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
2021-01-03 11:47:24	ga_s2am_ard_3-2-1_50HMG_2021-01-03_final
2021-01-09 12:03:45	ga_s2am_ard_3-2-1_49KHS_2021-01-09_final
2021-01-19 12:03:45	ga_s2am_ard_3-2-1_49KHS_2021-01-19_final
2021-01-23 11:47:25	ga_s2am_ard_3-2-1_50HMG_2021-01-23_final
2021-01-29 12:03:45	ga_s2am_ard_3-2-1_49KHS_2021-01-29_final
2021-02-01 12:13:40	ga_s2am_ard_3-2-1_49KHS_2021-02-01_final
2021-02-18 12:03:44	ga_s2am_ard_3-2-1_49KHS_2021-02-18_final
2021-02-22 11:47:24	ga_s2am_ard_3-2-1_50HMG_2021-02-22_final
2021-03-20 12:03:43	ga_s2am_ard_3-2-1_49KHS_2021-03-20_final
2021-03-27 11:57:18	ga_s2am_ard_3-2-1_50HMG_2021-03-27_final
2021-04-16 11:57:14	ga_s2am_ard_3-2-1_50HMG_2021-04-16_final
2021-04-19 12:03:39	ga_s2am_ard_3-2-1_49KHS_2021-04-19_final
2021-04-23 11:47:20	ga_s2am_ard_3-2-1_50HMG_2021-04-23_final
2021-05-12 12:13:39	ga_s2am_ard_3-2-1_49KHS_2021-05-12_final
2021-05-19 12:03:45	ga_s2am_ard_3-2-1_49KHS_2021-05-19_final
2021-06-11 12:13:40	ga_s2am_ard_3-2-1_49KHS_2021-06-11_final
2021-06-15 11:57:21	ga_s2am_ard_3-2-1_50HMG_2021-06-15_final
2021-06-18 12:03:45	ga_s2am_ard_3-2-1_49KHS_2021-06-18_final
2021-06-21 12:13:40	ga_s2am_ard_3-2-1_49KHS_2021-06-21_final
2021-07-01 12:13:42	ga_s2am_ard_3-2-1_49KHS_2021-07-01_final
2021-07-25 11:57:15	ga_s2am_ard_3-2-1_50HMG_2021-07-25_final
2021-07-31 12:13:43	ga_s2am_ard_3-2-1_49KHS_2021-07-31_final
2021-08-01 11:47:27	ga_s2am_ard_3-2-1_50HMG_2021-08-01_final
2021-08-04 11:57:23	ga_s2am_ard_3-2-1_50HMG_2021-08-04_final
2021-08-08 11:37:20	ga_s2am_ard_3-2-1_50HQG_2021-08-08_final
2021-08-20 12:13:42	ga_s2am_ard_3-2-1_49KHS_2021-08-20_final
2021-08-28 11:37:19	ga_s2am_ard_3-2-1_50HQG_2021-08-28_final
2021-08-30 12:13:41	ga_s2am_ard_3-2-1_49KHS_2021-08-30_final
2021-09-29 12:13:44	ga_s2am_ard_3-2-1_49KHS_2021-09-29_final
2021-10-09 12:13:45	ga_s2am_ard_3-2-1_49KHS_2021-10-09_final
2021-10-29 12:13:45	ga_s2am_ard_3-2-1_49KHS_2021-10-29_final
2021-11-02 11:57:24	ga_s2am_ard_3-2-1_50HMG_2021-11-02_final
2021-11-25 12:03:44	ga_s2am_ard_3-2-1_49KHS_2021-11-25_final
2021-11-26 11:37:15	ga_s2am_ard_3-2-1_50HQG_2021-11-26_final
2021-11-26 11:37:22	ga_s2am_ard_3-2-1_50HQG_2021-11-26_final
2021-12-08 12:13:39	ga_s2am_ard_3-2-1_49KHS_2021-12-08_final
2021-12-19 11:47:25	ga_s2am_ard_3-2-1_50HMG_2021-12-19_final
2023-08-11 11:43:09	ga_s2am_ard_3-2-1_50KRD_2023-08-11_final
2023-08-11 11:43:41	ga_s2am_ard_3-2-1_50KQB_2023-08-11_final
2023-08-11 11:43:56	ga_s2am_ard_3-2-1_50KQA_2023-08-11_final
2023-08-11 11:44:21	ga_s2am_ard_3-2-1_50KRU_2023-08-11_final
2023-08-11 11:44:24	ga_s2am_ard_3-2-1_50KQU_2023-08-11_final
2023-08-11 11:44:39	ga_s2am_ard_3-2-1_50JQT_2023-08-11_final
2023-08-11 11:44:49	ga_s2am_ard_3-2-1_50JRS_2023-08-11_final
2023-08-11 11:44:56	ga_s2am_ard_3-2-1_51JUL_2023-08-11_final
2023-08-11 11:45:04	ga_s2am_ard_3-2-1_51JTL_2023-08-11_final
2023-08-11 11:45:11	ga_s2am_ard_3-2-1_50JPR_2023-08-11_final
2023-08-11 11:45:44	ga_s2am_ard_3-2-1_50JNP_2023-08-11_final
2023-08-11 11:45:48	ga_s2am_ard_3-2-1_51JTH_2023-08-11_final
2023-08-11 11:46:12	ga_s2am_ard_3-2-1_51JTF_2023-08-11_final
2023-08-11 11:46:14	ga_s2am_ard_3-2-1_50JMM_2023-08-11_final
2023-08-11 11:46:15	ga_s2am_ard_3-2-1_50JQL_2023-08-11_final
2023-08-11 11:46:15	ga_s2am_ard_3-2-1_51JTF_2023-08-11_final
2023-08-11 11:46:16	ga_s2am_ard_3-2-1_50JPL_2023-08-11_final
2023-08-11 11:46:20	ga_s2am_ard_3-2-1_50JMM_2023-08-11_final
2023-08-11 11:46:21	ga_s2am_ard_3-2-1_50JQL_2023-08-11_final
2023-08-11 11:46:24	ga_s2am_ard_3-2-1_50JPL_2023-08-11_final
2023-08-11 11:46:27	ga_s2am_ard_3-2-1_50JNL_2023-08-11_final
2023-08-11 11:46:38	ga_s2am_ard_3-2-1_50HPK_2023-08-11_final
2023-08-11 11:46:42	ga_s2am_ard_3-2-1_50HNK_2023-08-11_final
2023-08-11 11:46:56	ga_s2am_ard_3-2-1_50HNJ_2023-08-11_final
2023-08-11 11:47:11	ga_s2am_ard_3-2-1_50HNH_2023-08-11_final
2023-08-11 11:47:29	ga_s2am_ard_3-2-1_50HMG_2023-08-11_final
2023-08-11 11:47:31	ga_s2am_ard_3-2-1_50HLG_2023-08-11_final
2023-08-12 11:14:46	ga_s2am_ard_3-2-1_51JYM_2023-08-12_final
2023-08-12 11:15:00	ga_s2am_ard_3-2-1_51JYL_2023-08-12_final
2023-08-12 11:15:47	ga_s2am_ard_3-2-1_51JXH_2023-08-12_final
2023-08-12 11:17:00	ga_s2am_ard_3-2-1_51HXC_2023-08-12_final
2023-08-12 11:17:10	ga_s2am_ard_3-2-1_51HXB_2023-08-12_final
2023-08-14 11:53:01	ga_s2am_ard_3-2-1_50KRD_2023-08-14_final
2023-08-14 11:53:08	ga_s2am_ard_3-2-1_50KRD_2023-08-14_final
2023-08-14 11:53:37	ga_s2am_ard_3-2-1_50KQB_2023-08-14_final
2023-08-14 11:53:41	ga_s2am_ard_3-2-1_50KPB_2023-08-14_final
2023-08-14 11:53:48	ga_s2am_ard_3-2-1_50KMB_2023-08-14_final
2023-08-14 11:53:52	ga_s2am_ard_3-2-1_50KQA_2023-08-14_final
2023-08-14 11:53:55	ga_s2am_ard_3-2-1_50KPA_2023-08-14_final
2023-08-14 11:54:13	ga_s2am_ard_3-2-1_50KNV_2023-08-14_final
2023-08-14 11:54:17	ga_s2am_ard_3-2-1_50KMV_2023-08-14_final
2023-08-14 11:54:18	ga_s2am_ard_3-2-1_50KQU_2023-08-14_final
2023-08-14 11:54:28	ga_s2am_ard_3-2-1_50JQT_2023-08-14_final
2023-08-14 11:54:42	ga_s2am_ard_3-2-1_50JNT_2023-08-14_final
2023-08-14 11:55:03	ga_s2am_ard_3-2-1_50JLS_2023-08-14_final
2023-08-14 11:55:08	ga_s2am_ard_3-2-1_50JPR_2023-08-14_final
2023-08-14 11:55:40	ga_s2am_ard_3-2-1_50JNP_2023-08-14_final
2023-08-14 11:55:47	ga_s2am_ard_3-2-1_50JLP_2023-08-14_final
2023-08-14 11:55:58	ga_s2am_ard_3-2-1_50JMN_2023-08-14_final
2023-08-14 11:56:05	ga_s2am_ard_3-2-1_50JKN_2023-08-14_final
2023-08-14 11:56:13	ga_s2am_ard_3-2-1_50JMM_2023-08-14_final
2023-08-14 11:56:23	ga_s2am_ard_3-2-1_50JNL_2023-08-14_final
2023-08-14 11:56:31	ga_s2am_ard_3-2-1_50JLL_2023-08-14_final
2023-08-14 11:56:35	ga_s2am_ard_3-2-1_50HNK_2023-08-14_final
2023-08-14 11:56:45	ga_s2am_ard_3-2-1_50HLK_2023-08-14_final
2023-08-14 11:57:25	ga_s2am_ard_3-2-1_50HMG_2023-08-14_final
2023-08-14 11:57:27	ga_s2am_ard_3-2-1_50HLG_2023-08-14_final
2023-08-24 11:50:55	ga_s2am_ard_3-2-1_50LRN_2023-08-24_nrt
2023-08-24 11:51:10	ga_s2am_ard_3-2-1_50LRM_2023-08-24_nrt
2023-08-24 11:52:22	ga_s2am_ard_3-2-1_50KRG_2023-08-24_nrt
2023-08-24 11:56:39	ga_s2am_ard_3-2-1_49JGF_2023-08-24_nrt
2023-08-24 11:57:30	ga_s2am_ard_3-2-1_50HKG_2023-08-24_nrt
2023-08-24 11:57:31	ga_s2am_ard_3-2-1_49HGB_2023-08-24_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

All years