<del>LS5_GM_NBART_9_-37_19870101.nc</del>

creation_dt 2020-05-05T06:20:35.370292 creation_time 2020-05-05 06:20:35 format NetCDF id 06655659-2069-4e4b-81ff-57e178acbf4b instrument TM label • lat -33.595 to -32.631 lon 141.683 to 142.845 platform LANDSAT_5 product_type surface_reflectance_statistical_summary time 1987-01-01 00:00:00 to 1987-01-01 00:00:00

Location 🔗

file:///g/data/fk4/datacube/002/stats/geomedian/annual/ls5_gm/9_-37/LS5_GM_NBART_9_-37_19870101.nc

Metadata Document 🔗

RAW As Stac

id: 06655659-2069-4e4b-81ff-57e178acbf4b

product_type: surface_reflectance_statistical_summary

creation_dt: 2020-05-05T06:20:35.370292

platform:

code: LANDSAT_5

instrument:

format:

extent:

coord:

ll:

lat: -33.59522287145778

lon: 141.76469067489674

lr:

lat: -33.523024156227606

lon: 142.84471028954962

ul:

lat: -32.70240494391343

lon: 141.6826197035814

ur:

lat: -32.6309223424897

lon: 142.75364382765136

to_dt: 1987-01-01T00:00:00

from_dt: 1987-01-01T00:00:00

center_dt: 1987-01-01T00:00:00

grid_spatial:

projection:

valid_data:

type: Polygon

coordinates: [

[

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-3700000.0

]

[

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-3600000.0

]

[

1000000.0

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[

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[

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geo_ref_points:

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y: -3700000.0

lr:

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y: -3700000.0

ul:

x: 900000.0

y: -3600000.0

ur:

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y: -3600000.0

spatial_reference: EPSG:3577

image:

bands:

nir:

path:

layer: nir

red:

path:

layer: red

blue:

path:

layer: blue

green:

path:

layer: green

swir1:

path:

layer: swir1

swir2:

path:

layer: swir2

lineage:

algorithm:

version: 1.0.2

repo_url: https://github.com/GeoscienceAustralia/datacube-stats.git

parameters:

configuration_file:

sources: [

masks: [

flags:

cloud_acca: no_cloud

contiguous: True

cloud_fmask: no_cloud

nir_saturated: False

red_saturated: False

blue_saturated: False

green_saturated: False

swir1_saturated: False

swir2_saturated: False

cloud_shadow_acca: no_cloud_shadow

cloud_shadow_fmask: no_cloud_shadow

product: ls5_pq_albers

group_by: solar_day

fuse_func: datacube.helpers.ga_pq_fuser

measurement: pixelquality

]

product: ls5_nbart_albers

group_by: solar_day

measurements: [

blue

green

red

nir

swir1

swir2

]

source_filter:

product: ls5_level1_scene

gqa_iterative_mean_xy: [

]

storage:

crs: EPSG:3577

driver: NetCDF CF

chunking:

x: 200

y: 200

time: 1

tile_size:

x: 100000.0

y: 100000.0

resolution:

x: 25

y: -25

dimension_order: [

time

]

location: ./output_ls5

computation:

chunking:

x: 4000

y: 4000

date_ranges:

end_date: 1988-01-01

step_size: 1y

start_date: 1987-01-01

stats_duration: 1y

input_region: null

filter_product:

var_attributes:

output_products: [

metadata:

format:

platform:

code: LANDSAT_5

instrument:

statistic: new_geomedian

product_type: surface_reflectance_statistical_summary

output_params:

zlib: True

fletcher32: True

statistic_args:

num_threads: 24

file_path_template: ls5_gm/{x}_{y}/LS5_GM_NBART_{x}_{y}_{epoch_start:%Y%m%d}.nc

]

global_attributes:

title: Surface Reflectance Geometric Median

source: geometric median v2017 of surface reflectance NBAR+T

license: CC BY Attribution 4.0 International License

summary: The surface reflectance geometric median (geomedian) is a pixel composite mosaic of a time series of earth observations. The value of a pixel in a geomedian image is the statistical median of all observations for that pixel from a period of time. For example the 2016 Landsat 8 geomedian image over an area will be the median of Landsat 8 pixels observed for that area in 2016. The geometric median products are available as annual datasets for years since 1987. Each annual composite is created from one calendar year of surface reflectance and for one sensor, i.e. Thematic Mapper (Landsat 5), Enhanced Thematic Mapper (Landsat 7) or Operational Land Imager (Landsat 8). The algorithm considers concurrent spectral measurements as a multi-dimensional set and outputs a multi-dimensional median that maintains the relationships among the measurements. This provides a median of the physical conditions measured by the earth observations used to create it, and provides a good representation of a typical pixel observation devoid of outliers. More information on the algorithm can be found in the article below: - Roberts, D., Mueller, N., & Mcintyre, A. (2017). High-Dimensional Pixel Composites From Earth Observation Time Series. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6254-6264. https://doi.org/10.1109/tgrs.2017.2723896

keywords: AU/GA,NASA/GSFC/SED/ESD/LANDSAT,REFLECTANCE,ETM+,TM,OLI,EARTH SCIENCE

platform: LANDSAT_5,LANDSAT_7,LANDSAT_8

instrument: TM,ETM+,OLI

references: - Roberts, D., Mueller, N., & Mcintyre, A. (2017). High-Dimensional Pixel Composites From Earth Observation Time Series. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6254-6264. https://doi.org/10.1109/tgrs.2017.2723896

description: Surface Reflectance Geometric Median 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577)

institution: Commonwealth of Australia (Geoscience Australia)

cdm_data_type: Grid

product_suite: Surface Reflectance Geometric Median 25m

publisher_url: http://www.ga.gov.au

acknowledgment: - Landsat data is provided by the United States Geological Survey (USGS) through direct reception of the data at Geoscience Australias satellite reception facility or download.

publisher_name: Section Leader, Operations Section, NEMO, Geoscience Australia

product_version: 2.0

publisher_email: earth.observation@ga.gov.au

keywords_vocabulary: GCMD

coverage_content_type: modelResult

machine:

uname: Linux gadi-cpu-clx-2007.gadi.nci.org.au 4.18.0-147.5.1.el8.nci.x86_64 #1 SMP Tue Mar 17 06:23:28 UTC 2020 x86_64

hostname: gadi-cpu-clx-2007.gadi.nci.org.au

software_versions:

python:

version: 3.6.10 | packaged by conda-forge | (default, Mar 5 2020, 10:05:08) [GCC 7.3.0]

datacube:

version: 1.8.0b7.dev54+g217c5baa

repo_url: https://github.com/opendatacube/datacube-core.git

source_datasets:

statistics:

step: 1y

period: 1y