Search ga_s2am_ard_3 on 4th October 2018

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
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56HKF_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56HKG_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56HLJ_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56HLK_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56HMJ_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56HMK_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_55GEN_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_55GEP_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_55GFN_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_55GFP_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_55GFQ_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_55GFR_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56JLL_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56JML_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56JMM_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56JMN_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56JMP_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56JMQ_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56JNL_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56JNM_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56JNN_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56JNS_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_56JNT_2018-10-03_final.stac-item
2018-10-04 09:22:41	ga_s2am_ard_3-2-1_55HGV_2018-10-03_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52JDS_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52JDT_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52JES_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52JET_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52JFS_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52JFT_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52JGS_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52JGT_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53KKA_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53KKB_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53KKR_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KDU_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53KKS_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KDV_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53KKT_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KEA_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53KKU_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KEB_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53KKV_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KEC_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53KLA_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KED_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53KLB_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KEU_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KEV_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KFA_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KFB_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KFC_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KFD_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KFE_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KFF_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KFG_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KFU_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KFV_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KGA_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KGB_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KGC_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KGD_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KGE_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KGF_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KGG_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KGU_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KGV_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KHA_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KHB_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KHC_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KHD_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KHE_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KHF_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KHG_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52KHV_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LFH_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LFJ_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LGH_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LGJ_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53LKC_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LGK_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53LKD_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53LKE_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53LKF_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LGL_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53LKG_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53LKH_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LGM_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LGN_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53LLC_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LHH_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LHJ_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53LLD_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LHK_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LHL_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53LLE_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LHM_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53LLF_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_52LHN_2018-10-04_final.stac-item
2018-10-04 10:57:12	ga_s2am_ard_3-2-1_53LLG_2018-10-04_final.stac-item

Return to ga_s2am_ard_3 on 4th October 2018 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

2018

October

4th