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
2018-10-25 12:14:37	ga_s2am_ard_3-2-1_49JGK_2018-10-25_final.stac-item
2018-10-25 12:14:37	ga_s2am_ard_3-2-1_49JGL_2018-10-25_final.stac-item
2018-11-04 12:13:17	ga_s2am_ard_3-2-1_49JGK_2018-11-04_final.stac-item
2018-11-04 12:13:17	ga_s2am_ard_3-2-1_49JGL_2018-11-04_final.stac-item
2018-11-04 12:13:17	ga_s2am_ard_3-2-1_49JGM_2018-11-04_final.stac-item
2018-11-04 12:13:17	ga_s2am_ard_3-2-1_49JGN_2018-11-04_final.stac-item
2018-11-04 12:13:17	ga_s2am_ard_3-2-1_49KGP_2018-11-04_final.stac-item
2018-11-04 12:13:17	ga_s2am_ard_3-2-1_49KGQ_2018-11-04_final.stac-item
2018-11-04 12:13:17	ga_s2am_ard_3-2-1_49KGR_2018-11-04_final.stac-item
2019-01-10 12:05:10	ga_s2am_ard_3-2-1_49JGL_2019-01-10_final.stac-item
2019-01-10 12:05:25	ga_s2am_ard_3-2-1_49JGK_2019-01-10_final.stac-item
2019-01-10 12:05:39	ga_s2am_ard_3-2-1_49JGJ_2019-01-10_final.stac-item
2019-01-13 12:13:54	ga_s2am_ard_3-2-1_49KGR_2019-01-13_final.stac-item
2019-01-13 12:14:08	ga_s2am_ard_3-2-1_49KGQ_2019-01-13_final.stac-item
2019-01-13 12:14:23	ga_s2am_ard_3-2-1_49KGP_2019-01-13_final.stac-item
2019-01-13 12:14:37	ga_s2am_ard_3-2-1_49JGN_2019-01-13_final.stac-item
2019-01-13 12:14:52	ga_s2am_ard_3-2-1_49JGM_2019-01-13_final.stac-item
2019-01-13 12:15:06	ga_s2am_ard_3-2-1_49JGL_2019-01-13_final.stac-item
2019-01-13 12:15:15	ga_s2am_ard_3-2-1_49JGK_2019-01-13_final.stac-item
2019-01-23 12:15:07	ga_s2am_ard_3-2-1_49JGL_2019-01-23_final.stac-item
2019-01-23 12:15:16	ga_s2am_ard_3-2-1_49JGK_2019-01-23_final.stac-item
2019-03-21 12:04:58	ga_s2am_ard_3-2-1_49JGM_2019-03-21_final.stac-item
2019-03-21 12:05:13	ga_s2am_ard_3-2-1_49JGL_2019-03-21_final.stac-item
2019-03-21 12:05:27	ga_s2am_ard_3-2-1_49JGK_2019-03-21_final.stac-item
2019-03-21 12:05:42	ga_s2am_ard_3-2-1_49JGJ_2019-03-21_final.stac-item
2019-06-29 12:05:05	ga_s2am_ard_3-2-1_49JGM_2019-06-29_final.stac-item
2019-06-29 12:05:19	ga_s2am_ard_3-2-1_49JGL_2019-06-29_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
2019-07-02 12:15:25	ga_s2am_ard_3-2-1_49JGK_2019-07-02_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
2019-07-12 12:15:25	ga_s2am_ard_3-2-1_49JGK_2019-07-12_final.stac-item
2023-04-26 11:57:14	ga_s2am_ard_3-2-1_49HGC_2023-04-26_nrt
2023-04-26 11:57:22	ga_s2am_ard_3-2-1_49HFC_2023-04-26_nrt
2023-04-26 11:57:25	ga_s2am_ard_3-2-1_49HGB_2023-04-26_nrt
2023-04-26 11:57:27	ga_s2am_ard_3-2-1_49HFB_2023-04-26_nrt
2023-05-06 11:57:15	ga_s2am_ard_3-2-1_49HGC_2023-05-06_nrt
2023-05-06 11:57:22	ga_s2am_ard_3-2-1_49HFC_2023-05-06_nrt
2023-05-06 11:57:25	ga_s2am_ard_3-2-1_49HGB_2023-05-06_nrt
2023-05-06 11:57:27	ga_s2am_ard_3-2-1_49HFB_2023-05-06_nrt
2023-05-26 11:57:18	ga_s2am_ard_3-2-1_49HGC_2023-05-26_nrt
2023-05-26 11:57:26	ga_s2am_ard_3-2-1_49HFC_2023-05-26_nrt
2023-05-26 11:57:28	ga_s2am_ard_3-2-1_49HGB_2023-05-26_nrt
2023-05-26 11:57:30	ga_s2am_ard_3-2-1_49HFB_2023-05-26_nrt
2023-06-01 12:13:27	ga_s2am_ard_3-2-1_49KGU_2023-06-01_nrt
2023-06-01 12:13:34	ga_s2am_ard_3-2-1_49KGT_2023-06-01_nrt
2023-06-01 12:13:48	ga_s2am_ard_3-2-1_49KGS_2023-06-01_nrt
2023-06-01 12:14:02	ga_s2am_ard_3-2-1_49KGR_2023-06-01_final
2023-06-01 12:14:16	ga_s2am_ard_3-2-1_49KGQ_2023-06-01_final
2023-06-01 12:14:31	ga_s2am_ard_3-2-1_49KGP_2023-06-01_final
2023-06-01 12:14:45	ga_s2am_ard_3-2-1_49JGN_2023-06-01_final
2023-06-01 12:15:00	ga_s2am_ard_3-2-1_49JGM_2023-06-01_final
2023-06-01 12:15:14	ga_s2am_ard_3-2-1_49JGL_2023-06-01_final
2023-06-01 12:15:23	ga_s2am_ard_3-2-1_49JGK_2023-06-01_final
2023-06-21 12:13:48	ga_s2am_ard_3-2-1_49KGS_2023-06-21_nrt
2023-06-28 12:05:04	ga_s2am_ard_3-2-1_49JGM_2023-06-28_final
2023-06-28 12:05:19	ga_s2am_ard_3-2-1_49JGL_2023-06-28_final
2023-06-28 12:05:33	ga_s2am_ard_3-2-1_49JGK_2023-06-28_final
2023-06-28 12:05:48	ga_s2am_ard_3-2-1_49JGJ_2023-06-28_final
2023-06-28 12:06:02	ga_s2am_ard_3-2-1_49JGH_2023-06-28_final
2023-06-28 12:06:17	ga_s2am_ard_3-2-1_49JGG_2023-06-28_nrt
2023-06-28 12:06:29	ga_s2am_ard_3-2-1_49JGF_2023-06-28_nrt
2023-10-09 12:13:30	ga_s2am_ard_3-2-1_49KGT_2023-10-09_nrt
2023-10-09 12:13:43	ga_s2am_ard_3-2-1_49KGS_2023-10-09_nrt
2023-10-09 12:13:58	ga_s2am_ard_3-2-1_49KGR_2023-10-09_final
2023-10-09 12:14:12	ga_s2am_ard_3-2-1_49KGQ_2023-10-09_final
2023-10-09 12:14:26	ga_s2am_ard_3-2-1_49KGP_2023-10-09_final
2023-10-09 12:14:41	ga_s2am_ard_3-2-1_49JGN_2023-10-09_final
2023-10-09 12:14:56	ga_s2am_ard_3-2-1_49JGM_2023-10-09_final
2023-10-09 12:15:10	ga_s2am_ard_3-2-1_49JGL_2023-10-09_final
2023-10-09 12:15:19	ga_s2am_ard_3-2-1_49JGK_2023-10-09_final
2023-10-13 11:57:12	ga_s2am_ard_3-2-1_49HGC_2023-10-13_nrt
2023-10-13 11:57:25	ga_s2am_ard_3-2-1_49HFB_2023-10-13_nrt
2023-10-16 12:04:59	ga_s2am_ard_3-2-1_49JGM_2023-10-16_final
2023-10-16 12:05:14	ga_s2am_ard_3-2-1_49JGL_2023-10-16_final
2023-10-16 12:05:28	ga_s2am_ard_3-2-1_49JGK_2023-10-16_final
2023-10-16 12:05:43	ga_s2am_ard_3-2-1_49JGJ_2023-10-16_final
2023-10-16 12:05:57	ga_s2am_ard_3-2-1_49JGH_2023-10-16_final
2023-10-16 12:06:12	ga_s2am_ard_3-2-1_49JGG_2023-10-16_nrt
2023-10-16 12:06:24	ga_s2am_ard_3-2-1_49JGF_2023-10-16_nrt
2023-10-19 12:13:30	ga_s2am_ard_3-2-1_49KGT_2023-10-19_nrt
2023-10-19 12:13:43	ga_s2am_ard_3-2-1_49KGS_2023-10-19_nrt
2023-10-19 12:13:58	ga_s2am_ard_3-2-1_49KGR_2023-10-19_final
2023-10-19 12:14:12	ga_s2am_ard_3-2-1_49KGQ_2023-10-19_final
2023-10-19 12:14:26	ga_s2am_ard_3-2-1_49KGP_2023-10-19_final
2023-10-19 12:14:41	ga_s2am_ard_3-2-1_49JGN_2023-10-19_final
2023-10-19 12:14:56	ga_s2am_ard_3-2-1_49JGM_2023-10-19_final
2023-10-19 12:15:10	ga_s2am_ard_3-2-1_49JGL_2023-10-19_final
2023-10-19 12:15:19	ga_s2am_ard_3-2-1_49JGK_2023-10-19_final
2023-10-29 12:15:10	ga_s2am_ard_3-2-1_49JGL_2023-10-29_final
2023-10-29 12:15:19	ga_s2am_ard_3-2-1_49JGK_2023-10-29_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