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
2023-12-02 10:13:15	ga_s2am_ard_3-2-1_54KYV_2023-12-02_final
2023-12-02 10:13:25	ga_s2am_ard_3-2-1_55KBP_2023-12-02_final
2023-12-02 10:13:25	ga_s2am_ard_3-2-1_54KZU_2023-12-02_final
2023-12-02 10:13:29	ga_s2am_ard_3-2-1_54KYU_2023-12-02_final
2023-12-02 10:13:33	ga_s2am_ard_3-2-1_54KXU_2023-12-02_final
2023-12-02 10:13:47	ga_s2am_ard_3-2-1_54JXT_2023-12-02_final
2023-12-02 10:13:51	ga_s2am_ard_3-2-1_54JWT_2023-12-02_final
2023-12-02 10:14:05	ga_s2am_ard_3-2-1_54JWS_2023-12-02_final
2023-12-02 10:14:09	ga_s2am_ard_3-2-1_54JVS_2023-12-02_final
2023-12-02 10:14:13	ga_s2am_ard_3-2-1_54JYR_2023-12-02_final
2023-12-02 10:14:16	ga_s2am_ard_3-2-1_54JXR_2023-12-02_final
2023-12-02 10:14:20	ga_s2am_ard_3-2-1_54JWR_2023-12-02_final
2023-12-02 10:14:23	ga_s2am_ard_3-2-1_54JVR_2023-12-02_final
2023-12-02 10:14:27	ga_s2am_ard_3-2-1_54JYQ_2023-12-02_final
2023-12-02 10:14:29	ga_s2am_ard_3-2-1_54JXQ_2023-12-02_final
2023-12-02 10:14:31	ga_s2am_ard_3-2-1_54JWQ_2023-12-02_final
2023-12-02 10:14:32	ga_s2am_ard_3-2-1_54JVQ_2023-12-02_final
2023-12-02 10:14:34	ga_s2am_ard_3-2-1_54JYP_2023-12-02_final
2023-12-02 10:14:36	ga_s2am_ard_3-2-1_54JXQ_2023-12-02_final
2023-12-02 10:17:30	ga_s2am_ard_3-2-1_54HUC_2023-12-02_nrt
2023-12-02 10:17:30	ga_s2am_ard_3-2-1_53HPU_2023-12-02_nrt
2023-12-02 10:17:31	ga_s2am_ard_3-2-1_54HTC_2023-12-02_nrt
2023-12-02 10:17:32	ga_s2am_ard_3-2-1_53HQT_2023-12-02_nrt
2023-12-02 11:50:16	ga_s2am_ard_3-2-1_51LVJ_2023-12-02_nrt
2023-12-02 11:50:22	ga_s2am_ard_3-2-1_51LUJ_2023-12-02_nrt
2023-12-02 11:50:25	ga_s2am_ard_3-2-1_51LTJ_2023-12-02_nrt
2023-12-02 11:50:26	ga_s2am_ard_3-2-1_51LVH_2023-12-02_nrt
2023-12-02 11:50:27	ga_s2am_ard_3-2-1_50LRP_2023-12-02_nrt
2023-12-02 11:50:30	ga_s2am_ard_3-2-1_50LQP_2023-12-02_nrt
2023-12-02 11:50:36	ga_s2am_ard_3-2-1_51LUH_2023-12-02_nrt
2023-12-02 11:50:40	ga_s2am_ard_3-2-1_51LTH_2023-12-02_nrt
2023-12-02 11:50:41	ga_s2am_ard_3-2-1_50LRN_2023-12-02_nrt
2023-12-02 11:50:44	ga_s2am_ard_3-2-1_50LQN_2023-12-02_nrt
2023-12-02 11:50:51	ga_s2am_ard_3-2-1_51LUG_2023-12-02_nrt
2023-12-02 11:50:54	ga_s2am_ard_3-2-1_51LTG_2023-12-02_nrt
2023-12-02 11:50:56	ga_s2am_ard_3-2-1_50LRM_2023-12-02_nrt
2023-12-02 11:50:59	ga_s2am_ard_3-2-1_50LQM_2023-12-02_nrt
2023-12-02 11:51:05	ga_s2am_ard_3-2-1_51LUF_2023-12-02_final
2023-12-02 11:51:09	ga_s2am_ard_3-2-1_51LTF_2023-12-02_nrt
2023-12-02 11:51:10	ga_s2am_ard_3-2-1_50LRL_2023-12-02_nrt
2023-12-02 11:51:14	ga_s2am_ard_3-2-1_50LQL_2023-12-02_nrt
2023-12-02 11:51:18	ga_s2am_ard_3-2-1_50LPL_2023-12-02_nrt
2023-12-02 11:51:19	ga_s2am_ard_3-2-1_51LUE_2023-12-02_final
2023-12-02 11:51:23	ga_s2am_ard_3-2-1_51LTE_2023-12-02_nrt
2023-12-02 11:51:25	ga_s2am_ard_3-2-1_50LRK_2023-12-02_nrt
2023-12-02 11:51:28	ga_s2am_ard_3-2-1_50LQK_2023-12-02_nrt
2023-12-02 11:51:31	ga_s2am_ard_3-2-1_51LUD_2023-12-02_final
2023-12-02 11:51:31	ga_s2am_ard_3-2-1_50LPK_2023-12-02_nrt
2023-12-02 11:51:37	ga_s2am_ard_3-2-1_51LTD_2023-12-02_nrt
2023-12-02 11:51:39	ga_s2am_ard_3-2-1_50LRJ_2023-12-02_nrt
2023-12-02 11:51:43	ga_s2am_ard_3-2-1_50LQJ_2023-12-02_nrt
2023-12-02 11:51:45	ga_s2am_ard_3-2-1_50LPJ_2023-12-02_nrt
2023-12-02 11:51:48	ga_s2am_ard_3-2-1_51LTC_2023-12-02_nrt
2023-12-02 11:51:49	ga_s2am_ard_3-2-1_50LRH_2023-12-02_nrt
2023-12-02 11:51:51	ga_s2am_ard_3-2-1_50LQJ_2023-12-02_nrt
2023-12-02 11:51:51	ga_s2am_ard_3-2-1_50LQH_2023-12-02_nrt
2023-12-02 11:51:52	ga_s2am_ard_3-2-1_50LPJ_2023-12-02_nrt
2023-12-02 11:51:52	ga_s2am_ard_3-2-1_50LPH_2023-12-02_nrt
2023-12-02 11:51:55	ga_s2am_ard_3-2-1_51LTC_2023-12-02_nrt
2023-12-02 11:51:55	ga_s2am_ard_3-2-1_50LRH_2023-12-02_nrt
2023-12-02 11:51:57	ga_s2am_ard_3-2-1_50LQH_2023-12-02_nrt
2023-12-02 11:52:00	ga_s2am_ard_3-2-1_50LPH_2023-12-02_nrt
2023-12-02 11:52:07	ga_s2am_ard_3-2-1_51KTB_2023-12-02_nrt
2023-12-02 11:52:08	ga_s2am_ard_3-2-1_50KRG_2023-12-02_nrt
2023-12-02 11:52:12	ga_s2am_ard_3-2-1_50KQG_2023-12-02_final
2023-12-02 11:52:15	ga_s2am_ard_3-2-1_50KPG_2023-12-02_nrt
2023-12-02 11:52:21	ga_s2am_ard_3-2-1_50KNG_2023-12-02_nrt
2023-12-02 11:52:21	ga_s2am_ard_3-2-1_51KTA_2023-12-02_nrt
2023-12-02 11:52:22	ga_s2am_ard_3-2-1_50KRF_2023-12-02_nrt
2023-12-02 11:52:26	ga_s2am_ard_3-2-1_50KQF_2023-12-02_final
2023-12-02 11:52:29	ga_s2am_ard_3-2-1_50KPF_2023-12-02_final
2023-12-02 11:52:33	ga_s2am_ard_3-2-1_50KNF_2023-12-02_nrt
2023-12-02 11:52:35	ga_s2am_ard_3-2-1_51KTV_2023-12-02_nrt
2023-12-02 11:52:37	ga_s2am_ard_3-2-1_50KRE_2023-12-02_nrt
2023-12-02 11:52:40	ga_s2am_ard_3-2-1_50KQE_2023-12-02_final
2023-12-02 11:52:44	ga_s2am_ard_3-2-1_50KPE_2023-12-02_nrt
2023-12-02 11:52:46	ga_s2am_ard_3-2-1_51KTU_2023-12-02_final
2023-12-02 11:56:14	ga_s2am_ard_3-2-1_50JML_2023-12-02_final
2023-12-02 11:56:17	ga_s2am_ard_3-2-1_50JLL_2023-12-02_final
2023-12-02 11:56:21	ga_s2am_ard_3-2-1_50JKL_2023-12-02_final
2023-12-02 11:56:22	ga_s2am_ard_3-2-1_50HNK_2023-12-02_final
2023-12-02 11:56:25	ga_s2am_ard_3-2-1_49JGF_2023-12-02_nrt
2023-12-02 11:56:28	ga_s2am_ard_3-2-1_50HMK_2023-12-02_final
2023-12-02 11:56:32	ga_s2am_ard_3-2-1_50HLK_2023-12-02_final
2023-12-02 11:56:35	ga_s2am_ard_3-2-1_50HKK_2023-12-02_nrt
2023-12-02 11:56:37	ga_s2am_ard_3-2-1_49HGE_2023-12-02_nrt
2023-12-02 11:56:43	ga_s2am_ard_3-2-1_50HMJ_2023-12-02_final
2023-12-02 11:56:47	ga_s2am_ard_3-2-1_50HLJ_2023-12-02_final
2023-12-02 11:56:50	ga_s2am_ard_3-2-1_50HKJ_2023-12-02_final
2023-12-02 11:56:52	ga_s2am_ard_3-2-1_49HGD_2023-12-02_nrt
2023-12-02 11:56:58	ga_s2am_ard_3-2-1_50HMH_2023-12-02_final
2023-12-02 11:57:01	ga_s2am_ard_3-2-1_50HLH_2023-12-02_final
2023-12-02 11:57:04	ga_s2am_ard_3-2-1_50HKH_2023-12-02_final
2023-12-02 11:57:06	ga_s2am_ard_3-2-1_49HGC_2023-12-02_nrt
2023-12-02 11:57:12	ga_s2am_ard_3-2-1_50HMG_2023-12-02_final
2023-12-02 11:57:14	ga_s2am_ard_3-2-1_50HLG_2023-12-02_final
2023-12-02 11:57:16	ga_s2am_ard_3-2-1_50HKG_2023-12-02_final
2023-12-02 11:57:17	ga_s2am_ard_3-2-1_49HGB_2023-12-02_nrt
2023-12-02 11:57:19	ga_s2am_ard_3-2-1_49HFB_2023-12-02_nrt
2023-12-02 11:57:19	ga_s2am_ard_3-2-1_50HMF_2023-12-02_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