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
2015-11-27 12:04:51	ga_s2am_ard_3-2-1_50JKR_2015-11-27_final.stac-item
2015-11-27 12:04:51	ga_s2am_ard_3-2-1_49JHM_2015-11-27_final.stac-item
2018-02-01 11:49:16	ga_s2am_ard_3-2-1_50JKM_2018-02-01_final.stac-item
2018-02-01 11:49:16	ga_s2am_ard_3-2-1_50JKN_2018-02-01_final.stac-item
2018-02-01 11:55:56	ga_s2am_ard_3-2-1_50JKM_2018-02-01_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_50JKM_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_50JKN_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_50JKP_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_50JKQ_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_50JKR_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_50JKS_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_50JKT_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_50KKA_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_50KKB_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_50KKU_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_50KKV_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_49JHM_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_49JHN_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_49KHP_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_49KHQ_2018-02-14_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_49KHR_2018-02-14_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_50JKM_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_50JKN_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_50JKP_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_50JKQ_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_50JKR_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_50JKS_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_50JKT_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_50KKA_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_50KKB_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_50KKU_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_50KKV_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_49JHM_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_49JHN_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_49KHP_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_49KHQ_2018-02-24_final.stac-item
2018-02-24 12:02:46	ga_s2am_ard_3-2-1_49KHR_2018-02-24_final.stac-item
2018-03-03 11:52:20	ga_s2am_ard_3-2-1_50JKM_2018-03-03_final.stac-item
2018-03-03 11:52:20	ga_s2am_ard_3-2-1_50JKN_2018-03-03_final.stac-item
2018-03-09 12:13:17	ga_s2am_ard_3-2-1_50KKB_2018-03-09_final.stac-item
2018-03-19 12:13:18	ga_s2am_ard_3-2-1_50KKB_2018-03-19_final.stac-item
2018-12-14 12:13:31	ga_s2am_ard_3-2-1_50KKB_2018-12-14_final.stac-item
2018-12-28 11:56:07	ga_s2am_ard_3-2-1_50JKM_2018-12-28_final.stac-item
2018-12-31 12:04:50	ga_s2am_ard_3-2-1_49JHM_2018-12-31_final.stac-item
2018-12-31 12:04:51	ga_s2am_ard_3-2-1_50JKS_2018-12-31_final.stac-item
2018-12-31 12:05:05	ga_s2am_ard_3-2-1_50JKR_2018-12-31_final.stac-item
2018-12-31 12:05:20	ga_s2am_ard_3-2-1_50JKQ_2018-12-31_final.stac-item
2018-12-31 12:05:34	ga_s2am_ard_3-2-1_50JKP_2018-12-31_final.stac-item
2018-12-31 12:05:49	ga_s2am_ard_3-2-1_50JKN_2018-12-31_final.stac-item
2018-12-31 12:06:03	ga_s2am_ard_3-2-1_50JKM_2018-12-31_final.stac-item
2019-01-03 12:13:34	ga_s2am_ard_3-2-1_50KKB_2019-01-03_final.stac-item
2019-04-17 11:56:02	ga_s2am_ard_3-2-1_50JKN_2019-04-17_final.stac-item
2019-04-17 11:56:16	ga_s2am_ard_3-2-1_50JKM_2019-04-17_final.stac-item
2019-04-27 11:56:03	ga_s2am_ard_3-2-1_50JKN_2019-04-27_final.stac-item
2019-05-13 12:13:43	ga_s2am_ard_3-2-1_50KKB_2019-05-13_final.stac-item
2019-05-17 11:56:03	ga_s2am_ard_3-2-1_50JKN_2019-05-17_final.stac-item
2019-05-17 11:56:18	ga_s2am_ard_3-2-1_50JKM_2019-05-17_final.stac-item
2019-05-23 12:13:43	ga_s2am_ard_3-2-1_50KKB_2019-05-23_final.stac-item
2023-02-01 12:13:28	ga_s2am_ard_3-2-1_50KKC_2023-02-01_nrt.stac-item
2023-02-01 12:13:28	ga_s2am_ard_3-2-1_49KHT_2023-02-01_nrt.stac-item
2023-02-01 12:13:38	ga_s2am_ard_3-2-1_50KKB_2023-02-01_final
2023-02-01 12:13:38	ga_s2am_ard_3-2-1_49KHS_2023-02-01_final
2023-02-05 11:55:51	ga_s2am_ard_3-2-1_50JKN_2023-02-05_final
2023-02-05 11:55:58	ga_s2am_ard_3-2-1_50JKN_2023-02-05_final
2023-02-05 11:56:13	ga_s2am_ard_3-2-1_50JKM_2023-02-05_final
2023-02-05 11:56:28	ga_s2am_ard_3-2-1_50JKL_2023-02-05_nrt.stac-item
2023-02-08 12:04:56	ga_s2am_ard_3-2-1_49JHM_2023-02-08_final
2023-02-08 12:04:56	ga_s2am_ard_3-2-1_50JKS_2023-02-08_final
2023-02-08 12:05:11	ga_s2am_ard_3-2-1_50JKR_2023-02-08_final
2023-02-08 12:05:25	ga_s2am_ard_3-2-1_50JKQ_2023-02-08_final
2023-02-08 12:05:40	ga_s2am_ard_3-2-1_50JKP_2023-02-08_final
2023-02-08 12:05:54	ga_s2am_ard_3-2-1_50JKN_2023-02-08_final
2023-02-08 12:06:09	ga_s2am_ard_3-2-1_50JKM_2023-02-08_final
2023-02-08 12:06:18	ga_s2am_ard_3-2-1_50JKL_2023-02-08_final
2023-02-08 12:06:22	ga_s2am_ard_3-2-1_50JKL_2023-02-08_final
2023-02-18 12:05:07	ga_s2am_ard_3-2-1_50JKR_2023-02-18_nrt.stac-item
2023-02-18 12:05:07	ga_s2am_ard_3-2-1_50JKR_2023-02-18_final
2023-02-18 12:05:14	ga_s2am_ard_3-2-1_50JKR_2023-02-18_nrt.stac-item
2023-02-18 12:05:14	ga_s2am_ard_3-2-1_50JKR_2023-02-18_final
2023-02-18 12:05:23	ga_s2am_ard_3-2-1_50JKQ_2023-02-18_nrt.stac-item
2023-02-18 12:05:23	ga_s2am_ard_3-2-1_50JKQ_2023-02-18_final
2023-02-18 12:05:38	ga_s2am_ard_3-2-1_50JKP_2023-02-18_nrt.stac-item
2023-02-18 12:05:38	ga_s2am_ard_3-2-1_50JKP_2023-02-18_final
2023-02-18 12:05:52	ga_s2am_ard_3-2-1_50JKN_2023-02-18_final
2023-02-18 12:06:06	ga_s2am_ard_3-2-1_50JKM_2023-02-18_nrt.stac-item
2023-02-18 12:06:06	ga_s2am_ard_3-2-1_50JKM_2023-02-18_final
2023-02-18 12:06:19	ga_s2am_ard_3-2-1_50JKL_2023-02-18_final
2023-02-21 12:13:27	ga_s2am_ard_3-2-1_50KKC_2023-02-21_nrt.stac-item
2023-02-21 12:13:27	ga_s2am_ard_3-2-1_49KHT_2023-02-21_nrt.stac-item
2023-02-21 12:13:37	ga_s2am_ard_3-2-1_50KKB_2023-02-21_nrt.stac-item
2023-03-17 11:55:57	ga_s2am_ard_3-2-1_50JKN_2023-03-17_final
2023-03-17 11:56:12	ga_s2am_ard_3-2-1_50JKM_2023-03-17_final
2023-03-17 11:56:26	ga_s2am_ard_3-2-1_50JKL_2023-03-17_nrt
2023-03-17 11:56:41	ga_s2am_ard_3-2-1_50HKK_2023-03-17_nrt
2023-03-17 11:56:55	ga_s2am_ard_3-2-1_50HKJ_2023-03-17_nrt
2023-03-17 11:57:10	ga_s2am_ard_3-2-1_50HKH_2023-03-17_nrt
2023-03-17 11:57:22	ga_s2am_ard_3-2-1_50HKG_2023-03-17_nrt
2023-03-20 12:03:28	ga_s2am_ard_3-2-1_50KKC_2023-03-20_final
2023-03-20 12:03:28	ga_s2am_ard_3-2-1_49KHT_2023-03-20_nrt
2023-03-23 12:13:28	ga_s2am_ard_3-2-1_50KKC_2023-03-23_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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