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
2016-05-25 12:04:47	ga_s2am_ard_3-2-1_49JGK_2016-05-25_final.stac-item
2016-05-25 12:04:47	ga_s2am_ard_3-2-1_49JGL_2016-05-25_final.stac-item
2016-05-25 12:04:47	ga_s2am_ard_3-2-1_49JGM_2016-05-25_final.stac-item
2016-09-12 12:02:46	ga_s2am_ard_3-2-1_49JGH_2016-09-12_final.stac-item
2016-09-12 12:02:46	ga_s2am_ard_3-2-1_49JGJ_2016-09-12_final.stac-item
2016-09-12 12:02:46	ga_s2am_ard_3-2-1_49JGK_2016-09-12_final.stac-item
2016-09-12 12:02:46	ga_s2am_ard_3-2-1_49JGL_2016-09-12_final.stac-item
2016-09-12 12:02:46	ga_s2am_ard_3-2-1_49JGM_2016-09-12_final.stac-item
2016-09-12 12:02:46	ga_s2am_ard_3-2-1_49JGN_2016-09-12_final.stac-item
2016-09-22 12:04:37	ga_s2am_ard_3-2-1_49JGL_2016-09-22_final.stac-item
2016-09-22 12:04:37	ga_s2am_ard_3-2-1_49JGM_2016-09-22_final.stac-item
2016-09-22 12:04:37	ga_s2am_ard_3-2-1_49JGN_2016-09-22_final.stac-item
2016-09-25 12:14:37	ga_s2am_ard_3-2-1_49JGL_2016-09-25_final.stac-item
2017-03-11 12:02:41	ga_s2am_ard_3-2-1_49JGJ_2017-03-11_final.stac-item
2017-03-11 12:02:41	ga_s2am_ard_3-2-1_49JGK_2017-03-11_final.stac-item
2017-03-11 12:02:41	ga_s2am_ard_3-2-1_49JGL_2017-03-11_final.stac-item
2017-03-11 12:02:41	ga_s2am_ard_3-2-1_49JGM_2017-03-11_final.stac-item
2017-03-11 12:02:41	ga_s2am_ard_3-2-1_49JGN_2017-03-11_final.stac-item
2017-03-14 12:13:38	ga_s2am_ard_3-2-1_49JGL_2017-03-14_final.stac-item
2018-03-26 12:02:47	ga_s2am_ard_3-2-1_49JGJ_2018-03-26_final.stac-item
2018-03-26 12:02:47	ga_s2am_ard_3-2-1_49JGK_2018-03-26_final.stac-item
2018-03-26 12:02:47	ga_s2am_ard_3-2-1_49JGL_2018-03-26_final.stac-item
2018-03-26 12:02:47	ga_s2am_ard_3-2-1_49JGM_2018-03-26_final.stac-item
2018-03-26 12:02:47	ga_s2am_ard_3-2-1_49JGN_2018-03-26_final.stac-item
2018-03-26 12:02:47	ga_s2am_ard_3-2-1_49KGP_2018-03-26_final.stac-item
2018-04-15 12:05:11	ga_s2am_ard_3-2-1_49JGJ_2018-04-15_final.stac-item
2018-04-15 12:05:11	ga_s2am_ard_3-2-1_49JGK_2018-04-15_final.stac-item
2018-04-15 12:05:11	ga_s2am_ard_3-2-1_49JGL_2018-04-15_final.stac-item
2018-04-18 12:13:22	ga_s2am_ard_3-2-1_49JGM_2018-04-18_final.stac-item
2018-04-18 12:13:22	ga_s2am_ard_3-2-1_49JGN_2018-04-18_final.stac-item
2018-04-18 12:13:22	ga_s2am_ard_3-2-1_49KGP_2018-04-18_final.stac-item
2018-04-18 12:13:22	ga_s2am_ard_3-2-1_49KGQ_2018-04-18_final.stac-item
2018-04-18 12:13:22	ga_s2am_ard_3-2-1_49KGR_2018-04-18_final.stac-item
2019-07-02 12:14:03	ga_s2am_ard_3-2-1_49KGR_2019-07-02_final.stac-item
2019-07-02 12:14:17	ga_s2am_ard_3-2-1_49KGQ_2019-07-02_final.stac-item
2019-07-02 12:14:32	ga_s2am_ard_3-2-1_49KGP_2019-07-02_final.stac-item
2019-07-02 12:14:46	ga_s2am_ard_3-2-1_49JGN_2019-07-02_final.stac-item
2019-07-02 12:15:01	ga_s2am_ard_3-2-1_49JGM_2019-07-02_final.stac-item
2019-07-02 12:15:15	ga_s2am_ard_3-2-1_49JGL_2019-07-02_final.stac-item
2019-07-09 12:04:36	ga_s2am_ard_3-2-1_49KGP_2019-07-09_final.stac-item
2019-07-09 12:04:51	ga_s2am_ard_3-2-1_49JGN_2019-07-09_final.stac-item
2019-07-09 12:05:06	ga_s2am_ard_3-2-1_49JGM_2019-07-09_final.stac-item
2019-07-12 12:14:03	ga_s2am_ard_3-2-1_49KGR_2019-07-12_final.stac-item
2019-07-12 12:14:18	ga_s2am_ard_3-2-1_49KGQ_2019-07-12_final.stac-item
2019-07-12 12:14:32	ga_s2am_ard_3-2-1_49KGP_2019-07-12_final.stac-item
2019-07-12 12:14:46	ga_s2am_ard_3-2-1_49JGN_2019-07-12_final.stac-item
2019-07-12 12:15:01	ga_s2am_ard_3-2-1_49JGM_2019-07-12_final.stac-item
2019-07-12 12:15:15	ga_s2am_ard_3-2-1_49JGL_2019-07-12_final.stac-item
2022-12-10 12:04:32	ga_s2am_ard_3-2-1_49KGP_2022-12-10_final.stac-item
2022-12-10 12:04:46	ga_s2am_ard_3-2-1_49JGN_2022-12-10_final.stac-item
2022-12-10 12:05:01	ga_s2am_ard_3-2-1_49JGM_2022-12-10_final.stac-item
2022-12-10 12:05:15	ga_s2am_ard_3-2-1_49JGL_2022-12-10_final.stac-item
2022-12-10 12:05:30	ga_s2am_ard_3-2-1_49JGK_2022-12-10_final.stac-item
2022-12-10 12:05:44	ga_s2am_ard_3-2-1_49JGJ_2022-12-10_final.stac-item
2022-12-13 12:13:31	ga_s2am_ard_3-2-1_49KGT_2022-12-13_nrt.stac-item
2022-12-13 12:13:44	ga_s2am_ard_3-2-1_49KGS_2022-12-13_nrt.stac-item
2022-12-13 12:13:58	ga_s2am_ard_3-2-1_49KGR_2022-12-13_final.stac-item
2022-12-13 12:14:10	ga_s2am_ard_3-2-1_49KGQ_2022-12-13_final.stac-item
2022-12-13 12:14:16	ga_s2am_ard_3-2-1_49KGQ_2022-12-13_final.stac-item
2022-12-13 12:14:27	ga_s2am_ard_3-2-1_49KGP_2022-12-13_final.stac-item
2022-12-13 12:14:41	ga_s2am_ard_3-2-1_49JGN_2022-12-13_final.stac-item
2022-12-13 12:14:56	ga_s2am_ard_3-2-1_49JGM_2022-12-13_final.stac-item
2022-12-13 12:15:10	ga_s2am_ard_3-2-1_49JGL_2022-12-13_final.stac-item
2022-12-17 11:56:59	ga_s2am_ard_3-2-1_49HGD_2022-12-17_nrt.stac-item
2022-12-17 11:57:14	ga_s2am_ard_3-2-1_49HGC_2022-12-17_nrt.stac-item
2022-12-17 11:57:25	ga_s2am_ard_3-2-1_49HGB_2022-12-17_nrt.stac-item
2023-02-08 12:04:30	ga_s2am_ard_3-2-1_49KGP_2023-02-08_final
2023-02-08 12:04:45	ga_s2am_ard_3-2-1_49JGN_2023-02-08_final
2023-03-20 12:06:11	ga_s2am_ard_3-2-1_49JGG_2023-03-20_nrt
2023-03-20 12:06:22	ga_s2am_ard_3-2-1_49JGF_2023-03-20_nrt
2024-05-06 12:13:33	ga_s2am_ard_3-2-1_49KGT_2024-05-06_nrt
2024-05-06 12:13:46	ga_s2am_ard_3-2-1_49KGS_2024-05-06_nrt
2024-05-06 12:14:01	ga_s2am_ard_3-2-1_49KGR_2024-05-06_final
2024-05-06 12:14:15	ga_s2am_ard_3-2-1_49KGQ_2024-05-06_final
2024-05-06 12:14:30	ga_s2am_ard_3-2-1_49KGP_2024-05-06_final
2024-05-06 12:14:44	ga_s2am_ard_3-2-1_49JGN_2024-05-06_final
2024-05-06 12:14:59	ga_s2am_ard_3-2-1_49JGM_2024-05-06_final
2024-05-06 12:15:13	ga_s2am_ard_3-2-1_49JGL_2024-05-06_final
2024-05-10 11:57:03	ga_s2am_ard_3-2-1_49HGD_2024-05-10_nrt
2024-05-10 11:57:17	ga_s2am_ard_3-2-1_49HGC_2024-05-10_nrt
2024-05-10 11:57:28	ga_s2am_ard_3-2-1_49HGB_2024-05-10_nrt
2024-05-16 12:14:59	ga_s2am_ard_3-2-1_49JGM_2024-05-16_nrt
2024-05-16 12:15:13	ga_s2am_ard_3-2-1_49JGL_2024-05-16_nrt
2024-05-20 11:57:01	ga_s2am_ard_3-2-1_49HGD_2024-05-20_nrt
2024-05-20 11:57:15	ga_s2am_ard_3-2-1_49HGC_2024-05-20_nrt
2024-05-20 11:57:26	ga_s2am_ard_3-2-1_49HGB_2024-05-20_nrt
2024-05-23 12:04:45	ga_s2am_ard_3-2-1_49JGN_2024-05-23_nrt
2024-05-23 12:05:00	ga_s2am_ard_3-2-1_49JGM_2024-05-23_nrt
2024-05-23 12:05:14	ga_s2am_ard_3-2-1_49JGL_2024-05-23_nrt
2024-05-23 12:05:28	ga_s2am_ard_3-2-1_49JGK_2024-05-23_nrt
2024-05-23 12:05:43	ga_s2am_ard_3-2-1_49JGJ_2024-05-23_nrt
2024-05-23 12:05:57	ga_s2am_ard_3-2-1_49JGH_2024-05-23_nrt
2024-05-23 12:06:12	ga_s2am_ard_3-2-1_49JGG_2024-05-23_nrt
2024-05-23 12:06:24	ga_s2am_ard_3-2-1_49JGF_2024-05-23_nrt
2024-05-26 12:13:31	ga_s2am_ard_3-2-1_49KGT_2024-05-26_nrt
2024-05-26 12:13:44	ga_s2am_ard_3-2-1_49KGS_2024-05-26_nrt
2024-05-26 12:13:58	ga_s2am_ard_3-2-1_49KGR_2024-05-26_nrt
2024-05-26 12:14:13	ga_s2am_ard_3-2-1_49KGQ_2024-05-26_nrt
2024-05-26 12:14:42	ga_s2am_ard_3-2-1_49JGN_2024-05-26_nrt
2024-05-26 12:14:56	ga_s2am_ard_3-2-1_49JGM_2024-05-26_nrt

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

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