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
2015-12-16 10:50:59	ga_s2am_ard_3-2-1_53LNF_2015-12-16_final.stac-item
2015-12-16 10:50:59	ga_s2am_ard_3-2-1_53LNG_2015-12-16_final.stac-item
2015-12-16 10:50:59	ga_s2am_ard_3-2-1_53LNH_2015-12-16_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53KNA_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53KNB_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53KNP_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53KNQ_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53KNR_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53KNS_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53KNT_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53KNU_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53KNV_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53JNH_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53JNJ_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53JNK_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53JNL_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53JNM_2016-02-01_final.stac-item
2016-02-01 10:39:39	ga_s2am_ard_3-2-1_53JNN_2016-02-01_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53JNH_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53JNJ_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53JNK_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53JNL_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53JNM_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53JNN_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53KNA_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53KNB_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53KNP_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53KNQ_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53KNR_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53KNS_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53KNT_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53KNU_2018-10-08_final.stac-item
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53KNV_2018-10-08_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53JNH_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53JNJ_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53JNK_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53JNL_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53JNM_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53JNN_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53KNA_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53KNB_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53KNP_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53KNQ_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53KNR_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53KNS_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53KNT_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53KNU_2018-10-18_final.stac-item
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53KNV_2018-10-18_final.stac-item
2019-09-17 10:26:53	ga_s2am_ard_3-2-1_53HNB_2019-09-17_final.stac-item
2019-09-27 10:26:55	ga_s2am_ard_3-2-1_53HNB_2019-09-27_final.stac-item
2019-10-07 10:26:56	ga_s2am_ard_3-2-1_53HNB_2019-10-07_final.stac-item
2019-10-10 10:34:42	ga_s2am_ard_3-2-1_53JNL_2019-10-10_final.stac-item
2019-10-10 10:34:57	ga_s2am_ard_3-2-1_53JNK_2019-10-10_final.stac-item
2019-10-10 10:35:11	ga_s2am_ard_3-2-1_53JNJ_2019-10-10_final.stac-item
2019-10-10 10:35:26	ga_s2am_ard_3-2-1_53JNH_2019-10-10_final.stac-item
2019-10-10 10:35:40	ga_s2am_ard_3-2-1_53JNG_2019-10-10_final.stac-item
2019-10-10 10:35:55	ga_s2am_ard_3-2-1_53JNF_2019-10-10_final.stac-item
2019-10-10 10:36:09	ga_s2am_ard_3-2-1_53HNE_2019-10-10_final.stac-item
2019-10-10 10:36:24	ga_s2am_ard_3-2-1_53HND_2019-10-10_final.stac-item
2019-10-10 10:36:38	ga_s2am_ard_3-2-1_53HNC_2019-10-10_final.stac-item
2019-10-10 10:36:53	ga_s2am_ard_3-2-1_53HNB_2019-10-10_final.stac-item
2019-10-13 10:41:58	ga_s2am_ard_3-2-1_53KNB_2019-10-13_final.stac-item
2019-10-13 10:42:13	ga_s2am_ard_3-2-1_53KNA_2019-10-13_final.stac-item
2019-10-13 10:42:28	ga_s2am_ard_3-2-1_53KNV_2019-10-13_final.stac-item
2019-10-13 10:42:42	ga_s2am_ard_3-2-1_53KNU_2019-10-13_final.stac-item
2019-10-13 10:42:56	ga_s2am_ard_3-2-1_53KNT_2019-10-13_final.stac-item
2019-10-13 10:43:11	ga_s2am_ard_3-2-1_53KNS_2019-10-13_final.stac-item
2019-10-13 10:43:26	ga_s2am_ard_3-2-1_53KNR_2019-10-13_final.stac-item
2019-10-13 10:43:40	ga_s2am_ard_3-2-1_53KNQ_2019-10-13_final.stac-item
2019-10-13 10:43:55	ga_s2am_ard_3-2-1_53KNP_2019-10-13_final.stac-item
2019-10-13 10:44:09	ga_s2am_ard_3-2-1_53JNN_2019-10-13_final.stac-item
2019-10-13 10:44:24	ga_s2am_ard_3-2-1_53JNM_2019-10-13_final.stac-item
2019-10-13 10:44:38	ga_s2am_ard_3-2-1_53JNL_2019-10-13_final.stac-item
2019-10-13 10:44:52	ga_s2am_ard_3-2-1_53JNK_2019-10-13_final.stac-item
2019-10-13 10:45:07	ga_s2am_ard_3-2-1_53JNJ_2019-10-13_final.stac-item
2019-10-13 10:45:21	ga_s2am_ard_3-2-1_53JNH_2019-10-13_final.stac-item
2019-10-16 10:50:41	ga_s2am_ard_3-2-1_53LNG_2019-10-16_final.stac-item
2019-10-16 10:50:56	ga_s2am_ard_3-2-1_53LNF_2019-10-16_final.stac-item
2019-10-16 10:51:10	ga_s2am_ard_3-2-1_53LNE_2019-10-16_final.stac-item
2019-10-16 10:51:25	ga_s2am_ard_3-2-1_53LND_2019-10-16_final.stac-item
2019-10-16 10:51:39	ga_s2am_ard_3-2-1_53LNC_2019-10-16_final.stac-item
2019-10-16 10:51:54	ga_s2am_ard_3-2-1_53KNB_2019-10-16_final.stac-item
2019-10-16 10:52:08	ga_s2am_ard_3-2-1_53KNA_2019-10-16_final.stac-item
2019-10-16 10:52:23	ga_s2am_ard_3-2-1_53KNV_2019-10-16_final.stac-item
2019-10-17 10:26:57	ga_s2am_ard_3-2-1_53HNB_2019-10-17_final.stac-item
2019-10-20 10:34:43	ga_s2am_ard_3-2-1_53JNL_2019-10-20_final.stac-item
2019-10-20 10:34:57	ga_s2am_ard_3-2-1_53JNK_2019-10-20_final.stac-item
2019-10-20 10:35:12	ga_s2am_ard_3-2-1_53JNJ_2019-10-20_final.stac-item
2019-10-20 10:35:26	ga_s2am_ard_3-2-1_53JNH_2019-10-20_final.stac-item
2019-10-20 10:35:41	ga_s2am_ard_3-2-1_53JNG_2019-10-20_final.stac-item
2019-10-20 10:35:55	ga_s2am_ard_3-2-1_53JNF_2019-10-20_final.stac-item
2019-10-20 10:36:10	ga_s2am_ard_3-2-1_53HNE_2019-10-20_final.stac-item
2019-10-20 10:36:24	ga_s2am_ard_3-2-1_53HND_2019-10-20_final.stac-item
2019-10-26 10:50:42	ga_s2am_ard_3-2-1_53LNG_2019-10-26_final.stac-item
2019-10-26 10:50:56	ga_s2am_ard_3-2-1_53LNF_2019-10-26_final.stac-item
2019-10-26 10:51:11	ga_s2am_ard_3-2-1_53LNE_2019-10-26_final.stac-item
2019-10-26 10:51:25	ga_s2am_ard_3-2-1_53LND_2019-10-26_final.stac-item
2019-10-26 10:51:39	ga_s2am_ard_3-2-1_53LNC_2019-10-26_final.stac-item
2019-10-26 10:51:54	ga_s2am_ard_3-2-1_53KNB_2019-10-26_final.stac-item
2019-10-26 10:52:09	ga_s2am_ard_3-2-1_53KNA_2019-10-26_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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