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
2022-12-17 10:12:21	ga_s2am_ard_3-2-1_54KZD_2022-12-17_final.stac-item
2022-12-17 10:12:24	ga_s2am_ard_3-2-1_54KYD_2022-12-17_final.stac-item
2022-12-17 10:12:28	ga_s2am_ard_3-2-1_54KXD_2022-12-17_final.stac-item
2022-12-17 10:12:35	ga_s2am_ard_3-2-1_54KZC_2022-12-17_final.stac-item
2022-12-17 10:12:39	ga_s2am_ard_3-2-1_54KYC_2022-12-17_final.stac-item
2022-12-17 10:12:42	ga_s2am_ard_3-2-1_54KXC_2022-12-17_final.stac-item
2022-12-17 10:12:49	ga_s2am_ard_3-2-1_55KBS_2022-12-17_final.stac-item
2022-12-17 10:12:49	ga_s2am_ard_3-2-1_54KZB_2022-12-17_final.stac-item
2022-12-17 10:12:53	ga_s2am_ard_3-2-1_54KYB_2022-12-17_final.stac-item
2022-12-17 10:12:57	ga_s2am_ard_3-2-1_54KXB_2022-12-17_final.stac-item
2022-12-17 10:13:03	ga_s2am_ard_3-2-1_54KWB_2022-12-17_final.stac-item
2022-12-17 10:13:04	ga_s2am_ard_3-2-1_55KBR_2022-12-17_final.stac-item
2022-12-17 10:13:04	ga_s2am_ard_3-2-1_54KZA_2022-12-17_final.stac-item
2022-12-17 10:13:08	ga_s2am_ard_3-2-1_54KYA_2022-12-17_final.stac-item
2022-12-17 10:13:11	ga_s2am_ard_3-2-1_54KXA_2022-12-17_final.stac-item
2022-12-17 10:13:15	ga_s2am_ard_3-2-1_54KWA_2022-12-17_final.stac-item
2022-12-17 10:13:18	ga_s2am_ard_3-2-1_55KBQ_2022-12-17_final.stac-item
2022-12-17 10:13:19	ga_s2am_ard_3-2-1_54KZV_2022-12-17_final.stac-item
2022-12-17 10:13:22	ga_s2am_ard_3-2-1_54KYV_2022-12-17_final.stac-item
2022-12-17 10:13:26	ga_s2am_ard_3-2-1_54KXV_2022-12-17_final.stac-item
2022-12-17 10:13:29	ga_s2am_ard_3-2-1_54KWV_2022-12-17_final.stac-item
2022-12-17 10:13:32	ga_s2am_ard_3-2-1_55KBP_2022-12-17_final.stac-item
2022-12-17 10:13:33	ga_s2am_ard_3-2-1_54KZU_2022-12-17_final.stac-item
2022-12-17 10:13:37	ga_s2am_ard_3-2-1_54KYU_2022-12-17_final.stac-item
2022-12-17 10:13:40	ga_s2am_ard_3-2-1_54KXU_2022-12-17_final.stac-item
2022-12-17 10:13:43	ga_s2am_ard_3-2-1_55JBN_2022-12-17_final.stac-item
2022-12-17 10:13:44	ga_s2am_ard_3-2-1_54KWU_2022-12-17_final.stac-item
2022-12-17 10:13:45	ga_s2am_ard_3-2-1_54JZT_2022-12-17_final.stac-item
2022-12-17 10:13:51	ga_s2am_ard_3-2-1_54JYT_2022-12-17_final.stac-item
2022-12-17 10:13:55	ga_s2am_ard_3-2-1_54JXT_2022-12-17_final.stac-item
2022-12-17 10:13:58	ga_s2am_ard_3-2-1_54JWT_2022-12-17_final.stac-item
2022-12-17 10:14:05	ga_s2am_ard_3-2-1_54JVT_2022-12-17_final.stac-item
2022-12-17 10:14:05	ga_s2am_ard_3-2-1_54JYS_2022-12-17_final.stac-item
2022-12-17 10:14:09	ga_s2am_ard_3-2-1_54JXS_2022-12-17_final.stac-item
2022-12-17 10:14:13	ga_s2am_ard_3-2-1_54JWS_2022-12-17_final.stac-item
2022-12-17 10:14:16	ga_s2am_ard_3-2-1_54JVS_2022-12-17_final.stac-item
2022-12-17 10:14:20	ga_s2am_ard_3-2-1_54JYR_2022-12-17_final.stac-item
2022-12-17 10:14:24	ga_s2am_ard_3-2-1_54JXR_2022-12-17_final.stac-item
2022-12-17 10:14:27	ga_s2am_ard_3-2-1_54JWR_2022-12-17_final.stac-item
2022-12-17 10:14:30	ga_s2am_ard_3-2-1_54JVR_2022-12-17_final.stac-item
2022-12-17 10:14:34	ga_s2am_ard_3-2-1_54JYQ_2022-12-17_final.stac-item
2022-12-17 10:14:38	ga_s2am_ard_3-2-1_54JXQ_2022-12-17_final.stac-item
2022-12-17 10:14:42	ga_s2am_ard_3-2-1_54JWQ_2022-12-17_final.stac-item
2022-12-17 10:14:45	ga_s2am_ard_3-2-1_54JVQ_2022-12-17_final.stac-item
2022-12-17 10:14:47	ga_s2am_ard_3-2-1_54JYP_2022-12-17_final.stac-item
2022-12-17 10:14:52	ga_s2am_ard_3-2-1_54JXP_2022-12-17_final.stac-item
2022-12-17 10:14:54	ga_s2am_ard_3-2-1_54JWP_2022-12-17_final.stac-item
2022-12-17 10:14:56	ga_s2am_ard_3-2-1_54JVP_2022-12-17_final.stac-item
2022-12-17 10:14:56	ga_s2am_ard_3-2-1_54JYP_2022-12-17_final.stac-item
2022-12-17 10:14:57	ga_s2am_ard_3-2-1_54JYN_2022-12-17_final.stac-item
2022-12-17 10:14:57	ga_s2am_ard_3-2-1_54JYN_2022-12-17_final.stac-item
2022-12-17 10:14:59	ga_s2am_ard_3-2-1_54JXN_2022-12-17_final.stac-item
2022-12-17 10:14:59	ga_s2am_ard_3-2-1_54JXP_2022-12-17_final.stac-item
2022-12-17 10:15:01	ga_s2am_ard_3-2-1_54JWP_2022-12-17_final.stac-item
2022-12-17 10:15:03	ga_s2am_ard_3-2-1_54JVP_2022-12-17_final.stac-item
2022-12-17 10:15:07	ga_s2am_ard_3-2-1_54JXN_2022-12-17_final.stac-item
2022-12-17 10:15:07	ga_s2am_ard_3-2-1_54JUP_2022-12-17_final.stac-item
2022-12-17 10:15:11	ga_s2am_ard_3-2-1_54JWN_2022-12-17_final.stac-item
2022-12-17 10:15:14	ga_s2am_ard_3-2-1_54JVN_2022-12-17_final.stac-item
2022-12-17 10:15:18	ga_s2am_ard_3-2-1_54JUN_2022-12-17_final.stac-item
2022-12-17 10:15:22	ga_s2am_ard_3-2-1_54JXM_2022-12-17_final.stac-item
2022-12-17 10:15:25	ga_s2am_ard_3-2-1_54JWM_2022-12-17_final.stac-item
2022-12-17 10:15:29	ga_s2am_ard_3-2-1_54JVM_2022-12-17_final.stac-item
2022-12-17 10:15:32	ga_s2am_ard_3-2-1_54JUM_2022-12-17_final.stac-item
2022-12-17 10:15:36	ga_s2am_ard_3-2-1_54JXL_2022-12-17_final.stac-item
2022-12-17 10:15:40	ga_s2am_ard_3-2-1_54JWL_2022-12-17_final.stac-item
2022-12-17 10:15:43	ga_s2am_ard_3-2-1_54JVL_2022-12-17_final.stac-item
2022-12-17 10:15:47	ga_s2am_ard_3-2-1_54JUL_2022-12-17_final.stac-item
2022-12-17 10:15:49	ga_s2am_ard_3-2-1_54HXK_2022-12-17_final.stac-item
2022-12-17 10:15:54	ga_s2am_ard_3-2-1_54HWK_2022-12-17_final.stac-item
2022-12-17 10:15:58	ga_s2am_ard_3-2-1_54HVK_2022-12-17_final.stac-item
2022-12-17 10:15:59	ga_s2am_ard_3-2-1_54HXJ_2022-12-17_final.stac-item
2022-12-17 10:16:01	ga_s2am_ard_3-2-1_54HUK_2022-12-17_final.stac-item
2022-12-17 10:16:09	ga_s2am_ard_3-2-1_54HWJ_2022-12-17_final.stac-item
2022-12-17 10:16:13	ga_s2am_ard_3-2-1_54HVJ_2022-12-17_final.stac-item
2022-12-17 10:16:16	ga_s2am_ard_3-2-1_54HUJ_2022-12-17_final.stac-item
2022-12-17 10:16:20	ga_s2am_ard_3-2-1_54HTJ_2022-12-17_final.stac-item
2022-12-17 10:16:23	ga_s2am_ard_3-2-1_54HWH_2022-12-17_final.stac-item
2022-12-17 10:16:27	ga_s2am_ard_3-2-1_54HVH_2022-12-17_final.stac-item
2022-12-17 10:16:30	ga_s2am_ard_3-2-1_54HUH_2022-12-17_final.stac-item
2022-12-17 10:16:33	ga_s2am_ard_3-2-1_54HTH_2022-12-17_final.stac-item
2022-12-17 10:16:38	ga_s2am_ard_3-2-1_54HWG_2022-12-17_final.stac-item
2022-12-17 10:16:42	ga_s2am_ard_3-2-1_54HVG_2022-12-17_final.stac-item
2022-12-17 10:16:45	ga_s2am_ard_3-2-1_54HUG_2022-12-17_final.stac-item
2022-12-17 10:16:48	ga_s2am_ard_3-2-1_54HTG_2022-12-17_final.stac-item
2022-12-17 10:16:51	ga_s2am_ard_3-2-1_53HQB_2022-12-17_final.stac-item
2022-12-17 10:16:51	ga_s2am_ard_3-2-1_54HWF_2022-12-17_final.stac-item
2022-12-17 10:16:56	ga_s2am_ard_3-2-1_54HVF_2022-12-17_final.stac-item
2022-12-17 10:16:59	ga_s2am_ard_3-2-1_54HUF_2022-12-17_final.stac-item
2022-12-17 10:17:03	ga_s2am_ard_3-2-1_54HWE_2022-12-17_final.stac-item
2022-12-17 10:17:03	ga_s2am_ard_3-2-1_54HTF_2022-12-17_final.stac-item
2022-12-17 10:17:04	ga_s2am_ard_3-2-1_53HQA_2022-12-17_final.stac-item
2022-12-17 10:17:10	ga_s2am_ard_3-2-1_54HVE_2022-12-17_final.stac-item
2022-12-17 10:17:25	ga_s2am_ard_3-2-1_54HVD_2022-12-17_final.stac-item
2022-12-17 10:17:32	ga_s2am_ard_3-2-1_54HTD_2022-12-17_nrt.stac-item
2022-12-17 10:17:33	ga_s2am_ard_3-2-1_53HQU_2022-12-17_nrt.stac-item
2022-12-17 10:17:38	ga_s2am_ard_3-2-1_54HUC_2022-12-17_nrt.stac-item
2022-12-17 10:17:39	ga_s2am_ard_3-2-1_53HPU_2022-12-17_nrt.stac-item
2022-12-17 10:17:40	ga_s2am_ard_3-2-1_54HTC_2022-12-17_nrt.stac-item
2022-12-17 10:17:41	ga_s2am_ard_3-2-1_53HQT_2022-12-17_nrt.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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