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
2016-03-02 10:44:00	ga_s2am_ard_3-2-1_53JKF_2016-03-02_final.stac-item
2016-03-02 10:44:00	ga_s2am_ard_3-2-1_53JKG_2016-03-02_final.stac-item
2016-03-02 10:44:00	ga_s2am_ard_3-2-1_53JKH_2016-03-02_final.stac-item
2016-03-02 10:44:00	ga_s2am_ard_3-2-1_52JGL_2016-03-02_final.stac-item
2016-03-02 10:44:00	ga_s2am_ard_3-2-1_52JGM_2016-03-02_final.stac-item
2016-03-02 10:44:00	ga_s2am_ard_3-2-1_53HKE_2016-03-02_final
2016-03-02 10:44:00	ga_s2am_ard_3-2-1_52HGK_2016-03-02_final
2016-03-25 10:52:14	ga_s2am_ard_3-2-1_53JKN_2016-03-25_final.stac-item
2016-03-25 10:52:14	ga_s2am_ard_3-2-1_52KHB_2016-03-25_final
2016-03-25 10:52:14	ga_s2am_ard_3-2-1_53KKQ_2016-03-25_final
2016-03-25 10:52:14	ga_s2am_ard_3-2-1_52KHV_2016-03-25_final
2016-03-28 10:57:16	ga_s2am_ard_3-2-1_52LHK_2016-03-28_final.stac-item
2016-03-28 10:57:16	ga_s2am_ard_3-2-1_52LHL_2016-03-28_final.stac-item
2016-03-28 10:57:16	ga_s2am_ard_3-2-1_52LHM_2016-03-28_final.stac-item
2016-03-28 10:57:16	ga_s2am_ard_3-2-1_52LHN_2016-03-28_final.stac-item
2016-03-28 11:01:10	ga_s2am_ard_3-2-1_52LHH_2016-03-28_final.stac-item
2016-03-28 11:01:10	ga_s2am_ard_3-2-1_52LHJ_2016-03-28_final.stac-item
2016-03-28 11:01:10	ga_s2am_ard_3-2-1_52LHK_2016-03-28_final.stac-item
2018-09-18 10:37:23	ga_s2am_ard_3-2-1_53JKF_2018-09-18_final.stac-item
2018-09-18 10:37:23	ga_s2am_ard_3-2-1_53JKG_2018-09-18_final.stac-item
2018-09-18 10:37:23	ga_s2am_ard_3-2-1_53JKH_2018-09-18_final.stac-item
2018-09-18 10:37:23	ga_s2am_ard_3-2-1_52JGL_2018-09-18_final.stac-item
2018-09-18 10:37:23	ga_s2am_ard_3-2-1_52JGM_2018-09-18_final.stac-item
2018-09-28 10:37:23	ga_s2am_ard_3-2-1_53JKF_2018-09-28_final.stac-item
2018-09-28 10:37:23	ga_s2am_ard_3-2-1_53JKG_2018-09-28_final.stac-item
2018-09-28 10:37:23	ga_s2am_ard_3-2-1_53JKH_2018-09-28_final.stac-item
2018-09-28 10:37:23	ga_s2am_ard_3-2-1_52JGL_2018-09-28_final.stac-item
2018-09-28 10:37:23	ga_s2am_ard_3-2-1_52JGM_2018-09-28_final.stac-item
2018-10-01 10:47:21	ga_s2am_ard_3-2-1_53KKQ_2018-10-01_final.stac-item
2018-10-01 10:47:21	ga_s2am_ard_3-2-1_53KKR_2018-10-01_final.stac-item
2018-10-01 10:47:21	ga_s2am_ard_3-2-1_53KKS_2018-10-01_final.stac-item
2018-10-01 10:47:21	ga_s2am_ard_3-2-1_53KKT_2018-10-01_final.stac-item
2018-10-01 10:47:21	ga_s2am_ard_3-2-1_52KHA_2018-10-01_final.stac-item
2018-10-01 10:47:21	ga_s2am_ard_3-2-1_52KHB_2018-10-01_final.stac-item
2018-10-01 10:47:21	ga_s2am_ard_3-2-1_52KHC_2018-10-01_final.stac-item
2018-10-01 10:47:21	ga_s2am_ard_3-2-1_52KHV_2018-10-01_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53JKM_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53JKN_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_52JHS_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_52JHT_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53KKP_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53KKQ_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53KKR_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53KKS_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53KKT_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_52KHA_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_52KHB_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_52KHC_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_52KHU_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_52KHV_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53JKF_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53JKG_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53JKH_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53JKJ_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53JKK_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53JKL_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_52JGL_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_52JGM_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_52JGN_2018-10-11_final.stac-item
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_52JGP_2018-10-11_final.stac-item
2018-10-21 10:47:22	ga_s2am_ard_3-2-1_53KKQ_2018-10-21_final.stac-item
2018-10-21 10:47:22	ga_s2am_ard_3-2-1_53KKR_2018-10-21_final.stac-item
2018-10-21 10:47:22	ga_s2am_ard_3-2-1_53KKS_2018-10-21_final.stac-item
2018-12-16 11:10:10	ga_s2am_ard_3-2-1_52LHN_2018-12-16_final.stac-item
2018-12-16 11:10:16	ga_s2am_ard_3-2-1_52LHN_2018-12-16_final.stac-item
2018-12-17 10:45:20	ga_s2am_ard_3-2-1_53JKH_2018-12-17_final.stac-item
2018-12-17 10:45:35	ga_s2am_ard_3-2-1_53JKG_2018-12-17_final.stac-item
2018-12-17 10:45:38	ga_s2am_ard_3-2-1_52JGM_2018-12-17_final.stac-item
2018-12-17 10:45:49	ga_s2am_ard_3-2-1_53JKF_2018-12-17_final.stac-item
2018-12-17 10:45:51	ga_s2am_ard_3-2-1_52JGL_2018-12-17_final.stac-item
2018-12-23 11:00:22	ga_s2am_ard_3-2-1_52LHN_2018-12-23_final.stac-item
2018-12-23 11:00:37	ga_s2am_ard_3-2-1_52LHM_2018-12-23_final.stac-item
2018-12-23 11:00:51	ga_s2am_ard_3-2-1_52LHL_2018-12-23_final.stac-item
2018-12-23 11:01:06	ga_s2am_ard_3-2-1_52LHK_2018-12-23_final.stac-item
2018-12-23 11:01:20	ga_s2am_ard_3-2-1_52LHJ_2018-12-23_final.stac-item
2018-12-23 11:01:35	ga_s2am_ard_3-2-1_52LHH_2018-12-23_final.stac-item
2018-12-23 11:01:49	ga_s2am_ard_3-2-1_52KHG_2018-12-23_final.stac-item
2018-12-23 11:02:02	ga_s2am_ard_3-2-1_53KKA_2018-12-23_final.stac-item
2018-12-23 11:02:04	ga_s2am_ard_3-2-1_52KHF_2018-12-23_final.stac-item
2018-12-23 11:02:17	ga_s2am_ard_3-2-1_53KKV_2018-12-23_final.stac-item
2018-12-23 11:02:18	ga_s2am_ard_3-2-1_52KHE_2018-12-23_final.stac-item
2018-12-26 11:10:18	ga_s2am_ard_3-2-1_52LHN_2018-12-26_final.stac-item
2019-01-02 11:00:24	ga_s2am_ard_3-2-1_52LHN_2019-01-02_final.stac-item
2019-01-02 11:00:39	ga_s2am_ard_3-2-1_52LHM_2019-01-02_final.stac-item
2019-01-02 11:00:53	ga_s2am_ard_3-2-1_52LHL_2019-01-02_final.stac-item
2019-01-02 11:01:08	ga_s2am_ard_3-2-1_52LHK_2019-01-02_final.stac-item
2019-01-02 11:01:22	ga_s2am_ard_3-2-1_52LHJ_2019-01-02_final.stac-item
2019-01-02 11:01:37	ga_s2am_ard_3-2-1_52LHH_2019-01-02_final.stac-item
2019-01-02 11:01:51	ga_s2am_ard_3-2-1_52KHG_2019-01-02_final.stac-item
2019-01-02 11:02:04	ga_s2am_ard_3-2-1_53KKA_2019-01-02_final.stac-item
2019-01-02 11:02:06	ga_s2am_ard_3-2-1_52KHF_2019-01-02_final.stac-item
2019-01-02 11:02:19	ga_s2am_ard_3-2-1_53KKV_2019-01-02_final.stac-item
2019-01-02 11:02:20	ga_s2am_ard_3-2-1_52KHE_2019-01-02_final.stac-item
2019-01-02 11:02:34	ga_s2am_ard_3-2-1_53KKU_2019-01-02_final.stac-item
2019-01-02 11:02:34	ga_s2am_ard_3-2-1_52KHD_2019-01-02_final.stac-item
2019-01-02 11:02:48	ga_s2am_ard_3-2-1_53KKT_2019-01-02_final.stac-item
2019-01-02 11:02:49	ga_s2am_ard_3-2-1_52KHC_2019-01-02_final.stac-item
2019-01-02 11:03:02	ga_s2am_ard_3-2-1_53KKS_2019-01-02_final.stac-item
2019-01-02 11:03:03	ga_s2am_ard_3-2-1_52KHB_2019-01-02_final.stac-item
2019-01-02 11:03:13	ga_s2am_ard_3-2-1_53KKR_2019-01-02_final.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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