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
2015-11-18 11:36:00	ga_s2am_ard_3-2-1_50JQL_2015-11-18_final.stac-item
2015-11-24 11:56:24	ga_s2am_ard_3-2-1_50HMK_2015-11-24_final.stac-item
2015-12-04 11:50:12	ga_s2am_ard_3-2-1_50JNR_2015-12-04_final.stac-item
2015-12-07 12:04:41	ga_s2am_ard_3-2-1_50JLR_2015-12-07_final.stac-item
2015-12-21 11:47:51	ga_s2am_ard_3-2-1_50HNH_2015-12-21_final.stac-item
2015-12-21 11:47:51	ga_s2am_ard_3-2-1_50JMM_2015-12-21_final.stac-item
2016-01-16 12:07:17	ga_s2am_ard_3-2-1_49JGK_2016-01-16_final.stac-item
2016-01-16 12:07:17	ga_s2am_ard_3-2-1_50JKS_2016-01-16_final.stac-item
2016-01-20 11:47:47	ga_s2am_ard_3-2-1_50JPS_2016-01-20_final.stac-item
2016-02-19 11:45:19	ga_s2am_ard_3-2-1_50JPN_2016-02-19_final.stac-item
2016-06-14 12:04:45	ga_s2am_ard_3-2-1_50JKS_2016-06-14_final.stac-item
2016-10-02 12:02:45	ga_s2am_ard_3-2-1_49JHM_2016-10-02_final.stac-item
2016-10-19 11:56:02	ga_s2am_ard_3-2-1_50JNL_2016-10-19_final.stac-item
2016-10-29 11:54:20	ga_s2am_ard_3-2-1_50JNS_2016-10-29_final.stac-item
2016-12-05 11:46:51	ga_s2am_ard_3-2-1_50HPH_2016-12-05_final.stac-item
2017-09-30 12:13:43	ga_s2am_ard_3-2-1_49JFM_2017-09-30_final.stac-item
2017-09-30 12:13:43	ga_s2am_ard_3-2-1_49JGM_2017-09-30_final.stac-item
2017-10-07 12:03:46	ga_s2am_ard_3-2-1_49JGL_2017-10-07_final.stac-item
2018-02-18 11:45:05	ga_s2am_ard_3-2-1_50HMJ_2018-02-18_final.stac-item
2018-02-28 11:44:11	ga_s2am_ard_3-2-1_50HMH_2018-02-28_final.stac-item
2018-09-12 12:02:46	ga_s2am_ard_3-2-1_49JHM_2018-09-12_final.stac-item
2018-10-02 12:02:46	ga_s2am_ard_3-2-1_49KHR_2018-10-02_final.stac-item
2018-11-15 11:46:13	ga_s2am_ard_3-2-1_50JPL_2018-11-15_final.stac-item
2019-01-07 11:56:46	ga_s2am_ard_3-2-1_50HMJ_2019-01-07_final.stac-item
2019-01-14 11:46:11	ga_s2am_ard_3-2-1_50JQL_2019-01-14_final.stac-item
2019-02-06 11:56:15	ga_s2am_ard_3-2-1_50JNL_2019-02-06_final.stac-item
2019-02-16 11:56:33	ga_s2am_ard_3-2-1_50HMK_2019-02-16_final.stac-item
2019-03-05 11:47:04	ga_s2am_ard_3-2-1_50HMH_2019-03-05_final.stac-item
2019-03-18 11:57:03	ga_s2am_ard_3-2-1_50HMH_2019-03-18_final.stac-item
2019-03-31 12:05:29	ga_s2am_ard_3-2-1_49JGK_2019-03-31_final.stac-item
2019-04-04 11:45:47	ga_s2am_ard_3-2-1_50JQN_2019-04-04_final.stac-item
2019-05-03 12:14:02	ga_s2am_ard_3-2-1_49KGR_2019-05-03_final.stac-item
2019-05-07 11:56:03	ga_s2am_ard_3-2-1_50JKN_2019-05-07_final.stac-item
2019-05-13 12:15:14	ga_s2am_ard_3-2-1_49JGL_2019-05-13_final.stac-item
2019-06-09 12:04:02	ga_s2am_ard_3-2-1_49KHR_2019-06-09_final
2019-06-22 12:14:02	ga_s2am_ard_3-2-1_49KGR_2019-06-22_final.stac-item
2019-09-17 12:05:51	ga_s2am_ard_3-2-1_50JLN_2019-09-17_final.stac-item
2019-11-13 11:56:39	ga_s2am_ard_3-2-1_50HMK_2019-11-13_final.stac-item
2020-01-09 11:47:04	ga_s2am_ard_3-2-1_50HMH_2020-01-09_final.stac-item
2020-02-04 12:06:00	ga_s2am_ard_3-2-1_50JKM_2020-02-04_final.stac-item
2020-02-18 11:46:49	ga_s2am_ard_3-2-1_50HMJ_2020-02-18_final.stac-item
2020-02-21 11:56:49	ga_s2am_ard_3-2-1_50HLJ_2020-02-21_final.stac-item
2020-04-01 11:56:26	ga_s2am_ard_3-2-1_50HNK_2020-04-01_final.stac-item
2020-04-04 12:04:45	ga_s2am_ard_3-2-1_50JMS_2020-04-04_final.stac-item
2020-04-08 11:46:02	ga_s2am_ard_3-2-1_50JPM_2020-04-08_final.stac-item
2020-04-08 11:46:27	ga_s2am_ard_3-2-1_50HQK_2020-04-08_final.stac-item
2020-05-05 11:36:41	ga_s2am_ard_3-2-1_50HPK_2020-05-05_final.stac-item
2020-06-06 12:14:15	ga_s2am_ard_3-2-1_49KHQ_2020-06-06_final.stac-item
2020-06-13 12:05:35	ga_s2am_ard_3-2-1_49JGK_2020-06-13_final.stac-item
2020-10-04 12:14:14	ga_s2am_ard_3-2-1_49KHQ_2020-10-04_final.stac-item
2020-10-04 12:14:22	ga_s2am_ard_3-2-1_49KHQ_2020-10-04_final.stac-item
2020-10-11 12:04:59	ga_s2am_ard_3-2-1_50JLS_2020-10-11_final.stac-item
2021-01-16 11:57:21	ga_s2am_ard_3-2-1_50HMG_2021-01-16_final
2021-01-19 12:05:52	ga_s2am_ard_3-2-1_50JLN_2021-01-19_final.stac-item
2021-01-29 12:04:59	ga_s2am_ard_3-2-1_50JMR_2021-01-29_final.stac-item
2021-02-22 11:47:14	ga_s2am_ard_3-2-1_50HLH_2021-02-22_final.stac-item
2021-05-02 12:14:54	ga_s2am_ard_3-2-1_49JGM_2021-05-02_final.stac-item
2021-05-22 12:13:40	ga_s2am_ard_3-2-1_49KHS_2021-05-22_final
2021-05-22 12:13:55	ga_s2am_ard_3-2-1_49KHR_2021-05-22_final.stac-item
2021-09-06 12:04:40	ga_s2am_ard_3-2-1_50JLT_2021-09-06_final.stac-item
2021-09-16 12:05:42	ga_s2am_ard_3-2-1_50JKP_2021-09-16_final.stac-item
2021-09-30 11:45:51	ga_s2am_ard_3-2-1_50JQN_2021-09-30_final.stac-item
2021-12-09 11:47:08	ga_s2am_ard_3-2-1_50HMH_2021-12-09_final.stac-item
2021-12-19 11:47:10	ga_s2am_ard_3-2-1_50HMH_2021-12-19_final.stac-item
2022-01-07 12:15:12	ga_s2am_ard_3-2-1_49JGL_2022-01-07_final.stac-item
2022-01-18 11:46:56	ga_s2am_ard_3-2-1_50HMJ_2022-01-18_final.stac-item
2022-03-25 12:06:07	ga_s2am_ard_3-2-1_50JLM_2022-03-25_final.stac-item
2022-04-08 11:45:47	ga_s2am_ard_3-2-1_50JQN_2022-04-08_final.stac-item
2022-04-11 11:56:53	ga_s2am_ard_3-2-1_50HMJ_2022-04-11_final.stac-item
2022-06-17 11:46:16	ga_s2am_ard_3-2-1_50JPM_2022-06-17_final.stac-item
2022-09-01 12:05:35	ga_s2am_ard_3-2-1_50JLQ_2022-09-01_final.stac-item
2022-10-01 12:05:59	ga_s2am_ard_3-2-1_50JLN_2022-10-01_final.stac-item
2022-10-11 12:05:28	ga_s2am_ard_3-2-1_50JLQ_2022-10-11_final.stac-item
2022-12-07 11:56:51	ga_s2am_ard_3-2-1_50HMJ_2022-12-07_final.stac-item
2023-02-08 12:05:54	ga_s2am_ard_3-2-1_50JKN_2023-02-08_final
2023-04-03 11:45:46	ga_s2am_ard_3-2-1_50JQN_2023-04-03_final
2023-04-03 11:47:09	ga_s2am_ard_3-2-1_50HMH_2023-04-03_final
2023-04-06 11:55:35	ga_s2am_ard_3-2-1_50JNP_2023-04-06_final
2023-04-09 12:05:08	ga_s2am_ard_3-2-1_50JLR_2023-04-09_final
2023-05-09 12:05:40	ga_s2am_ard_3-2-1_50JKP_2023-05-09_final
2023-06-12 11:46:23	ga_s2am_ard_3-2-1_50JPL_2023-06-12_final
2023-07-15 11:56:19	ga_s2am_ard_3-2-1_50JKM_2023-07-15_final
2023-09-06 12:06:15	ga_s2am_ard_3-2-1_50JKM_2023-09-06_final
2023-09-07 11:37:12	ga_s2am_ard_3-2-1_50HPH_2023-09-07_final
2023-09-09 12:14:18	ga_s2am_ard_3-2-1_49KGQ_2023-09-09_final
2023-09-09 12:14:32	ga_s2am_ard_3-2-1_49KGP_2023-09-09_final
2023-09-16 12:05:41	ga_s2am_ard_3-2-1_50JLP_2023-09-16_final
2023-09-20 11:46:03	ga_s2am_ard_3-2-1_50JQM_2023-09-20_final
2023-09-20 11:46:40	ga_s2am_ard_3-2-1_50HNK_2023-09-20_final
2023-09-26 12:04:45	ga_s2am_ard_3-2-1_49JHN_2023-09-26_final
2023-09-30 11:46:17	ga_s2am_ard_3-2-1_50JQL_2023-09-30_final
2023-10-13 11:56:05	ga_s2am_ard_3-2-1_50JMM_2023-10-13_final
2023-10-16 12:04:52	ga_s2am_ard_3-2-1_50JLS_2023-10-16_final
2023-10-26 12:05:55	ga_s2am_ard_3-2-1_50JKN_2023-10-26_final
2023-11-19 11:47:07	ga_s2am_ard_3-2-1_50HMH_2023-11-19_final
2023-11-29 11:47:02	ga_s2am_ard_3-2-1_50HMH_2023-11-29_final
2023-12-06 11:35:44	ga_s2am_ard_3-2-1_50JQN_2023-12-06_final
2024-01-18 11:47:01	ga_s2am_ard_3-2-1_50HMH_2024-01-18_final
2024-01-21 11:56:44	ga_s2am_ard_3-2-1_50HLJ_2024-01-21_final
2024-01-21 11:56:50	ga_s2am_ard_3-2-1_50HLJ_2024-01-21_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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