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
2019-03-06 09:32:45	ga_s2am_ard_3-2-1_56KNA_2019-03-06_final.stac-item
2022-12-22 09:23:20	ga_s2am_ard_3-2-1_56KPU_2022-12-21_nrt.stac-item
2023-01-01 09:23:21	ga_s2am_ard_3-2-1_56KPU_2022-12-31_nrt.stac-item
2023-01-04 09:33:16	ga_s2am_ard_3-2-1_56KPU_2023-01-04_nrt.stac-item
2023-01-11 09:23:20	ga_s2am_ard_3-2-1_56KPU_2023-01-10_nrt.stac-item
2023-01-14 09:32:46	ga_s2am_ard_3-2-1_56KPA_2023-01-14_nrt.stac-item
2023-01-14 09:33:15	ga_s2am_ard_3-2-1_56KPU_2023-01-14_nrt.stac-item
2023-01-21 09:23:17	ga_s2am_ard_3-2-1_56KPU_2023-01-20_nrt.stac-item
2023-01-24 09:32:45	ga_s2am_ard_3-2-1_56KPA_2023-01-24_nrt.stac-item
2023-01-24 09:33:14	ga_s2am_ard_3-2-1_56KPU_2023-01-24_nrt.stac-item
2023-01-31 09:23:20	ga_s2am_ard_3-2-1_56KPU_2023-01-30_nrt.stac-item
2023-02-03 09:32:47	ga_s2am_ard_3-2-1_56KPA_2023-02-03_nrt.stac-item
2023-02-03 09:33:16	ga_s2am_ard_3-2-1_56KPU_2023-02-03_nrt.stac-item
2023-02-10 09:23:21	ga_s2am_ard_3-2-1_56KPU_2023-02-09_nrt.stac-item
2023-02-13 09:32:46	ga_s2am_ard_3-2-1_56KPA_2023-02-13_nrt.stac-item
2023-02-13 09:33:15	ga_s2am_ard_3-2-1_56KPU_2023-02-13_nrt.stac-item
2023-02-23 09:32:46	ga_s2am_ard_3-2-1_56KPA_2023-02-23_nrt.stac-item
2023-02-23 09:33:15	ga_s2am_ard_3-2-1_56KPU_2023-02-23_nrt.stac-item
2023-03-02 09:23:19	ga_s2am_ard_3-2-1_56KPU_2023-03-01_nrt.stac-item
2023-03-05 09:33:14	ga_s2am_ard_3-2-1_56KPU_2023-03-05_nrt.stac-item
2023-03-12 09:23:18	ga_s2am_ard_3-2-1_56KPU_2023-03-11_nrt.stac-item
2023-03-22 09:23:21	ga_s2am_ard_3-2-1_56KPU_2023-03-21_nrt.stac-item
2023-04-01 09:23:21	ga_s2am_ard_3-2-1_56KPU_2023-03-31_nrt.stac-item
2023-04-04 09:33:17	ga_s2am_ard_3-2-1_56KPU_2023-04-04_nrt.stac-item
2023-04-11 09:23:22	ga_s2am_ard_3-2-1_56KPU_2023-04-10_nrt.stac-item
2023-04-14 09:33:17	ga_s2am_ard_3-2-1_56KPU_2023-04-14_nrt.stac-item
2023-04-21 09:23:20	ga_s2am_ard_3-2-1_56KPU_2023-04-20_nrt.stac-item
2023-04-24 09:33:17	ga_s2am_ard_3-2-1_56KPU_2023-04-24_nrt.stac-item
2023-05-01 09:23:22	ga_s2am_ard_3-2-1_56KPU_2023-04-30_nrt.stac-item
2023-05-04 09:33:18	ga_s2am_ard_3-2-1_56KPU_2023-05-04_nrt.stac-item
2023-05-11 09:23:21	ga_s2am_ard_3-2-1_56KPU_2023-05-10_nrt.stac-item
2023-05-14 09:33:05	ga_s2am_ard_3-2-1_56KNV_2023-05-14_nrt.stac-item
2023-05-14 09:33:17	ga_s2am_ard_3-2-1_56KPU_2023-05-14_nrt.stac-item
2023-05-21 09:23:25	ga_s2am_ard_3-2-1_56KPU_2023-05-20_nrt.stac-item
2023-05-24 09:33:20	ga_s2am_ard_3-2-1_56KPU_2023-05-24_nrt.stac-item
2023-05-24 09:33:27	ga_s2am_ard_3-2-1_56KPU_2023-05-24_nrt.stac-item
2023-05-31 09:23:26	ga_s2am_ard_3-2-1_56KPU_2023-05-30_nrt.stac-item
2023-06-03 09:33:21	ga_s2am_ard_3-2-1_56KPU_2023-06-03_nrt.stac-item
2023-06-10 09:23:26	ga_s2am_ard_3-2-1_56KPU_2023-06-09_nrt.stac-item
2023-06-20 09:23:26	ga_s2am_ard_3-2-1_56KPU_2023-06-19_nrt.stac-item
2023-06-23 09:33:21	ga_s2am_ard_3-2-1_56KPU_2023-06-23_nrt.stac-item
2023-06-30 09:23:26	ga_s2am_ard_3-2-1_56KPU_2023-06-29_nrt.stac-item
2023-07-10 09:23:27	ga_s2am_ard_3-2-1_56KPU_2023-07-09_nrt.stac-item
2023-07-13 09:33:22	ga_s2am_ard_3-2-1_56KPU_2023-07-13_nrt.stac-item
2023-07-20 09:23:26	ga_s2am_ard_3-2-1_56KPU_2023-07-19_nrt.stac-item
2023-07-23 09:33:22	ga_s2am_ard_3-2-1_56KPU_2023-07-23_nrt.stac-item
2023-07-30 09:23:27	ga_s2am_ard_3-2-1_56KPU_2023-07-29_nrt.stac-item
2023-08-02 09:33:23	ga_s2am_ard_3-2-1_56KPU_2023-08-02_nrt.stac-item
2023-08-09 09:23:25	ga_s2am_ard_3-2-1_56KNU_2023-08-08_final.stac-item
2023-08-09 09:23:30	ga_s2am_ard_3-2-1_56KPU_2023-08-08_nrt.stac-item
2023-08-12 09:33:22	ga_s2am_ard_3-2-1_56KPU_2023-08-12_nrt.stac-item
2023-08-19 09:23:28	ga_s2am_ard_3-2-1_56KPU_2023-08-18_nrt.stac-item
2023-08-22 09:33:23	ga_s2am_ard_3-2-1_56KPU_2023-08-22_nrt.stac-item
2023-08-29 09:23:28	ga_s2am_ard_3-2-1_56KPU_2023-08-28_nrt.stac-item
2023-09-08 09:23:27	ga_s2am_ard_3-2-1_56KPU_2023-09-07_nrt.stac-item
2023-09-11 09:33:04	ga_s2am_ard_3-2-1_56KNV_2023-09-11_nrt.stac-item
2023-09-11 09:33:22	ga_s2am_ard_3-2-1_56KPU_2023-09-11_nrt.stac-item
2023-09-18 09:23:25	ga_s2am_ard_3-2-1_56KPU_2023-09-17_nrt.stac-item
2023-09-28 09:23:24	ga_s2am_ard_3-2-1_56KPU_2023-09-27_nrt.stac-item
2023-10-01 09:33:19	ga_s2am_ard_3-2-1_56KPU_2023-10-01_nrt.stac-item
2023-10-08 09:23:22	ga_s2am_ard_3-2-1_56KPU_2023-10-07_nrt.stac-item
2023-10-11 09:33:16	ga_s2am_ard_3-2-1_56KPU_2023-10-11_nrt.stac-item
2023-10-18 09:23:21	ga_s2am_ard_3-2-1_56KPU_2023-10-17_nrt.stac-item
2023-10-21 09:33:17	ga_s2am_ard_3-2-1_56KPU_2023-10-21_nrt.stac-item
2023-10-28 09:23:22	ga_s2am_ard_3-2-1_56KPU_2023-10-27_nrt.stac-item
2023-10-31 09:33:17	ga_s2am_ard_3-2-1_56KPU_2023-10-31_nrt.stac-item
2023-11-10 09:33:17	ga_s2am_ard_3-2-1_56KPU_2023-11-10_nrt.stac-item
2023-11-17 09:23:20	ga_s2am_ard_3-2-1_56KPU_2023-11-16_nrt.stac-item
2023-11-20 09:33:14	ga_s2am_ard_3-2-1_56KPU_2023-11-20_nrt.stac-item
2023-11-27 09:23:16	ga_s2am_ard_3-2-1_56KPU_2023-11-26_nrt.stac-item
2023-12-07 09:23:16	ga_s2am_ard_3-2-1_56KPU_2023-12-06_nrt.stac-item
2023-12-10 09:33:12	ga_s2am_ard_3-2-1_56KPU_2023-12-10_nrt.stac-item
2023-12-17 09:23:18	ga_s2am_ard_3-2-1_56KPU_2023-12-16_nrt.stac-item
2023-12-27 09:23:19	ga_s2am_ard_3-2-1_56KPU_2023-12-26_nrt.stac-item
2023-12-30 09:33:14	ga_s2am_ard_3-2-1_56KPU_2023-12-30_nrt.stac-item
2024-01-06 09:23:18	ga_s2am_ard_3-2-1_56KPU_2024-01-05_nrt.stac-item
2024-01-09 09:33:13	ga_s2am_ard_3-2-1_56KPU_2024-01-09_nrt.stac-item
2024-01-16 09:23:15	ga_s2am_ard_3-2-1_56KPU_2024-01-15_nrt.stac-item
2024-01-19 09:33:08	ga_s2am_ard_3-2-1_56KPU_2024-01-19_nrt.stac-item
2024-01-26 09:23:16	ga_s2am_ard_3-2-1_56KPU_2024-01-25_nrt.stac-item
2024-01-29 09:33:12	ga_s2am_ard_3-2-1_56KPU_2024-01-29_nrt.stac-item
2024-02-05 09:23:18	ga_s2am_ard_3-2-1_56KPU_2024-02-04_nrt.stac-item
2024-02-08 09:33:13	ga_s2am_ard_3-2-1_56KPU_2024-02-08_nrt.stac-item
2024-02-15 09:23:20	ga_s2am_ard_3-2-1_56KPU_2024-02-14_nrt.stac-item
2024-02-18 09:33:15	ga_s2am_ard_3-2-1_56KPU_2024-02-18_nrt.stac-item
2024-02-25 09:23:21	ga_s2am_ard_3-2-1_56KPU_2024-02-24_nrt.stac-item
2024-02-28 09:33:17	ga_s2am_ard_3-2-1_56KPU_2024-02-28_nrt.stac-item
2024-03-06 09:23:23	ga_s2am_ard_3-2-1_56KPU_2024-03-05_nrt.stac-item
2024-03-16 09:23:22	ga_s2am_ard_3-2-1_56KPU_2024-03-15_nrt.stac-item
2024-03-26 09:23:23	ga_s2am_ard_3-2-1_56KPU_2024-03-25_nrt.stac-item
2024-03-29 09:33:17	ga_s2am_ard_3-2-1_56KPU_2024-03-29_nrt.stac-item
2024-04-05 09:23:19	ga_s2am_ard_3-2-1_56KPU_2024-04-04_nrt.stac-item
2024-04-08 09:33:16	ga_s2am_ard_3-2-1_56KPU_2024-04-08_nrt.stac-item
2024-04-15 09:23:24	ga_s2am_ard_3-2-1_56KPU_2024-04-14_nrt.stac-item
2024-04-18 09:33:20	ga_s2am_ard_3-2-1_56KPU_2024-04-18_nrt.stac-item
2024-04-25 09:23:24	ga_s2am_ard_3-2-1_56KPU_2024-04-24_nrt.stac-item
2024-05-05 09:23:25	ga_s2am_ard_3-2-1_56KPU_2024-05-04_nrt.stac-item
2024-05-08 09:33:20	ga_s2am_ard_3-2-1_56KPU_2024-05-08_nrt.stac-item
2024-05-15 09:23:25	ga_s2am_ard_3-2-1_56KPU_2024-05-14_nrt.stac-item
2024-05-25 09:23:22	ga_s2am_ard_3-2-1_56KPU_2024-05-24_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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