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
2015-12-17 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
2016-01-03 11:57:46	ga_s2am_ard_3-2-1_50JNN_2016-01-03_final.stac-item
2016-02-15 11:59:34	ga_s2am_ard_3-2-1_50JLS_2016-02-15_final.stac-item
2016-04-15 11:55:08	ga_s2am_ard_3-2-1_50JMR_2016-04-15_final.stac-item
2016-11-21 12:02:48	ga_s2am_ard_3-2-1_49KHQ_2016-11-21_final.stac-item
2017-01-30 12:02:43	ga_s2am_ard_3-2-1_50JLT_2017-01-30_final.stac-item
2017-03-05 11:44:15	ga_s2am_ard_3-2-1_50HQH_2017-03-05_final.stac-item
2017-04-14 11:45:07	ga_s2am_ard_3-2-1_50HQH_2017-04-14_final.stac-item
2017-10-30 12:13:43	ga_s2am_ard_3-2-1_49KHQ_2017-10-30_final.stac-item
2017-11-09 12:13:20	ga_s2am_ard_3-2-1_49KHQ_2017-11-09_final.stac-item
2019-02-09 12:04:32	ga_s2am_ard_3-2-1_50JMT_2019-02-09_final.stac-item
2019-02-16 11:55:09	ga_s2am_ard_3-2-1_50JLR_2019-02-16_final.stac-item
2019-02-19 12:04:35	ga_s2am_ard_3-2-1_50JLT_2019-02-19_final.stac-item
2019-02-19 12:05:04	ga_s2am_ard_3-2-1_50JLR_2019-02-19_final.stac-item
2019-03-01 12:04:44	ga_s2am_ard_3-2-1_50JMS_2019-03-01_final.stac-item
2019-03-01 12:04:54	ga_s2am_ard_3-2-1_50JMR_2019-03-01_final.stac-item
2019-03-01 12:05:07	ga_s2am_ard_3-2-1_50JLR_2019-03-01_final.stac-item
2019-11-03 11:55:30	ga_s2am_ard_3-2-1_50JLQ_2019-11-03_final.stac-item
2019-11-26 12:04:36	ga_s2am_ard_3-2-1_50JMT_2019-11-26_final.stac-item
2019-11-26 12:04:49	ga_s2am_ard_3-2-1_50JMS_2019-11-26_final.stac-item
2019-11-29 12:14:08	ga_s2am_ard_3-2-1_49KHQ_2019-11-29_final.stac-item
2019-12-13 11:54:40	ga_s2am_ard_3-2-1_50JLT_2019-12-13_final.stac-item
2019-12-13 11:55:08	ga_s2am_ard_3-2-1_50JLR_2019-12-13_final.stac-item
2019-12-13 11:55:23	ga_s2am_ard_3-2-1_50JLQ_2019-12-13_final.stac-item
2019-12-16 12:04:48	ga_s2am_ard_3-2-1_50JLS_2019-12-16_final.stac-item
2020-08-02 12:04:54	ga_s2am_ard_3-2-1_50JMS_2020-08-02_final.stac-item
2020-08-02 12:05:29	ga_s2am_ard_3-2-1_50JLQ_2020-08-02_final.stac-item
2020-09-18 11:55:17	ga_s2am_ard_3-2-1_50JLR_2020-09-18_final.stac-item
2020-10-01 12:04:41	ga_s2am_ard_3-2-1_50JMT_2020-10-01_final.stac-item
2020-10-01 12:05:14	ga_s2am_ard_3-2-1_49JFM_2020-10-01_final.stac-item
2020-11-13 12:14:25	ga_s2am_ard_3-2-1_49KHP_2020-11-13_final.stac-item
2021-01-29 12:04:36	ga_s2am_ard_3-2-1_50JMT_2021-01-29_final.stac-item
2021-11-22 11:55:35	ga_s2am_ard_3-2-1_50JNP_2021-11-22_final.stac-item
2021-12-12 11:55:34	ga_s2am_ard_3-2-1_50JNP_2021-12-12_final.stac-item
2021-12-29 11:46:55	ga_s2am_ard_3-2-1_50HQH_2021-12-29_final.stac-item
2022-01-01 11:55:14	ga_s2am_ard_3-2-1_50JLR_2022-01-01_final.stac-item
2022-01-04 12:05:00	ga_s2am_ard_3-2-1_50JMR_2022-01-04_final.stac-item
2022-01-14 12:05:24	ga_s2am_ard_3-2-1_50JLQ_2022-01-14_final.stac-item
2022-02-10 11:55:12	ga_s2am_ard_3-2-1_50JLR_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
2022-03-02 11:55:16	ga_s2am_ard_3-2-1_50JLR_2022-03-02_final.stac-item
2022-03-06 11:36:33	ga_s2am_ard_3-2-1_50HQK_2022-03-06_final.stac-item
2022-04-21 11:55:32	ga_s2am_ard_3-2-1_50JLQ_2022-04-21_final.stac-item
2022-05-04 12:05:09	ga_s2am_ard_3-2-1_50JKR_2022-05-04_final.stac-item
2022-05-04 12:05:28	ga_s2am_ard_3-2-1_50JLQ_2022-05-04_final.stac-item
2022-05-24 12:04:40	ga_s2am_ard_3-2-1_49JHN_2022-05-24_final.stac-item
2022-06-30 11:55:26	ga_s2am_ard_3-2-1_50JLR_2022-06-30_final.stac-item
2022-07-20 11:55:39	ga_s2am_ard_3-2-1_50JLQ_2022-07-20_final.stac-item
2022-08-12 12:05:11	ga_s2am_ard_3-2-1_50JMR_2022-08-12_final.stac-item
2022-08-12 12:05:20	ga_s2am_ard_3-2-1_50JLR_2022-08-12_final.stac-item
2022-08-29 11:55:40	ga_s2am_ard_3-2-1_50JLQ_2022-08-29_final.stac-item
2022-10-08 11:55:34	ga_s2am_ard_3-2-1_50JLQ_2022-10-08_final.stac-item
2022-11-20 12:05:09	ga_s2am_ard_3-2-1_50JLR_2022-11-20_final.stac-item
2022-12-17 11:55:27	ga_s2am_ard_3-2-1_50JLQ_2022-12-17_final.stac-item
2023-01-09 12:05:21	ga_s2am_ard_3-2-1_50JLQ_2023-01-09_final.stac-item
2023-01-13 11:46:51	ga_s2am_ard_3-2-1_50HQH_2023-01-13_final.stac-item
2023-02-15 11:55:32	ga_s2am_ard_3-2-1_50JNP_2023-02-15_nrt.stac-item
2023-02-15 11:55:32	ga_s2am_ard_3-2-1_50JNP_2023-02-15_final
2023-02-18 12:04:55	ga_s2am_ard_3-2-1_50JMR_2023-02-18_nrt.stac-item
2023-02-18 12:04:55	ga_s2am_ard_3-2-1_50JMR_2023-02-18_final
2023-02-18 12:05:12	ga_s2am_ard_3-2-1_50JLR_2023-02-18_nrt.stac-item
2023-02-18 12:05:12	ga_s2am_ard_3-2-1_50JLR_2023-02-18_final
2023-02-25 11:55:10	ga_s2am_ard_3-2-1_50JLR_2023-02-25_final
2023-02-25 11:55:25	ga_s2am_ard_3-2-1_50JLQ_2023-02-25_final
2023-03-07 11:55:31	ga_s2am_ard_3-2-1_50JNP_2023-03-07_final
2023-03-10 12:05:18	ga_s2am_ard_3-2-1_50JLQ_2023-03-10_final
2023-04-19 12:05:07	ga_s2am_ard_3-2-1_50JLR_2023-04-19_final
2023-05-03 11:45:40	ga_s2am_ard_3-2-1_50JNP_2023-05-03_final
2023-05-16 11:55:14	ga_s2am_ard_3-2-1_50JLR_2023-05-16_final
2023-05-26 11:55:31	ga_s2am_ard_3-2-1_50JLQ_2023-05-26_final
2023-06-28 12:04:49	ga_s2am_ard_3-2-1_50JMS_2023-06-28_final
2023-06-28 12:05:02	ga_s2am_ard_3-2-1_50JMR_2023-06-28_final
2023-06-28 12:05:12	ga_s2am_ard_3-2-1_50JLR_2023-06-28_final
2023-08-04 11:55:32	ga_s2am_ard_3-2-1_50JLQ_2023-08-04_final
2023-08-31 11:47:19	ga_s2am_ard_3-2-1_50HLH_2023-08-31_final
2023-11-05 12:05:22	ga_s2am_ard_3-2-1_50JLQ_2023-11-05_final
2023-11-25 12:04:53	ga_s2am_ard_3-2-1_50JMR_2023-11-25_final
2023-11-25 12:05:03	ga_s2am_ard_3-2-1_50JLR_2023-11-25_final
2023-12-02 11:55:19	ga_s2am_ard_3-2-1_50JLQ_2023-12-02_final
2023-12-05 12:05:16	ga_s2am_ard_3-2-1_50JLQ_2023-12-05_final
2023-12-08 12:14:03	ga_s2am_ard_3-2-1_49KHQ_2023-12-08_final
2023-12-15 12:04:44	ga_s2am_ard_3-2-1_50JMS_2023-12-15_final
2023-12-25 12:04:55	ga_s2am_ard_3-2-1_50JMR_2023-12-25_final
2024-01-31 11:55:08	ga_s2am_ard_3-2-1_50JLR_2024-01-31_final
2024-01-31 11:55:23	ga_s2am_ard_3-2-1_50JLQ_2024-01-31_final
2024-02-03 12:05:18	ga_s2am_ard_3-2-1_50JLQ_2024-02-03_final
2024-02-20 11:55:25	ga_s2am_ard_3-2-1_50JLQ_2024-02-20_final
2024-03-17 12:14:21	ga_s2am_ard_3-2-1_49KHP_2024-03-17_final
2024-04-03 12:04:37	ga_s2am_ard_3-2-1_50JLT_2024-04-03_final
2024-04-03 12:04:51	ga_s2am_ard_3-2-1_50JLS_2024-04-03_final
2024-04-03 12:05:06	ga_s2am_ard_3-2-1_50JLR_2024-04-03_final
2024-04-04 11:37:24	ga_s2am_ard_3-2-1_50HNG_2024-04-04_final
2024-04-10 11:55:13	ga_s2am_ard_3-2-1_50JLR_2024-04-10_final
2024-04-20 11:55:31	ga_s2am_ard_3-2-1_50JLQ_2024-04-20_final
2024-05-10 11:55:15	ga_s2am_ard_3-2-1_50JLR_2024-05-10_final
2024-05-10 11:55:30	ga_s2am_ard_3-2-1_50JLQ_2024-05-10_final
2024-05-20 11:55:28	ga_s2am_ard_3-2-1_50JLQ_2024-05-20_nrt
2024-05-23 12:05:07	ga_s2am_ard_3-2-1_50JLR_2024-05-23_nrt
2024-05-23 12:05:22	ga_s2am_ard_3-2-1_50JLQ_2024-05-23_nrt

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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