Search ga_s2bm_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
2022-06-15 11:54:36	ga_s2bm_ard_3-2-1_50KLU_2022-06-15_final.stac-item
2022-06-15 11:54:48	ga_s2bm_ard_3-2-1_50JLT_2022-06-15_final.stac-item
2022-06-15 11:55:01	ga_s2bm_ard_3-2-1_50JLS_2022-06-15_final.stac-item
2022-06-15 11:55:16	ga_s2bm_ard_3-2-1_50JLR_2022-06-15_final.stac-item
2022-06-15 11:55:31	ga_s2bm_ard_3-2-1_50JLQ_2022-06-15_final.stac-item
2022-06-22 11:47:00	ga_s2bm_ard_3-2-1_50HMJ_2022-06-22_final.stac-item
2022-06-22 11:47:14	ga_s2bm_ard_3-2-1_50HMH_2022-06-22_final.stac-item
2022-06-22 11:47:19	ga_s2bm_ard_3-2-1_50HLH_2022-06-22_final.stac-item
2022-07-02 11:47:00	ga_s2bm_ard_3-2-1_50HMJ_2022-07-02_final.stac-item
2022-07-02 11:47:15	ga_s2bm_ard_3-2-1_50HMH_2022-07-02_final.stac-item
2022-07-02 11:47:19	ga_s2bm_ard_3-2-1_50HLH_2022-07-02_final.stac-item
2022-07-05 11:54:38	ga_s2bm_ard_3-2-1_50KLU_2022-07-05_final.stac-item
2022-07-05 11:54:49	ga_s2bm_ard_3-2-1_50JLT_2022-07-05_final.stac-item
2022-07-05 11:55:03	ga_s2bm_ard_3-2-1_50JLS_2022-07-05_final.stac-item
2022-07-05 11:55:18	ga_s2bm_ard_3-2-1_50JLR_2022-07-05_final.stac-item
2022-07-05 11:55:32	ga_s2bm_ard_3-2-1_50JLQ_2022-07-05_final.stac-item
2022-07-12 11:47:00	ga_s2bm_ard_3-2-1_50HMJ_2022-07-12_final.stac-item
2022-07-12 11:47:14	ga_s2bm_ard_3-2-1_50HMH_2022-07-12_final.stac-item
2022-07-12 11:47:18	ga_s2bm_ard_3-2-1_50HLH_2022-07-12_final.stac-item
2022-10-26 12:02:58	ga_s2bm_ard_3-2-1_50KLE_2022-10-26_nrt.stac-item
2022-10-26 12:03:11	ga_s2bm_ard_3-2-1_50KLD_2022-10-26_nrt.stac-item
2022-10-26 12:03:26	ga_s2bm_ard_3-2-1_50KLC_2022-10-26_final.stac-item
2022-10-26 12:03:40	ga_s2bm_ard_3-2-1_50KLB_2022-10-26_final.stac-item
2022-10-26 12:03:55	ga_s2bm_ard_3-2-1_50KLA_2022-10-26_final.stac-item
2022-10-26 12:04:09	ga_s2bm_ard_3-2-1_50KLV_2022-10-26_final.stac-item
2022-10-26 12:04:24	ga_s2bm_ard_3-2-1_50KLU_2022-10-26_final.stac-item
2022-10-26 12:04:38	ga_s2bm_ard_3-2-1_50JLT_2022-10-26_final.stac-item
2022-10-26 12:04:53	ga_s2bm_ard_3-2-1_50JLS_2022-10-26_final.stac-item
2022-10-26 12:05:07	ga_s2bm_ard_3-2-1_50JLR_2022-10-26_final.stac-item
2022-10-30 11:46:54	ga_s2bm_ard_3-2-1_50HMJ_2022-10-30_final.stac-item
2022-10-30 11:47:08	ga_s2bm_ard_3-2-1_50HMH_2022-10-30_final.stac-item
2022-10-30 11:47:13	ga_s2bm_ard_3-2-1_50HLH_2022-10-30_final.stac-item
2022-10-30 11:47:32	ga_s2bm_ard_3-2-1_50HMF_2022-10-30_nrt.stac-item
2022-10-30 11:47:33	ga_s2bm_ard_3-2-1_50HLF_2022-10-30_nrt.stac-item
2023-06-27 11:47:00	ga_s2bm_ard_3-2-1_50HMJ_2023-06-27_final
2023-06-27 11:47:14	ga_s2bm_ard_3-2-1_50HMH_2023-06-27_final
2023-06-27 11:47:19	ga_s2bm_ard_3-2-1_50HLH_2023-06-27_final
2023-06-27 11:47:29	ga_s2bm_ard_3-2-1_50HMG_2023-06-27_final
2023-06-27 11:47:31	ga_s2bm_ard_3-2-1_50HLG_2023-06-27_final
2023-06-30 11:54:49	ga_s2bm_ard_3-2-1_50JLT_2023-06-30_final
2023-06-30 11:55:03	ga_s2bm_ard_3-2-1_50JLS_2023-06-30_final
2023-06-30 11:55:18	ga_s2bm_ard_3-2-1_50JLR_2023-06-30_final
2023-06-30 11:55:32	ga_s2bm_ard_3-2-1_50JLQ_2023-06-30_final
2023-06-30 11:55:44	ga_s2bm_ard_3-2-1_50JMP_2023-06-30_final
2023-06-30 11:55:47	ga_s2bm_ard_3-2-1_50JLP_2023-06-30_final
2023-06-30 11:55:58	ga_s2bm_ard_3-2-1_50JMN_2023-06-30_final
2023-06-30 11:56:01	ga_s2bm_ard_3-2-1_50JLN_2023-06-30_final
2023-06-30 11:56:08	ga_s2bm_ard_3-2-1_50JMM_2023-06-30_final
2023-06-30 11:56:09	ga_s2bm_ard_3-2-1_50JLN_2023-06-30_final
2023-06-30 11:56:09	ga_s2bm_ard_3-2-1_50JLM_2023-06-30_final
2023-06-30 11:56:14	ga_s2bm_ard_3-2-1_50JMM_2023-06-30_final
2023-06-30 11:56:16	ga_s2bm_ard_3-2-1_50JLM_2023-06-30_final
2023-06-30 11:56:27	ga_s2bm_ard_3-2-1_50JML_2023-06-30_final
2023-06-30 11:56:31	ga_s2bm_ard_3-2-1_50JLL_2023-06-30_final
2023-06-30 11:56:42	ga_s2bm_ard_3-2-1_50HMK_2023-06-30_final
2023-06-30 11:56:45	ga_s2bm_ard_3-2-1_50HLK_2023-06-30_final
2023-06-30 11:56:56	ga_s2bm_ard_3-2-1_50HMJ_2023-06-30_final
2023-06-30 11:57:00	ga_s2bm_ard_3-2-1_50HLJ_2023-06-30_final
2023-06-30 11:57:11	ga_s2bm_ard_3-2-1_50HMH_2023-06-30_final
2023-06-30 11:57:14	ga_s2bm_ard_3-2-1_50HLH_2023-06-30_final
2023-06-30 11:57:25	ga_s2bm_ard_3-2-1_50HMG_2023-06-30_final
2023-06-30 11:57:27	ga_s2bm_ard_3-2-1_50HLG_2023-06-30_final
2023-06-30 11:57:32	ga_s2bm_ard_3-2-1_50HMF_2023-06-30_nrt
2023-07-03 12:03:04	ga_s2bm_ard_3-2-1_50KLE_2023-07-03_nrt
2023-07-03 12:03:17	ga_s2bm_ard_3-2-1_50KLD_2023-07-03_nrt
2023-07-03 12:04:45	ga_s2bm_ard_3-2-1_50JLT_2023-07-03_final
2023-07-03 12:04:59	ga_s2bm_ard_3-2-1_50JLS_2023-07-03_final
2023-07-03 12:05:13	ga_s2bm_ard_3-2-1_50JLR_2023-07-03_final
2023-07-07 11:47:01	ga_s2bm_ard_3-2-1_50HMJ_2023-07-07_final
2023-07-07 11:47:15	ga_s2bm_ard_3-2-1_50HMH_2023-07-07_final
2023-07-07 11:47:19	ga_s2bm_ard_3-2-1_50HLH_2023-07-07_final
2023-07-07 11:47:29	ga_s2bm_ard_3-2-1_50HMG_2023-07-07_final
2023-07-07 11:47:32	ga_s2bm_ard_3-2-1_50HLG_2023-07-07_final
2023-07-07 11:47:37	ga_s2bm_ard_3-2-1_50HMF_2023-07-07_nrt
2023-07-07 11:47:38	ga_s2bm_ard_3-2-1_50HLF_2023-07-07_nrt
2023-07-10 11:54:49	ga_s2bm_ard_3-2-1_50JLT_2023-07-10_final
2023-07-10 11:55:03	ga_s2bm_ard_3-2-1_50JLS_2023-07-10_final
2023-07-10 11:55:18	ga_s2bm_ard_3-2-1_50JLR_2023-07-10_final
2023-07-10 11:55:32	ga_s2bm_ard_3-2-1_50JLQ_2023-07-10_final
2023-07-10 11:55:44	ga_s2bm_ard_3-2-1_50JMP_2023-07-10_final
2023-07-10 11:55:47	ga_s2bm_ard_3-2-1_50JLP_2023-07-10_final
2023-07-10 11:55:58	ga_s2bm_ard_3-2-1_50JMN_2023-07-10_final
2023-07-10 11:56:01	ga_s2bm_ard_3-2-1_50JLN_2023-07-10_final
2023-07-10 11:56:13	ga_s2bm_ard_3-2-1_50JMM_2023-07-10_final
2023-07-10 11:56:16	ga_s2bm_ard_3-2-1_50JLM_2023-07-10_final
2023-07-10 11:56:27	ga_s2bm_ard_3-2-1_50JML_2023-07-10_final
2023-07-10 11:56:31	ga_s2bm_ard_3-2-1_50JLL_2023-07-10_final
2023-07-10 11:56:41	ga_s2bm_ard_3-2-1_50HMK_2023-07-10_final
2023-07-10 11:56:45	ga_s2bm_ard_3-2-1_50HLK_2023-07-10_final
2023-07-10 11:56:56	ga_s2bm_ard_3-2-1_50HMJ_2023-07-10_final
2023-07-10 11:57:00	ga_s2bm_ard_3-2-1_50HLJ_2023-07-10_final
2023-07-10 11:57:11	ga_s2bm_ard_3-2-1_50HMH_2023-07-10_final
2023-07-10 11:57:14	ga_s2bm_ard_3-2-1_50HLH_2023-07-10_final
2023-07-10 11:57:25	ga_s2bm_ard_3-2-1_50HMG_2023-07-10_final
2023-07-10 11:57:27	ga_s2bm_ard_3-2-1_50HLG_2023-07-10_final
2023-07-10 11:57:32	ga_s2bm_ard_3-2-1_50HMF_2023-07-10_nrt
2023-07-30 11:54:50	ga_s2bm_ard_3-2-1_50JLT_2023-07-30_final
2023-07-30 11:55:03	ga_s2bm_ard_3-2-1_50JLS_2023-07-30_final
2023-07-30 11:55:18	ga_s2bm_ard_3-2-1_50JLR_2023-07-30_final
2023-07-30 11:55:33	ga_s2bm_ard_3-2-1_50JLQ_2023-07-30_final

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