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
2019-03-23 09:23:53	ga_s2am_ard_3-2-1_56JNS_2019-03-22_final.stac-item
2019-03-23 11:04:23	ga_s2am_ard_3-2-1_52JGS_2019-03-23_final.stac-item
2019-03-23 11:04:27	ga_s2am_ard_3-2-1_52JFS_2019-03-23_final.stac-item
2019-03-23 11:04:31	ga_s2am_ard_3-2-1_52JES_2019-03-23_final.stac-item
2019-03-23 11:04:34	ga_s2am_ard_3-2-1_52JDS_2019-03-23_final.stac-item
2019-04-03 12:14:57	ga_s2am_ard_3-2-1_49JGM_2019-04-03_final.stac-item
2019-04-03 12:15:00	ga_s2am_ard_3-2-1_49JFM_2019-04-03_final.stac-item
2019-04-04 10:04:01	ga_s2am_ard_3-2-1_55JDM_2019-04-04_final.stac-item
2019-04-04 10:04:05	ga_s2am_ard_3-2-1_55JCM_2019-04-04_final.stac-item
2019-04-04 10:04:08	ga_s2am_ard_3-2-1_54JZS_2019-04-04_final.stac-item
2019-04-04 10:04:08	ga_s2am_ard_3-2-1_55JBM_2019-04-04_final.stac-item
2019-04-04 10:04:12	ga_s2am_ard_3-2-1_54JYS_2019-04-04_final.stac-item
2019-04-08 09:43:54	ga_s2am_ard_3-2-1_56JLS_2019-04-08_final.stac-item
2019-04-08 09:43:57	ga_s2am_ard_3-2-1_55JHM_2019-04-08_final.stac-item
2019-04-08 09:43:57	ga_s2am_ard_3-2-1_56JKS_2019-04-08_final.stac-item
2019-04-08 09:44:00	ga_s2am_ard_3-2-1_55JGM_2019-04-08_final.stac-item
2019-04-08 09:44:04	ga_s2am_ard_3-2-1_55JFM_2019-04-08_final.stac-item
2019-04-10 12:04:50	ga_s2am_ard_3-2-1_50JMS_2019-04-10_final.stac-item
2019-04-10 12:04:55	ga_s2am_ard_3-2-1_50JLS_2019-04-10_final.stac-item
2019-04-10 12:04:58	ga_s2am_ard_3-2-1_49JHM_2019-04-10_final.stac-item
2019-04-10 12:04:59	ga_s2am_ard_3-2-1_50JKS_2019-04-10_final.stac-item
2019-04-10 12:05:02	ga_s2am_ard_3-2-1_49JGM_2019-04-10_final.stac-item
2019-04-11 09:54:00	ga_s2am_ard_3-2-1_55JFM_2019-04-11_final.stac-item
2019-04-11 09:54:04	ga_s2am_ard_3-2-1_55JEM_2019-04-11_final.stac-item
2019-04-11 09:54:07	ga_s2am_ard_3-2-1_55JDM_2019-04-11_final.stac-item
2019-04-11 09:54:11	ga_s2am_ard_3-2-1_55JCM_2019-04-11_final.stac-item
2019-07-20 11:34:45	ga_s2am_ard_3-2-1_51JWM_2019-07-20_final.stac-item
2019-07-20 11:34:47	ga_s2am_ard_3-2-1_51JVM_2019-07-20_final.stac-item
2019-07-20 11:34:49	ga_s2am_ard_3-2-1_51JUM_2019-07-20_final.stac-item
2019-07-20 11:34:50	ga_s2am_ard_3-2-1_50JRS_2019-07-20_final.stac-item
2019-07-20 11:34:50	ga_s2am_ard_3-2-1_51JTM_2019-07-20_final.stac-item
2019-07-20 11:34:55	ga_s2am_ard_3-2-1_51JUM_2019-07-20_final.stac-item
2019-07-20 11:34:57	ga_s2am_ard_3-2-1_50JRS_2019-07-20_final.stac-item
2019-07-20 11:34:57	ga_s2am_ard_3-2-1_51JTM_2019-07-20_final.stac-item
2019-07-26 11:54:53	ga_s2am_ard_3-2-1_50JPS_2019-07-26_final.stac-item
2019-07-26 11:54:56	ga_s2am_ard_3-2-1_50JNS_2019-07-26_final.stac-item
2019-07-26 11:55:00	ga_s2am_ard_3-2-1_50JMS_2019-07-26_final.stac-item
2019-07-26 11:55:03	ga_s2am_ard_3-2-1_50JLS_2019-07-26_final.stac-item
2019-07-30 11:34:44	ga_s2am_ard_3-2-1_51JWM_2019-07-30_final.stac-item
2019-07-30 11:34:48	ga_s2am_ard_3-2-1_51JVM_2019-07-30_final.stac-item
2019-07-30 11:34:51	ga_s2am_ard_3-2-1_51JUM_2019-07-30_final.stac-item
2019-07-30 11:34:54	ga_s2am_ard_3-2-1_50JRS_2019-07-30_final.stac-item
2019-07-30 11:34:54	ga_s2am_ard_3-2-1_51JTM_2019-07-30_final.stac-item
2019-08-02 11:44:45	ga_s2am_ard_3-2-1_51JUM_2019-08-02_final.stac-item
2019-08-02 11:44:49	ga_s2am_ard_3-2-1_50JRS_2019-08-02_final.stac-item
2019-08-02 11:44:50	ga_s2am_ard_3-2-1_51JTM_2019-08-02_final.stac-item
2019-08-02 11:44:53	ga_s2am_ard_3-2-1_50JQS_2019-08-02_final.stac-item
2019-08-02 11:44:57	ga_s2am_ard_3-2-1_50JPS_2019-08-02_final.stac-item
2019-08-05 11:54:52	ga_s2am_ard_3-2-1_50JPS_2019-08-05_final.stac-item
2019-08-05 11:54:56	ga_s2am_ard_3-2-1_50JNS_2019-08-05_final.stac-item
2019-08-05 11:54:59	ga_s2am_ard_3-2-1_50JMS_2019-08-05_final.stac-item
2019-08-05 11:55:02	ga_s2am_ard_3-2-1_50JLS_2019-08-05_final.stac-item
2019-08-09 11:34:43	ga_s2am_ard_3-2-1_51JWM_2019-08-09_final.stac-item
2019-08-09 11:34:47	ga_s2am_ard_3-2-1_51JVM_2019-08-09_final.stac-item
2019-08-09 11:34:50	ga_s2am_ard_3-2-1_51JUM_2019-08-09_final.stac-item
2019-08-09 11:34:53	ga_s2am_ard_3-2-1_50JRS_2019-08-09_final.stac-item
2019-08-09 11:34:53	ga_s2am_ard_3-2-1_51JTM_2019-08-09_final.stac-item
2019-08-12 11:44:44	ga_s2am_ard_3-2-1_51JUM_2019-08-12_final.stac-item
2019-08-12 11:44:48	ga_s2am_ard_3-2-1_50JRS_2019-08-12_final.stac-item
2019-08-12 11:44:49	ga_s2am_ard_3-2-1_51JTM_2019-08-12_final.stac-item
2019-08-12 11:44:52	ga_s2am_ard_3-2-1_50JQS_2019-08-12_final.stac-item
2019-08-12 11:44:56	ga_s2am_ard_3-2-1_50JPS_2019-08-12_final.stac-item
2019-08-15 11:54:51	ga_s2am_ard_3-2-1_50JPS_2019-08-15_final.stac-item
2019-08-15 11:54:55	ga_s2am_ard_3-2-1_50JNS_2019-08-15_final.stac-item
2019-08-15 11:54:58	ga_s2am_ard_3-2-1_50JMS_2019-08-15_final.stac-item
2019-08-15 11:55:01	ga_s2am_ard_3-2-1_50JLS_2019-08-15_final.stac-item
2023-08-17 10:24:14	ga_s2am_ard_3-2-1_54JWS_2023-08-17_final
2023-08-17 10:24:18	ga_s2am_ard_3-2-1_54JVS_2023-08-17_final
2023-08-17 10:24:22	ga_s2am_ard_3-2-1_54JUS_2023-08-17_final
2023-08-17 10:24:25	ga_s2am_ard_3-2-1_53JRM_2023-08-17_final
2023-08-17 10:24:25	ga_s2am_ard_3-2-1_54JTS_2023-08-17_final
2023-08-18 09:54:08	ga_s2am_ard_3-2-1_55JEM_2023-08-18_final
2023-08-18 09:54:15	ga_s2am_ard_3-2-1_55JCM_2023-08-18_final
2023-08-18 11:34:46	ga_s2am_ard_3-2-1_51JWM_2023-08-18_final
2023-08-18 11:34:49	ga_s2am_ard_3-2-1_51JVM_2023-08-18_final
2023-08-18 11:34:52	ga_s2am_ard_3-2-1_51JUM_2023-08-18_final
2023-08-18 11:34:55	ga_s2am_ard_3-2-1_50JRS_2023-08-18_final
2023-08-18 11:34:55	ga_s2am_ard_3-2-1_51JTM_2023-08-18_final
2023-08-19 09:23:57	ga_s2am_ard_3-2-1_56JPS_2023-08-18_nrt
2023-08-19 09:24:00	ga_s2am_ard_3-2-1_56JNS_2023-08-18_final
2023-08-19 11:04:30	ga_s2am_ard_3-2-1_52JGS_2023-08-19_final
2023-08-19 11:04:35	ga_s2am_ard_3-2-1_52JFS_2023-08-19_final
2023-08-19 11:04:38	ga_s2am_ard_3-2-1_52JES_2023-08-19_final
2023-08-19 11:04:42	ga_s2am_ard_3-2-1_52JDS_2023-08-19_final
2023-08-21 10:04:07	ga_s2am_ard_3-2-1_55JDM_2023-08-21_final
2023-08-21 10:04:10	ga_s2am_ard_3-2-1_55JCM_2023-08-21_final
2023-08-21 10:04:13	ga_s2am_ard_3-2-1_54JZS_2023-08-21_final
2023-08-21 10:04:14	ga_s2am_ard_3-2-1_55JBM_2023-08-21_final
2023-08-21 10:04:17	ga_s2am_ard_3-2-1_54JYS_2023-08-21_final
2023-08-21 11:44:47	ga_s2am_ard_3-2-1_51JUM_2023-08-21_final
2023-08-21 11:44:51	ga_s2am_ard_3-2-1_50JRS_2023-08-21_final
2023-08-21 11:44:51	ga_s2am_ard_3-2-1_51JTM_2023-08-21_final
2023-08-21 11:44:55	ga_s2am_ard_3-2-1_50JQS_2023-08-21_final
2023-08-21 11:44:59	ga_s2am_ard_3-2-1_50JPS_2023-08-21_final
2023-08-22 11:14:44	ga_s2am_ard_3-2-1_51JZM_2023-08-22_final
2023-08-22 11:14:44	ga_s2am_ard_3-2-1_52JBS_2023-08-22_final
2023-08-22 11:14:47	ga_s2am_ard_3-2-1_51JYM_2023-08-22_final
2023-08-23 10:44:23	ga_s2am_ard_3-2-1_53JPM_2023-08-23_final
2023-08-23 10:44:27	ga_s2am_ard_3-2-1_53JNM_2023-08-23_final
2023-08-23 10:44:30	ga_s2am_ard_3-2-1_53JMM_2023-08-23_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