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
2023-12-21 14:13:35	ga_s2am_ard_3-2-1_42FYG_2023-12-21_final
2023-12-21 14:13:35	ga_s2am_ard_3-2-1_43FCB_2023-12-21_final
2023-12-21 14:13:40	ga_s2am_ard_3-2-1_42FYF_2023-12-21_final
2023-12-22 08:34:16	ga_s2am_ard_3-2-1_59JKH_2023-12-21_final
2023-12-22 08:34:18	ga_s2am_ard_3-2-1_58JGN_2023-12-21_final
2023-12-22 08:41:35	ga_s2am_ard_3-2-1_57FVV_2023-12-21_final
2023-12-22 10:09:36	ga_s2am_ard_3-2-1_55LBK_2023-12-22_final
2023-12-22 10:09:41	ga_s2am_ard_3-2-1_55LEJ_2023-12-22_nrt
2023-12-22 10:09:45	ga_s2am_ard_3-2-1_55LDJ_2023-12-22_nrt
2023-12-22 10:09:48	ga_s2am_ard_3-2-1_55LCJ_2023-12-22_nrt
2023-12-22 10:09:51	ga_s2am_ard_3-2-1_55LBJ_2023-12-22_final
2023-12-22 10:09:54	ga_s2am_ard_3-2-1_54LZP_2023-12-22_final
2023-12-22 10:09:54	ga_s2am_ard_3-2-1_55LEH_2023-12-22_nrt
2023-12-22 10:09:59	ga_s2am_ard_3-2-1_55LDH_2023-12-22_nrt
2023-12-22 10:10:03	ga_s2am_ard_3-2-1_55LCH_2023-12-22_nrt
2023-12-22 10:10:05	ga_s2am_ard_3-2-1_55LEG_2023-12-22_nrt
2023-12-22 10:10:06	ga_s2am_ard_3-2-1_55LBH_2023-12-22_nrt
2023-12-22 10:10:07	ga_s2am_ard_3-2-1_54LZN_2023-12-22_final
2023-12-22 10:10:14	ga_s2am_ard_3-2-1_55LDG_2023-12-22_nrt
2023-12-22 10:10:17	ga_s2am_ard_3-2-1_55LCG_2023-12-22_nrt
2023-12-22 10:10:20	ga_s2am_ard_3-2-1_55LBG_2023-12-22_final
2023-12-22 10:10:22	ga_s2am_ard_3-2-1_54LZM_2023-12-22_final
2023-12-22 10:10:28	ga_s2am_ard_3-2-1_55LDF_2023-12-22_nrt
2023-12-22 10:10:32	ga_s2am_ard_3-2-1_55LCF_2023-12-22_nrt
2023-12-22 10:10:35	ga_s2am_ard_3-2-1_55LBF_2023-12-22_final
2023-12-22 10:10:36	ga_s2am_ard_3-2-1_54LZL_2023-12-22_final
2023-12-22 10:10:43	ga_s2am_ard_3-2-1_55LDE_2023-12-22_nrt
2023-12-22 10:10:46	ga_s2am_ard_3-2-1_55LCE_2023-12-22_final
2023-12-22 10:10:50	ga_s2am_ard_3-2-1_55LBE_2023-12-22_final
2023-12-22 10:10:51	ga_s2am_ard_3-2-1_54LZK_2023-12-22_final
2023-12-22 10:10:56	ga_s2am_ard_3-2-1_54LYK_2023-12-22_final
2023-12-22 10:10:57	ga_s2am_ard_3-2-1_55LDD_2023-12-22_nrt
2023-12-22 10:11:01	ga_s2am_ard_3-2-1_55LCD_2023-12-22_final
2023-12-22 10:11:04	ga_s2am_ard_3-2-1_55LBD_2023-12-22_final
2023-12-22 10:11:05	ga_s2am_ard_3-2-1_54LZJ_2023-12-22_final
2023-12-22 10:11:09	ga_s2am_ard_3-2-1_54LYJ_2023-12-22_final
2023-12-22 10:11:09	ga_s2am_ard_3-2-1_55LDC_2023-12-22_final
2023-12-22 10:11:15	ga_s2am_ard_3-2-1_55LCC_2023-12-22_final
2023-12-22 10:11:19	ga_s2am_ard_3-2-1_55LBC_2023-12-22_final
2023-12-22 10:11:20	ga_s2am_ard_3-2-1_54LZH_2023-12-22_final
2023-12-22 10:11:23	ga_s2am_ard_3-2-1_54LYH_2023-12-22_final
2023-12-22 10:11:30	ga_s2am_ard_3-2-1_55KCB_2023-12-22_final
2023-12-22 10:11:33	ga_s2am_ard_3-2-1_55KBB_2023-12-22_final
2023-12-22 10:11:34	ga_s2am_ard_3-2-1_54KZG_2023-12-22_final
2023-12-22 10:11:38	ga_s2am_ard_3-2-1_54KYG_2023-12-22_final
2023-12-22 10:11:44	ga_s2am_ard_3-2-1_55KCA_2023-12-22_final
2023-12-22 10:11:48	ga_s2am_ard_3-2-1_55KBA_2023-12-22_final
2023-12-22 10:11:49	ga_s2am_ard_3-2-1_54KZF_2023-12-22_final
2023-12-22 10:11:52	ga_s2am_ard_3-2-1_54KYF_2023-12-22_final
2023-12-22 10:11:58	ga_s2am_ard_3-2-1_54KXF_2023-12-22_final
2023-12-22 10:11:59	ga_s2am_ard_3-2-1_55KCV_2023-12-22_final
2023-12-22 10:12:02	ga_s2am_ard_3-2-1_55KBV_2023-12-22_final
2023-12-22 10:12:03	ga_s2am_ard_3-2-1_54KZE_2023-12-22_final
2023-12-22 10:12:07	ga_s2am_ard_3-2-1_54KYE_2023-12-22_final
2023-12-22 10:12:10	ga_s2am_ard_3-2-1_54KXE_2023-12-22_final
2023-12-22 10:12:13	ga_s2am_ard_3-2-1_55KCU_2023-12-22_final
2023-12-22 10:12:17	ga_s2am_ard_3-2-1_55KBU_2023-12-22_final
2023-12-22 10:12:18	ga_s2am_ard_3-2-1_54KZD_2023-12-22_final
2023-12-22 10:12:21	ga_s2am_ard_3-2-1_54KYD_2023-12-22_final
2023-12-22 11:52:37	ga_s2am_ard_3-2-1_50KNF_2023-12-22_nrt
2023-12-22 11:53:06	ga_s2am_ard_3-2-1_50KND_2023-12-22_nrt
2023-12-22 11:55:20	ga_s2am_ard_3-2-1_50JMQ_2023-12-22_final
2023-12-22 11:55:22	ga_s2am_ard_3-2-1_50JPP_2023-12-22_final
2023-12-22 11:55:23	ga_s2am_ard_3-2-1_50JLQ_2023-12-22_final
2023-12-22 11:55:31	ga_s2am_ard_3-2-1_50JNP_2023-12-22_final
2023-12-22 11:55:35	ga_s2am_ard_3-2-1_50JMP_2023-12-22_final
2023-12-22 11:55:38	ga_s2am_ard_3-2-1_50JLP_2023-12-22_final
2023-12-22 11:55:42	ga_s2am_ard_3-2-1_50JKP_2023-12-22_final
2023-12-22 11:55:45	ga_s2am_ard_3-2-1_50JNN_2023-12-22_final
2023-12-22 11:55:49	ga_s2am_ard_3-2-1_50JMN_2023-12-22_final
2023-12-22 11:55:52	ga_s2am_ard_3-2-1_50JLN_2023-12-22_final
2023-12-22 11:55:55	ga_s2am_ard_3-2-1_50JKN_2023-12-22_final
2023-12-22 11:56:00	ga_s2am_ard_3-2-1_50JNM_2023-12-22_final
2023-12-22 11:56:04	ga_s2am_ard_3-2-1_50JMM_2023-12-22_final
2023-12-22 11:56:07	ga_s2am_ard_3-2-1_50JLM_2023-12-22_final
2023-12-22 11:56:10	ga_s2am_ard_3-2-1_50JKM_2023-12-22_final
2023-12-22 11:56:14	ga_s2am_ard_3-2-1_50JNL_2023-12-22_final
2023-12-22 11:56:18	ga_s2am_ard_3-2-1_50JML_2023-12-22_final
2023-12-22 11:56:22	ga_s2am_ard_3-2-1_50JLL_2023-12-22_final
2023-12-22 11:56:25	ga_s2am_ard_3-2-1_50JKL_2023-12-22_final
2023-12-22 11:56:26	ga_s2am_ard_3-2-1_50HNK_2023-12-22_final
2023-12-22 11:56:29	ga_s2am_ard_3-2-1_49JGF_2023-12-22_nrt
2023-12-22 11:56:33	ga_s2am_ard_3-2-1_50HMK_2023-12-22_final
2023-12-22 11:56:36	ga_s2am_ard_3-2-1_50HLK_2023-12-22_final
2023-12-22 11:56:39	ga_s2am_ard_3-2-1_50HKK_2023-12-22_nrt
2023-12-22 11:56:41	ga_s2am_ard_3-2-1_49HGE_2023-12-22_nrt
2023-12-22 11:56:47	ga_s2am_ard_3-2-1_50HMJ_2023-12-22_final
2023-12-22 11:56:51	ga_s2am_ard_3-2-1_50HLJ_2023-12-22_final
2023-12-22 11:56:54	ga_s2am_ard_3-2-1_50HKJ_2023-12-22_final
2023-12-22 11:56:56	ga_s2am_ard_3-2-1_49HGD_2023-12-22_nrt
2023-12-22 11:57:02	ga_s2am_ard_3-2-1_50HMH_2023-12-22_final
2023-12-22 11:57:05	ga_s2am_ard_3-2-1_50HLH_2023-12-22_final
2023-12-22 11:57:09	ga_s2am_ard_3-2-1_50HKH_2023-12-22_final
2023-12-22 11:57:11	ga_s2am_ard_3-2-1_49HGC_2023-12-22_nrt
2023-12-22 11:57:16	ga_s2am_ard_3-2-1_50HMG_2023-12-22_final
2023-12-22 11:57:18	ga_s2am_ard_3-2-1_50HLG_2023-12-22_final
2023-12-22 11:57:19	ga_s2am_ard_3-2-1_49HFC_2023-12-22_nrt
2023-12-22 11:57:20	ga_s2am_ard_3-2-1_50HKG_2023-12-22_final
2023-12-22 11:57:21	ga_s2am_ard_3-2-1_49HGB_2023-12-22_nrt
2023-12-22 11:57:23	ga_s2am_ard_3-2-1_49HFB_2023-12-22_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