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
2018-11-25 10:02:22	ga_s2am_ard_3-2-1_55KET_2018-11-25_final.stac-item
2018-11-25 10:02:26	ga_s2am_ard_3-2-1_55KDT_2018-11-25_final.stac-item
2018-11-25 10:02:29	ga_s2am_ard_3-2-1_55KCT_2018-11-25_final.stac-item
2018-11-25 10:02:32	ga_s2am_ard_3-2-1_55KBT_2018-11-25_final.stac-item
2018-11-25 10:02:34	ga_s2am_ard_3-2-1_54KZC_2018-11-25_final.stac-item
2018-11-25 10:02:47	ga_s2am_ard_3-2-1_54KZB_2018-11-25_final.stac-item
2018-12-02 09:52:19	ga_s2am_ard_3-2-1_55KGT_2018-12-02_final.stac-item
2018-12-02 09:52:23	ga_s2am_ard_3-2-1_55KFT_2018-12-02_final.stac-item
2018-12-02 09:52:26	ga_s2am_ard_3-2-1_55KET_2018-12-02_final.stac-item
2018-12-03 11:02:46	ga_s2am_ard_3-2-1_53KKT_2018-12-03_final.stac-item
2018-12-03 11:02:46	ga_s2am_ard_3-2-1_52KHC_2018-12-03_final.stac-item
2018-12-03 11:02:50	ga_s2am_ard_3-2-1_52KGC_2018-12-03_final.stac-item
2018-12-03 11:02:53	ga_s2am_ard_3-2-1_52KFC_2018-12-03_final.stac-item
2018-12-03 11:02:57	ga_s2am_ard_3-2-1_52KEC_2018-12-03_final.stac-item
2018-12-15 11:43:06	ga_s2am_ard_3-2-1_51KUT_2018-12-15_final.stac-item
2018-12-15 11:43:09	ga_s2am_ard_3-2-1_51KTT_2018-12-15_final.stac-item
2018-12-15 11:43:10	ga_s2am_ard_3-2-1_50KRC_2018-12-15_final.stac-item
2018-12-15 11:43:13	ga_s2am_ard_3-2-1_50KQC_2018-12-15_final.stac-item
2018-12-15 11:43:24	ga_s2am_ard_3-2-1_50KRB_2018-12-15_final.stac-item
2018-12-18 11:53:08	ga_s2am_ard_3-2-1_50KQC_2018-12-18_final.stac-item
2018-12-18 11:53:12	ga_s2am_ard_3-2-1_50KPC_2018-12-18_final.stac-item
2018-12-18 11:53:16	ga_s2am_ard_3-2-1_50KNC_2018-12-18_final.stac-item
2018-12-18 11:53:22	ga_s2am_ard_3-2-1_50KMC_2018-12-18_final.stac-item
2018-12-25 11:43:04	ga_s2am_ard_3-2-1_51KVT_2018-12-25_final.stac-item
2018-12-25 11:43:07	ga_s2am_ard_3-2-1_51KUT_2018-12-25_final.stac-item
2018-12-25 11:43:10	ga_s2am_ard_3-2-1_51KTT_2018-12-25_final.stac-item
2018-12-25 11:43:11	ga_s2am_ard_3-2-1_50KRC_2018-12-25_final.stac-item
2018-12-25 11:43:14	ga_s2am_ard_3-2-1_50KQC_2018-12-25_final.stac-item
2018-12-25 11:43:25	ga_s2am_ard_3-2-1_50KRB_2018-12-25_final.stac-item
2018-12-28 11:53:10	ga_s2am_ard_3-2-1_50KQC_2018-12-28_final.stac-item
2018-12-28 11:53:14	ga_s2am_ard_3-2-1_50KPC_2018-12-28_final.stac-item
2018-12-28 11:53:18	ga_s2am_ard_3-2-1_50KNC_2018-12-28_final.stac-item
2018-12-28 11:53:24	ga_s2am_ard_3-2-1_50KMC_2018-12-28_final.stac-item
2019-01-03 10:32:38	ga_s2am_ard_3-2-1_54KUC_2019-01-03_final.stac-item
2019-01-04 11:43:06	ga_s2am_ard_3-2-1_51KVT_2019-01-04_final.stac-item
2019-01-04 11:43:09	ga_s2am_ard_3-2-1_51KUT_2019-01-04_final.stac-item
2019-01-04 11:43:12	ga_s2am_ard_3-2-1_51KTT_2019-01-04_final.stac-item
2019-01-04 11:43:13	ga_s2am_ard_3-2-1_50KRC_2019-01-04_final.stac-item
2019-01-04 11:43:16	ga_s2am_ard_3-2-1_50KQC_2019-01-04_final.stac-item
2019-01-04 11:43:27	ga_s2am_ard_3-2-1_50KRB_2019-01-04_final.stac-item
2023-09-24 09:42:24	ga_s2am_ard_3-2-1_56KMC_2023-09-24_nrt
2023-09-24 09:42:27	ga_s2am_ard_3-2-1_56KLC_2023-09-24_nrt
2023-09-24 09:42:30	ga_s2am_ard_3-2-1_56KKC_2023-09-24_nrt
2023-09-24 09:42:31	ga_s2am_ard_3-2-1_55KHT_2023-09-24_nrt
2023-09-24 09:42:35	ga_s2am_ard_3-2-1_55KGT_2023-09-24_final
2023-09-24 09:42:45	ga_s2am_ard_3-2-1_55KHS_2023-09-24_final
2023-09-26 10:22:41	ga_s2am_ard_3-2-1_54KXC_2023-09-26_final
2023-09-26 10:22:44	ga_s2am_ard_3-2-1_54KWC_2023-09-26_final
2023-09-26 10:22:47	ga_s2am_ard_3-2-1_54KVC_2023-09-26_final
2023-09-26 10:22:50	ga_s2am_ard_3-2-1_54KUC_2023-09-26_final
2023-09-29 10:32:46	ga_s2am_ard_3-2-1_54KUC_2023-09-29_final
2023-09-29 10:32:49	ga_s2am_ard_3-2-1_54KTC_2023-09-29_final
2023-09-29 10:32:50	ga_s2am_ard_3-2-1_53KRT_2023-09-29_final
2023-09-29 10:32:54	ga_s2am_ard_3-2-1_53KQT_2023-09-29_final
2023-09-29 10:33:04	ga_s2am_ard_3-2-1_53KRS_2023-09-29_final
2023-09-29 12:13:30	ga_s2am_ard_3-2-1_50KKC_2023-09-29_nrt
2023-09-29 12:13:31	ga_s2am_ard_3-2-1_49KHT_2023-09-29_nrt
2023-09-29 12:13:33	ga_s2am_ard_3-2-1_49KGT_2023-09-29_nrt
2023-09-29 12:13:35	ga_s2am_ard_3-2-1_49KFT_2023-09-29_nrt
2023-09-30 10:02:32	ga_s2am_ard_3-2-1_55KET_2023-09-30_final
2023-09-30 10:02:36	ga_s2am_ard_3-2-1_55KDT_2023-09-30_final
2023-09-30 10:02:39	ga_s2am_ard_3-2-1_55KCT_2023-09-30_final
2023-09-30 10:02:42	ga_s2am_ard_3-2-1_55KBT_2023-09-30_final
2023-09-30 10:02:44	ga_s2am_ard_3-2-1_54KZC_2023-09-30_final
2023-09-30 10:02:57	ga_s2am_ard_3-2-1_54KZB_2023-09-30_final
2023-09-30 11:43:13	ga_s2am_ard_3-2-1_51KVT_2023-09-30_final
2023-09-30 11:43:17	ga_s2am_ard_3-2-1_51KUT_2023-09-30_final
2023-09-30 11:43:20	ga_s2am_ard_3-2-1_51KTT_2023-09-30_final
2023-09-30 11:43:20	ga_s2am_ard_3-2-1_50KRC_2023-09-30_final
2023-09-30 11:43:24	ga_s2am_ard_3-2-1_50KQC_2023-09-30_final
2023-09-30 11:43:35	ga_s2am_ard_3-2-1_50KRB_2023-09-30_final
2023-10-01 09:32:28	ga_s2am_ard_3-2-1_56KPC_2023-10-01_final
2023-10-01 09:32:30	ga_s2am_ard_3-2-1_56KNC_2023-10-01_nrt
2023-10-01 09:32:32	ga_s2am_ard_3-2-1_56KMC_2023-10-01_nrt
2023-10-03 10:12:37	ga_s2am_ard_3-2-1_55KBT_2023-10-03_final
2023-10-03 10:12:38	ga_s2am_ard_3-2-1_54KZC_2023-10-03_final
2023-10-03 10:12:41	ga_s2am_ard_3-2-1_54KYC_2023-10-03_final
2023-10-03 10:12:45	ga_s2am_ard_3-2-1_54KXC_2023-10-03_final
2023-10-03 10:12:52	ga_s2am_ard_3-2-1_54KZB_2023-10-03_final
2023-10-07 09:52:24	ga_s2am_ard_3-2-1_55KHT_2023-10-07_final
2023-10-07 09:52:28	ga_s2am_ard_3-2-1_55KGT_2023-10-07_final
2023-10-07 09:52:32	ga_s2am_ard_3-2-1_55KFT_2023-10-07_final
2023-10-07 09:52:35	ga_s2am_ard_3-2-1_55KET_2023-10-07_final
2023-10-07 11:33:09	ga_s2am_ard_3-2-1_51KXT_2023-10-07_final
2023-10-07 11:33:13	ga_s2am_ard_3-2-1_51KWT_2023-10-07_final
2023-10-07 11:33:17	ga_s2am_ard_3-2-1_51KVT_2023-10-07_final
2023-10-07 11:33:23	ga_s2am_ard_3-2-1_51KUT_2023-10-07_final
2023-10-23 10:12:35	ga_s2am_ard_3-2-1_55KBT_2023-10-23_final
2023-10-23 10:12:36	ga_s2am_ard_3-2-1_54KZC_2023-10-23_final
2023-10-23 10:12:40	ga_s2am_ard_3-2-1_54KYC_2023-10-23_final
2023-10-23 10:12:43	ga_s2am_ard_3-2-1_54KXC_2023-10-23_final
2023-10-23 10:12:50	ga_s2am_ard_3-2-1_54KZB_2023-10-23_final
2023-10-29 10:32:43	ga_s2am_ard_3-2-1_54KUC_2023-10-29_final
2023-10-29 10:32:47	ga_s2am_ard_3-2-1_54KTC_2023-10-29_final
2023-10-29 10:32:47	ga_s2am_ard_3-2-1_53KRT_2023-10-29_final
2023-10-29 10:32:51	ga_s2am_ard_3-2-1_53KQT_2023-10-29_final
2023-10-29 10:32:58	ga_s2am_ard_3-2-1_53KRS_2023-10-29_final
2023-10-29 12:13:28	ga_s2am_ard_3-2-1_50KKC_2023-10-29_nrt
2023-10-29 12:13:28	ga_s2am_ard_3-2-1_49KHT_2023-10-29_nrt
2023-10-29 12:13:30	ga_s2am_ard_3-2-1_49KGT_2023-10-29_nrt

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