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
2023-11-25 10:26:35	ga_s2am_ard_3-2-1_54HUG_2023-11-25_final
2023-11-25 12:03:18	ga_s2am_ard_3-2-1_50KMC_2023-11-25_final
2023-11-26 09:54:58	ga_s2am_ard_3-2-1_55JDH_2023-11-26_final
2023-11-27 11:01:17	ga_s2am_ard_3-2-1_53LLD_2023-11-27_final
2023-11-29 11:43:39	ga_s2am_ard_3-2-1_51KTR_2023-11-29_final
2023-11-30 11:14:38	ga_s2am_ard_3-2-1_52JDR_2023-11-30_final
2023-11-30 11:16:50	ga_s2am_ard_3-2-1_51HXC_2023-11-30_final
2023-12-01 10:40:28	ga_s2am_ard_3-2-1_53LQG_2023-12-01_final
2023-12-01 10:40:31	ga_s2am_ard_3-2-1_53LPG_2023-12-01_final
2023-12-01 10:40:42	ga_s2am_ard_3-2-1_53LQF_2023-12-01_final
2023-12-01 10:41:15	ga_s2am_ard_3-2-1_53LPD_2023-12-01_final
2023-12-01 10:41:34	ga_s2am_ard_3-2-1_54KTG_2023-12-01_final
2023-12-01 10:41:37	ga_s2am_ard_3-2-1_53KRB_2023-12-01_final
2023-12-01 10:41:40	ga_s2am_ard_3-2-1_53KQB_2023-12-01_final
2023-12-01 10:41:55	ga_s2am_ard_3-2-1_53KQA_2023-12-01_final
2023-12-01 10:42:16	ga_s2am_ard_3-2-1_53KNV_2023-12-01_final
2023-12-01 10:42:23	ga_s2am_ard_3-2-1_53KQU_2023-12-01_final
2023-12-01 10:42:30	ga_s2am_ard_3-2-1_53KNU_2023-12-01_final
2023-12-01 10:42:35	ga_s2am_ard_3-2-1_53KMU_2023-12-01_final
2023-12-01 10:42:48	ga_s2am_ard_3-2-1_53KMT_2023-12-01_final
2023-12-01 10:43:15	ga_s2am_ard_3-2-1_53KNR_2023-12-01_final
2023-12-01 10:43:29	ga_s2am_ard_3-2-1_53KNQ_2023-12-01_final
2023-12-01 10:43:50	ga_s2am_ard_3-2-1_53KLP_2023-12-01_final
2023-12-01 10:44:01	ga_s2am_ard_3-2-1_53JMN_2023-12-01_final
2023-12-01 10:44:26	ga_s2am_ard_3-2-1_53JNL_2023-12-01_final
2023-12-01 10:44:30	ga_s2am_ard_3-2-1_53JML_2023-12-01_final
2023-12-01 10:44:40	ga_s2am_ard_3-2-1_53JKL_2023-12-01_final
2023-12-01 10:44:41	ga_s2am_ard_3-2-1_53JNK_2023-12-01_final
2023-12-01 10:45:16	ga_s2am_ard_3-2-1_53JMH_2023-12-01_final
2023-12-01 10:45:32	ga_s2am_ard_3-2-1_53JLG_2023-12-01_final
2023-12-01 10:46:22	ga_s2am_ard_3-2-1_53HMC_2023-12-01_final
2023-12-02 10:10:32	ga_s2am_ard_3-2-1_54LZL_2023-12-02_final
2023-12-02 10:10:47	ga_s2am_ard_3-2-1_54LZK_2023-12-02_final
2023-12-02 10:11:01	ga_s2am_ard_3-2-1_54LZJ_2023-12-02_final
2023-12-02 10:11:05	ga_s2am_ard_3-2-1_54LYJ_2023-12-02_final
2023-12-02 10:11:11	ga_s2am_ard_3-2-1_55LCC_2023-12-02_final
2023-12-02 10:11:26	ga_s2am_ard_3-2-1_55KCB_2023-12-02_final
2023-12-02 10:11:54	ga_s2am_ard_3-2-1_54KXF_2023-12-02_final
2023-12-02 10:11:55	ga_s2am_ard_3-2-1_55KCV_2023-12-02_final
2023-12-02 10:12:13	ga_s2am_ard_3-2-1_55KBU_2023-12-02_final
2023-12-02 10:12:17	ga_s2am_ard_3-2-1_54KYD_2023-12-02_final
2023-12-02 10:12:56	ga_s2am_ard_3-2-1_55KBR_2023-12-02_final
2023-12-02 10:13:04	ga_s2am_ard_3-2-1_54KXA_2023-12-02_final
2023-12-02 10:13:25	ga_s2am_ard_3-2-1_55KBP_2023-12-02_final
2023-12-02 10:14:02	ga_s2am_ard_3-2-1_54JXS_2023-12-02_final
2023-12-02 10:14:34	ga_s2am_ard_3-2-1_54JYQ_2023-12-02_final
2023-12-02 10:15:42	ga_s2am_ard_3-2-1_54HXK_2023-12-02_final
2023-12-02 10:16:13	ga_s2am_ard_3-2-1_54HTJ_2023-12-02_final
2023-12-02 10:16:16	ga_s2am_ard_3-2-1_54HWH_2023-12-02_final
2023-12-02 10:16:20	ga_s2am_ard_3-2-1_54HVH_2023-12-02_final
2023-12-02 10:16:49	ga_s2am_ard_3-2-1_54HVF_2023-12-02_final
2023-12-02 10:16:55	ga_s2am_ard_3-2-1_54HWE_2023-12-02_final
2023-12-02 10:17:03	ga_s2am_ard_3-2-1_54HVE_2023-12-02_final
2023-12-02 10:17:10	ga_s2am_ard_3-2-1_54HTE_2023-12-02_final
2023-12-02 10:17:17	ga_s2am_ard_3-2-1_54HVD_2023-12-02_final
2023-12-02 11:51:19	ga_s2am_ard_3-2-1_51LUE_2023-12-02_final
2023-12-02 11:51:31	ga_s2am_ard_3-2-1_51LUD_2023-12-02_final
2023-12-02 11:52:29	ga_s2am_ard_3-2-1_50KPF_2023-12-02_final
2023-12-02 11:52:46	ga_s2am_ard_3-2-1_51KTU_2023-12-02_final
2023-12-02 11:52:55	ga_s2am_ard_3-2-1_50KQD_2023-12-02_final
2023-12-02 11:52:58	ga_s2am_ard_3-2-1_50KPD_2023-12-02_final
2023-12-02 11:53:09	ga_s2am_ard_3-2-1_50KQC_2023-12-02_final
2023-12-02 11:53:24	ga_s2am_ard_3-2-1_50KQB_2023-12-02_final
2023-12-02 11:53:38	ga_s2am_ard_3-2-1_50KQA_2023-12-02_final
2023-12-02 11:53:45	ga_s2am_ard_3-2-1_50KNA_2023-12-02_final
2023-12-02 11:54:14	ga_s2am_ard_3-2-1_50KNU_2023-12-02_final
2023-12-02 11:54:32	ga_s2am_ard_3-2-1_50JMT_2023-12-02_final
2023-12-02 11:54:40	ga_s2am_ard_3-2-1_50JPS_2023-12-02_final
2023-12-02 11:54:47	ga_s2am_ard_3-2-1_50JMS_2023-12-02_final
2023-12-02 11:54:58	ga_s2am_ard_3-2-1_50JNR_2023-12-02_final
2023-12-02 11:55:12	ga_s2am_ard_3-2-1_50JNQ_2023-12-02_final
2023-12-02 11:55:16	ga_s2am_ard_3-2-1_50JMQ_2023-12-02_final
2023-12-02 11:55:18	ga_s2am_ard_3-2-1_50JPP_2023-12-02_final
2023-12-02 11:56:17	ga_s2am_ard_3-2-1_50JLL_2023-12-02_final
2023-12-02 11:56:22	ga_s2am_ard_3-2-1_50HNK_2023-12-02_final
2023-12-12 10:09:41	ga_s2am_ard_3-2-1_55LEJ_2023-12-12_nrt
2023-12-12 10:09:47	ga_s2am_ard_3-2-1_55LCJ_2023-12-12_nrt
2023-12-12 10:09:54	ga_s2am_ard_3-2-1_55LEH_2023-12-12_nrt
2023-12-12 10:10:02	ga_s2am_ard_3-2-1_55LCH_2023-12-12_nrt
2023-12-12 10:10:04	ga_s2am_ard_3-2-1_55LEG_2023-12-12_nrt
2023-12-12 10:10:17	ga_s2am_ard_3-2-1_55LCG_2023-12-12_nrt
2023-12-12 10:10:31	ga_s2am_ard_3-2-1_55LCF_2023-12-12_nrt
2023-12-12 10:17:28	ga_s2am_ard_3-2-1_54HTD_2023-12-12_nrt
2023-12-12 10:17:34	ga_s2am_ard_3-2-1_53HPU_2023-12-12_nrt
2023-12-12 10:17:35	ga_s2am_ard_3-2-1_54HTC_2023-12-12_nrt
2023-12-12 11:50:19	ga_s2am_ard_3-2-1_51LVJ_2023-12-12_nrt
2023-12-12 11:51:14	ga_s2am_ard_3-2-1_50LRL_2023-12-12_nrt
2023-12-12 11:51:26	ga_s2am_ard_3-2-1_51LTE_2023-12-12_nrt
2023-12-12 11:51:41	ga_s2am_ard_3-2-1_51LTD_2023-12-12_nrt
2023-12-12 11:51:42	ga_s2am_ard_3-2-1_50LRJ_2023-12-12_nrt
2023-12-12 11:52:10	ga_s2am_ard_3-2-1_50KRG_2023-12-12_nrt
2023-12-12 11:52:16	ga_s2am_ard_3-2-1_50KRF_2023-12-12_nrt
2023-12-12 11:52:24	ga_s2am_ard_3-2-1_50KNG_2023-12-12_nrt
2023-12-12 11:52:25	ga_s2am_ard_3-2-1_50KRF_2023-12-12_nrt
2023-12-12 11:52:36	ga_s2am_ard_3-2-1_50KNF_2023-12-12_nrt
2023-12-12 11:52:40	ga_s2am_ard_3-2-1_50KRE_2023-12-12_nrt
2023-12-12 11:52:47	ga_s2am_ard_3-2-1_50KPE_2023-12-12_nrt
2023-12-12 11:53:05	ga_s2am_ard_3-2-1_50KND_2023-12-12_nrt
2023-12-12 11:56:28	ga_s2am_ard_3-2-1_49JGF_2023-12-12_nrt
2023-12-12 11:57:10	ga_s2am_ard_3-2-1_49HGC_2023-12-12_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