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
2017-04-03 12:14:19	ga_s2am_ard_3-2-1_49JFM_2017-04-03_final.stac-item
2018-10-02 12:02:46	ga_s2am_ard_3-2-1_49JFM_2018-10-02_final.stac-item
2018-10-05 12:13:18	ga_s2am_ard_3-2-1_49JFM_2018-10-05_final.stac-item
2018-10-05 12:13:18	ga_s2am_ard_3-2-1_49JFN_2018-10-05_final.stac-item
2018-10-05 12:13:18	ga_s2am_ard_3-2-1_49KGP_2018-10-05_final.stac-item
2018-10-05 12:13:18	ga_s2am_ard_3-2-1_49KGQ_2018-10-05_final.stac-item
2018-10-05 12:13:18	ga_s2am_ard_3-2-1_49KGR_2018-10-05_final.stac-item
2018-10-15 12:13:18	ga_s2am_ard_3-2-1_49JFM_2018-10-15_final.stac-item
2018-10-15 12:13:18	ga_s2am_ard_3-2-1_49JFN_2018-10-15_final.stac-item
2018-10-15 12:13:18	ga_s2am_ard_3-2-1_49KGP_2018-10-15_final.stac-item
2018-10-15 12:13:18	ga_s2am_ard_3-2-1_49KGQ_2018-10-15_final.stac-item
2018-10-15 12:13:18	ga_s2am_ard_3-2-1_49KGR_2018-10-15_final.stac-item
2019-08-21 12:14:01	ga_s2am_ard_3-2-1_49KGR_2019-08-21_final.stac-item
2019-08-21 12:14:15	ga_s2am_ard_3-2-1_49KGQ_2019-08-21_final.stac-item
2019-08-21 12:14:29	ga_s2am_ard_3-2-1_49KGP_2019-08-21_final.stac-item
2019-08-21 12:14:48	ga_s2am_ard_3-2-1_49JFN_2019-08-21_final.stac-item
2019-08-21 12:15:02	ga_s2am_ard_3-2-1_49JFM_2019-08-21_final.stac-item
2019-08-31 12:13:59	ga_s2am_ard_3-2-1_49KGR_2019-08-31_final.stac-item
2019-08-31 12:14:13	ga_s2am_ard_3-2-1_49KGQ_2019-08-31_final.stac-item
2019-08-31 12:14:28	ga_s2am_ard_3-2-1_49KGP_2019-08-31_final.stac-item
2019-08-31 12:14:46	ga_s2am_ard_3-2-1_49JFN_2019-08-31_final.stac-item
2019-08-31 12:15:00	ga_s2am_ard_3-2-1_49JFM_2019-08-31_final.stac-item
2019-09-10 12:13:57	ga_s2am_ard_3-2-1_49KGR_2019-09-10_final.stac-item
2019-09-10 12:14:11	ga_s2am_ard_3-2-1_49KGQ_2019-09-10_final.stac-item
2019-09-10 12:14:25	ga_s2am_ard_3-2-1_49KGP_2019-09-10_final.stac-item
2019-09-10 12:14:44	ga_s2am_ard_3-2-1_49JFN_2019-09-10_final.stac-item
2019-09-10 12:14:58	ga_s2am_ard_3-2-1_49JFM_2019-09-10_final.stac-item
2019-09-20 12:13:58	ga_s2am_ard_3-2-1_49KGR_2019-09-20_final.stac-item
2019-09-20 12:14:13	ga_s2am_ard_3-2-1_49KGQ_2019-09-20_final.stac-item
2019-09-20 12:14:27	ga_s2am_ard_3-2-1_49KGP_2019-09-20_final.stac-item
2019-09-20 12:14:45	ga_s2am_ard_3-2-1_49JFN_2019-09-20_final.stac-item
2019-09-20 12:15:00	ga_s2am_ard_3-2-1_49JFM_2019-09-20_final.stac-item
2019-09-30 12:14:00	ga_s2am_ard_3-2-1_49KGR_2019-09-30_final.stac-item
2019-09-30 12:14:14	ga_s2am_ard_3-2-1_49KGQ_2019-09-30_final.stac-item
2019-09-30 12:14:28	ga_s2am_ard_3-2-1_49KGP_2019-09-30_final.stac-item
2019-09-30 12:14:47	ga_s2am_ard_3-2-1_49JFN_2019-09-30_final.stac-item
2019-09-30 12:15:01	ga_s2am_ard_3-2-1_49JFM_2019-09-30_final.stac-item
2023-04-29 12:05:19	ga_s2am_ard_3-2-1_49JFL_2023-04-29_final
2023-04-29 12:05:33	ga_s2am_ard_3-2-1_49JFK_2023-04-29_nrt
2023-04-29 12:05:48	ga_s2am_ard_3-2-1_49JFJ_2023-04-29_nrt
2023-04-29 12:06:02	ga_s2am_ard_3-2-1_49JFH_2023-04-29_nrt
2023-04-29 12:06:16	ga_s2am_ard_3-2-1_49JFG_2023-04-29_nrt
2023-04-29 12:06:25	ga_s2am_ard_3-2-1_49JFF_2023-04-29_nrt
2023-05-02 12:13:25	ga_s2am_ard_3-2-1_49KFU_2023-05-02_nrt
2023-05-02 12:13:31	ga_s2am_ard_3-2-1_49KGT_2023-05-02_nrt
2023-05-02 12:13:33	ga_s2am_ard_3-2-1_49KFT_2023-05-02_nrt
2023-05-02 12:13:44	ga_s2am_ard_3-2-1_49KGS_2023-05-02_nrt
2023-05-02 12:13:47	ga_s2am_ard_3-2-1_49KFS_2023-05-02_nrt
2023-05-02 12:13:59	ga_s2am_ard_3-2-1_49KGR_2023-05-02_final
2023-05-02 12:14:02	ga_s2am_ard_3-2-1_49KFR_2023-05-02_nrt
2023-05-02 12:14:13	ga_s2am_ard_3-2-1_49KGQ_2023-05-02_final
2023-05-02 12:14:17	ga_s2am_ard_3-2-1_49KFQ_2023-05-02_nrt
2023-05-02 12:14:27	ga_s2am_ard_3-2-1_49KGP_2023-05-02_final
2023-05-02 12:14:31	ga_s2am_ard_3-2-1_49KFP_2023-05-02_nrt
2023-05-02 12:14:46	ga_s2am_ard_3-2-1_49JFN_2023-05-02_final
2023-05-02 12:15:00	ga_s2am_ard_3-2-1_49JFM_2023-05-02_final
2023-05-02 12:15:14	ga_s2am_ard_3-2-1_49JFL_2023-05-02_nrt
2023-05-02 12:15:21	ga_s2am_ard_3-2-1_49JFK_2023-05-02_nrt
2023-05-09 12:05:19	ga_s2am_ard_3-2-1_49JFL_2023-05-09_final
2023-05-09 12:05:32	ga_s2am_ard_3-2-1_49JFK_2023-05-09_nrt
2023-05-09 12:05:47	ga_s2am_ard_3-2-1_49JFJ_2023-05-09_nrt
2023-05-09 12:06:02	ga_s2am_ard_3-2-1_49JFH_2023-05-09_nrt
2023-05-09 12:06:16	ga_s2am_ard_3-2-1_49JFG_2023-05-09_nrt
2023-05-09 12:06:24	ga_s2am_ard_3-2-1_49JFF_2023-05-09_nrt
2023-05-12 12:13:24	ga_s2am_ard_3-2-1_49KFU_2023-05-12_nrt
2023-05-12 12:13:30	ga_s2am_ard_3-2-1_49KGT_2023-05-12_nrt
2023-05-12 12:13:32	ga_s2am_ard_3-2-1_49KFT_2023-05-12_nrt
2023-05-12 12:13:43	ga_s2am_ard_3-2-1_49KGS_2023-05-12_nrt
2023-05-12 12:13:46	ga_s2am_ard_3-2-1_49KFS_2023-05-12_nrt
2023-05-12 12:13:57	ga_s2am_ard_3-2-1_49KGR_2023-05-12_final
2023-05-12 12:14:01	ga_s2am_ard_3-2-1_49KFR_2023-05-12_nrt
2023-05-12 12:14:12	ga_s2am_ard_3-2-1_49KGQ_2023-05-12_final
2023-05-12 12:14:15	ga_s2am_ard_3-2-1_49KFQ_2023-05-12_nrt
2023-05-12 12:14:26	ga_s2am_ard_3-2-1_49KGP_2023-05-12_final
2023-05-12 12:14:30	ga_s2am_ard_3-2-1_49KFP_2023-05-12_nrt
2023-05-12 12:14:44	ga_s2am_ard_3-2-1_49JFN_2023-05-12_final
2023-05-12 12:14:59	ga_s2am_ard_3-2-1_49JFM_2023-05-12_final
2023-05-12 12:15:13	ga_s2am_ard_3-2-1_49JFL_2023-05-12_nrt
2023-05-12 12:15:20	ga_s2am_ard_3-2-1_49JFK_2023-05-12_nrt
2023-05-19 12:05:21	ga_s2am_ard_3-2-1_49JFL_2023-05-19_final
2023-05-19 12:05:35	ga_s2am_ard_3-2-1_49JFK_2023-05-19_nrt
2023-05-19 12:05:49	ga_s2am_ard_3-2-1_49JFJ_2023-05-19_nrt
2023-05-19 12:06:04	ga_s2am_ard_3-2-1_49JFH_2023-05-19_nrt
2023-05-19 12:06:18	ga_s2am_ard_3-2-1_49JFG_2023-05-19_nrt
2023-05-19 12:06:28	ga_s2am_ard_3-2-1_49JFF_2023-05-19_nrt
2023-05-22 12:13:26	ga_s2am_ard_3-2-1_49KGU_2023-05-22_nrt
2023-05-22 12:13:28	ga_s2am_ard_3-2-1_49KFU_2023-05-22_nrt
2023-05-22 12:13:33	ga_s2am_ard_3-2-1_49KGT_2023-05-22_nrt
2023-05-22 12:13:36	ga_s2am_ard_3-2-1_49KFT_2023-05-22_nrt
2023-05-22 12:13:47	ga_s2am_ard_3-2-1_49KGS_2023-05-22_nrt
2023-05-22 12:13:50	ga_s2am_ard_3-2-1_49KFS_2023-05-22_nrt
2023-05-22 12:14:01	ga_s2am_ard_3-2-1_49KGR_2023-05-22_final
2023-05-22 12:14:05	ga_s2am_ard_3-2-1_49KFR_2023-05-22_nrt
2023-05-22 12:14:15	ga_s2am_ard_3-2-1_49KGQ_2023-05-22_final
2023-05-22 12:14:19	ga_s2am_ard_3-2-1_49KFQ_2023-05-22_nrt
2023-05-22 12:14:30	ga_s2am_ard_3-2-1_49KGP_2023-05-22_final
2023-05-22 12:14:34	ga_s2am_ard_3-2-1_49KFP_2023-05-22_nrt
2023-05-22 12:14:48	ga_s2am_ard_3-2-1_49JFN_2023-05-22_final
2023-05-22 12:15:02	ga_s2am_ard_3-2-1_49JFM_2023-05-22_final
2023-05-22 12:15:17	ga_s2am_ard_3-2-1_49JFL_2023-05-22_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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