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
2015-10-28 11:59:52	ga_s2am_ard_3-2-1_49KHP_2015-10-28_final.stac-item
2015-12-17 12:07:19	ga_s2am_ard_3-2-1_49KHP_2015-12-17_final.stac-item
2015-12-17 12:07:19	ga_s2am_ard_3-2-1_50JKS_2015-12-17_final.stac-item
2016-02-08 12:10:34	ga_s2am_ard_3-2-1_49KHP_2016-02-08_final.stac-item
2016-04-25 12:04:45	ga_s2am_ard_3-2-1_50JLR_2016-04-25_final.stac-item
2016-08-13 12:04:45	ga_s2am_ard_3-2-1_50JLR_2016-08-13_final.stac-item
2016-11-21 12:02:48	ga_s2am_ard_3-2-1_50JLQ_2016-11-21_final.stac-item
2016-12-01 12:03:46	ga_s2am_ard_3-2-1_49KHP_2016-12-01_final.stac-item
2016-12-01 12:03:46	ga_s2am_ard_3-2-1_50JLQ_2016-12-01_final.stac-item
2016-12-11 12:02:45	ga_s2am_ard_3-2-1_50JLR_2016-12-11_final.stac-item
2016-12-21 12:02:43	ga_s2am_ard_3-2-1_49KHQ_2016-12-21_final.stac-item
2017-10-20 12:13:21	ga_s2am_ard_3-2-1_49KHQ_2017-10-20_final.stac-item
2017-10-20 12:14:08	ga_s2am_ard_3-2-1_49KHP_2017-10-20_final.stac-item
2017-12-10 11:42:24	ga_s2am_ard_3-2-1_50HQH_2017-12-10_final.stac-item
2019-01-17 11:55:23	ga_s2am_ard_3-2-1_50JLQ_2019-01-17_final.stac-item
2019-02-06 11:55:24	ga_s2am_ard_3-2-1_50JLQ_2019-02-06_final.stac-item
2019-02-09 12:04:25	ga_s2am_ard_3-2-1_49KHP_2019-02-09_final.stac-item
2019-03-01 12:04:49	ga_s2am_ard_3-2-1_50JLS_2019-03-01_final.stac-item
2019-03-11 12:04:09	ga_s2am_ard_3-2-1_49KHQ_2019-03-11_final.stac-item
2019-07-22 12:14:26	ga_s2am_ard_3-2-1_49KHP_2019-07-22_final.stac-item
2019-07-29 12:04:33	ga_s2am_ard_3-2-1_49KHP_2019-07-29_final.stac-item
2019-11-10 11:46:56	ga_s2am_ard_3-2-1_50HQH_2019-11-10_final.stac-item
2019-11-13 11:55:15	ga_s2am_ard_3-2-1_50JLR_2019-11-13_final.stac-item
2019-11-26 12:05:08	ga_s2am_ard_3-2-1_50JLR_2019-11-26_final.stac-item
2019-11-30 11:46:53	ga_s2am_ard_3-2-1_50HQH_2019-11-30_final.stac-item
2019-12-16 12:04:22	ga_s2am_ard_3-2-1_49KHP_2019-12-16_final.stac-item
2019-12-16 12:04:37	ga_s2am_ard_3-2-1_49JHN_2019-12-16_final.stac-item
2019-12-16 12:04:52	ga_s2am_ard_3-2-1_50JKS_2019-12-16_final.stac-item
2020-01-22 11:55:21	ga_s2am_ard_3-2-1_50JLQ_2020-01-22_final.stac-item
2020-03-08 12:14:27	ga_s2am_ard_3-2-1_49JHN_2020-03-08_final.stac-item
2020-05-18 11:46:58	ga_s2am_ard_3-2-1_50HQH_2020-05-18_final.stac-item
2020-06-20 11:57:12	ga_s2am_ard_3-2-1_50HMH_2020-06-20_final.stac-item
2020-07-20 11:56:56	ga_s2am_ard_3-2-1_50HMJ_2020-07-20_final.stac-item
2020-07-30 11:56:57	ga_s2am_ard_3-2-1_50HMJ_2020-07-30_final.stac-item
2020-08-22 12:04:59	ga_s2am_ard_3-2-1_50JLS_2020-08-22_final.stac-item
2020-09-05 11:46:58	ga_s2am_ard_3-2-1_50HQH_2020-09-05_final.stac-item
2020-09-21 12:05:13	ga_s2am_ard_3-2-1_50JLR_2020-09-21_final.stac-item
2021-09-13 11:55:28	ga_s2am_ard_3-2-1_50JLQ_2021-09-13_final.stac-item
2021-11-15 12:05:09	ga_s2am_ard_3-2-1_50JLR_2021-11-15_final.stac-item
2021-12-02 11:55:12	ga_s2am_ard_3-2-1_50JLR_2021-12-02_final.stac-item
2022-01-14 12:05:09	ga_s2am_ard_3-2-1_50JLR_2022-01-14_final.stac-item
2022-01-18 11:46:54	ga_s2am_ard_3-2-1_50HQH_2022-01-18_final.stac-item
2022-01-31 11:55:27	ga_s2am_ard_3-2-1_50JLQ_2022-01-31_final.stac-item
2022-02-03 12:05:23	ga_s2am_ard_3-2-1_50JLQ_2022-02-03_final.stac-item
2022-02-23 12:05:10	ga_s2am_ard_3-2-1_50JLR_2022-02-23_final.stac-item
2022-03-08 12:14:13	ga_s2am_ard_3-2-1_49KHQ_2022-03-08_final.stac-item
2022-03-08 12:14:25	ga_s2am_ard_3-2-1_49KHP_2022-03-08_final.stac-item
2022-04-14 12:05:00	ga_s2am_ard_3-2-1_50JKS_2022-04-14_final.stac-item
2022-04-24 12:05:13	ga_s2am_ard_3-2-1_50JLR_2022-04-24_final.stac-item
2022-05-04 12:05:09	ga_s2am_ard_3-2-1_50JKS_2022-05-04_final.stac-item
2022-06-07 11:47:22	ga_s2am_ard_3-2-1_50HLH_2022-06-07_final.stac-item
2022-06-20 11:55:10	ga_s2am_ard_3-2-1_50JLS_2022-06-20_final.stac-item
2022-06-30 11:57:19	ga_s2am_ard_3-2-1_50HMH_2022-06-30_final.stac-item
2022-07-03 12:05:36	ga_s2am_ard_3-2-1_50JLQ_2022-07-03_final.stac-item
2022-07-23 12:05:19	ga_s2am_ard_3-2-1_50JLR_2022-07-23_final.stac-item
2022-08-12 12:05:06	ga_s2am_ard_3-2-1_50JLS_2022-08-12_final.stac-item
2022-08-19 11:55:10	ga_s2am_ard_3-2-1_50JLS_2022-08-19_final.stac-item
2022-10-08 11:55:19	ga_s2am_ard_3-2-1_50JLR_2022-10-08_final.stac-item
2022-12-10 12:05:09	ga_s2am_ard_3-2-1_50JLR_2022-12-10_final.stac-item
2023-01-06 11:55:26	ga_s2am_ard_3-2-1_50JLQ_2023-01-06_final.stac-item
2023-01-16 11:55:09	ga_s2am_ard_3-2-1_50JLR_2023-01-16_final.stac-item
2023-01-29 12:05:21	ga_s2am_ard_3-2-1_50JLQ_2023-01-29_final.stac-item
2023-02-02 11:46:52	ga_s2am_ard_3-2-1_50HQH_2023-02-02_final
2023-02-15 11:54:57	ga_s2am_ard_3-2-1_50JLS_2023-02-15_nrt.stac-item
2023-02-15 11:54:57	ga_s2am_ard_3-2-1_50JLS_2023-02-15_final
2023-03-07 11:54:54	ga_s2am_ard_3-2-1_50JLS_2023-03-07_final
2023-03-07 11:57:16	ga_s2am_ard_3-2-1_50HMG_2023-03-07_final
2023-03-10 12:04:48	ga_s2am_ard_3-2-1_50JLS_2023-03-10_final
2023-03-13 12:14:17	ga_s2am_ard_3-2-1_49KHP_2023-03-13_final
2023-03-27 11:54:56	ga_s2am_ard_3-2-1_50JLS_2023-03-27_final
2023-04-16 11:55:12	ga_s2am_ard_3-2-1_50JLR_2023-04-16_final
2023-05-16 11:57:21	ga_s2am_ard_3-2-1_50HMG_2023-05-16_final
2023-05-19 12:05:10	ga_s2am_ard_3-2-1_50JLR_2023-05-19_final
2023-06-08 12:05:12	ga_s2am_ard_3-2-1_50JLR_2023-06-08_final
2023-06-15 11:55:32	ga_s2am_ard_3-2-1_50JLQ_2023-06-15_final
2023-07-08 12:05:13	ga_s2am_ard_3-2-1_50JLR_2023-07-08_final
2023-07-15 11:57:14	ga_s2am_ard_3-2-1_50HLH_2023-07-15_final
2023-08-04 11:57:00	ga_s2am_ard_3-2-1_50HLJ_2023-08-04_final
2023-08-17 12:05:14	ga_s2am_ard_3-2-1_50JLR_2023-08-17_final
2023-08-24 11:55:04	ga_s2am_ard_3-2-1_50JLS_2023-08-24_final
2023-09-13 11:55:32	ga_s2am_ard_3-2-1_50JLQ_2023-09-13_final
2023-11-02 11:55:11	ga_s2am_ard_3-2-1_50JLR_2023-11-02_final
2023-12-02 11:55:05	ga_s2am_ard_3-2-1_50JLR_2023-12-02_final
2023-12-25 12:05:05	ga_s2am_ard_3-2-1_50JLR_2023-12-25_final
2023-12-25 12:05:20	ga_s2am_ard_3-2-1_50JLQ_2023-12-25_final
2024-01-11 11:55:07	ga_s2am_ard_3-2-1_50JLR_2024-01-11_final
2024-01-11 11:55:22	ga_s2am_ard_3-2-1_50JLQ_2024-01-11_final
2024-01-14 12:05:02	ga_s2am_ard_3-2-1_50JLR_2024-01-14_final
2024-01-14 12:05:16	ga_s2am_ard_3-2-1_50JLQ_2024-01-14_final
2024-03-01 11:56:37	ga_s2am_ard_3-2-1_50HLK_2024-03-01_final
2024-03-17 12:14:09	ga_s2am_ard_3-2-1_49KHQ_2024-03-17_final
2024-03-21 11:57:23	ga_s2am_ard_3-2-1_50HLG_2024-03-21_final
2024-03-27 12:14:09	ga_s2am_ard_3-2-1_49KHQ_2024-03-27_final
2024-04-06 12:14:07	ga_s2am_ard_3-2-1_49KHQ_2024-04-06_final
2024-04-13 12:05:10	ga_s2am_ard_3-2-1_50JLR_2024-04-13_final
2024-04-16 12:14:23	ga_s2am_ard_3-2-1_49KHP_2024-04-16_final
2024-04-23 12:05:26	ga_s2am_ard_3-2-1_50JLQ_2024-04-23_final
2024-04-26 12:14:23	ga_s2am_ard_3-2-1_49KHP_2024-04-26_final
2024-05-06 12:14:23	ga_s2am_ard_3-2-1_49KHP_2024-05-06_final
2024-05-07 11:46:56	ga_s2am_ard_3-2-1_50HQH_2024-05-07_final

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

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