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
2023-04-04 09:32:26	ga_s2am_ard_3-2-1_56KPC_2023-04-04_final
2023-04-04 09:32:33	ga_s2am_ard_3-2-1_56KPB_2023-04-04_final
2023-04-04 09:32:46	ga_s2am_ard_3-2-1_56KLB_2023-04-04_final
2023-04-04 09:33:12	ga_s2am_ard_3-2-1_56KLV_2023-04-04_final
2023-04-04 09:33:35	ga_s2am_ard_3-2-1_56JNT_2023-04-04_final
2023-04-04 09:33:42	ga_s2am_ard_3-2-1_56JLT_2023-04-04_final
2023-04-04 09:33:56	ga_s2am_ard_3-2-1_56JLS_2023-04-04_final
2023-04-04 09:34:00	ga_s2am_ard_3-2-1_55JHM_2023-04-04_final
2023-04-04 09:34:08	ga_s2am_ard_3-2-1_56JMR_2023-04-04_final
2023-04-04 09:34:14	ga_s2am_ard_3-2-1_56JKR_2023-04-04_final
2023-04-04 09:34:22	ga_s2am_ard_3-2-1_56JMQ_2023-04-04_final
2023-04-04 09:34:32	ga_s2am_ard_3-2-1_56JNP_2023-04-04_final
2023-04-04 09:34:58	ga_s2am_ard_3-2-1_56JKN_2023-04-04_final
2023-04-04 09:35:09	ga_s2am_ard_3-2-1_56JLM_2023-04-04_final
2023-04-04 09:35:24	ga_s2am_ard_3-2-1_56JLL_2023-04-04_final
2023-04-04 09:35:27	ga_s2am_ard_3-2-1_56JKL_2023-04-04_final
2023-04-04 09:35:29	ga_s2am_ard_3-2-1_55JGF_2023-04-04_final
2023-04-04 09:35:53	ga_s2am_ard_3-2-1_56HLJ_2023-04-04_final
2023-04-04 09:36:07	ga_s2am_ard_3-2-1_56HLH_2023-04-04_final
2023-04-04 09:36:11	ga_s2am_ard_3-2-1_56HKH_2023-04-04_final
2023-04-04 09:36:13	ga_s2am_ard_3-2-1_55HGC_2023-04-04_final
2023-04-04 09:36:25	ga_s2am_ard_3-2-1_56HKG_2023-04-04_final
2023-04-04 09:36:27	ga_s2am_ard_3-2-1_55HGB_2023-04-04_final
2023-04-04 09:36:31	ga_s2am_ard_3-2-1_55HFB_2023-04-04_final
2023-04-04 09:36:50	ga_s2am_ard_3-2-1_55HEA_2023-04-04_final
2023-04-04 09:37:14	ga_s2am_ard_3-2-1_55HFU_2023-04-04_final
2023-04-04 09:37:18	ga_s2am_ard_3-2-1_55HEU_2023-04-04_final
2023-04-04 09:37:43	ga_s2am_ard_3-2-1_55HFS_2023-04-04_final
2023-04-04 09:38:01	ga_s2am_ard_3-2-1_55GER_2023-04-04_final
2023-04-04 09:38:05	ga_s2am_ard_3-2-1_55GDR_2023-04-04_final
2023-04-04 09:38:16	ga_s2am_ard_3-2-1_55GEQ_2023-04-04_final
2023-04-04 09:38:19	ga_s2am_ard_3-2-1_55GDQ_2023-04-04_final
2023-04-04 09:38:26	ga_s2am_ard_3-2-1_55GFP_2023-04-04_final
2023-04-04 09:38:48	ga_s2am_ard_3-2-1_55GDN_2023-04-04_final
2023-04-04 09:39:03	ga_s2am_ard_3-2-1_55GCM_2023-04-04_final
2023-04-04 11:10:03	ga_s2am_ard_3-2-1_53LKJ_2023-04-04_final
2023-04-04 11:10:28	ga_s2am_ard_3-2-1_52LGN_2023-04-04_final
2023-04-04 11:10:32	ga_s2am_ard_3-2-1_52LFN_2023-04-04_final
2023-04-04 11:10:43	ga_s2am_ard_3-2-1_52LGM_2023-04-04_final
2023-04-04 11:10:46	ga_s2am_ard_3-2-1_52LFM_2023-04-04_final
2023-04-04 11:10:57	ga_s2am_ard_3-2-1_52LEL_2023-04-04_final
2023-04-04 11:11:04	ga_s2am_ard_3-2-1_52LEL_2023-04-04_final
2023-04-04 11:11:19	ga_s2am_ard_3-2-1_52LEK_2023-04-04_final
2023-04-04 11:11:33	ga_s2am_ard_3-2-1_52LEJ_2023-04-04_final
2023-04-04 11:11:44	ga_s2am_ard_3-2-1_52LFH_2023-04-04_final
2023-04-04 11:11:51	ga_s2am_ard_3-2-1_52LDH_2023-04-04_final
2023-04-04 11:11:58	ga_s2am_ard_3-2-1_52KFG_2023-04-04_final
2023-04-04 11:12:13	ga_s2am_ard_3-2-1_52KFF_2023-04-04_final
2023-04-04 11:12:20	ga_s2am_ard_3-2-1_52KDF_2023-04-04_final
2023-04-04 11:12:46	ga_s2am_ard_3-2-1_52KED_2023-04-04_final
2023-04-04 11:12:49	ga_s2am_ard_3-2-1_52KDD_2023-04-04_final
2023-04-04 11:12:52	ga_s2am_ard_3-2-1_52KCD_2023-04-04_final
2023-04-04 11:13:00	ga_s2am_ard_3-2-1_52KEC_2023-04-04_final
2023-04-04 11:13:04	ga_s2am_ard_3-2-1_52KDC_2023-04-04_final
2023-04-04 11:13:21	ga_s2am_ard_3-2-1_52KCB_2023-04-04_final
2023-04-04 11:13:29	ga_s2am_ard_3-2-1_52KEA_2023-04-04_final
2023-04-04 11:13:39	ga_s2am_ard_3-2-1_52KBA_2023-04-04_final
2023-04-04 11:13:44	ga_s2am_ard_3-2-1_52KEV_2023-04-04_final
2023-04-04 11:13:47	ga_s2am_ard_3-2-1_52KDV_2023-04-04_final
2023-04-04 11:13:54	ga_s2am_ard_3-2-1_51KZQ_2023-04-04_final
2023-04-04 11:13:54	ga_s2am_ard_3-2-1_52KBV_2023-04-04_final
2023-04-04 11:13:57	ga_s2am_ard_3-2-1_52KEU_2023-04-04_final
2023-04-04 11:14:08	ga_s2am_ard_3-2-1_51KZP_2023-04-04_final
2023-04-04 11:14:20	ga_s2am_ard_3-2-1_52JCT_2023-04-04_final
2023-04-04 11:14:23	ga_s2am_ard_3-2-1_51JZN_2023-04-04_final
2023-04-04 11:14:28	ga_s2am_ard_3-2-1_51JYN_2023-04-04_final
2023-04-04 11:14:34	ga_s2am_ard_3-2-1_52JCS_2023-04-04_final
2023-04-04 11:14:37	ga_s2am_ard_3-2-1_51JZM_2023-04-04_final
2023-04-04 11:14:45	ga_s2am_ard_3-2-1_52JDR_2023-04-04_final
2023-04-04 11:15:07	ga_s2am_ard_3-2-1_52JBQ_2023-04-04_final
2023-04-04 11:15:10	ga_s2am_ard_3-2-1_51JYK_2023-04-04_final
2023-04-04 11:15:18	ga_s2am_ard_3-2-1_52JCP_2023-04-04_final
2023-04-04 11:15:21	ga_s2am_ard_3-2-1_52JBP_2023-04-04_final
2023-04-04 11:15:24	ga_s2am_ard_3-2-1_51JYJ_2023-04-04_final
2023-04-04 11:15:29	ga_s2am_ard_3-2-1_51JXJ_2023-04-04_final
2023-04-04 11:15:32	ga_s2am_ard_3-2-1_52JCN_2023-04-04_final
2023-04-04 11:15:36	ga_s2am_ard_3-2-1_52JBN_2023-04-04_final
2023-04-04 11:15:38	ga_s2am_ard_3-2-1_51JYH_2023-04-04_final
2023-04-04 11:15:42	ga_s2am_ard_3-2-1_51JXH_2023-04-04_final
2023-04-04 11:15:46	ga_s2am_ard_3-2-1_52JCM_2023-04-04_final
2023-04-04 11:15:48	ga_s2am_ard_3-2-1_52JBM_2023-04-04_final
2023-04-04 11:15:49	ga_s2am_ard_3-2-1_51JYG_2023-04-04_final
2023-04-04 11:15:56	ga_s2am_ard_3-2-1_51JYG_2023-04-04_final
2023-04-04 11:16:02	ga_s2am_ard_3-2-1_52JCL_2023-04-04_final
2023-04-04 11:16:19	ga_s2am_ard_3-2-1_52HBK_2023-04-04_final
2023-04-04 11:16:40	ga_s2am_ard_3-2-1_51HXD_2023-04-04_final
2023-04-04 11:16:43	ga_s2am_ard_3-2-1_51HWD_2023-04-04_final
2023-04-04 11:16:58	ga_s2am_ard_3-2-1_51HWC_2023-04-04_final
2023-04-15 10:40:18	ga_s2am_ard_3-2-1_53LRH_2023-04-15_nrt
2023-04-16 10:09:45	ga_s2am_ard_3-2-1_55LEJ_2023-04-16_nrt
2023-04-16 10:09:55	ga_s2am_ard_3-2-1_55LBJ_2023-04-16_nrt
2023-04-16 10:09:58	ga_s2am_ard_3-2-1_55LEH_2023-04-16_nrt
2023-04-16 10:10:09	ga_s2am_ard_3-2-1_55LBH_2023-04-16_nrt
2023-04-16 10:10:24	ga_s2am_ard_3-2-1_55LBG_2023-04-16_nrt
2023-04-16 10:10:38	ga_s2am_ard_3-2-1_55LBF_2023-04-16_nrt
2023-04-16 10:17:32	ga_s2am_ard_3-2-1_54HTD_2023-04-16_nrt
2023-04-16 10:17:38	ga_s2am_ard_3-2-1_54HUC_2023-04-16_nrt
2023-04-16 10:17:38	ga_s2am_ard_3-2-1_53HPU_2023-04-16_nrt
2023-04-16 10:17:39	ga_s2am_ard_3-2-1_54HTC_2023-04-16_nrt
2023-04-16 11:52:59	ga_s2am_ard_3-2-1_50KNE_2023-04-16_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