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
2019-07-19 12:04:48	ga_s2am_ard_3-2-1_49JHN_2019-07-19_final.stac-item
2019-07-19 12:04:51	ga_s2am_ard_3-2-1_49JGN_2019-07-19_final.stac-item
2019-07-20 09:53:30	ga_s2am_ard_3-2-1_55KGP_2019-07-20_final.stac-item
2019-07-20 09:53:34	ga_s2am_ard_3-2-1_55KFP_2019-07-20_final.stac-item
2019-07-20 09:53:38	ga_s2am_ard_3-2-1_55KEP_2019-07-20_final.stac-item
2019-07-20 09:53:41	ga_s2am_ard_3-2-1_55KDP_2019-07-20_final.stac-item
2019-08-03 09:33:29	ga_s2am_ard_3-2-1_56KMU_2019-08-03_final.stac-item
2019-08-03 09:33:32	ga_s2am_ard_3-2-1_56KLU_2019-08-03_final.stac-item
2019-08-11 10:33:45	ga_s2am_ard_3-2-1_54KUU_2019-08-11_final.stac-item
2019-08-11 10:33:49	ga_s2am_ard_3-2-1_54KTU_2019-08-11_final.stac-item
2019-08-11 10:33:49	ga_s2am_ard_3-2-1_53KRP_2019-08-11_final.stac-item
2019-08-11 10:33:53	ga_s2am_ard_3-2-1_53KQP_2019-08-11_final.stac-item
2019-08-11 10:33:56	ga_s2am_ard_3-2-1_53KPP_2019-08-11_final.stac-item
2019-08-11 10:34:03	ga_s2am_ard_3-2-1_53JRN_2019-08-11_final.stac-item
2019-08-11 10:34:07	ga_s2am_ard_3-2-1_53JQN_2019-08-11_final.stac-item
2023-04-01 11:03:55	ga_s2am_ard_3-2-1_52KGU_2023-04-01_final
2023-04-01 11:03:59	ga_s2am_ard_3-2-1_52KFU_2023-04-01_final
2023-04-01 11:04:02	ga_s2am_ard_3-2-1_52KEU_2023-04-01_final
2023-04-01 11:04:05	ga_s2am_ard_3-2-1_52KDU_2023-04-01_final
2023-04-02 10:33:40	ga_s2am_ard_3-2-1_54KUU_2023-04-02_final
2023-04-02 10:33:44	ga_s2am_ard_3-2-1_54KTU_2023-04-02_final
2023-04-02 10:33:44	ga_s2am_ard_3-2-1_53KRP_2023-04-02_final
2023-04-02 10:33:48	ga_s2am_ard_3-2-1_53KQP_2023-04-02_final
2023-04-02 10:33:52	ga_s2am_ard_3-2-1_53KPP_2023-04-02_final
2023-04-02 10:33:59	ga_s2am_ard_3-2-1_53JRN_2023-04-02_final
2023-04-02 10:34:03	ga_s2am_ard_3-2-1_53JQN_2023-04-02_final
2023-04-03 10:03:31	ga_s2am_ard_3-2-1_55KDP_2023-04-03_final
2023-04-03 10:03:34	ga_s2am_ard_3-2-1_55KCP_2023-04-03_final
2023-04-03 10:03:37	ga_s2am_ard_3-2-1_55KBP_2023-04-03_final
2023-04-03 10:03:37	ga_s2am_ard_3-2-1_54KZU_2023-04-03_final
2023-04-03 10:03:42	ga_s2am_ard_3-2-1_54KYU_2023-04-03_final
2023-04-03 10:03:52	ga_s2am_ard_3-2-1_54JZT_2023-04-03_final
2023-04-03 10:03:55	ga_s2am_ard_3-2-1_54JYT_2023-04-03_final
2023-04-03 11:44:12	ga_s2am_ard_3-2-1_51KUP_2023-04-03_final
2023-04-03 11:44:15	ga_s2am_ard_3-2-1_50KRU_2023-04-03_final
2023-04-03 11:44:15	ga_s2am_ard_3-2-1_51KTP_2023-04-03_final
2023-04-03 11:44:19	ga_s2am_ard_3-2-1_50KQU_2023-04-03_final
2023-04-03 11:44:22	ga_s2am_ard_3-2-1_50KPU_2023-04-03_final
2023-04-03 11:44:30	ga_s2am_ard_3-2-1_50JRT_2023-04-03_final
2023-04-03 11:44:34	ga_s2am_ard_3-2-1_50JQT_2023-04-03_final
2023-04-04 09:33:17	ga_s2am_ard_3-2-1_56KPU_2023-04-04_nrt
2023-04-04 09:33:21	ga_s2am_ard_3-2-1_56KNU_2023-04-04_nrt
2023-04-04 09:33:24	ga_s2am_ard_3-2-1_56KMU_2023-04-04_final
2023-04-04 09:33:27	ga_s2am_ard_3-2-1_56KLU_2023-04-04_final
2023-04-04 11:13:57	ga_s2am_ard_3-2-1_52KEU_2023-04-04_final
2023-04-04 11:14:02	ga_s2am_ard_3-2-1_52KDU_2023-04-04_final
2023-04-04 11:14:05	ga_s2am_ard_3-2-1_52KCU_2023-04-04_final
2023-04-04 11:14:08	ga_s2am_ard_3-2-1_51KZP_2023-04-04_final
2023-04-04 11:14:08	ga_s2am_ard_3-2-1_52KBU_2023-04-04_final
2023-04-04 11:14:23	ga_s2am_ard_3-2-1_51JZN_2023-04-04_final
2023-04-06 10:13:32	ga_s2am_ard_3-2-1_55KBP_2023-04-06_final
2023-04-06 10:13:33	ga_s2am_ard_3-2-1_54KZU_2023-04-06_final
2023-04-06 10:13:37	ga_s2am_ard_3-2-1_54KYU_2023-04-06_final
2023-04-06 10:13:41	ga_s2am_ard_3-2-1_54KXU_2023-04-06_final
2023-04-06 10:13:44	ga_s2am_ard_3-2-1_54KWU_2023-04-06_final
2023-04-06 10:13:45	ga_s2am_ard_3-2-1_54JZT_2023-04-06_final
2023-04-06 10:13:51	ga_s2am_ard_3-2-1_54JYT_2023-04-06_final
2023-04-06 11:54:13	ga_s2am_ard_3-2-1_50KQU_2023-04-06_final
2023-04-06 11:54:19	ga_s2am_ard_3-2-1_50KPU_2023-04-06_final
2023-04-06 11:54:22	ga_s2am_ard_3-2-1_50KNU_2023-04-06_final
2023-04-06 11:54:26	ga_s2am_ard_3-2-1_50KMU_2023-04-06_final
2023-04-06 11:54:33	ga_s2am_ard_3-2-1_50KLU_2023-04-06_final
2023-04-07 11:24:04	ga_s2am_ard_3-2-1_51KZP_2023-04-07_final
2023-04-07 11:24:04	ga_s2am_ard_3-2-1_52KBU_2023-04-07_final
2023-04-07 11:24:08	ga_s2am_ard_3-2-1_51KYP_2023-04-07_final
2023-04-07 11:24:12	ga_s2am_ard_3-2-1_51KXP_2023-04-07_final
2023-04-07 11:24:15	ga_s2am_ard_3-2-1_51KWP_2023-04-07_final
2023-04-07 11:24:19	ga_s2am_ard_3-2-1_51JZN_2023-04-07_final
2023-04-07 11:24:23	ga_s2am_ard_3-2-1_51JYN_2023-04-07_final
2023-04-08 10:53:49	ga_s2am_ard_3-2-1_53KMP_2023-04-08_final
2023-04-08 10:53:54	ga_s2am_ard_3-2-1_53KLP_2023-04-08_final
2023-04-08 10:53:57	ga_s2am_ard_3-2-1_53KKP_2023-04-08_final
2023-04-08 10:53:57	ga_s2am_ard_3-2-1_52KHU_2023-04-08_final
2023-04-08 10:54:01	ga_s2am_ard_3-2-1_52KGU_2023-04-08_final
2023-04-08 10:54:12	ga_s2am_ard_3-2-1_52JHT_2023-04-08_final
2023-04-08 10:54:15	ga_s2am_ard_3-2-1_52JGT_2023-04-08_final
2023-04-16 10:13:33	ga_s2am_ard_3-2-1_54KZU_2023-04-16_final
2023-04-16 10:13:41	ga_s2am_ard_3-2-1_54KXU_2023-04-16_final
2023-04-16 11:54:13	ga_s2am_ard_3-2-1_50KQU_2023-04-16_final
2023-04-16 11:54:18	ga_s2am_ard_3-2-1_50KPU_2023-04-16_final
2023-04-16 11:54:22	ga_s2am_ard_3-2-1_50KNU_2023-04-16_final
2023-04-16 11:54:26	ga_s2am_ard_3-2-1_50KMU_2023-04-16_final
2023-04-16 11:54:33	ga_s2am_ard_3-2-1_50KLU_2023-04-16_final
2023-04-18 10:53:48	ga_s2am_ard_3-2-1_53KMP_2023-04-18_final
2023-04-18 10:53:53	ga_s2am_ard_3-2-1_53KLP_2023-04-18_final
2023-04-18 10:53:56	ga_s2am_ard_3-2-1_53KKP_2023-04-18_final
2023-04-18 10:53:56	ga_s2am_ard_3-2-1_52KHU_2023-04-18_final
2023-04-18 10:54:00	ga_s2am_ard_3-2-1_52KGU_2023-04-18_final
2023-04-18 10:54:10	ga_s2am_ard_3-2-1_52JHT_2023-04-18_final
2023-04-18 10:54:11	ga_s2am_ard_3-2-1_52JGT_2023-04-18_final
2023-04-20 11:34:04	ga_s2am_ard_3-2-1_51KXP_2023-04-20_final
2023-04-20 11:34:09	ga_s2am_ard_3-2-1_51KWP_2023-04-20_final
2023-04-20 11:34:13	ga_s2am_ard_3-2-1_51KVP_2023-04-20_final
2023-04-20 11:34:16	ga_s2am_ard_3-2-1_51KUP_2023-04-20_final
2023-04-22 10:33:40	ga_s2am_ard_3-2-1_54KUU_2023-04-22_final
2023-04-22 10:33:44	ga_s2am_ard_3-2-1_54KTU_2023-04-22_final
2023-04-22 10:33:44	ga_s2am_ard_3-2-1_53KRP_2023-04-22_final
2023-04-22 10:33:48	ga_s2am_ard_3-2-1_53KQP_2023-04-22_final
2023-04-22 10:33:52	ga_s2am_ard_3-2-1_53KPP_2023-04-22_final
2023-04-22 10:33:58	ga_s2am_ard_3-2-1_53JRN_2023-04-22_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