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

91 results

Time (local)	Label
2023-01-15 10:40:37	ga_s2am_ard_3-2-1_53LPG_2023-01-15_final.stac-item
2023-01-15 10:41:06	ga_s2am_ard_3-2-1_53LPE_2023-01-15_final.stac-item
2023-01-15 10:41:56	ga_s2am_ard_3-2-1_53KRA_2023-01-15_final.stac-item
2023-01-15 10:42:04	ga_s2am_ard_3-2-1_53KPA_2023-01-15_final.stac-item
2023-01-15 10:42:09	ga_s2am_ard_3-2-1_53KRV_2023-01-15_final.stac-item
2023-01-15 10:42:14	ga_s2am_ard_3-2-1_53KQV_2023-01-15_final.stac-item
2023-01-15 10:42:50	ga_s2am_ard_3-2-1_53KNT_2023-01-15_final.stac-item
2023-01-15 10:43:11	ga_s2am_ard_3-2-1_53KQR_2023-01-15_final.stac-item
2023-01-15 10:44:46	ga_s2am_ard_3-2-1_53JNK_2023-01-15_final.stac-item
2023-01-15 10:45:13	ga_s2am_ard_3-2-1_53JLJ_2023-01-15_final.stac-item
2023-01-15 10:45:40	ga_s2am_ard_3-2-1_53JKG_2023-01-15_final.stac-item
2023-01-15 10:45:57	ga_s2am_ard_3-2-1_52JGL_2023-01-15_final.stac-item
2023-01-15 10:46:21	ga_s2am_ard_3-2-1_53HLD_2023-01-15_final.stac-item
2023-01-26 10:10:18	ga_s2am_ard_3-2-1_55LCG_2023-01-26_nrt.stac-item
2023-01-26 10:10:21	ga_s2am_ard_3-2-1_55LBG_2023-01-26_nrt.stac-item
2023-01-26 10:10:33	ga_s2am_ard_3-2-1_55LCF_2023-01-26_nrt.stac-item
2023-01-26 10:10:36	ga_s2am_ard_3-2-1_55LBF_2023-01-26_nrt.stac-item
2023-01-26 10:17:36	ga_s2am_ard_3-2-1_54HUC_2023-01-26_nrt.stac-item
2023-01-26 11:50:21	ga_s2am_ard_3-2-1_51LVJ_2023-01-26_nrt.stac-item
2023-01-26 11:50:27	ga_s2am_ard_3-2-1_51LUJ_2023-01-26_nrt.stac-item
2023-01-26 11:50:30	ga_s2am_ard_3-2-1_51LTJ_2023-01-26_nrt.stac-item
2023-01-26 11:50:31	ga_s2am_ard_3-2-1_51LVH_2023-01-26_nrt.stac-item
2023-01-26 11:50:45	ga_s2am_ard_3-2-1_51LTH_2023-01-26_nrt.stac-item
2023-01-26 11:50:56	ga_s2am_ard_3-2-1_51LUG_2023-01-26_nrt.stac-item
2023-01-26 11:50:59	ga_s2am_ard_3-2-1_51LTG_2023-01-26_nrt.stac-item
2023-01-26 11:51:14	ga_s2am_ard_3-2-1_51LTF_2023-01-26_nrt.stac-item
2023-01-26 11:51:28	ga_s2am_ard_3-2-1_51LTE_2023-01-26_nrt.stac-item
2023-01-26 11:51:43	ga_s2am_ard_3-2-1_51LTD_2023-01-26_nrt.stac-item
2023-01-26 11:51:57	ga_s2am_ard_3-2-1_51LTC_2023-01-26_nrt.stac-item
2023-01-26 11:52:12	ga_s2am_ard_3-2-1_51KTB_2023-01-26_nrt.stac-item
2023-01-26 11:52:13	ga_s2am_ard_3-2-1_50KRG_2023-01-26_nrt.stac-item
2023-01-26 11:52:20	ga_s2am_ard_3-2-1_50KPG_2023-01-26_nrt.stac-item
2023-01-26 11:52:26	ga_s2am_ard_3-2-1_51KTA_2023-01-26_nrt.stac-item
2023-01-26 11:52:27	ga_s2am_ard_3-2-1_50KRF_2023-01-26_nrt.stac-item
2023-01-26 11:52:38	ga_s2am_ard_3-2-1_50KNF_2023-01-26_nrt.stac-item
2023-01-26 11:52:40	ga_s2am_ard_3-2-1_51KTV_2023-01-26_nrt.stac-item
2023-01-26 11:52:42	ga_s2am_ard_3-2-1_50KRE_2023-01-26_nrt.stac-item
2023-01-26 11:52:49	ga_s2am_ard_3-2-1_50KPE_2023-01-26_nrt.stac-item
2023-01-26 11:52:52	ga_s2am_ard_3-2-1_50KNE_2023-01-26_nrt.stac-item
2023-01-26 11:53:07	ga_s2am_ard_3-2-1_50KND_2023-01-26_nrt.stac-item
2023-01-26 11:56:26	ga_s2am_ard_3-2-1_50JKL_2023-01-26_nrt.stac-item
2023-01-26 11:56:30	ga_s2am_ard_3-2-1_49JGF_2023-01-26_nrt.stac-item
2023-01-26 11:56:42	ga_s2am_ard_3-2-1_49HGE_2023-01-26_nrt.stac-item
2023-01-26 11:57:12	ga_s2am_ard_3-2-1_49HGC_2023-01-26_nrt.stac-item
2023-01-26 11:57:23	ga_s2am_ard_3-2-1_49HGB_2023-01-26_nrt.stac-item
2023-01-26 11:57:25	ga_s2am_ard_3-2-1_49HFB_2023-01-26_nrt.stac-item
2023-01-27 09:39:32	ga_s2am_ard_3-2-1_56LPP_2023-01-27_nrt.stac-item
2023-01-27 09:39:36	ga_s2am_ard_3-2-1_56LNP_2023-01-27_nrt.stac-item
2023-01-27 09:39:39	ga_s2am_ard_3-2-1_56LMP_2023-01-27_nrt.stac-item
2023-01-27 09:39:46	ga_s2am_ard_3-2-1_56LLP_2023-01-27_nrt.stac-item
2023-01-27 09:39:47	ga_s2am_ard_3-2-1_56LPN_2023-01-27_nrt.stac-item
2023-01-27 09:39:51	ga_s2am_ard_3-2-1_56LNN_2023-01-27_nrt.stac-item
2023-01-27 09:39:54	ga_s2am_ard_3-2-1_56LMN_2023-01-27_nrt.stac-item
2023-01-27 09:39:57	ga_s2am_ard_3-2-1_56LLN_2023-01-27_nrt.stac-item
2023-01-27 09:40:01	ga_s2am_ard_3-2-1_56LPM_2023-01-27_nrt.stac-item
2023-01-27 09:40:03	ga_s2am_ard_3-2-1_56LNM_2023-01-27_nrt.stac-item
2023-01-27 09:40:05	ga_s2am_ard_3-2-1_56LMM_2023-01-27_nrt.stac-item
2023-01-27 09:40:06	ga_s2am_ard_3-2-1_56LLM_2023-01-27_nrt.stac-item
2023-01-27 09:40:09	ga_s2am_ard_3-2-1_56LPL_2023-01-27_nrt.stac-item
2023-01-27 09:40:11	ga_s2am_ard_3-2-1_56LNL_2023-01-27_nrt.stac-item
2023-01-27 09:41:11	ga_s2am_ard_3-2-1_56LNH_2023-01-27_nrt.stac-item
2023-01-27 09:41:12	ga_s2am_ard_3-2-1_56LMH_2023-01-27_nrt.stac-item
2023-01-27 09:41:14	ga_s2am_ard_3-2-1_56LLH_2023-01-27_nrt.stac-item
2023-01-27 09:41:18	ga_s2am_ard_3-2-1_56KNG_2023-01-27_nrt.stac-item
2023-01-27 09:41:31	ga_s2am_ard_3-2-1_56KNF_2023-01-27_nrt.stac-item
2023-01-27 09:41:43	ga_s2am_ard_3-2-1_55KHA_2023-01-27_nrt.stac-item
2023-01-27 09:41:44	ga_s2am_ard_3-2-1_56KNE_2023-01-27_nrt.stac-item
2023-01-27 09:41:57	ga_s2am_ard_3-2-1_56KKE_2023-01-27_nrt.stac-item
2023-01-27 09:41:57	ga_s2am_ard_3-2-1_55KHV_2023-01-27_nrt.stac-item
2023-01-27 09:42:11	ga_s2am_ard_3-2-1_56KKD_2023-01-27_nrt.stac-item
2023-01-27 09:42:12	ga_s2am_ard_3-2-1_55KHU_2023-01-27_nrt.stac-item
2023-01-27 09:42:22	ga_s2am_ard_3-2-1_56KLC_2023-01-27_nrt.stac-item
2023-01-27 09:42:26	ga_s2am_ard_3-2-1_56KKC_2023-01-27_nrt.stac-item
2023-01-27 09:42:26	ga_s2am_ard_3-2-1_55KHT_2023-01-27_nrt.stac-item
2023-01-27 09:42:58	ga_s2am_ard_3-2-1_56KMV_2023-01-27_nrt.stac-item
2023-01-27 09:48:35	ga_s2am_ard_3-2-1_55GBP_2023-01-27_nrt.stac-item
2023-01-27 09:48:40	ga_s2am_ard_3-2-1_54GXU_2023-01-27_nrt.stac-item
2023-01-27 09:48:53	ga_s2am_ard_3-2-1_54GXT_2023-01-27_nrt.stac-item
2023-01-27 09:49:01	ga_s2am_ard_3-2-1_54GYS_2023-01-27_nrt.stac-item
2023-01-27 09:49:02	ga_s2am_ard_3-2-1_54GXS_2023-01-27_nrt.stac-item
2023-01-27 11:20:20	ga_s2am_ard_3-2-1_52LCP_2023-01-27_nrt.stac-item
2023-01-27 11:20:23	ga_s2am_ard_3-2-1_52LBP_2023-01-27_nrt.stac-item
2023-01-27 11:20:24	ga_s2am_ard_3-2-1_52LEN_2023-01-27_nrt.stac-item
2023-01-27 11:20:26	ga_s2am_ard_3-2-1_52LDN_2023-01-27_nrt.stac-item
2023-01-27 11:20:27	ga_s2am_ard_3-2-1_52LCN_2023-01-27_nrt.stac-item
2023-01-27 11:20:28	ga_s2am_ard_3-2-1_52LBN_2023-01-27_nrt.stac-item
2023-01-27 11:20:28	ga_s2am_ard_3-2-1_52LBP_2023-01-27_nrt.stac-item
2023-01-27 11:20:42	ga_s2am_ard_3-2-1_52LEM_2023-01-27_nrt.stac-item
2023-01-27 11:20:46	ga_s2am_ard_3-2-1_52LDM_2023-01-27_nrt.stac-item
2023-01-27 11:20:55	ga_s2am_ard_3-2-1_51LZG_2023-01-27_nrt.stac-item
2023-01-27 11:21:00	ga_s2am_ard_3-2-1_52LDL_2023-01-27_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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