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

99 results

Time (local)	Label
2022-12-15 09:31:33	ga_s2am_ard_3-2-1_56KNF_2022-12-15_nrt.stac-item
2022-12-18 09:41:33	ga_s2am_ard_3-2-1_56KNF_2022-12-18_nrt.stac-item
2022-12-28 09:41:33	ga_s2am_ard_3-2-1_56KNF_2022-12-28_nrt.stac-item
2023-01-04 09:31:33	ga_s2am_ard_3-2-1_56KNF_2023-01-04_nrt.stac-item
2023-01-07 09:41:33	ga_s2am_ard_3-2-1_56KNF_2023-01-07_nrt.stac-item
2023-01-14 09:31:31	ga_s2am_ard_3-2-1_56KNF_2023-01-14_nrt.stac-item
2023-01-17 09:41:30	ga_s2am_ard_3-2-1_56KNF_2023-01-17_nrt.stac-item
2023-01-24 09:31:30	ga_s2am_ard_3-2-1_56KNF_2023-01-24_nrt.stac-item
2023-01-27 09:41:31	ga_s2am_ard_3-2-1_56KNF_2023-01-27_nrt.stac-item
2023-02-03 09:31:32	ga_s2am_ard_3-2-1_56KNF_2023-02-03_nrt.stac-item
2023-02-06 09:41:33	ga_s2am_ard_3-2-1_56KNF_2023-02-06_nrt.stac-item
2023-02-13 09:31:32	ga_s2am_ard_3-2-1_56KNF_2023-02-13_nrt.stac-item
2023-02-16 09:41:31	ga_s2am_ard_3-2-1_56KNF_2023-02-16_nrt.stac-item
2023-02-23 09:31:31	ga_s2am_ard_3-2-1_56KNF_2023-02-23_nrt.stac-item
2023-02-26 09:41:32	ga_s2am_ard_3-2-1_56KNF_2023-02-26_nrt.stac-item
2023-03-05 09:31:30	ga_s2am_ard_3-2-1_56KNF_2023-03-05_nrt.stac-item
2023-03-08 09:41:30	ga_s2am_ard_3-2-1_56KNF_2023-03-08_nrt.stac-item
2023-03-15 09:31:31	ga_s2am_ard_3-2-1_56KNF_2023-03-15_nrt.stac-item
2023-03-18 09:41:32	ga_s2am_ard_3-2-1_56KNF_2023-03-18_nrt.stac-item
2023-03-25 09:31:32	ga_s2am_ard_3-2-1_56KNF_2023-03-25_nrt.stac-item
2023-03-28 09:41:32	ga_s2am_ard_3-2-1_56KNF_2023-03-28_nrt.stac-item
2023-04-04 09:31:33	ga_s2am_ard_3-2-1_56KNF_2023-04-04_nrt.stac-item
2023-04-07 09:41:34	ga_s2am_ard_3-2-1_56KNF_2023-04-07_nrt.stac-item
2023-04-14 09:31:33	ga_s2am_ard_3-2-1_56KNF_2023-04-14_nrt.stac-item
2023-04-17 09:41:33	ga_s2am_ard_3-2-1_56KNF_2023-04-17_nrt.stac-item
2023-04-24 09:31:31	ga_s2am_ard_3-2-1_56KNF_2023-04-24_nrt.stac-item
2023-04-27 09:41:34	ga_s2am_ard_3-2-1_56KNF_2023-04-27_nrt.stac-item
2023-05-04 09:31:32	ga_s2am_ard_3-2-1_56KNF_2023-05-04_nrt.stac-item
2023-05-07 09:41:34	ga_s2am_ard_3-2-1_56KNF_2023-05-07_nrt.stac-item
2023-05-14 09:31:32	ga_s2am_ard_3-2-1_56KNF_2023-05-14_nrt.stac-item
2023-05-17 09:41:35	ga_s2am_ard_3-2-1_56KNF_2023-05-17_nrt.stac-item
2023-05-24 09:31:35	ga_s2am_ard_3-2-1_56KNF_2023-05-24_nrt.stac-item
2023-05-27 09:41:37	ga_s2am_ard_3-2-1_56KNF_2023-05-27_nrt.stac-item
2023-06-03 09:31:37	ga_s2am_ard_3-2-1_56KNF_2023-06-03_nrt.stac-item
2023-06-06 09:41:38	ga_s2am_ard_3-2-1_56KNF_2023-06-06_nrt.stac-item
2023-06-13 09:31:36	ga_s2am_ard_3-2-1_56KNF_2023-06-13_nrt.stac-item
2023-06-16 09:41:38	ga_s2am_ard_3-2-1_56KNF_2023-06-16_nrt.stac-item
2023-06-23 09:31:35	ga_s2am_ard_3-2-1_56KNF_2023-06-23_nrt.stac-item
2023-06-26 09:41:37	ga_s2am_ard_3-2-1_56KNF_2023-06-26_nrt.stac-item
2023-07-03 09:31:36	ga_s2am_ard_3-2-1_56KNF_2023-07-03_nrt.stac-item
2023-07-06 09:41:39	ga_s2am_ard_3-2-1_56KNF_2023-07-06_nrt.stac-item
2023-07-16 09:41:39	ga_s2am_ard_3-2-1_56KNF_2023-07-16_nrt.stac-item
2023-07-23 09:31:36	ga_s2am_ard_3-2-1_56KNF_2023-07-23_nrt.stac-item
2023-07-26 09:41:39	ga_s2am_ard_3-2-1_56KNF_2023-07-26_nrt.stac-item
2023-08-02 09:31:37	ga_s2am_ard_3-2-1_56KNF_2023-08-02_nrt.stac-item
2023-08-05 09:41:39	ga_s2am_ard_3-2-1_56KNF_2023-08-05_nrt.stac-item
2023-08-12 09:31:36	ga_s2am_ard_3-2-1_56KNF_2023-08-12_nrt.stac-item
2023-08-15 09:41:40	ga_s2am_ard_3-2-1_56KNF_2023-08-15_nrt.stac-item
2023-08-22 09:31:38	ga_s2am_ard_3-2-1_56KNF_2023-08-22_nrt.stac-item
2023-08-25 09:41:40	ga_s2am_ard_3-2-1_56KNF_2023-08-25_nrt.stac-item
2023-09-01 09:31:37	ga_s2am_ard_3-2-1_56KNF_2023-09-01_nrt.stac-item
2023-09-04 09:41:39	ga_s2am_ard_3-2-1_56KNF_2023-09-04_nrt.stac-item
2023-09-11 09:31:38	ga_s2am_ard_3-2-1_56KNF_2023-09-11_nrt.stac-item
2023-09-21 09:31:33	ga_s2am_ard_3-2-1_56KNF_2023-09-21_nrt.stac-item
2023-09-24 09:41:36	ga_s2am_ard_3-2-1_56KNF_2023-09-24_nrt.stac-item
2023-10-01 09:31:34	ga_s2am_ard_3-2-1_56KNF_2023-10-01_nrt.stac-item
2023-10-04 09:41:36	ga_s2am_ard_3-2-1_56KNF_2023-10-04_nrt.stac-item
2023-10-11 09:31:30	ga_s2am_ard_3-2-1_56KNF_2023-10-11_nrt.stac-item
2023-10-14 09:41:32	ga_s2am_ard_3-2-1_56KNF_2023-10-14_nrt.stac-item
2023-10-21 09:31:33	ga_s2am_ard_3-2-1_56KNF_2023-10-21_nrt.stac-item
2023-10-24 09:41:34	ga_s2am_ard_3-2-1_56KNF_2023-10-24_nrt.stac-item
2023-10-31 09:31:31	ga_s2am_ard_3-2-1_56KNF_2023-10-31_nrt.stac-item
2023-11-03 09:41:33	ga_s2am_ard_3-2-1_56KNF_2023-11-03_nrt.stac-item
2023-11-10 09:31:31	ga_s2am_ard_3-2-1_56KNF_2023-11-10_nrt.stac-item
2023-11-13 09:41:33	ga_s2am_ard_3-2-1_56KNF_2023-11-13_nrt.stac-item
2023-11-20 09:31:30	ga_s2am_ard_3-2-1_56KNF_2023-11-20_nrt.stac-item
2023-11-23 09:41:30	ga_s2am_ard_3-2-1_56KNF_2023-11-23_nrt.stac-item
2023-12-03 09:41:27	ga_s2am_ard_3-2-1_56KNF_2023-12-03_nrt.stac-item
2023-12-10 09:31:26	ga_s2am_ard_3-2-1_56KNF_2023-12-10_nrt.stac-item
2023-12-13 09:41:30	ga_s2am_ard_3-2-1_56KNF_2023-12-13_nrt.stac-item
2023-12-20 09:31:28	ga_s2am_ard_3-2-1_56KNF_2023-12-20_nrt.stac-item
2023-12-23 09:41:30	ga_s2am_ard_3-2-1_56KNF_2023-12-23_nrt.stac-item
2023-12-30 09:31:30	ga_s2am_ard_3-2-1_56KNF_2023-12-30_nrt.stac-item
2024-01-02 09:41:31	ga_s2am_ard_3-2-1_56KNF_2024-01-02_nrt.stac-item
2024-01-09 09:31:27	ga_s2am_ard_3-2-1_56KNF_2024-01-09_nrt.stac-item
2024-01-12 09:41:29	ga_s2am_ard_3-2-1_56KNF_2024-01-12_nrt.stac-item
2024-01-19 09:31:24	ga_s2am_ard_3-2-1_56KNF_2024-01-19_nrt.stac-item
2024-01-22 09:41:27	ga_s2am_ard_3-2-1_56KNF_2024-01-22_nrt.stac-item
2024-01-29 09:31:28	ga_s2am_ard_3-2-1_56KNF_2024-01-29_nrt.stac-item
2024-02-01 09:41:30	ga_s2am_ard_3-2-1_56KNF_2024-02-01_nrt.stac-item
2024-02-08 09:31:27	ga_s2am_ard_3-2-1_56KNF_2024-02-08_nrt.stac-item
2024-02-11 09:41:31	ga_s2am_ard_3-2-1_56KNF_2024-02-11_nrt.stac-item
2024-02-18 09:31:31	ga_s2am_ard_3-2-1_56KNF_2024-02-18_nrt.stac-item
2024-02-21 09:41:32	ga_s2am_ard_3-2-1_56KNF_2024-02-21_nrt.stac-item
2024-02-28 09:31:31	ga_s2am_ard_3-2-1_56KNF_2024-02-28_nrt.stac-item
2024-03-02 09:41:34	ga_s2am_ard_3-2-1_56KNF_2024-03-02_nrt.stac-item
2024-03-09 09:31:32	ga_s2am_ard_3-2-1_56KNF_2024-03-09_nrt.stac-item
2024-03-12 09:41:34	ga_s2am_ard_3-2-1_56KNF_2024-03-12_nrt.stac-item
2024-03-19 09:31:32	ga_s2am_ard_3-2-1_56KNF_2024-03-19_nrt.stac-item
2024-03-22 09:41:35	ga_s2am_ard_3-2-1_56KNF_2024-03-22_nrt.stac-item
2024-04-01 09:41:33	ga_s2am_ard_3-2-1_56KNF_2024-04-01_nrt.stac-item
2024-04-11 09:41:35	ga_s2am_ard_3-2-1_56KNF_2024-04-11_nrt.stac-item
2024-04-21 09:41:37	ga_s2am_ard_3-2-1_56KNF_2024-04-21_nrt.stac-item
2024-04-28 09:31:34	ga_s2am_ard_3-2-1_56KNF_2024-04-28_nrt.stac-item
2024-05-01 09:41:37	ga_s2am_ard_3-2-1_56KNF_2024-05-01_nrt.stac-item
2024-05-11 09:41:37	ga_s2am_ard_3-2-1_56KNF_2024-05-11_nrt.stac-item
2024-05-18 09:31:35	ga_s2am_ard_3-2-1_56KNF_2024-05-18_nrt.stac-item
2024-05-21 09:41:35	ga_s2am_ard_3-2-1_56KNF_2024-05-21_nrt.stac-item
2024-05-31 09:41:37	ga_s2am_ard_3-2-1_56KNF_2024-05-31_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

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

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