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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

66 results

Time (local)	Label
2019-10-19 11:51:15	ga_s2bm_ard_3-2-1_51LUF_2019-10-19_final.stac-item
2019-10-19 11:51:28	ga_s2bm_ard_3-2-1_51LUE_2019-10-19_final.stac-item
2019-10-19 11:52:55	ga_s2bm_ard_3-2-1_51KTU_2019-10-19_final.stac-item
2019-10-19 11:53:00	ga_s2bm_ard_3-2-1_50KRD_2019-10-19_final.stac-item
2019-10-19 11:53:12	ga_s2bm_ard_3-2-1_50KRC_2019-10-19_final.stac-item
2019-10-19 11:53:18	ga_s2bm_ard_3-2-1_50KQC_2019-10-19_final.stac-item
2019-10-19 11:53:22	ga_s2bm_ard_3-2-1_50KPC_2019-10-19_final.stac-item
2019-10-19 11:53:26	ga_s2bm_ard_3-2-1_50KNC_2019-10-19_final.stac-item
2019-10-19 11:53:32	ga_s2bm_ard_3-2-1_50KMC_2019-10-19_final.stac-item
2019-10-19 11:53:33	ga_s2bm_ard_3-2-1_50KQB_2019-10-19_final.stac-item
2019-10-19 11:53:37	ga_s2bm_ard_3-2-1_50KPB_2019-10-19_final.stac-item
2019-10-19 11:53:40	ga_s2bm_ard_3-2-1_50KNB_2019-10-19_final.stac-item
2019-10-19 11:53:44	ga_s2bm_ard_3-2-1_50KMB_2019-10-19_final.stac-item
2019-10-19 11:53:48	ga_s2bm_ard_3-2-1_50KQA_2019-10-19_final.stac-item
2019-10-19 11:53:51	ga_s2bm_ard_3-2-1_50KPA_2019-10-19_final.stac-item
2019-10-19 11:53:55	ga_s2bm_ard_3-2-1_50KNA_2019-10-19_final.stac-item
2019-10-19 11:53:58	ga_s2bm_ard_3-2-1_50KMA_2019-10-19_final.stac-item
2019-10-19 11:54:02	ga_s2bm_ard_3-2-1_50KQV_2019-10-19_final.stac-item
2019-10-19 11:54:06	ga_s2bm_ard_3-2-1_50KPV_2019-10-19_final.stac-item
2019-10-19 11:54:09	ga_s2bm_ard_3-2-1_50KNV_2019-10-19_final.stac-item
2019-10-19 11:54:12	ga_s2bm_ard_3-2-1_50KMV_2019-10-19_final.stac-item
2019-10-19 11:54:15	ga_s2bm_ard_3-2-1_50KQU_2019-10-19_final.stac-item
2019-10-19 11:54:20	ga_s2bm_ard_3-2-1_50KPU_2019-10-19_final.stac-item
2019-10-19 11:54:24	ga_s2bm_ard_3-2-1_50KNU_2019-10-19_final.stac-item
2019-10-19 11:54:24	ga_s2bm_ard_3-2-1_50JQT_2019-10-19_final.stac-item
2019-10-19 11:54:27	ga_s2bm_ard_3-2-1_50KMU_2019-10-19_final.stac-item
2019-10-19 11:54:34	ga_s2bm_ard_3-2-1_50JPT_2019-10-19_final.stac-item
2019-10-19 11:54:35	ga_s2bm_ard_3-2-1_50KLU_2019-10-19_final.stac-item
2019-10-19 11:54:38	ga_s2bm_ard_3-2-1_50JNT_2019-10-19_final.stac-item
2019-10-19 11:54:42	ga_s2bm_ard_3-2-1_50JMT_2019-10-19_final.stac-item
2019-10-19 11:54:46	ga_s2bm_ard_3-2-1_50JLT_2019-10-19_final.stac-item
2019-10-19 11:54:49	ga_s2bm_ard_3-2-1_50JPS_2019-10-19_final.stac-item
2019-10-19 11:54:53	ga_s2bm_ard_3-2-1_50JNS_2019-10-19_final.stac-item
2019-10-19 11:54:56	ga_s2bm_ard_3-2-1_50JMS_2019-10-19_final.stac-item
2019-10-19 11:54:59	ga_s2bm_ard_3-2-1_50JLS_2019-10-19_final.stac-item
2019-10-19 11:55:04	ga_s2bm_ard_3-2-1_50JPR_2019-10-19_final.stac-item
2019-10-19 11:55:07	ga_s2bm_ard_3-2-1_50JNR_2019-10-19_final.stac-item
2019-10-19 11:55:11	ga_s2bm_ard_3-2-1_50JMR_2019-10-19_final.stac-item
2019-10-19 11:55:14	ga_s2bm_ard_3-2-1_50JLR_2019-10-19_final.stac-item
2019-10-19 11:55:17	ga_s2bm_ard_3-2-1_50JPQ_2019-10-19_final.stac-item
2019-10-19 11:55:22	ga_s2bm_ard_3-2-1_50JNQ_2019-10-19_final.stac-item
2019-10-19 11:55:25	ga_s2bm_ard_3-2-1_50JMQ_2019-10-19_final.stac-item
2019-10-19 11:55:27	ga_s2bm_ard_3-2-1_50JPP_2019-10-19_final.stac-item
2019-10-19 11:55:28	ga_s2bm_ard_3-2-1_50JLQ_2019-10-19_final.stac-item
2019-10-19 11:55:36	ga_s2bm_ard_3-2-1_50JNP_2019-10-19_final.stac-item
2019-10-19 11:55:40	ga_s2bm_ard_3-2-1_50JMP_2019-10-19_final.stac-item
2019-10-19 11:55:43	ga_s2bm_ard_3-2-1_50JLP_2019-10-19_final.stac-item
2019-10-19 11:55:48	ga_s2bm_ard_3-2-1_50JKP_2019-10-19_final.stac-item
2019-10-19 11:55:51	ga_s2bm_ard_3-2-1_50JNN_2019-10-19_final.stac-item
2019-10-19 11:55:54	ga_s2bm_ard_3-2-1_50JMN_2019-10-19_final.stac-item
2019-10-19 11:55:57	ga_s2bm_ard_3-2-1_50JLN_2019-10-19_final.stac-item
2019-10-19 11:56:01	ga_s2bm_ard_3-2-1_50JKN_2019-10-19_final.stac-item
2019-10-19 11:56:05	ga_s2bm_ard_3-2-1_50JNM_2019-10-19_final.stac-item
2019-10-19 11:56:09	ga_s2bm_ard_3-2-1_50JMM_2019-10-19_final.stac-item
2019-10-19 11:56:12	ga_s2bm_ard_3-2-1_50JLM_2019-10-19_final.stac-item
2019-10-19 11:56:15	ga_s2bm_ard_3-2-1_50JKM_2019-10-19_final.stac-item
2019-10-19 11:56:19	ga_s2bm_ard_3-2-1_50JNL_2019-10-19_final.stac-item
2019-10-19 11:56:23	ga_s2bm_ard_3-2-1_50JML_2019-10-19_final.stac-item
2019-10-19 11:56:27	ga_s2bm_ard_3-2-1_50JLL_2019-10-19_final.stac-item
2019-10-19 11:56:31	ga_s2bm_ard_3-2-1_50HNK_2019-10-19_final.stac-item
2019-10-19 11:56:38	ga_s2bm_ard_3-2-1_50HMK_2019-10-19_final.stac-item
2019-10-19 11:56:41	ga_s2bm_ard_3-2-1_50HLK_2019-10-19_final.stac-item
2019-10-19 11:56:52	ga_s2bm_ard_3-2-1_50HMJ_2019-10-19_final.stac-item
2019-10-19 11:56:56	ga_s2bm_ard_3-2-1_50HLJ_2019-10-19_final.stac-item
2019-10-19 11:57:07	ga_s2bm_ard_3-2-1_50HMH_2019-10-19_final.stac-item
2019-10-19 11:57:10	ga_s2bm_ard_3-2-1_50HLH_2019-10-19_final.stac-item

Return to ga_s2bm_ard_3 whole collection overview

ga_s2bm_ard_3: Geoscience Australia Sentinel 2B MSI Analysis Ready Data Collection 3

Baseline satellite data

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

All years