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
2018-01-31 10:43:03	ga_s2am_ard_3-2-1_53KNR_2018-01-31_final.stac-item
2018-01-31 10:43:03	ga_s2am_ard_3-2-1_53KNS_2018-01-31_final.stac-item
2018-01-31 10:43:07	ga_s2am_ard_3-2-1_53JNM_2018-01-31_final.stac-item
2018-01-31 10:43:07	ga_s2am_ard_3-2-1_53JNN_2018-01-31_final.stac-item
2018-01-31 10:43:07	ga_s2am_ard_3-2-1_53KNP_2018-01-31_final.stac-item
2018-01-31 10:43:07	ga_s2am_ard_3-2-1_53KNQ_2018-01-31_final.stac-item
2018-01-31 10:43:07	ga_s2am_ard_3-2-1_53KNR_2018-01-31_final.stac-item
2018-02-03 10:47:21	ga_s2am_ard_3-2-1_53KNB_2018-02-03_final.stac-item
2018-02-03 10:47:21	ga_s2am_ard_3-2-1_53LNC_2018-02-03_final.stac-item
2018-02-03 10:47:21	ga_s2am_ard_3-2-1_53LND_2018-02-03_final.stac-item
2018-02-03 10:47:21	ga_s2am_ard_3-2-1_53LNE_2018-02-03_final.stac-item
2018-02-03 10:47:21	ga_s2am_ard_3-2-1_53LNF_2018-02-03_final.stac-item
2018-02-03 10:47:21	ga_s2am_ard_3-2-1_53LNG_2018-02-03_final.stac-item
2018-02-03 10:47:21	ga_s2am_ard_3-2-1_53LPG_2018-02-03_final.stac-item
2018-02-17 10:27:04	ga_s2am_ard_3-2-1_53JNM_2018-02-17_final.stac-item
2018-02-17 10:27:04	ga_s2am_ard_3-2-1_53JNN_2018-02-17_final.stac-item
2018-02-17 10:27:04	ga_s2am_ard_3-2-1_53HNB_2018-02-17_final.stac-item
2018-02-17 10:27:04	ga_s2am_ard_3-2-1_53HNC_2018-02-17_final.stac-item
2018-02-17 10:27:04	ga_s2am_ard_3-2-1_53HND_2018-02-17_final.stac-item
2018-02-17 10:27:04	ga_s2am_ard_3-2-1_53HNE_2018-02-17_final.stac-item
2018-02-17 10:27:04	ga_s2am_ard_3-2-1_53JNF_2018-02-17_final.stac-item
2018-02-17 10:27:04	ga_s2am_ard_3-2-1_53JNG_2018-02-17_final.stac-item
2018-02-17 10:27:04	ga_s2am_ard_3-2-1_53JNH_2018-02-17_final.stac-item
2018-02-17 10:27:04	ga_s2am_ard_3-2-1_53JNJ_2018-02-17_final.stac-item
2018-02-17 10:27:04	ga_s2am_ard_3-2-1_53JNK_2018-02-17_final.stac-item
2018-02-17 10:27:04	ga_s2am_ard_3-2-1_53JNL_2018-02-17_final.stac-item
2018-12-21 10:26:32	ga_s2am_ard_3-2-1_53HNC_2018-12-21_final.stac-item
2018-12-21 10:26:45	ga_s2am_ard_3-2-1_53HNB_2018-12-21_final.stac-item
2019-06-08 10:50:37	ga_s2am_ard_3-2-1_53LPG_2019-06-08_final.stac-item
2019-06-08 10:50:41	ga_s2am_ard_3-2-1_53LNG_2019-06-08_final.stac-item
2019-06-08 10:50:55	ga_s2am_ard_3-2-1_53LNF_2019-06-08_final.stac-item
2019-06-08 10:51:10	ga_s2am_ard_3-2-1_53LNE_2019-06-08_final.stac-item
2019-06-08 10:51:24	ga_s2am_ard_3-2-1_53LND_2019-06-08_final.stac-item
2019-06-08 10:51:31	ga_s2am_ard_3-2-1_53LND_2019-06-08_final.stac-item
2019-06-08 10:51:31	ga_s2am_ard_3-2-1_53LNC_2019-06-08_final.stac-item
2019-06-08 10:51:38	ga_s2am_ard_3-2-1_53LNC_2019-06-08_final.stac-item
2019-06-08 10:51:53	ga_s2am_ard_3-2-1_53KNB_2019-06-08_final.stac-item
2019-06-15 10:41:58	ga_s2am_ard_3-2-1_53KNB_2019-06-15_final.stac-item
2019-06-15 10:42:13	ga_s2am_ard_3-2-1_53KNA_2019-06-15_final.stac-item
2019-06-15 10:43:26	ga_s2am_ard_3-2-1_53KNR_2019-06-15_final.stac-item
2019-06-15 10:43:41	ga_s2am_ard_3-2-1_53KNQ_2019-06-15_final.stac-item
2019-06-15 10:43:55	ga_s2am_ard_3-2-1_53KNP_2019-06-15_final.stac-item
2023-10-22 10:42:39	ga_s2am_ard_3-2-1_53KNU_2023-10-22_final
2023-10-22 10:42:53	ga_s2am_ard_3-2-1_53KNT_2023-10-22_final
2023-10-22 10:42:59	ga_s2am_ard_3-2-1_53KNS_2023-10-22_final
2023-10-22 10:43:00	ga_s2am_ard_3-2-1_53KNT_2023-10-22_final
2023-10-22 10:43:08	ga_s2am_ard_3-2-1_53KNS_2023-10-22_final
2023-10-22 10:43:23	ga_s2am_ard_3-2-1_53KNR_2023-10-22_final
2023-10-22 10:43:37	ga_s2am_ard_3-2-1_53KNQ_2023-10-22_final
2023-10-22 10:43:52	ga_s2am_ard_3-2-1_53KNP_2023-10-22_final
2023-10-22 10:44:06	ga_s2am_ard_3-2-1_53JNN_2023-10-22_final
2023-10-22 10:44:21	ga_s2am_ard_3-2-1_53JNM_2023-10-22_final
2023-10-22 10:44:35	ga_s2am_ard_3-2-1_53JNL_2023-10-22_final
2023-10-22 10:44:50	ga_s2am_ard_3-2-1_53JNK_2023-10-22_final
2023-10-25 10:50:06	ga_s2am_ard_3-2-1_53LPJ_2023-10-25_final
2023-10-25 10:50:09	ga_s2am_ard_3-2-1_53LNJ_2023-10-25_nrt
2023-10-25 10:50:20	ga_s2am_ard_3-2-1_53LPH_2023-10-25_final
2023-10-25 10:50:24	ga_s2am_ard_3-2-1_53LNH_2023-10-25_final
2023-10-25 10:50:35	ga_s2am_ard_3-2-1_53LPG_2023-10-25_final
2023-10-25 10:50:38	ga_s2am_ard_3-2-1_53LNG_2023-10-25_final
2023-10-25 10:50:53	ga_s2am_ard_3-2-1_53LNF_2023-10-25_final
2023-10-25 10:51:07	ga_s2am_ard_3-2-1_53LNE_2023-10-25_final
2023-10-25 10:51:22	ga_s2am_ard_3-2-1_53LND_2023-10-25_final
2023-10-25 10:51:36	ga_s2am_ard_3-2-1_53LNC_2023-10-25_final
2023-10-25 10:51:51	ga_s2am_ard_3-2-1_53KNB_2023-10-25_final
2023-10-29 10:34:13	ga_s2am_ard_3-2-1_53JNN_2023-10-29_final
2023-10-29 10:34:25	ga_s2am_ard_3-2-1_53JNM_2023-10-29_final
2023-10-29 10:34:31	ga_s2am_ard_3-2-1_53JNM_2023-10-29_final
2023-10-29 10:34:32	ga_s2am_ard_3-2-1_53JNL_2023-10-29_final
2023-10-29 10:34:39	ga_s2am_ard_3-2-1_53JNL_2023-10-29_final
2023-10-29 10:34:54	ga_s2am_ard_3-2-1_53JNK_2023-10-29_final
2023-10-29 10:35:08	ga_s2am_ard_3-2-1_53JNJ_2023-10-29_final
2023-10-29 10:35:23	ga_s2am_ard_3-2-1_53JNH_2023-10-29_final
2023-10-29 10:35:37	ga_s2am_ard_3-2-1_53JNG_2023-10-29_final
2023-10-29 10:35:52	ga_s2am_ard_3-2-1_53JNF_2023-10-29_final
2023-11-08 10:34:13	ga_s2am_ard_3-2-1_53JNN_2023-11-08_final
2023-11-08 10:34:25	ga_s2am_ard_3-2-1_53JNM_2023-11-08_final
2023-11-08 10:34:39	ga_s2am_ard_3-2-1_53JNL_2023-11-08_final
2023-11-08 10:34:54	ga_s2am_ard_3-2-1_53JNK_2023-11-08_final
2023-11-08 10:35:08	ga_s2am_ard_3-2-1_53JNJ_2023-11-08_final
2023-11-08 10:35:23	ga_s2am_ard_3-2-1_53JNH_2023-11-08_final
2023-11-08 10:35:37	ga_s2am_ard_3-2-1_53JNG_2023-11-08_final
2023-11-08 10:35:52	ga_s2am_ard_3-2-1_53JNF_2023-11-08_final
2023-11-08 10:36:06	ga_s2am_ard_3-2-1_53HNE_2023-11-08_final
2023-11-08 10:36:21	ga_s2am_ard_3-2-1_53HND_2023-11-08_final
2023-11-11 10:41:29	ga_s2am_ard_3-2-1_53LND_2023-11-11_final
2023-11-11 10:41:41	ga_s2am_ard_3-2-1_53LNC_2023-11-11_final
2023-11-11 10:41:53	ga_s2am_ard_3-2-1_53KNB_2023-11-11_final
2023-11-11 10:41:59	ga_s2am_ard_3-2-1_53KNB_2023-11-11_final
2023-11-11 10:42:09	ga_s2am_ard_3-2-1_53KNA_2023-11-11_final
2023-11-11 10:42:24	ga_s2am_ard_3-2-1_53KNV_2023-11-11_final
2023-11-11 10:42:38	ga_s2am_ard_3-2-1_53KNU_2023-11-11_final
2023-11-11 10:42:53	ga_s2am_ard_3-2-1_53KNT_2023-11-11_final
2023-11-11 10:43:08	ga_s2am_ard_3-2-1_53KNS_2023-11-11_final
2023-11-11 10:43:22	ga_s2am_ard_3-2-1_53KNR_2023-11-11_final
2023-11-11 10:43:37	ga_s2am_ard_3-2-1_53KNQ_2023-11-11_final
2023-11-11 10:43:51	ga_s2am_ard_3-2-1_53KNP_2023-11-11_final
2023-11-11 10:43:59	ga_s2am_ard_3-2-1_53JNN_2023-11-11_final
2023-11-11 10:44:06	ga_s2am_ard_3-2-1_53JNN_2023-11-11_final
2023-11-11 10:44:20	ga_s2am_ard_3-2-1_53JNM_2023-11-11_final

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

All years