Search ga_s2am_ard_3 whole collection

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels. For these ARD products, this value comes from the Fmask algorithm. from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels. For these ARD products, this value comes from the Fmask algorithm. 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 (e.g. ‘epsg:32654’) :

One of - final|interim|nrt (near real time):

ID of a dataset type:

Ground sampling distance of the sensor’s best resolution band in metres; represents the size (or spatial resolution) of one pixel. from:

Ground sampling distance of the sensor’s best resolution band in metres; represents the size (or spatial resolution) of one pixel. to (exclusive):

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north from:

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north to (exclusive):

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon. from:

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon. 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 from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm 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 from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm 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 from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm 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 from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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. For Sentinel it is MGRS code. :

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless to (exclusive):

Unique identifier for a datastrip relative to a given Datatake. (e.g. ‘S2A_OPER_MSI_L1C_DS_SGS__20161214T040601_S20161214T005840_N02.04’) :

ESA product URI, with the '.SAFE' ending removed. (e.g. 'S2A_MSIL1C_20220303T000731_N0400_R073_T56LNM_20220303T012845') :

Granule ID according to the ESA naming convention (e.g. ‘S2A_OPER_MSI_L1C_TL_SGS__20161214T040601_A007721_T53KRB_N02.04’) :

Acquisition time range From

Acquisition time range to (exclusive)

Dataset URI:

Add field

First 100 results (query limit)

Time (local)	Label
2015-08-01 11:02:42	ga_s2am_ard_3-2-1_52KDU_2015-08-01_final.stac-item
2015-11-29 10:57:18	ga_s2am_ard_3-2-1_52KDU_2015-11-29_final.stac-item
2021-05-14 11:14:02	ga_s2am_ard_3-2-1_52KDU_2021-05-14_final
2021-06-03 11:14:03	ga_s2am_ard_3-2-1_52KDU_2021-06-03_final
2021-06-23 11:14:03	ga_s2am_ard_3-2-1_52KDU_2021-06-23_final
2021-06-30 11:04:08	ga_s2am_ard_3-2-1_52KDU_2021-06-30_final
2021-07-10 11:04:09	ga_s2am_ard_3-2-1_52KDU_2021-07-10_final
2021-07-23 11:14:06	ga_s2am_ard_3-2-1_52KDU_2021-07-23_final
2021-10-08 11:04:12	ga_s2am_ard_3-2-1_52KDU_2021-10-08_final
2021-10-31 11:14:07	ga_s2am_ard_3-2-1_52KDU_2021-10-31_final
2021-11-30 11:14:02	ga_s2am_ard_3-2-1_52KDU_2021-11-30_final
2021-12-07 11:04:05	ga_s2am_ard_3-2-1_52KDU_2021-12-07_final
2021-12-10 11:14:01	ga_s2am_ard_3-2-1_52KDU_2021-12-10_final
2021-12-17 11:04:07	ga_s2am_ard_3-2-1_52KDU_2021-12-17_final
2021-12-20 11:13:59	ga_s2am_ard_3-2-1_52KDU_2021-12-20_final
2021-12-20 11:14:06	ga_s2am_ard_3-2-1_52KDU_2021-12-20_final
2021-12-27 11:04:00	ga_s2am_ard_3-2-1_52KDU_2021-12-27_final
2021-12-27 11:04:08	ga_s2am_ard_3-2-1_52KDU_2021-12-27_final
2021-12-30 11:13:58	ga_s2am_ard_3-2-1_52KDU_2021-12-30_final
2021-12-30 11:14:05	ga_s2am_ard_3-2-1_52KDU_2021-12-30_final
2022-01-06 11:04:08	ga_s2am_ard_3-2-1_52KDU_2022-01-06_final
2022-01-09 11:14:04	ga_s2am_ard_3-2-1_52KDU_2022-01-09_final
2022-01-16 11:04:07	ga_s2am_ard_3-2-1_52KDU_2022-01-16_final
2022-01-19 11:14:02	ga_s2am_ard_3-2-1_52KDU_2022-01-19_final
2022-01-26 11:04:07	ga_s2am_ard_3-2-1_52KDU_2022-01-26_final
2022-01-29 11:14:02	ga_s2am_ard_3-2-1_52KDU_2022-01-29_final
2022-02-05 11:04:06	ga_s2am_ard_3-2-1_52KDU_2022-02-05_final
2022-02-08 11:14:01	ga_s2am_ard_3-2-1_52KDU_2022-02-08_final
2022-02-15 11:04:07	ga_s2am_ard_3-2-1_52KDU_2022-02-15_final
2022-02-18 11:14:03	ga_s2am_ard_3-2-1_52KDU_2022-02-18_final
2022-02-25 11:04:09	ga_s2am_ard_3-2-1_52KDU_2022-02-25_final
2022-02-28 11:14:05	ga_s2am_ard_3-2-1_52KDU_2022-02-28_final
2022-03-07 11:04:10	ga_s2am_ard_3-2-1_52KDU_2022-03-07_final
2022-03-10 11:13:56	ga_s2am_ard_3-2-1_52KDU_2022-03-10_final
2022-03-10 11:14:06	ga_s2am_ard_3-2-1_52KDU_2022-03-10_final
2022-03-17 11:04:10	ga_s2am_ard_3-2-1_52KDU_2022-03-17_final
2022-03-20 11:14:06	ga_s2am_ard_3-2-1_52KDU_2022-03-20_final
2022-03-27 11:04:09	ga_s2am_ard_3-2-1_52KDU_2022-03-27_final
2022-03-30 11:14:05	ga_s2am_ard_3-2-1_52KDU_2022-03-30_final
2022-04-06 11:04:08	ga_s2am_ard_3-2-1_52KDU_2022-04-06_final
2022-04-09 11:14:03	ga_s2am_ard_3-2-1_52KDU_2022-04-09_final
2022-04-16 11:04:10	ga_s2am_ard_3-2-1_52KDU_2022-04-16_final
2022-04-19 11:14:06	ga_s2am_ard_3-2-1_52KDU_2022-04-19_final
2022-04-26 11:04:12	ga_s2am_ard_3-2-1_52KDU_2022-04-26_final
2022-04-29 11:14:07	ga_s2am_ard_3-2-1_52KDU_2022-04-29_final
2022-05-06 11:04:11	ga_s2am_ard_3-2-1_52KDU_2022-05-06_final
2022-05-09 11:14:06	ga_s2am_ard_3-2-1_52KDU_2022-05-09_final
2022-05-16 11:04:11	ga_s2am_ard_3-2-1_52KDU_2022-05-16_final
2022-05-19 11:14:08	ga_s2am_ard_3-2-1_52KDU_2022-05-19_final
2022-05-26 11:04:14	ga_s2am_ard_3-2-1_52KDU_2022-05-26_final
2022-05-29 11:14:10	ga_s2am_ard_3-2-1_52KDU_2022-05-29_final
2022-06-05 11:04:14	ga_s2am_ard_3-2-1_52KDU_2022-06-05_final
2022-06-08 11:14:11	ga_s2am_ard_3-2-1_52KDU_2022-06-08_final
2022-06-15 11:04:17	ga_s2am_ard_3-2-1_52KDU_2022-06-15_final
2022-06-15 11:04:23	ga_s2am_ard_3-2-1_52KDU_2022-06-15_final
2022-06-18 11:14:14	ga_s2am_ard_3-2-1_52KDU_2022-06-18_final
2022-06-25 11:04:19	ga_s2am_ard_3-2-1_52KDU_2022-06-25_final
2022-06-25 11:04:25	ga_s2am_ard_3-2-1_52KDU_2022-06-25_final
2022-06-28 11:14:15	ga_s2am_ard_3-2-1_52KDU_2022-06-28_final
2022-07-05 11:04:20	ga_s2am_ard_3-2-1_52KDU_2022-07-05_final
2022-07-08 11:14:15	ga_s2am_ard_3-2-1_52KDU_2022-07-08_final
2022-07-15 11:04:19	ga_s2am_ard_3-2-1_52KDU_2022-07-15_final
2022-07-18 11:14:14	ga_s2am_ard_3-2-1_52KDU_2022-07-18_final
2022-07-25 11:04:17	ga_s2am_ard_3-2-1_52KDU_2022-07-25_final
2022-07-28 11:14:12	ga_s2am_ard_3-2-1_52KDU_2022-07-28_final
2022-08-04 11:04:17	ga_s2am_ard_3-2-1_52KDU_2022-08-04_final
2022-08-07 11:14:14	ga_s2am_ard_3-2-1_52KDU_2022-08-07_final
2022-08-14 11:04:19	ga_s2am_ard_3-2-1_52KDU_2022-08-14_final
2022-08-17 11:14:15	ga_s2am_ard_3-2-1_52KDU_2022-08-17_final
2022-08-27 11:14:15	ga_s2am_ard_3-2-1_52KDU_2022-08-27_final
2023-02-10 11:04:05	ga_s2am_ard_3-2-1_52KDU_2023-02-10_final.stac-item
2023-02-13 11:14:00	ga_s2am_ard_3-2-1_52KDU_2023-02-13_final.stac-item
2023-02-20 11:04:04	ga_s2am_ard_3-2-1_52KDU_2023-02-20_final.stac-item
2023-02-23 11:14:00	ga_s2am_ard_3-2-1_52KDU_2023-02-23_final.stac-item
2023-03-02 11:04:04	ga_s2am_ard_3-2-1_52KDU_2023-03-02_final.stac-item
2023-03-05 11:13:59	ga_s2am_ard_3-2-1_52KDU_2023-03-05_final.stac-item
2023-03-12 11:04:02	ga_s2am_ard_3-2-1_52KDU_2023-03-12_final.stac-item
2023-03-15 11:13:59	ga_s2am_ard_3-2-1_52KDU_2023-03-15_final.stac-item
2023-03-22 11:04:05	ga_s2am_ard_3-2-1_52KDU_2023-03-22_final.stac-item
2023-04-01 11:04:05	ga_s2am_ard_3-2-1_52KDU_2023-04-01_final.stac-item
2023-04-04 11:14:02	ga_s2am_ard_3-2-1_52KDU_2023-04-04_final.stac-item
2023-04-11 11:04:07	ga_s2am_ard_3-2-1_52KDU_2023-04-11_final.stac-item
2023-04-14 11:14:02	ga_s2am_ard_3-2-1_52KDU_2023-04-14_final.stac-item
2023-04-21 11:04:05	ga_s2am_ard_3-2-1_52KDU_2023-04-21_final.stac-item
2023-04-24 11:14:02	ga_s2am_ard_3-2-1_52KDU_2023-04-24_final.stac-item
2023-05-01 11:04:07	ga_s2am_ard_3-2-1_52KDU_2023-05-01_final.stac-item
2023-05-04 11:14:02	ga_s2am_ard_3-2-1_52KDU_2023-05-04_final.stac-item
2023-05-11 11:04:06	ga_s2am_ard_3-2-1_52KDU_2023-05-11_final.stac-item
2023-05-14 11:14:02	ga_s2am_ard_3-2-1_52KDU_2023-05-14_final.stac-item
2023-05-21 11:04:09	ga_s2am_ard_3-2-1_52KDU_2023-05-21_final.stac-item
2023-05-24 11:14:05	ga_s2am_ard_3-2-1_52KDU_2023-05-24_final.stac-item
2023-05-31 11:04:10	ga_s2am_ard_3-2-1_52KDU_2023-05-31_final.stac-item
2023-06-03 11:14:06	ga_s2am_ard_3-2-1_52KDU_2023-06-03_final.stac-item
2023-06-10 11:04:10	ga_s2am_ard_3-2-1_52KDU_2023-06-10_final.stac-item
2023-06-13 11:14:06	ga_s2am_ard_3-2-1_52KDU_2023-06-13_final.stac-item
2023-10-18 11:04:06	ga_s2am_ard_3-2-1_52KDU_2023-10-18_final.stac-item
2023-10-21 11:14:02	ga_s2am_ard_3-2-1_52KDU_2023-10-21_final.stac-item
2023-10-28 11:04:06	ga_s2am_ard_3-2-1_52KDU_2023-10-28_final.stac-item
2023-10-31 11:14:01	ga_s2am_ard_3-2-1_52KDU_2023-10-31_final.stac-item
2023-11-17 11:04:05	ga_s2am_ard_3-2-1_52KDU_2023-11-17_final.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

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

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