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
2015-10-22 11:38:10	ga_s2am_ard_3-2-1_50HMK_2015-10-22_final.stac-item
2015-12-07 12:04:41	ga_s2am_ard_3-2-1_50JKS_2015-12-07_final.stac-item
2016-01-03 11:57:46	ga_s2am_ard_3-2-1_50HLK_2016-01-03_final.stac-item
2016-05-15 12:02:55	ga_s2am_ard_3-2-1_50JKS_2016-05-15_final.stac-item
2016-05-16 11:32:16	ga_s2am_ard_3-2-1_50HQG_2016-05-16_final
2016-06-17 12:13:46	ga_s2am_ard_3-2-1_49KGR_2016-06-17_final.stac-item
2016-06-24 12:02:50	ga_s2am_ard_3-2-1_50JLP_2016-06-24_final.stac-item
2016-07-01 11:48:31	ga_s2am_ard_3-2-1_50JLN_2016-07-01_final.stac-item
2016-07-24 12:04:46	ga_s2am_ard_3-2-1_50JLR_2016-07-24_final.stac-item
2016-09-15 12:13:17	ga_s2am_ard_3-2-1_49JGN_2016-09-15_final.stac-item
2016-09-25 12:13:39	ga_s2am_ard_3-2-1_49KHR_2016-09-25_final.stac-item
2016-12-18 11:56:02	ga_s2am_ard_3-2-1_50JLL_2016-12-18_final.stac-item
2016-12-31 12:02:43	ga_s2am_ard_3-2-1_49JGN_2016-12-31_final.stac-item
2017-02-03 11:41:40	ga_s2am_ard_3-2-1_50HMJ_2017-02-03_final.stac-item
2017-02-20 11:36:27	ga_s2am_ard_3-2-1_50HPK_2017-02-20_final.stac-item
2017-03-11 12:02:41	ga_s2am_ard_3-2-1_49KHQ_2017-03-11_final.stac-item
2017-04-10 12:04:40	ga_s2am_ard_3-2-1_50JLR_2017-04-10_final.stac-item
2017-05-13 12:13:38	ga_s2am_ard_3-2-1_49JGN_2017-05-13_final.stac-item
2017-07-03 11:43:16	ga_s2am_ard_3-2-1_50HQH_2017-07-03_final.stac-item
2017-12-03 11:53:28	ga_s2am_ard_3-2-1_50HLH_2017-12-03_final.stac-item
2018-01-25 12:04:03	ga_s2am_ard_3-2-1_50JLN_2018-01-25_final.stac-item
2018-02-11 11:55:28	ga_s2am_ard_3-2-1_50JLP_2018-02-11_final.stac-item
2018-02-27 12:13:18	ga_s2am_ard_3-2-1_49KGP_2018-02-27_final.stac-item
2018-04-15 12:05:11	ga_s2am_ard_3-2-1_50JLN_2018-04-15_final.stac-item
2018-06-14 12:03:57	ga_s2am_ard_3-2-1_50JLQ_2018-06-14_final.stac-item
2018-06-21 11:53:43	ga_s2am_ard_3-2-1_50HNK_2018-06-21_final.stac-item
2018-07-01 11:52:21	ga_s2am_ard_3-2-1_50JLT_2018-07-01_final.stac-item
2018-08-10 11:51:15	ga_s2am_ard_3-2-1_50JKP_2018-08-10_final.stac-item
2018-09-15 12:14:38	ga_s2am_ard_3-2-1_49JGL_2018-09-15_final.stac-item
2019-02-26 11:55:37	ga_s2am_ard_3-2-1_50JLP_2019-02-26_final.stac-item
2019-04-07 11:56:15	ga_s2am_ard_3-2-1_50JKM_2019-04-07_final.stac-item
2019-06-03 11:46:58	ga_s2am_ard_3-2-1_50HMJ_2019-06-03_final.stac-item
2019-08-02 11:47:19	ga_s2am_ard_3-2-1_50HLH_2019-08-02_final.stac-item
2019-08-05 11:55:32	ga_s2am_ard_3-2-1_50JLQ_2019-08-05_final.stac-item
2019-09-01 11:46:30	ga_s2am_ard_3-2-1_50HQK_2019-09-01_final.stac-item
2019-09-14 11:55:45	ga_s2am_ard_3-2-1_50JKP_2019-09-14_final.stac-item
2019-10-10 12:13:57	ga_s2am_ard_3-2-1_49KHR_2019-10-10_final.stac-item
2019-10-17 12:05:00	ga_s2am_ard_3-2-1_50JKS_2019-10-17_final.stac-item
2019-10-17 12:05:41	ga_s2am_ard_3-2-1_50JLP_2019-10-17_final.stac-item
2019-10-30 12:14:44	ga_s2am_ard_3-2-1_49JGN_2019-10-30_final.stac-item
2019-11-03 11:56:15	ga_s2am_ard_3-2-1_50JLM_2019-11-03_final.stac-item
2019-11-06 12:04:45	ga_s2am_ard_3-2-1_49JHN_2019-11-06_final.stac-item
2019-11-26 12:05:51	ga_s2am_ard_3-2-1_50JLN_2019-11-26_final.stac-item
2019-12-03 11:56:49	ga_s2am_ard_3-2-1_50HMJ_2019-12-03_final.stac-item
2019-12-30 11:46:38	ga_s2am_ard_3-2-1_50HQJ_2019-12-30_final.stac-item
2020-01-22 11:56:04	ga_s2am_ard_3-2-1_50JLM_2020-01-22_final.stac-item
2020-02-24 12:05:46	ga_s2am_ard_3-2-1_50JLN_2020-02-24_final.stac-item
2020-04-08 11:46:49	ga_s2am_ard_3-2-1_50HNJ_2020-04-08_final.stac-item
2020-05-21 11:56:17	ga_s2am_ard_3-2-1_50JLM_2020-05-21_final.stac-item
2020-10-11 12:04:34	ga_s2am_ard_3-2-1_49KHP_2020-10-11_final.stac-item
2020-10-25 11:47:01	ga_s2am_ard_3-2-1_50HMJ_2020-10-25_final.stac-item
2021-01-10 11:37:22	ga_s2am_ard_3-2-1_50HPG_2021-01-10_final
2021-01-26 11:55:42	ga_s2am_ard_3-2-1_50JLP_2021-01-26_final.stac-item
2021-02-25 11:57:09	ga_s2am_ard_3-2-1_50HLH_2021-02-25_final.stac-item
2021-03-14 11:47:24	ga_s2am_ard_3-2-1_50HMG_2021-03-14_final
2021-03-24 11:47:08	ga_s2am_ard_3-2-1_50HMH_2021-03-24_final.stac-item
2021-04-26 11:56:47	ga_s2am_ard_3-2-1_50HMJ_2021-04-26_final.stac-item
2021-05-16 11:55:27	ga_s2am_ard_3-2-1_50JLQ_2021-05-16_final.stac-item
2021-06-02 11:47:22	ga_s2am_ard_3-2-1_50HNG_2021-06-02_final
2021-08-24 11:57:22	ga_s2am_ard_3-2-1_50HMG_2021-08-24_final
2021-09-06 12:05:27	ga_s2am_ard_3-2-1_50JKQ_2021-09-06_final.stac-item
2021-10-16 12:05:28	ga_s2am_ard_3-2-1_50JLQ_2021-10-16_final.stac-item
2021-11-15 12:04:43	ga_s2am_ard_3-2-1_49JHN_2021-11-15_final.stac-item
2021-11-29 11:46:48	ga_s2am_ard_3-2-1_50HPJ_2021-11-29_final.stac-item
2021-12-18 12:13:59	ga_s2am_ard_3-2-1_49KGR_2021-12-18_final.stac-item
2022-03-12 11:55:46	ga_s2am_ard_3-2-1_50JLP_2022-03-12_final.stac-item
2022-04-08 11:46:38	ga_s2am_ard_3-2-1_50HNK_2022-04-08_final.stac-item
2022-04-18 11:46:47	ga_s2am_ard_3-2-1_50HQJ_2022-04-18_final.stac-item
2022-06-20 11:55:58	ga_s2am_ard_3-2-1_50JKP_2022-06-20_final.stac-item
2022-06-26 12:15:23	ga_s2am_ard_3-2-1_49JGL_2022-06-26_final.stac-item
2022-07-30 11:55:37	ga_s2am_ard_3-2-1_50JLQ_2022-07-30_final.stac-item
2022-08-13 11:36:52	ga_s2am_ard_3-2-1_50HQK_2022-08-13_final.stac-item
2022-09-01 12:04:58	ga_s2am_ard_3-2-1_49JGN_2022-09-01_final.stac-item
2022-11-07 11:56:29	ga_s2am_ard_3-2-1_50JLL_2022-11-07_final.stac-item
2022-12-27 11:56:09	ga_s2am_ard_3-2-1_50JLM_2022-12-27_final.stac-item
2023-01-03 11:46:47	ga_s2am_ard_3-2-1_50HPJ_2023-01-03_final.stac-item
2023-01-16 11:55:43	ga_s2am_ard_3-2-1_50JKP_2023-01-16_final.stac-item
2023-02-02 11:47:16	ga_s2am_ard_3-2-1_50HLH_2023-02-02_final
2023-02-09 11:36:37	ga_s2am_ard_3-2-1_50HPK_2023-02-09_final.stac-item
2023-02-11 12:14:40	ga_s2am_ard_3-2-1_49JGN_2023-02-11_nrt.stac-item
2023-02-11 12:14:40	ga_s2am_ard_3-2-1_49JGN_2023-02-11_final
2023-02-12 11:46:50	ga_s2am_ard_3-2-1_50HNJ_2023-02-12_nrt.stac-item
2023-04-16 11:55:46	ga_s2am_ard_3-2-1_50JKP_2023-04-16_final
2023-04-23 11:46:51	ga_s2am_ard_3-2-1_50HNJ_2023-04-23_final
2023-05-06 11:57:22	ga_s2am_ard_3-2-1_50HLG_2023-05-06_final
2023-05-09 12:05:11	ga_s2am_ard_3-2-1_50JKR_2023-05-09_final
2023-06-08 12:04:31	ga_s2am_ard_3-2-1_49KHP_2023-06-08_final
2023-08-17 12:05:43	ga_s2am_ard_3-2-1_50JLP_2023-08-17_final
2023-09-26 12:04:59	ga_s2am_ard_3-2-1_50JKS_2023-09-26_final
2023-10-07 11:37:22	ga_s2am_ard_3-2-1_50HPG_2023-10-07_final
2023-11-25 12:04:08	ga_s2am_ard_3-2-1_49KHQ_2023-11-25_final
2023-11-25 12:05:17	ga_s2am_ard_3-2-1_50JLQ_2023-11-25_final
2023-12-19 11:46:44	ga_s2am_ard_3-2-1_50HPJ_2023-12-19_final
2024-01-15 11:36:56	ga_s2am_ard_3-2-1_50HQH_2024-01-15_final
2024-02-07 11:46:31	ga_s2am_ard_3-2-1_50HMK_2024-02-07_final
2024-02-27 11:46:42	ga_s2am_ard_3-2-1_50HQJ_2024-02-27_final
2024-03-25 11:37:08	ga_s2am_ard_3-2-1_50HPH_2024-03-25_final
2024-03-31 11:56:29	ga_s2am_ard_3-2-1_50HNK_2024-03-31_final
2024-04-03 12:03:49	ga_s2am_ard_3-2-1_49KHS_2024-04-03_final
2024-04-23 12:05:40	ga_s2am_ard_3-2-1_50JLP_2024-04-23_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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