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
2023-10-03 10:11:02	ga_s2am_ard_3-2-1_54LYK_2023-10-03_final
2023-10-03 10:12:16	ga_s2am_ard_3-2-1_54KXE_2023-10-03_final
2023-10-03 10:12:30	ga_s2am_ard_3-2-1_54KXD_2023-10-03_final
2023-10-03 10:13:25	ga_s2am_ard_3-2-1_54KYV_2023-10-03_final
2023-10-03 10:13:31	ga_s2am_ard_3-2-1_54KWV_2023-10-03_final
2023-10-03 10:13:39	ga_s2am_ard_3-2-1_54KYU_2023-10-03_final
2023-10-03 10:14:07	ga_s2am_ard_3-2-1_54JVT_2023-10-03_final
2023-10-03 10:14:12	ga_s2am_ard_3-2-1_54JXS_2023-10-03_final
2023-10-03 10:14:26	ga_s2am_ard_3-2-1_54JXR_2023-10-03_final
2023-10-03 10:14:37	ga_s2am_ard_3-2-1_54JYQ_2023-10-03_final
2023-10-03 10:14:55	ga_s2am_ard_3-2-1_54JXP_2023-10-03_final
2023-10-03 10:15:09	ga_s2am_ard_3-2-1_54JUP_2023-10-03_final
2023-10-03 10:15:42	ga_s2am_ard_3-2-1_54JWL_2023-10-03_final
2023-10-03 10:16:00	ga_s2am_ard_3-2-1_54HVK_2023-10-03_final
2023-10-03 10:16:04	ga_s2am_ard_3-2-1_54HUK_2023-10-03_final
2023-10-03 10:16:11	ga_s2am_ard_3-2-1_54HWJ_2023-10-03_final
2023-10-03 10:16:15	ga_s2am_ard_3-2-1_54HVJ_2023-10-03_final
2023-10-03 10:17:06	ga_s2am_ard_3-2-1_53HQA_2023-10-03_final
2023-10-03 10:17:17	ga_s2am_ard_3-2-1_54HUE_2023-10-03_final
2023-10-03 10:17:21	ga_s2am_ard_3-2-1_53HQV_2023-10-03_final
2023-10-03 10:17:31	ga_s2am_ard_3-2-1_54HUD_2023-10-03_final
2023-10-03 10:17:38	ga_s2am_ard_3-2-1_54HVC_2023-10-03_final
2023-10-03 11:51:40	ga_s2am_ard_3-2-1_51LUD_2023-10-03_final
2023-10-03 11:52:21	ga_s2am_ard_3-2-1_50KQG_2023-10-03_final
2023-10-03 11:52:36	ga_s2am_ard_3-2-1_50KQF_2023-10-03_final
2023-10-03 11:52:39	ga_s2am_ard_3-2-1_50KPF_2023-10-03_final
2023-10-03 11:52:50	ga_s2am_ard_3-2-1_50KQE_2023-10-03_final
2023-10-03 11:52:56	ga_s2am_ard_3-2-1_51KTU_2023-10-03_final
2023-10-03 11:53:05	ga_s2am_ard_3-2-1_50KQD_2023-10-03_final
2023-10-03 11:53:08	ga_s2am_ard_3-2-1_50KPD_2023-10-03_final
2023-10-03 11:53:12	ga_s2am_ard_3-2-1_50KRC_2023-10-03_final
2023-10-03 11:53:19	ga_s2am_ard_3-2-1_50KQC_2023-10-03_final
2023-10-03 11:53:23	ga_s2am_ard_3-2-1_50KPC_2023-10-03_final
2023-10-03 11:53:32	ga_s2am_ard_3-2-1_50KMC_2023-10-03_final
2023-10-03 11:53:33	ga_s2am_ard_3-2-1_50KQB_2023-10-03_final
2023-10-03 11:53:37	ga_s2am_ard_3-2-1_50KPB_2023-10-03_final
2023-10-03 11:53:41	ga_s2am_ard_3-2-1_50KNB_2023-10-03_final
2023-10-03 11:53:44	ga_s2am_ard_3-2-1_50KMB_2023-10-03_final
2023-10-03 11:53:48	ga_s2am_ard_3-2-1_50KQA_2023-10-03_final
2023-10-03 11:53:52	ga_s2am_ard_3-2-1_50KPA_2023-10-03_final
2023-10-03 11:53:55	ga_s2am_ard_3-2-1_50KNA_2023-10-03_final
2023-10-03 11:53:58	ga_s2am_ard_3-2-1_50KMA_2023-10-03_final
2023-10-03 11:54:02	ga_s2am_ard_3-2-1_50KQV_2023-10-03_final
2023-10-03 11:54:10	ga_s2am_ard_3-2-1_50KNV_2023-10-03_final
2023-10-03 11:54:13	ga_s2am_ard_3-2-1_50KMV_2023-10-03_final
2023-10-03 11:54:25	ga_s2am_ard_3-2-1_50JQT_2023-10-03_final
2023-10-03 11:54:35	ga_s2am_ard_3-2-1_50JPT_2023-10-03_final
2023-10-03 11:54:39	ga_s2am_ard_3-2-1_50JNT_2023-10-03_final
2023-10-03 11:54:42	ga_s2am_ard_3-2-1_50JMT_2023-10-03_final
2023-10-03 11:54:50	ga_s2am_ard_3-2-1_50JPS_2023-10-03_final
2023-10-03 11:54:53	ga_s2am_ard_3-2-1_50JNS_2023-10-03_final
2023-10-03 11:55:00	ga_s2am_ard_3-2-1_50JLS_2023-10-03_final
2023-10-03 11:55:08	ga_s2am_ard_3-2-1_50JNR_2023-10-03_final
2023-10-03 11:55:11	ga_s2am_ard_3-2-1_50JMR_2023-10-03_final
2023-10-03 11:55:14	ga_s2am_ard_3-2-1_50JLR_2023-10-03_final
2023-10-03 11:55:17	ga_s2am_ard_3-2-1_50JPQ_2023-10-03_final
2023-10-03 11:55:28	ga_s2am_ard_3-2-1_50JPP_2023-10-03_final
2023-10-03 11:55:36	ga_s2am_ard_3-2-1_50JNP_2023-10-03_final
2023-10-03 11:55:44	ga_s2am_ard_3-2-1_50JLP_2023-10-03_final
2023-10-03 11:55:48	ga_s2am_ard_3-2-1_50JKP_2023-10-03_final
2023-10-03 11:55:48	ga_s2am_ard_3-2-1_50JNN_2023-10-03_final
2023-10-03 11:55:50	ga_s2am_ard_3-2-1_50JMN_2023-10-03_final
2023-10-03 11:55:51	ga_s2am_ard_3-2-1_50JLP_2023-10-03_final
2023-10-03 11:55:52	ga_s2am_ard_3-2-1_50JLN_2023-10-03_final
2023-10-03 11:55:53	ga_s2am_ard_3-2-1_50JKP_2023-10-03_final
2023-10-03 11:55:53	ga_s2am_ard_3-2-1_50JKN_2023-10-03_final
2023-10-03 11:55:55	ga_s2am_ard_3-2-1_50JNN_2023-10-03_final
2023-10-03 11:55:57	ga_s2am_ard_3-2-1_50JMN_2023-10-03_final
2023-10-03 11:55:58	ga_s2am_ard_3-2-1_50JLN_2023-10-03_final
2023-10-03 11:56:01	ga_s2am_ard_3-2-1_50JKN_2023-10-03_final
2023-10-03 11:56:09	ga_s2am_ard_3-2-1_50JMM_2023-10-03_final
2023-10-03 11:56:20	ga_s2am_ard_3-2-1_50JNL_2023-10-03_final
2023-10-03 11:56:24	ga_s2am_ard_3-2-1_50JML_2023-10-03_final
2023-10-03 11:56:27	ga_s2am_ard_3-2-1_50JLL_2023-10-03_final
2023-10-03 11:56:31	ga_s2am_ard_3-2-1_50HNK_2023-10-03_final
2023-10-03 11:56:53	ga_s2am_ard_3-2-1_50HMJ_2023-10-03_final
2023-10-03 11:56:56	ga_s2am_ard_3-2-1_50HLJ_2023-10-03_final
2023-10-03 11:57:08	ga_s2am_ard_3-2-1_50HMH_2023-10-03_final
2023-10-03 11:57:11	ga_s2am_ard_3-2-1_50HLH_2023-10-03_final
2023-10-03 11:57:24	ga_s2am_ard_3-2-1_50HLG_2023-10-03_final
2023-10-13 11:51:00	ga_s2am_ard_3-2-1_51LTG_2023-10-13_nrt
2023-10-13 11:51:05	ga_s2am_ard_3-2-1_50LQM_2023-10-13_nrt
2023-10-13 11:51:15	ga_s2am_ard_3-2-1_51LTF_2023-10-13_nrt
2023-10-13 11:51:19	ga_s2am_ard_3-2-1_50LQL_2023-10-13_nrt
2023-10-13 11:51:24	ga_s2am_ard_3-2-1_50LPL_2023-10-13_nrt
2023-10-13 11:51:29	ga_s2am_ard_3-2-1_51LTE_2023-10-13_nrt
2023-10-13 11:51:34	ga_s2am_ard_3-2-1_50LQK_2023-10-13_nrt
2023-10-13 11:51:37	ga_s2am_ard_3-2-1_50LPK_2023-10-13_nrt
2023-10-13 11:51:43	ga_s2am_ard_3-2-1_51LTD_2023-10-13_nrt
2023-10-13 11:51:48	ga_s2am_ard_3-2-1_50LQJ_2023-10-13_nrt
2023-10-13 11:51:51	ga_s2am_ard_3-2-1_50LPJ_2023-10-13_nrt
2023-10-13 11:52:03	ga_s2am_ard_3-2-1_50LQH_2023-10-13_nrt
2023-10-13 11:52:06	ga_s2am_ard_3-2-1_50LPH_2023-10-13_nrt
2023-10-13 11:52:50	ga_s2am_ard_3-2-1_50KPE_2023-10-13_nrt
2023-10-13 11:56:26	ga_s2am_ard_3-2-1_50JKL_2023-10-13_nrt
2023-10-13 11:56:31	ga_s2am_ard_3-2-1_49JGF_2023-10-13_nrt
2023-10-13 11:56:41	ga_s2am_ard_3-2-1_50HKK_2023-10-13_nrt
2023-10-13 11:56:56	ga_s2am_ard_3-2-1_50HKJ_2023-10-13_nrt
2023-10-13 11:57:10	ga_s2am_ard_3-2-1_50HKH_2023-10-13_nrt
2023-10-13 11:57:25	ga_s2am_ard_3-2-1_49HFB_2023-10-13_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