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
2022-10-01 10:23:18	ga_s2am_ard_3-2-1_54KWA_2022-10-01_final.stac-item
2022-10-01 10:24:59	ga_s2am_ard_3-2-1_53JQK_2022-10-01_final.stac-item
2022-10-02 09:58:21	ga_s2am_ard_3-2-1_55GCQ_2022-10-02_final.stac-item
2022-10-02 11:32:06	ga_s2am_ard_3-2-1_51KZB_2022-10-02_final.stac-item
2022-10-03 09:27:08	ga_s2am_ard_3-2-1_55HGV_2022-10-02_final.stac-item
2022-10-03 11:01:59	ga_s2am_ard_3-2-1_53KLB_2022-10-03_final.stac-item
2022-10-03 11:02:40	ga_s2am_ard_3-2-1_52KFE_2022-10-03_final.stac-item
2022-10-03 11:05:56	ga_s2am_ard_3-2-1_52JDM_2022-10-03_final.stac-item
2022-10-03 11:06:06	ga_s2am_ard_3-2-1_52JEL_2022-10-03_final.stac-item
2022-10-04 10:33:45	ga_s2am_ard_3-2-1_53KPQ_2022-10-04_final.stac-item
2022-10-04 10:34:07	ga_s2am_ard_3-2-1_53JRN_2022-10-04_final.stac-item
2022-10-04 10:34:21	ga_s2am_ard_3-2-1_53JRM_2022-10-04_final.stac-item
2022-10-04 10:34:29	ga_s2am_ard_3-2-1_53JPM_2022-10-04_final.stac-item
2022-10-06 11:15:56	ga_s2am_ard_3-2-1_52JCM_2022-10-06_final.stac-item
2022-10-06 11:16:09	ga_s2am_ard_3-2-1_52JCL_2022-10-06_final.stac-item
2022-10-06 11:16:15	ga_s2am_ard_3-2-1_51JYF_2022-10-06_final.stac-item
2022-10-07 10:42:50	ga_s2am_ard_3-2-1_53KMU_2022-10-07_final.stac-item
2022-10-07 10:42:57	ga_s2am_ard_3-2-1_53KNT_2022-10-07_final.stac-item
2022-10-07 10:43:26	ga_s2am_ard_3-2-1_53KPR_2022-10-07_final.stac-item
2022-10-07 10:43:29	ga_s2am_ard_3-2-1_53KNR_2022-10-07_final.stac-item
2022-10-07 10:43:32	ga_s2am_ard_3-2-1_53KQQ_2022-10-07_final.stac-item
2022-10-07 10:43:40	ga_s2am_ard_3-2-1_53KPQ_2022-10-07_final.stac-item
2022-10-07 10:44:35	ga_s2am_ard_3-2-1_53JPL_2022-10-07_final.stac-item
2022-10-07 10:45:25	ga_s2am_ard_3-2-1_53JNH_2022-10-07_final.stac-item
2022-10-08 10:13:22	ga_s2am_ard_3-2-1_54KWA_2022-10-08_final.stac-item
2022-10-08 11:56:20	ga_s2am_ard_3-2-1_50JKM_2022-10-08_final.stac-item
2022-10-10 10:52:03	ga_s2am_ard_3-2-1_53KLB_2022-10-10_final.stac-item
2022-10-10 10:52:08	ga_s2am_ard_3-2-1_53KKB_2022-10-10_final.stac-item
2022-10-10 10:52:35	ga_s2am_ard_3-2-1_53KKV_2022-10-10_final.stac-item
2022-10-10 10:52:51	ga_s2am_ard_3-2-1_53KNT_2022-10-10_final.stac-item
2022-10-11 10:22:51	ga_s2am_ard_3-2-1_54KVC_2022-10-11_final.stac-item
2022-10-11 10:22:54	ga_s2am_ard_3-2-1_54KUC_2022-10-11_final.stac-item
2022-10-11 10:23:35	ga_s2am_ard_3-2-1_54KVV_2022-10-11_final.stac-item
2022-10-11 10:24:10	ga_s2am_ard_3-2-1_53JRN_2022-10-11_final.stac-item
2022-10-11 10:24:22	ga_s2am_ard_3-2-1_53JRM_2022-10-11_final.stac-item
2022-10-11 10:24:29	ga_s2am_ard_3-2-1_53JRM_2022-10-11_final.stac-item
2022-10-11 12:06:15	ga_s2am_ard_3-2-1_50JKM_2022-10-11_final.stac-item
2022-10-13 11:02:20	ga_s2am_ard_3-2-1_52KGF_2022-10-13_final.stac-item
2022-10-13 11:02:25	ga_s2am_ard_3-2-1_52KHE_2022-10-13_final.stac-item
2022-10-13 11:02:26	ga_s2am_ard_3-2-1_52KGE_2022-10-13_final.stac-item
2022-10-13 11:02:31	ga_s2am_ard_3-2-1_53KKV_2022-10-13_final.stac-item
2022-10-13 11:05:57	ga_s2am_ard_3-2-1_52JCM_2022-10-13_final.stac-item
2022-10-14 10:33:21	ga_s2am_ard_3-2-1_54KTA_2022-10-14_final.stac-item
2022-10-14 10:34:04	ga_s2am_ard_3-2-1_53JRN_2022-10-14_final.stac-item
2022-10-14 10:34:18	ga_s2am_ard_3-2-1_53JNN_2022-10-14_final.stac-item
2022-10-14 10:34:26	ga_s2am_ard_3-2-1_53JPM_2022-10-14_final.stac-item
2022-10-15 10:03:13	ga_s2am_ard_3-2-1_54KZA_2022-10-15_final.stac-item
2022-10-16 11:11:48	ga_s2am_ard_3-2-1_52LFH_2022-10-16_final.stac-item
2022-10-16 11:16:12	ga_s2am_ard_3-2-1_51JYF_2022-10-16_final.stac-item
2022-10-17 10:42:54	ga_s2am_ard_3-2-1_53KNT_2022-10-17_final.stac-item
2022-10-17 10:43:00	ga_s2am_ard_3-2-1_53KNT_2022-10-17_final.stac-item
2022-10-17 10:43:28	ga_s2am_ard_3-2-1_53KQQ_2022-10-17_final.stac-item
2022-10-17 10:43:37	ga_s2am_ard_3-2-1_53KPQ_2022-10-17_final.stac-item
2022-10-17 10:43:41	ga_s2am_ard_3-2-1_53KNQ_2022-10-17_final.stac-item
2022-10-17 10:43:51	ga_s2am_ard_3-2-1_53KPP_2022-10-17_final.stac-item
2022-10-17 10:44:10	ga_s2am_ard_3-2-1_53JNN_2022-10-17_final.stac-item
2022-10-18 10:15:22	ga_s2am_ard_3-2-1_54JUN_2022-10-18_final.stac-item
2022-10-19 11:22:29	ga_s2am_ard_3-2-1_51KYA_2022-10-19_final.stac-item
2022-10-19 11:22:40	ga_s2am_ard_3-2-1_51KZV_2022-10-19_final.stac-item
2022-10-20 10:51:59	ga_s2am_ard_3-2-1_53KLB_2022-10-20_final.stac-item
2022-10-20 10:52:28	ga_s2am_ard_3-2-1_53KLV_2022-10-20_final.stac-item
2022-10-21 10:23:01	ga_s2am_ard_3-2-1_54KVB_2022-10-21_final.stac-item
2022-10-21 10:24:05	ga_s2am_ard_3-2-1_53JRN_2022-10-21_final.stac-item
2022-10-21 10:24:20	ga_s2am_ard_3-2-1_53JRM_2022-10-21_final.stac-item
2022-10-21 10:24:27	ga_s2am_ard_3-2-1_53JRM_2022-10-21_final.stac-item
2022-10-21 10:25:16	ga_s2am_ard_3-2-1_54JUN_2022-10-21_final.stac-item
2022-10-21 12:06:11	ga_s2am_ard_3-2-1_50JKM_2022-10-21_final.stac-item
2022-10-22 09:58:07	ga_s2am_ard_3-2-1_54GYA_2022-10-22_final.stac-item
2022-10-23 11:02:17	ga_s2am_ard_3-2-1_52KGF_2022-10-23_final.stac-item
2022-10-23 11:02:37	ga_s2am_ard_3-2-1_52KHE_2022-10-23_final.stac-item
2022-10-23 11:02:43	ga_s2am_ard_3-2-1_52KHD_2022-10-23_final.stac-item
2022-10-23 11:05:51	ga_s2am_ard_3-2-1_52JDM_2022-10-23_final.stac-item
2022-10-23 11:06:01	ga_s2am_ard_3-2-1_52JEL_2022-10-23_final.stac-item
2022-10-23 11:06:08	ga_s2am_ard_3-2-1_52JCL_2022-10-23_final.stac-item
2022-10-24 10:29:42	ga_s2am_ard_3-2-1_54LXQ_2022-10-24_final.stac-item
2022-10-24 10:32:50	ga_s2am_ard_3-2-1_54KUC_2022-10-24_final.stac-item
2022-10-24 10:33:52	ga_s2am_ard_3-2-1_53KQP_2022-10-24_final.stac-item
2022-10-24 10:34:16	ga_s2am_ard_3-2-1_53JRM_2022-10-24_final.stac-item
2022-10-24 10:34:46	ga_s2am_ard_3-2-1_54JTQ_2022-10-24_final.stac-item
2022-10-25 10:03:12	ga_s2am_ard_3-2-1_54KZA_2022-10-25_final.stac-item
2022-10-26 11:11:48	ga_s2am_ard_3-2-1_52LFH_2022-10-26_final.stac-item
2022-10-26 11:12:02	ga_s2am_ard_3-2-1_52KFG_2022-10-26_final.stac-item
2022-10-27 10:43:18	ga_s2am_ard_3-2-1_53KQR_2022-10-27_final.stac-item
2022-10-27 10:43:37	ga_s2am_ard_3-2-1_53KPQ_2022-10-27_final.stac-item
2022-10-27 10:43:51	ga_s2am_ard_3-2-1_53KPP_2022-10-27_final.stac-item
2022-10-27 10:43:55	ga_s2am_ard_3-2-1_53KNP_2022-10-27_final.stac-item
2022-10-27 10:44:09	ga_s2am_ard_3-2-1_53JNN_2022-10-27_final.stac-item
2022-10-27 10:45:08	ga_s2am_ard_3-2-1_53JNJ_2022-10-27_final.stac-item
2022-10-27 10:45:22	ga_s2am_ard_3-2-1_53JNH_2022-10-27_final.stac-item
2022-10-28 11:56:17	ga_s2am_ard_3-2-1_50JKM_2022-10-28_final.stac-item
2022-10-29 09:48:12	ga_s2am_ard_3-2-1_54GYA_2022-10-29_final.stac-item
2022-10-29 11:22:11	ga_s2am_ard_3-2-1_52KBG_2022-10-29_final.stac-item
2022-10-30 10:52:06	ga_s2am_ard_3-2-1_53KKB_2022-10-30_final.stac-item
2022-10-30 10:52:42	ga_s2am_ard_3-2-1_53KMU_2022-10-30_final.stac-item
2022-10-31 10:22:49	ga_s2am_ard_3-2-1_54KVC_2022-10-31_final.stac-item
2022-10-31 10:23:04	ga_s2am_ard_3-2-1_54KVB_2022-10-31_final.stac-item
2022-10-31 10:23:15	ga_s2am_ard_3-2-1_54KWA_2022-10-31_final.stac-item
2022-10-31 10:23:33	ga_s2am_ard_3-2-1_54KVV_2022-10-31_final.stac-item
2022-10-31 10:23:44	ga_s2am_ard_3-2-1_53KRQ_2022-10-31_final.stac-item
2022-10-31 10:24:22	ga_s2am_ard_3-2-1_53JRM_2022-10-31_final.stac-item

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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