:55:03	ga_s2am_ard_3-2-1_50JLS_2019-07-16_final.stac-item
2019-07-16 11:55:08	ga_s2am_ard_3-2-1_50JPR_2019-07-16_final.stac-item
2019-07-16 11:55:18	ga_s2am_ard_3-2-1_50JLR_2019-07-16_final.stac-item
2023-05-24 11:14:34	ga_s2am_ard_3-2-1_52JDS_2023-05-24_final
2023-05-24 11:14:38	ga_s2am_ard_3-2-1_52JCS_2023-05-24_final
2023-05-24 11:14:41	ga_s2am_ard_3-2-1_51JZM_2023-05-24_final
2023-05-24 11:14:41	ga_s2am_ard_3-2-1_52JBS_2023-05-24_final
2023-05-24 11:14:44	ga_s2am_ard_3-2-1_51JYM_2023-05-24_final
2023-05-24 11:14:53	ga_s2am_ard_3-2-1_52JCR_2023-05-24_final
2023-05-24 11:14:56	ga_s2am_ard_3-2-1_52JBR_2023-05-24_final
2023-05-24 11:14:59	ga_s2am_ard_3-2-1_51JYL_2023-05-24_final
2023-05-25 10:44:20	ga_s2am_ard_3-2-1_53JPM_2023-05-25_final
2023-05-25 10:44:24	ga_s2am_ard_3-2-1_53JNM_2023-05-25_final
2023-05-25 10:44:28	ga_s2am_ard_3-2-1_53JMM_2023-05-25_final
2023-05-25 10:44:31	ga_s2am_ard_3-2-1_53JLM_2023-05-25_final
2023-05-26 11:54:51	ga_s2am_ard_3-2-1_50JPS_2023-05-26_final
2023-05-26 11:54:55	ga_s2am_ard_3-2-1_50JNS_2023-05-26_final
2023-05-26 11:54:58	ga_s2am_ard_3-2-1_50JMS_2023-05-26_final
2023-05-26 11:55:01	ga_s2am_ard_3-2-1_50JLS_2023-05-26_final
2023-05-26 11:55:06	ga_s2am_ard_3-2-1_50JPR_2023-05-26_final
2023-05-26 11:55:16	ga_s2am_ard_3-2-1_50JLR_2023-05-26_final
2023-05-29 12:04:52	ga_s2am_ard_3-2-1_50JMS_2023-05-29_final
2023-05-29 12:04:57	ga_s2am_ard_3-2-1_50JLS_2023-05-29_final
2023-05-29 12:05:00	ga_s2am_ard_3-2-1_49JHM_2023-05-29_final
2023-05-29 12:05:01	ga_s2am_ard_3-2-1_50JKS_2023-05-29_final
2023-05-29 12:05:04	ga_s2am_ard_3-2-1_49JGM_2023-05-29_final
2023-05-29 12:05:12	ga_s2am_ard_3-2-1_50JLR_2023-05-29_final
2023-05-29 12:05:12	ga_s2am_ard_3-2-1_49JFM_2023-05-29_final
2023-05-29 12:05:15	ga_s2am_ard_3-2-1_50JKR_2023-05-29_final
2023-05-29 12:05:18	ga_s2am_ard_3-2-1_49JGL_2023-05-29_final
2023-05-29 12:05:23	ga_s2am_ard_3-2-1_49JFL_2023-05-29_final
2023-05-30 11:34:43	ga_s2am_ard_3-2-1_51JWM_2023-05-30_final
2023-05-30 11:34:47	ga_s2am_ard_3-2-1_51JVM_2023-05-30_final
2023-05-30 11:34:50	ga_s2am_ard_3-2-1_51JUM_2023-05-30_final
2023-05-30 11:34:53	ga_s2am_ard_3-2-1_50JRS_2023-05-30_final
2023-05-30 11:34:53	ga_s2am_ard_3-2-1_51JTM_2023-05-30_final
2023-05-30 11:35:05	ga_s2am_ard_3-2-1_51JUL_2023-05-30_final
2023-05-30 11:35:08	ga_s2am_ard_3-2-1_51JTL_2023-05-30_final
2023-06-15 11:54:52	ga_s2am_ard_3-2-1_50JPS_2023-06-15_final
2023-06-15 11:54:56	ga_s2am_ard_3-2-1_50JNS_2023-06-15_final
2023-06-15 11:54:59	ga_s2am_ard_3-2-1_50JMS_2023-06-15_final
2023-06-15 11:55:02	ga_s2am_ard_3-2-1_50JLS_2023-06-15_final
2023-06-15 11:55:07	ga_s2am_ard_3-2-1_50JPR_2023-06-15_final
2023-06-15 11:55:17	ga_s2am_ard_3-2-1_50JLR_2023-06-15_final
2023-07-13 11:14:36	ga_s2am_ard_3-2-1_52JDS_2023-07-13_final
2023-07-13 11:14:40	ga_s2am_ard_3-2-1_52JCS_2023-07-13_final
2023-07-13 11:14:43	ga_s2am_ard_3-2-1_51JZM_2023-07-13_final
2023-07-13 11:14:43	ga_s2am_ard_3-2-1_52JBS_2023-07-13_final
2023-07-13 11:14:46	ga_s2am_ard_3-2-1_51JYM_2023-07-13_final
2023-07-13 11:14:54	ga_s2am_ard_3-2-1_52JCR_2023-07-13_final
2023-07-13 11:14:58	ga_s2am_ard_3-2-1_52JBR_2023-07-13_final
2023-07-13 11:15:01	ga_s2am_ard_3-2-1_51JYL_2023-07-13_final
2023-07-15 11:54:53	ga_s2am_ard_3-2-1_50JPS_2023-07-15_final
2023-07-15 11:54:56	ga_s2am_ard_3-2-1_50JNS_2023-07-15_final
2023-07-15 11:55:00	ga_s2am_ard_3-2-1_50JMS_2023-07-15_final
2023-07-15 11:55:03	ga_s2am_ard_3-2-1_50JLS_2023-07-15_final
2023-07-15 11:55:07	ga_s2am_ard_3-2-1_50JPR_2023-07-15_final
2023-07-15 11:55:18	ga_s2am_ard_3-2-1_50JLR_2023-07-15_final
2023-07-17 10:54:27	ga_s2am_ard_3-2-1_53JLM_2023-07-17_final
2023-07-17 10:54:31	ga_s2am_ard_3-2-1_52JHS_2023-07-17_final
2023-07-17 10:54:31	ga_s2am_ard_3-2-1_53JKM_2023-07-17_final
2023-07-17 10:54:34	ga_s2am_ard_3-2-1_52JGS_2023-07-17_final
2023-07-17 10:54:38	ga_s2am_ard_3-2-1_52JFS_2023-07-17_final
2023-07-17 10:54:42	ga_s2am_ard_3-2-1_53JLL_2023-07-17_final
2023-07-17 10:54:46	ga_s2am_ard_3-2-1_53JKL_2023-07-17_final
2023-07-17 10:54:49	ga_s2am_ard_3-2-1_52JGR_2023-07-17_final
2023-07-17 10:54:52	ga_s2am_ard_3-2-1_52JFR_2023-07-17_final
2023-07-20 11:04:28	ga_s2am_ard_3-2-1_52JGS_2023-07-20_final
2023-07-20 11:04:33	ga_s2am_ard_3-2-1_52JFS_2023-07-20_final
2023-07-20 11:04:36	ga_s2am_ard_3-2-1_52JES_2023-07-20_final
2023-07-20 11:04:39	ga_s2am_ard_3-2-1_52JGR_2023-07-20_final
2023-07-20 11:04:40	ga_s2am_ard_3-2-1_52JDS_2023-07-20_final
2023-07-20 11:04:47	ga_s2am_ard_3-2-1_52JFR_2023-07-20_final
2023-07-23 11:14:36	ga_s2am_ard_3-2-1_52JDS_2023-07-23_final
2023-07-25 11:55:07	ga_s2am_ard_3-2-1_50JPR_2023-07-25_final
2023-07-25 11:55:17	ga_s2am_ard_3-2-1_50JLR_2023-07-25_final
2023-11-05 10:24:12	ga_s2am_ard_3-2-1_54JWS_2023-11-05_final
2023-11-05 10:24:14	ga_s2am_ard_3-2-1_54JVS_2023-11-05_final
2023-11-05 10:24:16	ga_s2am_ard_3-2-1_54JUS_2023-11-05_final
2023-11-05 10:24:18	ga_s2am_ard_3-2-1_53JRM_2023-11-05_final
2023-11-05 10:24:18	ga_s2am_ard_3-2-1_54JTS_2023-11-05_final
2023-11-05 10:24:30	ga_s2am_ard_3-2-1_54JUR_2023-11-05_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