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
2023-12-18 12:14:27	ga_s2am_ard_3-2-1_49KFP_2023-12-18_nrt
2023-12-18 12:14:27	ga_s2am_ard_3-2-1_49JHN_2023-12-18_final
2023-12-18 12:14:37	ga_s2am_ard_3-2-1_49JGN_2023-12-18_final
2023-12-18 12:14:41	ga_s2am_ard_3-2-1_49JFN_2023-12-18_final
2023-12-18 12:15:17	ga_s2am_ard_3-2-1_49JFK_2023-12-18_nrt
2023-12-19 09:59:44	ga_s2am_ard_3-2-1_55LHH_2023-12-19_nrt
2023-12-19 09:59:47	ga_s2am_ard_3-2-1_55LEJ_2023-12-19_nrt
2023-12-19 10:00:18	ga_s2am_ard_3-2-1_55LDG_2023-12-19_nrt
2023-12-19 10:00:45	ga_s2am_ard_3-2-1_55LEE_2023-12-19_nrt
2023-12-19 10:00:48	ga_s2am_ard_3-2-1_55LGD_2023-12-19_nrt
2023-12-19 10:02:01	ga_s2am_ard_3-2-1_55KDV_2023-12-19_final
2023-12-19 10:02:01	ga_s2am_ard_3-2-1_55KFU_2023-12-19_final
2023-12-19 10:02:04	ga_s2am_ard_3-2-1_55KCV_2023-12-19_final
2023-12-19 10:02:11	ga_s2am_ard_3-2-1_55KBV_2023-12-19_final
2023-12-19 10:02:11	ga_s2am_ard_3-2-1_55KEU_2023-12-19_final
2023-12-19 10:02:15	ga_s2am_ard_3-2-1_55KDU_2023-12-19_final
2023-12-19 10:02:18	ga_s2am_ard_3-2-1_55KCU_2023-12-19_final
2023-12-19 10:02:22	ga_s2am_ard_3-2-1_55KBU_2023-12-19_final
2023-12-19 10:02:26	ga_s2am_ard_3-2-1_55KET_2023-12-19_final
2023-12-19 10:02:30	ga_s2am_ard_3-2-1_55KDT_2023-12-19_final
2023-12-19 10:02:33	ga_s2am_ard_3-2-1_55KCT_2023-12-19_final
2023-12-19 10:02:36	ga_s2am_ard_3-2-1_55KBT_2023-12-19_final
2023-12-19 10:02:38	ga_s2am_ard_3-2-1_54KZC_2023-12-19_final
2023-12-19 10:02:41	ga_s2am_ard_3-2-1_55KES_2023-12-19_final
2023-12-19 10:02:44	ga_s2am_ard_3-2-1_55KDS_2023-12-19_final
2023-12-19 10:02:48	ga_s2am_ard_3-2-1_55KCS_2023-12-19_final
2023-12-19 10:02:50	ga_s2am_ard_3-2-1_55KBS_2023-12-19_final
2023-12-19 10:02:51	ga_s2am_ard_3-2-1_54KZB_2023-12-19_final
2023-12-19 10:02:54	ga_s2am_ard_3-2-1_55KER_2023-12-19_final
2023-12-19 10:02:59	ga_s2am_ard_3-2-1_55KDR_2023-12-19_final
2023-12-19 10:03:02	ga_s2am_ard_3-2-1_55KCR_2023-12-19_final
2023-12-19 10:03:05	ga_s2am_ard_3-2-1_55KEQ_2023-12-19_final
2023-12-19 10:03:05	ga_s2am_ard_3-2-1_55KBR_2023-12-19_final
2023-12-19 10:03:05	ga_s2am_ard_3-2-1_54KZA_2023-12-19_final
2023-12-19 10:03:13	ga_s2am_ard_3-2-1_55KDQ_2023-12-19_final
2023-12-19 10:03:17	ga_s2am_ard_3-2-1_55KCQ_2023-12-19_final
2023-12-19 10:03:20	ga_s2am_ard_3-2-1_55KBQ_2023-12-19_final
2023-12-19 10:03:20	ga_s2am_ard_3-2-1_54KZV_2023-12-19_final
2023-12-19 10:03:28	ga_s2am_ard_3-2-1_55KDP_2023-12-19_final
2023-12-19 10:03:31	ga_s2am_ard_3-2-1_55KCP_2023-12-19_final
2023-12-19 10:03:34	ga_s2am_ard_3-2-1_55KBP_2023-12-19_final
2023-12-19 10:03:34	ga_s2am_ard_3-2-1_54KZU_2023-12-19_final
2023-12-19 10:03:39	ga_s2am_ard_3-2-1_54KYU_2023-12-19_final
2023-12-19 10:03:42	ga_s2am_ard_3-2-1_55JDN_2023-12-19_final
2023-12-19 10:03:46	ga_s2am_ard_3-2-1_55JCN_2023-12-19_final
2023-12-19 10:03:49	ga_s2am_ard_3-2-1_54JZT_2023-12-19_final
2023-12-19 10:03:49	ga_s2am_ard_3-2-1_55JBN_2023-12-19_final
2023-12-19 10:03:52	ga_s2am_ard_3-2-1_54JYT_2023-12-19_final
2023-12-19 10:03:57	ga_s2am_ard_3-2-1_55JDM_2023-12-19_final
2023-12-19 10:04:00	ga_s2am_ard_3-2-1_55JCM_2023-12-19_final
2023-12-19 10:04:03	ga_s2am_ard_3-2-1_54JZS_2023-12-19_final
2023-12-19 10:04:04	ga_s2am_ard_3-2-1_55JBM_2023-12-19_final
2023-12-19 11:44:47	ga_s2am_ard_3-2-1_51JUL_2023-12-19_final
2023-12-19 11:44:49	ga_s2am_ard_3-2-1_50JPS_2023-12-19_final
2023-12-19 11:44:56	ga_s2am_ard_3-2-1_51JTL_2023-12-19_final
2023-12-19 11:44:59	ga_s2am_ard_3-2-1_50JQR_2023-12-19_final
2023-12-19 11:45:03	ga_s2am_ard_3-2-1_50JPR_2023-12-19_final
2023-12-19 11:45:07	ga_s2am_ard_3-2-1_50JNR_2023-12-19_final
2023-12-19 11:45:10	ga_s2am_ard_3-2-1_51JTK_2023-12-19_final
2023-12-19 11:45:14	ga_s2am_ard_3-2-1_50JQQ_2023-12-19_final
2023-12-19 11:45:17	ga_s2am_ard_3-2-1_50JPQ_2023-12-19_final
2023-12-19 11:45:21	ga_s2am_ard_3-2-1_50JNQ_2023-12-19_final
2023-12-19 11:45:25	ga_s2am_ard_3-2-1_51JTJ_2023-12-19_final
2023-12-19 11:45:28	ga_s2am_ard_3-2-1_50JQP_2023-12-19_final
2023-12-19 11:45:32	ga_s2am_ard_3-2-1_50JPP_2023-12-19_final
2023-12-19 11:45:35	ga_s2am_ard_3-2-1_50JNP_2023-12-19_final
2023-12-19 11:45:40	ga_s2am_ard_3-2-1_51JTH_2023-12-19_final
2023-12-19 11:45:43	ga_s2am_ard_3-2-1_50JQN_2023-12-19_final
2023-12-19 11:45:46	ga_s2am_ard_3-2-1_50JPN_2023-12-19_final
2023-12-19 11:45:50	ga_s2am_ard_3-2-1_50JNN_2023-12-19_final
2023-12-19 11:45:53	ga_s2am_ard_3-2-1_51JTG_2023-12-19_final
2023-12-19 11:45:57	ga_s2am_ard_3-2-1_50JQM_2023-12-19_final
2023-12-19 11:46:01	ga_s2am_ard_3-2-1_50JPM_2023-12-19_final
2023-12-19 11:46:04	ga_s2am_ard_3-2-1_51JTF_2023-12-19_final
2023-12-19 11:46:04	ga_s2am_ard_3-2-1_50JNM_2023-12-19_final
2023-12-19 11:46:09	ga_s2am_ard_3-2-1_50JMM_2023-12-19_final
2023-12-19 11:46:11	ga_s2am_ard_3-2-1_50JQL_2023-12-19_final
2023-12-19 11:46:15	ga_s2am_ard_3-2-1_50JPL_2023-12-19_final
2023-12-19 11:46:19	ga_s2am_ard_3-2-1_50JNL_2023-12-19_final
2023-12-19 11:46:22	ga_s2am_ard_3-2-1_50JML_2023-12-19_final
2023-12-19 11:46:26	ga_s2am_ard_3-2-1_50HQK_2023-12-19_final
2023-12-19 11:46:30	ga_s2am_ard_3-2-1_50HPK_2023-12-19_final
2023-12-19 11:46:33	ga_s2am_ard_3-2-1_50HNK_2023-12-19_final
2023-12-19 11:46:37	ga_s2am_ard_3-2-1_50HMK_2023-12-19_final
2023-12-19 11:46:39	ga_s2am_ard_3-2-1_50HQJ_2023-12-19_final
2023-12-19 11:46:44	ga_s2am_ard_3-2-1_50HPJ_2023-12-19_final
2023-12-19 11:46:48	ga_s2am_ard_3-2-1_50HNJ_2023-12-19_final
2023-12-19 11:46:49	ga_s2am_ard_3-2-1_50HQH_2023-12-19_final
2023-12-19 11:46:52	ga_s2am_ard_3-2-1_50HMJ_2023-12-19_final
2023-12-19 11:46:58	ga_s2am_ard_3-2-1_50HPH_2023-12-19_final
2023-12-19 11:47:03	ga_s2am_ard_3-2-1_50HNH_2023-12-19_final
2023-12-19 11:47:06	ga_s2am_ard_3-2-1_50HMH_2023-12-19_final
2023-12-19 11:47:10	ga_s2am_ard_3-2-1_50HLH_2023-12-19_final
2023-12-19 11:47:13	ga_s2am_ard_3-2-1_50HPG_2023-12-19_final
2023-12-19 11:47:17	ga_s2am_ard_3-2-1_50HNG_2023-12-19_final
2023-12-19 11:47:20	ga_s2am_ard_3-2-1_50HMG_2023-12-19_final
2023-12-19 11:47:23	ga_s2am_ard_3-2-1_50HLG_2023-12-19_final
2023-12-19 11:47:24	ga_s2am_ard_3-2-1_50HPF_2023-12-19_nrt
2023-12-19 11:47:26	ga_s2am_ard_3-2-1_50HNF_2023-12-19_final
2023-12-19 11:47:28	ga_s2am_ard_3-2-1_50HMF_2023-12-19_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