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
2015-10-21 12:09:42	ga_s2am_ard_3-2-1_49KGP_2015-10-21_final.stac-item
2015-10-21 12:09:42	ga_s2am_ard_3-2-1_49KHQ_2015-10-21_final.stac-item
2015-11-14 11:54:02	ga_s2am_ard_3-2-1_50JPR_2015-11-14_final.stac-item
2015-12-17 12:07:19	ga_s2am_ard_3-2-1_50JKQ_2015-12-17_final.stac-item
2015-12-17 12:07:19	ga_s2am_ard_3-2-1_50JKR_2015-12-17_final.stac-item
2015-12-24 11:56:44	ga_s2am_ard_3-2-1_50JML_2015-12-24_final.stac-item
2016-02-02 11:48:17	ga_s2am_ard_3-2-1_50JNP_2016-02-02_final.stac-item
2016-02-02 11:48:17	ga_s2am_ard_3-2-1_50JNQ_2016-02-02_final.stac-item
2016-03-06 12:04:37	ga_s2am_ard_3-2-1_50JLS_2016-03-06_final.stac-item
2016-03-16 11:57:24	ga_s2am_ard_3-2-1_50JLS_2016-03-16_final.stac-item
2016-04-02 11:49:05	ga_s2am_ard_3-2-1_50JPQ_2016-04-02_final.stac-item
2016-06-21 11:56:09	ga_s2am_ard_3-2-1_50HMK_2016-06-21_final.stac-item
2016-11-14 12:13:24	ga_s2am_ard_3-2-1_49KGQ_2016-11-14_final.stac-item
2016-11-21 12:02:48	ga_s2am_ard_3-2-1_49KHQ_2016-11-21_final.stac-item
2016-12-11 12:02:45	ga_s2am_ard_3-2-1_50JMR_2016-12-11_final.stac-item
2016-12-14 12:13:17	ga_s2am_ard_3-2-1_49KGR_2016-12-14_final.stac-item
2016-12-21 12:02:43	ga_s2am_ard_3-2-1_49KGQ_2016-12-21_final.stac-item
2016-12-21 12:02:43	ga_s2am_ard_3-2-1_49KHQ_2016-12-21_final.stac-item
2017-01-24 11:44:18	ga_s2am_ard_3-2-1_50JPQ_2017-01-24_final.stac-item
2017-03-14 12:13:38	ga_s2am_ard_3-2-1_49KHP_2017-03-14_final.stac-item
2017-04-14 11:45:07	ga_s2am_ard_3-2-1_50HQH_2017-04-14_final.stac-item
2017-06-29 12:04:40	ga_s2am_ard_3-2-1_50JMS_2017-06-29_final.stac-item
2017-07-22 12:13:45	ga_s2am_ard_3-2-1_49KHQ_2017-07-22_final.stac-item
2017-11-09 12:13:20	ga_s2am_ard_3-2-1_49KHQ_2017-11-09_final.stac-item
2019-01-30 12:04:10	ga_s2am_ard_3-2-1_49KHQ_2019-01-30_final.stac-item
2019-01-30 12:05:20	ga_s2am_ard_3-2-1_50JLQ_2019-01-30_final.stac-item
2019-02-09 12:04:10	ga_s2am_ard_3-2-1_49KHQ_2019-02-09_final.stac-item
2019-02-16 11:55:24	ga_s2am_ard_3-2-1_50JLQ_2019-02-16_final.stac-item
2019-02-19 12:06:00	ga_s2am_ard_3-2-1_50JLM_2019-02-19_final.stac-item
2019-02-22 12:13:54	ga_s2am_ard_3-2-1_49KGR_2019-02-22_final.stac-item
2019-02-22 12:14:09	ga_s2am_ard_3-2-1_49KGQ_2019-02-22_final.stac-item
2019-02-26 11:56:06	ga_s2am_ard_3-2-1_50JLM_2019-02-26_final.stac-item
2019-03-02 11:36:47	ga_s2am_ard_3-2-1_50HPJ_2019-03-02_final.stac-item
2019-12-16 12:05:59	ga_s2am_ard_3-2-1_50JLM_2019-12-16_final.stac-item
2020-01-28 12:14:24	ga_s2am_ard_3-2-1_49JHN_2020-01-28_final.stac-item
2020-02-17 12:14:25	ga_s2am_ard_3-2-1_49JHN_2020-02-17_final.stac-item
2020-08-15 12:14:27	ga_s2am_ard_3-2-1_49KHP_2020-08-15_final.stac-item
2020-09-04 12:14:25	ga_s2am_ard_3-2-1_49KHP_2020-09-04_final.stac-item
2020-09-18 11:55:32	ga_s2am_ard_3-2-1_50JLQ_2020-09-18_final.stac-item
2020-09-18 11:56:16	ga_s2am_ard_3-2-1_50JLM_2020-09-18_final.stac-item
2021-04-10 11:37:13	ga_s2am_ard_3-2-1_50HQG_2021-04-10_final
2021-08-24 11:56:16	ga_s2am_ard_3-2-1_50JKM_2021-08-24_final.stac-item
2021-08-24 11:57:25	ga_s2am_ard_3-2-1_50HLG_2021-08-24_final
2021-09-23 11:56:14	ga_s2am_ard_3-2-1_50JLM_2021-09-23_final.stac-item
2021-10-17 11:37:12	ga_s2am_ard_3-2-1_50HPH_2021-10-17_final.stac-item
2021-11-29 11:46:53	ga_s2am_ard_3-2-1_50HQH_2021-11-29_final.stac-item
2021-12-12 11:55:25	ga_s2am_ard_3-2-1_50JPP_2021-12-12_final.stac-item
2021-12-22 11:55:28	ga_s2am_ard_3-2-1_50JLQ_2021-12-22_final.stac-item
2021-12-22 11:56:27	ga_s2am_ard_3-2-1_50JLL_2021-12-22_final.stac-item
2022-01-04 12:06:06	ga_s2am_ard_3-2-1_50JLM_2022-01-04_final.stac-item
2022-01-05 11:36:53	ga_s2am_ard_3-2-1_50HPJ_2022-01-05_final.stac-item
2022-01-11 11:56:41	ga_s2am_ard_3-2-1_50HLK_2022-01-11_final.stac-item
2022-01-15 11:36:53	ga_s2am_ard_3-2-1_50HPJ_2022-01-15_final.stac-item
2022-02-10 11:55:26	ga_s2am_ard_3-2-1_50JLQ_2022-02-10_final.stac-item
2022-02-13 12:04:58	ga_s2am_ard_3-2-1_50JMR_2022-02-13_final.stac-item
2022-02-13 12:06:04	ga_s2am_ard_3-2-1_50JLM_2022-02-13_final.stac-item
2022-03-05 12:05:13	ga_s2am_ard_3-2-1_50JLR_2022-03-05_final.stac-item
2022-03-06 11:36:56	ga_s2am_ard_3-2-1_50HPJ_2022-03-06_final.stac-item
2022-03-09 11:46:34	ga_s2am_ard_3-2-1_50HQK_2022-03-09_final.stac-item
2022-03-09 11:46:52	ga_s2am_ard_3-2-1_50HPJ_2022-03-09_final.stac-item
2022-03-22 11:56:14	ga_s2am_ard_3-2-1_50JLM_2022-03-22_final.stac-item
2022-04-14 12:05:06	ga_s2am_ard_3-2-1_50JLR_2022-04-14_final.stac-item
2022-04-24 12:06:09	ga_s2am_ard_3-2-1_50JLM_2022-04-24_final.stac-item
2022-05-04 12:05:08	ga_s2am_ard_3-2-1_50JLR_2022-05-04_final.stac-item
2022-12-10 12:06:04	ga_s2am_ard_3-2-1_50JLM_2022-12-10_final.stac-item
2022-12-14 11:45:21	ga_s2am_ard_3-2-1_50JPQ_2022-12-14_final.stac-item
2022-12-21 11:36:32	ga_s2am_ard_3-2-1_50HQK_2022-12-21_final.stac-item
2022-12-24 11:46:51	ga_s2am_ard_3-2-1_50HQH_2022-12-24_final.stac-item
2023-02-15 11:55:10	ga_s2am_ard_3-2-1_50JLR_2023-02-15_nrt.stac-item
2023-02-15 11:55:10	ga_s2am_ard_3-2-1_50JLR_2023-02-15_final
2023-02-18 12:05:05	ga_s2am_ard_3-2-1_50JLR_2023-02-18_nrt.stac-item
2023-02-18 12:05:05	ga_s2am_ard_3-2-1_50JLR_2023-02-18_final
2023-03-07 11:56:07	ga_s2am_ard_3-2-1_50JLM_2023-03-07_final
2023-03-07 11:56:21	ga_s2am_ard_3-2-1_50JLL_2023-03-07_final
2023-03-07 11:56:36	ga_s2am_ard_3-2-1_50HLK_2023-03-07_final
2023-03-27 11:55:26	ga_s2am_ard_3-2-1_50JLQ_2023-03-27_final
2023-05-03 11:45:27	ga_s2am_ard_3-2-1_50JPQ_2023-05-03_final
2023-05-16 11:55:28	ga_s2am_ard_3-2-1_50JLQ_2023-05-16_final
2023-05-26 11:57:13	ga_s2am_ard_3-2-1_50HLH_2023-05-26_final
2023-06-28 12:05:02	ga_s2am_ard_3-2-1_50JMR_2023-06-28_final
2023-08-04 11:57:11	ga_s2am_ard_3-2-1_50HMH_2023-08-04_final
2023-08-04 11:57:14	ga_s2am_ard_3-2-1_50HLH_2023-08-04_final
2023-08-14 11:55:18	ga_s2am_ard_3-2-1_50JLR_2023-08-14_final
2023-08-14 11:56:56	ga_s2am_ard_3-2-1_50HMJ_2023-08-14_final
2023-11-15 12:05:21	ga_s2am_ard_3-2-1_50JLQ_2023-11-15_final
2023-12-02 11:56:03	ga_s2am_ard_3-2-1_50JLM_2023-12-02_final
2023-12-12 11:56:35	ga_s2am_ard_3-2-1_50HLK_2023-12-12_final
2023-12-15 12:05:04	ga_s2am_ard_3-2-1_50JLR_2023-12-15_final
2023-12-22 11:56:22	ga_s2am_ard_3-2-1_50JLL_2023-12-22_final
2024-01-15 11:36:45	ga_s2am_ard_3-2-1_50HPJ_2024-01-15_final
2024-01-21 11:56:32	ga_s2am_ard_3-2-1_50HLK_2024-01-21_final
2024-01-25 11:36:46	ga_s2am_ard_3-2-1_50HPJ_2024-01-25_final
2024-01-28 11:46:43	ga_s2am_ard_3-2-1_50HPJ_2024-01-28_final
2024-01-31 11:55:22	ga_s2am_ard_3-2-1_50JPP_2024-01-31_final
2024-02-23 12:04:57	ga_s2am_ard_3-2-1_50JMR_2024-02-23_final
2024-03-01 11:55:16	ga_s2am_ard_3-2-1_50JPQ_2024-03-01_final
2024-03-01 11:55:27	ga_s2am_ard_3-2-1_50JLQ_2024-03-01_final
2024-03-04 12:05:08	ga_s2am_ard_3-2-1_50JLR_2024-03-04_final
2024-04-03 12:05:06	ga_s2am_ard_3-2-1_50JLR_2024-04-03_final
2024-04-10 11:55:26	ga_s2am_ard_3-2-1_50JPP_2024-04-10_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

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