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
2019-10-10 12:14:48	ga_s2am_ard_3-2-1_49JFN_2019-10-10_final.stac-item
2019-10-20 12:14:48	ga_s2am_ard_3-2-1_49JFN_2019-10-20_final.stac-item
2019-11-09 12:14:48	ga_s2am_ard_3-2-1_49JFN_2019-11-09_final.stac-item
2019-11-19 12:14:47	ga_s2am_ard_3-2-1_49JFN_2019-11-19_final.stac-item
2019-11-29 12:14:45	ga_s2am_ard_3-2-1_49JFN_2019-11-29_final.stac-item
2019-12-29 12:14:40	ga_s2am_ard_3-2-1_49JFN_2019-12-29_final.stac-item
2020-01-28 12:14:38	ga_s2am_ard_3-2-1_49JFN_2020-01-28_final.stac-item
2020-02-07 12:14:37	ga_s2am_ard_3-2-1_49JFN_2020-02-07_final.stac-item
2020-02-27 12:14:40	ga_s2am_ard_3-2-1_49JFN_2020-02-27_final.stac-item
2020-03-28 12:14:41	ga_s2am_ard_3-2-1_49JFN_2020-03-28_final.stac-item
2020-04-17 12:14:45	ga_s2am_ard_3-2-1_49JFN_2020-04-17_final.stac-item
2020-04-27 12:14:48	ga_s2am_ard_3-2-1_49JFN_2020-04-27_final.stac-item
2020-05-17 12:14:50	ga_s2am_ard_3-2-1_49JFN_2020-05-17_final.stac-item
2020-07-16 12:14:50	ga_s2am_ard_3-2-1_49JFN_2020-07-16_final.stac-item
2020-09-04 12:14:50	ga_s2am_ard_3-2-1_49JFN_2020-09-04_final.stac-item
2020-09-24 12:14:50	ga_s2am_ard_3-2-1_49JFN_2020-09-24_final.stac-item
2020-11-13 12:14:49	ga_s2am_ard_3-2-1_49JFN_2020-11-13_final.stac-item
2020-11-23 12:14:47	ga_s2am_ard_3-2-1_49JFN_2020-11-23_final.stac-item
2020-12-03 12:14:44	ga_s2am_ard_3-2-1_49JFN_2020-12-03_final.stac-item
2020-12-13 12:14:42	ga_s2am_ard_3-2-1_49JFN_2020-12-13_final.stac-item
2020-12-23 12:14:44	ga_s2am_ard_3-2-1_49JFN_2020-12-23_final.stac-item
2021-01-02 12:14:45	ga_s2am_ard_3-2-1_49JFN_2021-01-02_final.stac-item
2021-01-12 12:14:46	ga_s2am_ard_3-2-1_49JFN_2021-01-12_final.stac-item
2021-01-22 12:14:46	ga_s2am_ard_3-2-1_49JFN_2021-01-22_final.stac-item
2021-02-01 12:14:45	ga_s2am_ard_3-2-1_49JFN_2021-02-01_final.stac-item
2021-02-11 12:14:44	ga_s2am_ard_3-2-1_49JFN_2021-02-11_final.stac-item
2021-02-21 12:14:45	ga_s2am_ard_3-2-1_49JFN_2021-02-21_final.stac-item
2021-03-03 12:14:45	ga_s2am_ard_3-2-1_49JFN_2021-03-03_final.stac-item
2021-03-13 12:14:45	ga_s2am_ard_3-2-1_49JFN_2021-03-13_final.stac-item
2021-03-23 12:14:44	ga_s2am_ard_3-2-1_49JFN_2021-03-23_final.stac-item
2021-04-02 12:14:42	ga_s2am_ard_3-2-1_49JFN_2021-04-02_final.stac-item
2021-04-12 12:14:40	ga_s2am_ard_3-2-1_49JFN_2021-04-12_final.stac-item
2021-04-22 12:14:41	ga_s2am_ard_3-2-1_49JFN_2021-04-22_final.stac-item
2021-04-22 12:14:41	ga_s2am_ard_3-2-1_49JFN_2021-04-22_final.stac-item
2021-05-02 12:14:43	ga_s2am_ard_3-2-1_49JFN_2021-05-02_final.stac-item
2021-05-12 12:14:45	ga_s2am_ard_3-2-1_49JFN_2021-05-12_final.stac-item
2021-05-22 12:14:46	ga_s2am_ard_3-2-1_49JFN_2021-05-22_final.stac-item
2021-06-01 12:14:46	ga_s2am_ard_3-2-1_49JFN_2021-06-01_final.stac-item
2021-06-11 12:14:46	ga_s2am_ard_3-2-1_49JFN_2021-06-11_final.stac-item
2021-06-21 12:14:46	ga_s2am_ard_3-2-1_49JFN_2021-06-21_final.stac-item
2021-07-01 12:14:47	ga_s2am_ard_3-2-1_49JFN_2021-07-01_final.stac-item
2021-07-11 12:14:48	ga_s2am_ard_3-2-1_49JFN_2021-07-11_final.stac-item
2021-07-21 12:14:49	ga_s2am_ard_3-2-1_49JFN_2021-07-21_final.stac-item
2021-07-31 12:14:49	ga_s2am_ard_3-2-1_49JFN_2021-07-31_final.stac-item
2021-08-10 12:14:48	ga_s2am_ard_3-2-1_49JFN_2021-08-10_final.stac-item
2021-08-20 12:14:48	ga_s2am_ard_3-2-1_49JFN_2021-08-20_final.stac-item
2021-08-30 12:14:47	ga_s2am_ard_3-2-1_49JFN_2021-08-30_final.stac-item
2021-09-09 12:14:45	ga_s2am_ard_3-2-1_49JFN_2021-09-09_final.stac-item
2021-09-19 12:14:48	ga_s2am_ard_3-2-1_49JFN_2021-09-19_final.stac-item
2021-09-29 12:14:49	ga_s2am_ard_3-2-1_49JFN_2021-09-29_final.stac-item
2021-10-09 12:14:50	ga_s2am_ard_3-2-1_49JFN_2021-10-09_final.stac-item
2021-10-19 12:14:51	ga_s2am_ard_3-2-1_49JFN_2021-10-19_final.stac-item
2021-10-29 12:14:50	ga_s2am_ard_3-2-1_49JFN_2021-10-29_final.stac-item
2021-11-08 12:14:48	ga_s2am_ard_3-2-1_49JFN_2021-11-08_final.stac-item
2021-11-18 12:14:46	ga_s2am_ard_3-2-1_49JFN_2021-11-18_final.stac-item
2021-11-28 12:14:45	ga_s2am_ard_3-2-1_49JFN_2021-11-28_final.stac-item
2021-12-08 12:14:44	ga_s2am_ard_3-2-1_49JFN_2021-12-08_final.stac-item
2021-12-18 12:14:46	ga_s2am_ard_3-2-1_49JFN_2021-12-18_final.stac-item
2021-12-28 12:14:47	ga_s2am_ard_3-2-1_49JFN_2021-12-28_final.stac-item
2022-01-07 12:14:47	ga_s2am_ard_3-2-1_49JFN_2022-01-07_final.stac-item
2022-01-17 12:14:46	ga_s2am_ard_3-2-1_49JFN_2022-01-17_final.stac-item
2022-01-27 12:14:45	ga_s2am_ard_3-2-1_49JFN_2022-01-27_final.stac-item
2022-02-06 12:14:45	ga_s2am_ard_3-2-1_49JFN_2022-02-06_final.stac-item
2022-02-16 12:14:46	ga_s2am_ard_3-2-1_49JFN_2022-02-16_final.stac-item
2022-02-26 12:14:48	ga_s2am_ard_3-2-1_49JFN_2022-02-26_final.stac-item
2022-03-08 12:14:49	ga_s2am_ard_3-2-1_49JFN_2022-03-08_final.stac-item
2022-03-18 12:14:49	ga_s2am_ard_3-2-1_49JFN_2022-03-18_final.stac-item
2022-03-28 12:14:48	ga_s2am_ard_3-2-1_49JFN_2022-03-28_final.stac-item
2022-04-07 12:14:46	ga_s2am_ard_3-2-1_49JFN_2022-04-07_final.stac-item
2022-04-17 12:14:49	ga_s2am_ard_3-2-1_49JFN_2022-04-17_final.stac-item
2022-04-27 12:14:50	ga_s2am_ard_3-2-1_49JFN_2022-04-27_final.stac-item
2022-05-07 12:14:50	ga_s2am_ard_3-2-1_49JFN_2022-05-07_final.stac-item
2022-05-17 12:14:50	ga_s2am_ard_3-2-1_49JFN_2022-05-17_final.stac-item
2022-05-27 12:14:53	ga_s2am_ard_3-2-1_49JFN_2022-05-27_final.stac-item
2022-06-06 12:14:53	ga_s2am_ard_3-2-1_49JFN_2022-06-06_final.stac-item
2022-06-16 12:14:56	ga_s2am_ard_3-2-1_49JFN_2022-06-16_final.stac-item
2022-06-26 12:14:58	ga_s2am_ard_3-2-1_49JFN_2022-06-26_final.stac-item
2022-07-06 12:14:58	ga_s2am_ard_3-2-1_49JFN_2022-07-06_final.stac-item
2022-07-16 12:14:58	ga_s2am_ard_3-2-1_49JFN_2022-07-16_final.stac-item
2022-07-26 12:14:55	ga_s2am_ard_3-2-1_49JFN_2022-07-26_final.stac-item
2022-08-05 12:14:56	ga_s2am_ard_3-2-1_49JFN_2022-08-05_final.stac-item
2022-08-15 12:14:58	ga_s2am_ard_3-2-1_49JFN_2022-08-15_final.stac-item
2022-09-04 12:14:57	ga_s2am_ard_3-2-1_49JFN_2022-09-04_final.stac-item
2022-09-14 12:14:55	ga_s2am_ard_3-2-1_49JFN_2022-09-14_final.stac-item
2022-09-24 12:14:52	ga_s2am_ard_3-2-1_49JFN_2022-09-24_final.stac-item
2022-10-04 12:14:52	ga_s2am_ard_3-2-1_49JFN_2022-10-04_final.stac-item
2022-10-14 12:14:50	ga_s2am_ard_3-2-1_49JFN_2022-10-14_final.stac-item
2022-10-24 12:14:48	ga_s2am_ard_3-2-1_49JFN_2022-10-24_final.stac-item
2022-11-03 12:14:49	ga_s2am_ard_3-2-1_49JFN_2022-11-03_final.stac-item
2022-11-13 12:14:48	ga_s2am_ard_3-2-1_49JFN_2022-11-13_final.stac-item
2022-11-23 12:14:45	ga_s2am_ard_3-2-1_49JFN_2022-11-23_final.stac-item
2022-12-03 12:14:46	ga_s2am_ard_3-2-1_49JFN_2022-12-03_final.stac-item
2022-12-13 12:14:45	ga_s2am_ard_3-2-1_49JFN_2022-12-13_final.stac-item
2022-12-23 12:14:43	ga_s2am_ard_3-2-1_49JFN_2022-12-23_final.stac-item
2023-01-02 12:14:44	ga_s2am_ard_3-2-1_49JFN_2023-01-02_final.stac-item
2023-01-12 12:14:43	ga_s2am_ard_3-2-1_49JFN_2023-01-12_final.stac-item
2023-01-22 12:14:41	ga_s2am_ard_3-2-1_49JFN_2023-01-22_final.stac-item
2023-02-01 12:14:44	ga_s2am_ard_3-2-1_49JFN_2023-02-01_final
2023-02-11 12:14:44	ga_s2am_ard_3-2-1_49JFN_2023-02-11_nrt.stac-item
2023-02-21 12:14:43	ga_s2am_ard_3-2-1_49JFN_2023-02-21_nrt.stac-item

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