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
2019-08-02 11:43:08	ga_s2am_ard_3-2-1_50KRD_2019-08-02_final.stac-item
2019-08-02 11:43:23	ga_s2am_ard_3-2-1_50KRC_2019-08-02_final.stac-item
2019-08-02 11:43:26	ga_s2am_ard_3-2-1_50KQC_2019-08-02_final.stac-item
2019-08-02 11:43:41	ga_s2am_ard_3-2-1_50KQB_2019-08-02_final.stac-item
2019-08-02 11:43:55	ga_s2am_ard_3-2-1_50KQA_2019-08-02_final.stac-item
2019-08-02 11:44:10	ga_s2am_ard_3-2-1_50KQV_2019-08-02_final.stac-item
2019-08-02 11:44:24	ga_s2am_ard_3-2-1_50KQU_2019-08-02_final.stac-item
2019-08-05 11:53:03	ga_s2am_ard_3-2-1_50KRD_2019-08-05_final.stac-item
2019-08-05 11:53:15	ga_s2am_ard_3-2-1_50KRC_2019-08-05_final.stac-item
2019-08-05 11:53:21	ga_s2am_ard_3-2-1_50KQC_2019-08-05_final.stac-item
2019-08-15 11:53:02	ga_s2am_ard_3-2-1_50KRD_2019-08-15_final.stac-item
2019-08-15 11:53:14	ga_s2am_ard_3-2-1_50KRC_2019-08-15_final.stac-item
2019-08-15 11:53:20	ga_s2am_ard_3-2-1_50KQC_2019-08-15_final.stac-item
2023-03-01 11:35:34	ga_s2am_ard_3-2-1_50JQP_2023-03-01_final
2023-03-01 11:35:48	ga_s2am_ard_3-2-1_50JQN_2023-03-01_final
2023-03-01 11:36:02	ga_s2am_ard_3-2-1_50JQM_2023-03-01_final
2023-03-01 11:36:14	ga_s2am_ard_3-2-1_51JTF_2023-03-01_final
2023-03-01 11:36:17	ga_s2am_ard_3-2-1_50JQL_2023-03-01_final
2023-03-01 11:36:29	ga_s2am_ard_3-2-1_51HTE_2023-03-01_final
2023-03-01 11:36:31	ga_s2am_ard_3-2-1_50HQK_2023-03-01_final
2023-03-01 11:36:44	ga_s2am_ard_3-2-1_51HTD_2023-03-01_final
2023-03-01 11:36:46	ga_s2am_ard_3-2-1_50HQJ_2023-03-01_final
2023-03-01 11:36:58	ga_s2am_ard_3-2-1_51HTC_2023-03-01_final
2023-03-01 11:37:00	ga_s2am_ard_3-2-1_50HQH_2023-03-01_final
2023-03-01 11:37:13	ga_s2am_ard_3-2-1_51HTB_2023-03-01_final
2023-03-01 11:37:15	ga_s2am_ard_3-2-1_50HQG_2023-03-01_final
2023-03-01 11:37:24	ga_s2am_ard_3-2-1_51HTA_2023-03-01_nrt
2023-03-01 11:37:25	ga_s2am_ard_3-2-1_50HQF_2023-03-01_nrt
2023-03-01 11:37:29	ga_s2am_ard_3-2-1_50HQF_2023-03-01_nrt
2023-03-01 11:37:29	ga_s2am_ard_3-2-1_51HTA_2023-03-01_nrt
2023-03-04 11:45:29	ga_s2am_ard_3-2-1_50JQP_2023-03-04_final
2023-03-04 11:45:43	ga_s2am_ard_3-2-1_50JQN_2023-03-04_final
2023-03-04 11:45:57	ga_s2am_ard_3-2-1_50JQM_2023-03-04_final
2023-03-04 11:46:04	ga_s2am_ard_3-2-1_51JTF_2023-03-04_final
2023-03-11 11:35:32	ga_s2am_ard_3-2-1_50JQP_2023-03-11_final
2023-03-11 11:35:46	ga_s2am_ard_3-2-1_50JQN_2023-03-11_final
2023-03-11 11:36:00	ga_s2am_ard_3-2-1_50JQM_2023-03-11_final
2023-03-11 11:36:12	ga_s2am_ard_3-2-1_51JTF_2023-03-11_final
2023-03-11 11:36:15	ga_s2am_ard_3-2-1_50JQL_2023-03-11_final
2023-03-11 11:36:27	ga_s2am_ard_3-2-1_51HTE_2023-03-11_final
2023-03-11 11:36:29	ga_s2am_ard_3-2-1_50HQK_2023-03-11_final
2023-03-11 11:36:42	ga_s2am_ard_3-2-1_51HTD_2023-03-11_final
2023-03-11 11:36:44	ga_s2am_ard_3-2-1_50HQJ_2023-03-11_final
2023-03-11 11:36:56	ga_s2am_ard_3-2-1_51HTC_2023-03-11_final
2023-03-11 11:36:58	ga_s2am_ard_3-2-1_50HQH_2023-03-11_final
2023-03-11 11:37:11	ga_s2am_ard_3-2-1_51HTB_2023-03-11_final
2023-03-11 11:37:13	ga_s2am_ard_3-2-1_50HQG_2023-03-11_final
2023-03-11 11:37:22	ga_s2am_ard_3-2-1_51HTA_2023-03-11_nrt
2023-03-11 11:37:23	ga_s2am_ard_3-2-1_50HQF_2023-03-11_nrt
2023-03-11 11:37:27	ga_s2am_ard_3-2-1_50HQF_2023-03-11_nrt
2023-03-11 11:37:27	ga_s2am_ard_3-2-1_51HTA_2023-03-11_nrt
2023-03-14 11:43:01	ga_s2am_ard_3-2-1_50KRD_2023-03-14_final
2023-03-14 11:43:05	ga_s2am_ard_3-2-1_50KQD_2023-03-14_final
2023-03-14 11:43:18	ga_s2am_ard_3-2-1_50KQC_2023-03-14_final
2023-03-14 11:43:33	ga_s2am_ard_3-2-1_50KQB_2023-03-14_final
2023-03-14 11:43:48	ga_s2am_ard_3-2-1_50KQA_2023-03-14_final
2023-03-14 11:44:02	ga_s2am_ard_3-2-1_50KQV_2023-03-14_final
2023-03-14 11:44:16	ga_s2am_ard_3-2-1_50KQU_2023-03-14_final
2023-03-14 11:44:31	ga_s2am_ard_3-2-1_50JQT_2023-03-14_final
2023-03-14 11:44:45	ga_s2am_ard_3-2-1_50JQS_2023-03-14_final
2023-03-14 11:44:55	ga_s2am_ard_3-2-1_50JQR_2023-03-14_final
2023-03-14 11:45:02	ga_s2am_ard_3-2-1_50JQR_2023-03-14_final
2023-03-14 11:45:14	ga_s2am_ard_3-2-1_50JQQ_2023-03-14_final
2023-03-14 11:45:29	ga_s2am_ard_3-2-1_50JQP_2023-03-14_final
2023-03-14 11:45:43	ga_s2am_ard_3-2-1_50JQN_2023-03-14_final
2023-03-14 11:45:57	ga_s2am_ard_3-2-1_50JQM_2023-03-14_final
2023-03-14 11:46:04	ga_s2am_ard_3-2-1_51JTF_2023-03-14_final
2023-03-14 11:46:12	ga_s2am_ard_3-2-1_50JQL_2023-03-14_final
2023-07-02 11:43:09	ga_s2am_ard_3-2-1_50KRD_2023-07-02_final
2023-07-02 11:43:13	ga_s2am_ard_3-2-1_50KQD_2023-07-02_final
2023-07-02 11:43:23	ga_s2am_ard_3-2-1_50KRC_2023-07-02_final
2023-07-02 11:43:26	ga_s2am_ard_3-2-1_50KQC_2023-07-02_final
2023-07-02 11:43:41	ga_s2am_ard_3-2-1_50KQB_2023-07-02_final
2023-07-02 11:43:56	ga_s2am_ard_3-2-1_50KQA_2023-07-02_final
2023-07-29 11:36:22	ga_s2am_ard_3-2-1_51JTF_2023-07-29_final
2023-07-29 11:36:25	ga_s2am_ard_3-2-1_50JQL_2023-07-29_final
2023-07-29 11:36:37	ga_s2am_ard_3-2-1_51HTE_2023-07-29_final
2023-07-29 11:36:39	ga_s2am_ard_3-2-1_50HQK_2023-07-29_final
2023-07-29 11:36:52	ga_s2am_ard_3-2-1_51HTD_2023-07-29_final
2023-07-29 11:36:54	ga_s2am_ard_3-2-1_50HQJ_2023-07-29_final
2023-07-29 11:37:06	ga_s2am_ard_3-2-1_51HTC_2023-07-29_final
2023-07-29 11:37:08	ga_s2am_ard_3-2-1_50HQH_2023-07-29_final
2023-07-29 11:37:21	ga_s2am_ard_3-2-1_51HTB_2023-07-29_final
2023-07-29 11:37:22	ga_s2am_ard_3-2-1_50HQG_2023-07-29_final
2023-07-29 11:37:29	ga_s2am_ard_3-2-1_51HTB_2023-07-29_final
2023-07-29 11:37:29	ga_s2am_ard_3-2-1_51HTA_2023-07-29_nrt
2023-07-29 11:37:30	ga_s2am_ard_3-2-1_50HQG_2023-07-29_final
2023-07-29 11:37:30	ga_s2am_ard_3-2-1_50HQF_2023-07-29_nrt
2023-07-29 11:37:33	ga_s2am_ard_3-2-1_51HTA_2023-07-29_nrt
2023-07-29 11:37:34	ga_s2am_ard_3-2-1_50HQF_2023-07-29_nrt
2023-08-01 11:43:09	ga_s2am_ard_3-2-1_50KRD_2023-08-01_final
2023-08-01 11:43:14	ga_s2am_ard_3-2-1_50KQD_2023-08-01_final
2023-08-01 11:43:24	ga_s2am_ard_3-2-1_50KRC_2023-08-01_final
2023-08-01 11:43:27	ga_s2am_ard_3-2-1_50KQC_2023-08-01_final
2023-08-01 11:43:42	ga_s2am_ard_3-2-1_50KQB_2023-08-01_final
2023-08-01 11:43:56	ga_s2am_ard_3-2-1_50KQA_2023-08-01_final
2023-08-01 11:44:11	ga_s2am_ard_3-2-1_50KQV_2023-08-01_final
2023-08-01 11:44:25	ga_s2am_ard_3-2-1_50KQU_2023-08-01_final
2023-08-01 11:44:40	ga_s2am_ard_3-2-1_50JQT_2023-08-01_final
2023-08-01 11:44:51	ga_s2am_ard_3-2-1_50JQS_2023-08-01_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

All years