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
2021-09-30 11:46:23	ga_s2am_ard_3-2-1_50JPL_2021-09-30_final
2021-10-10 11:46:24	ga_s2am_ard_3-2-1_50JPL_2021-10-10_final
2021-10-17 11:36:32	ga_s2am_ard_3-2-1_50JPL_2021-10-17_final
2021-10-20 11:46:24	ga_s2am_ard_3-2-1_50JPL_2021-10-20_final
2021-10-27 11:36:31	ga_s2am_ard_3-2-1_50JPL_2021-10-27_final
2021-10-30 11:46:24	ga_s2am_ard_3-2-1_50JPL_2021-10-30_final
2021-11-06 11:36:30	ga_s2am_ard_3-2-1_50JPL_2021-11-06_final
2021-11-16 11:36:27	ga_s2am_ard_3-2-1_50JPL_2021-11-16_final
2021-11-19 11:46:19	ga_s2am_ard_3-2-1_50JPL_2021-11-19_final
2021-11-26 11:36:26	ga_s2am_ard_3-2-1_50JPL_2021-11-26_final
2021-11-29 11:46:19	ga_s2am_ard_3-2-1_50JPL_2021-11-29_final
2021-12-06 11:36:26	ga_s2am_ard_3-2-1_50JPL_2021-12-06_final
2021-12-09 11:46:18	ga_s2am_ard_3-2-1_50JPL_2021-12-09_final
2021-12-16 11:36:26	ga_s2am_ard_3-2-1_50JPL_2021-12-16_final
2021-12-19 11:46:20	ga_s2am_ard_3-2-1_50JPL_2021-12-19_final
2021-12-26 11:36:28	ga_s2am_ard_3-2-1_50JPL_2021-12-26_final
2021-12-29 11:46:21	ga_s2am_ard_3-2-1_50JPL_2021-12-29_final
2022-01-05 11:36:28	ga_s2am_ard_3-2-1_50JPL_2022-01-05_final
2022-01-15 11:36:27	ga_s2am_ard_3-2-1_50JPL_2022-01-15_final
2022-01-15 11:36:30	ga_s2am_ard_3-2-1_50JPL_2022-01-15_final
2022-01-18 11:46:19	ga_s2am_ard_3-2-1_50JPL_2022-01-18_final
2022-01-25 11:36:27	ga_s2am_ard_3-2-1_50JPL_2022-01-25_final
2022-02-04 11:36:26	ga_s2am_ard_3-2-1_50JPL_2022-02-04_final
2022-02-07 11:46:19	ga_s2am_ard_3-2-1_50JPL_2022-02-07_final
2022-02-14 11:36:26	ga_s2am_ard_3-2-1_50JPL_2022-02-14_final
2022-02-17 11:46:20	ga_s2am_ard_3-2-1_50JPL_2022-02-17_final
2022-02-24 11:36:29	ga_s2am_ard_3-2-1_50JPL_2022-02-24_final
2022-03-06 11:36:30	ga_s2am_ard_3-2-1_50JPL_2022-03-06_final
2022-03-09 11:46:23	ga_s2am_ard_3-2-1_50JPL_2022-03-09_final
2022-03-16 11:36:30	ga_s2am_ard_3-2-1_50JPL_2022-03-16_final
2022-03-19 11:46:23	ga_s2am_ard_3-2-1_50JPL_2022-03-19_final
2022-03-26 11:36:29	ga_s2am_ard_3-2-1_50JPL_2022-03-26_final
2022-03-29 11:46:22	ga_s2am_ard_3-2-1_50JPL_2022-03-29_final
2022-04-05 11:36:28	ga_s2am_ard_3-2-1_50JPL_2022-04-05_final
2022-04-08 11:46:20	ga_s2am_ard_3-2-1_50JPL_2022-04-08_final
2022-04-15 11:36:29	ga_s2am_ard_3-2-1_50JPL_2022-04-15_final
2022-04-18 11:46:23	ga_s2am_ard_3-2-1_50JPL_2022-04-18_final
2022-04-25 11:36:31	ga_s2am_ard_3-2-1_50JPL_2022-04-25_final
2022-04-28 11:46:24	ga_s2am_ard_3-2-1_50JPL_2022-04-28_final
2022-05-05 11:36:31	ga_s2am_ard_3-2-1_50JPL_2022-05-05_final
2022-05-15 11:36:31	ga_s2am_ard_3-2-1_50JPL_2022-05-15_final
2022-05-18 11:46:25	ga_s2am_ard_3-2-1_50JPL_2022-05-18_final
2022-05-25 11:36:34	ga_s2am_ard_3-2-1_50JPL_2022-05-25_final
2022-05-28 11:46:27	ga_s2am_ard_3-2-1_50JPL_2022-05-28_final
2022-06-04 11:36:34	ga_s2am_ard_3-2-1_50JPL_2022-06-04_final
2022-06-07 11:46:27	ga_s2am_ard_3-2-1_50JPL_2022-06-07_final
2022-06-14 11:36:37	ga_s2am_ard_3-2-1_50JPL_2022-06-14_final
2022-06-17 11:46:30	ga_s2am_ard_3-2-1_50JPL_2022-06-17_final
2022-06-24 11:36:39	ga_s2am_ard_3-2-1_50JPL_2022-06-24_final
2022-06-27 11:46:32	ga_s2am_ard_3-2-1_50JPL_2022-06-27_final
2022-07-04 11:36:39	ga_s2am_ard_3-2-1_50JPL_2022-07-04_final
2022-07-07 11:46:32	ga_s2am_ard_3-2-1_50JPL_2022-07-07_final
2022-07-14 11:36:39	ga_s2am_ard_3-2-1_50JPL_2022-07-14_final
2022-07-17 11:46:31	ga_s2am_ard_3-2-1_50JPL_2022-07-17_final
2022-07-24 11:36:37	ga_s2am_ard_3-2-1_50JPL_2022-07-24_final
2022-07-27 11:46:29	ga_s2am_ard_3-2-1_50JPL_2022-07-27_final
2022-08-03 11:36:37	ga_s2am_ard_3-2-1_50JPL_2022-08-03_final
2022-08-06 11:46:30	ga_s2am_ard_3-2-1_50JPL_2022-08-06_final
2022-08-13 11:36:38	ga_s2am_ard_3-2-1_50JPL_2022-08-13_final
2022-08-16 11:46:32	ga_s2am_ard_3-2-1_50JPL_2022-08-16_final
2022-08-26 11:46:32	ga_s2am_ard_3-2-1_50JPL_2022-08-26_final
2023-02-12 11:46:12	ga_s2am_ard_3-2-1_50JPL_2023-02-12_final.stac-item
2023-02-12 11:46:19	ga_s2am_ard_3-2-1_50JPL_2023-02-12_final.stac-item
2023-02-19 11:36:23	ga_s2am_ard_3-2-1_50JPL_2023-02-19_final.stac-item
2023-02-22 11:46:17	ga_s2am_ard_3-2-1_50JPL_2023-02-22_final.stac-item
2023-03-01 11:36:24	ga_s2am_ard_3-2-1_50JPL_2023-03-01_final.stac-item
2023-03-04 11:46:16	ga_s2am_ard_3-2-1_50JPL_2023-03-04_final.stac-item
2023-03-11 11:36:22	ga_s2am_ard_3-2-1_50JPL_2023-03-11_final.stac-item
2023-03-14 11:46:16	ga_s2am_ard_3-2-1_50JPL_2023-03-14_final.stac-item
2023-03-21 11:36:25	ga_s2am_ard_3-2-1_50JPL_2023-03-21_final.stac-item
2023-03-24 11:46:18	ga_s2am_ard_3-2-1_50JPL_2023-03-24_final.stac-item
2023-03-31 11:36:25	ga_s2am_ard_3-2-1_50JPL_2023-03-31_final.stac-item
2023-04-03 11:46:18	ga_s2am_ard_3-2-1_50JPL_2023-04-03_final.stac-item
2023-04-10 11:36:26	ga_s2am_ard_3-2-1_50JPL_2023-04-10_final.stac-item
2023-04-13 11:46:19	ga_s2am_ard_3-2-1_50JPL_2023-04-13_final.stac-item
2023-04-20 11:36:25	ga_s2am_ard_3-2-1_50JPL_2023-04-20_final.stac-item
2023-04-23 11:46:18	ga_s2am_ard_3-2-1_50JPL_2023-04-23_final.stac-item
2023-04-30 11:36:27	ga_s2am_ard_3-2-1_50JPL_2023-04-30_final.stac-item
2023-05-03 11:46:19	ga_s2am_ard_3-2-1_50JPL_2023-05-03_final.stac-item
2023-05-10 11:36:26	ga_s2am_ard_3-2-1_50JPL_2023-05-10_final.stac-item
2023-05-13 11:46:19	ga_s2am_ard_3-2-1_50JPL_2023-05-13_final.stac-item
2023-05-20 11:36:29	ga_s2am_ard_3-2-1_50JPL_2023-05-20_final.stac-item
2023-05-23 11:46:22	ga_s2am_ard_3-2-1_50JPL_2023-05-23_final.stac-item
2023-05-30 11:36:30	ga_s2am_ard_3-2-1_50JPL_2023-05-30_final.stac-item
2023-06-02 11:46:23	ga_s2am_ard_3-2-1_50JPL_2023-06-02_final.stac-item
2023-06-09 11:36:30	ga_s2am_ard_3-2-1_50JPL_2023-06-09_final.stac-item
2023-06-12 11:46:23	ga_s2am_ard_3-2-1_50JPL_2023-06-12_final.stac-item
2023-06-29 11:36:30	ga_s2am_ard_3-2-1_50JPL_2023-06-29_final.stac-item
2023-07-02 11:46:24	ga_s2am_ard_3-2-1_50JPL_2023-07-02_final.stac-item
2023-07-09 11:36:28	ga_s2am_ard_3-2-1_50JPL_2023-07-09_final.stac-item
2023-07-09 11:36:32	ga_s2am_ard_3-2-1_50JPL_2023-07-09_final.stac-item
2023-07-12 11:46:24	ga_s2am_ard_3-2-1_50JPL_2023-07-12_final.stac-item
2023-07-29 11:36:32	ga_s2am_ard_3-2-1_50JPL_2023-07-29_final.stac-item
2023-08-08 11:36:31	ga_s2am_ard_3-2-1_50JPL_2023-08-08_final.stac-item
2023-10-17 11:36:26	ga_s2am_ard_3-2-1_50JPL_2023-10-17_final.stac-item
2023-10-20 11:46:19	ga_s2am_ard_3-2-1_50JPL_2023-10-20_final.stac-item
2023-10-27 11:36:26	ga_s2am_ard_3-2-1_50JPL_2023-10-27_final.stac-item
2023-10-30 11:46:19	ga_s2am_ard_3-2-1_50JPL_2023-10-30_final.stac-item
2023-11-16 11:36:25	ga_s2am_ard_3-2-1_50JPL_2023-11-16_final.stac-item
2023-11-19 11:46:16	ga_s2am_ard_3-2-1_50JPL_2023-11-19_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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