<del>f5cc2904-a539-4485-9dbe-ca553982a621</del>

creation_dt 2018-07-06T03:12:39.020375 creation_time 2018-07-06 03:12:39 format NetCDF id f5cc2904-a539-4485-9dbe-ca553982a621 instrument TM label • lat -40.815 to -39.824 lon 142.381 to 143.633 platform LANDSAT_5 product_type surface_reflectance_statistical_summary time 2003-01-01 00:00:00 to 2003-01-01 00:00:00

Location 🔗

No known active locations

Archived 3 years ago:

file:///g/data/fk4/datacube/002/stats/geomedian/annual/ls5_gm/9_-45/LS5_GM_NBART_9_-45_20030101.nc

Metadata Document 🔗

RAW As Stac

id: f5cc2904-a539-4485-9dbe-ca553982a621

product_type: surface_reflectance_statistical_summary

creation_dt: 2018-07-06T03:12:39.020375

platform:

code: LANDSAT_5

instrument:

format:

extent:

coord:

ll:

lat: -40.81549652739979

lon: 142.47486764530746

lr:

lat: -40.73617647569014

lon: 143.63263809417236

ul:

lat: -39.90247960587763

lon: 142.3805094687774

ur:

lat: -39.82420792699151

lon: 143.52795889914728

to_dt: 2003-01-01T00:00:00

from_dt: 2003-01-01T00:00:00

center_dt: 2003-01-01T00:00:00

grid_spatial:

projection:

valid_data:

type: Polygon

coordinates: [

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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: 0.9b1

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

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swir2

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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: /g/data/u46/users/ia1511/Work/data/geomedian/output

computation:

chunking:

x: 2000

y: 2000

date_ranges:

end_date: 2005-01-01

step_size: 1y

start_date: 2002-01-01

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: 3

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 r2955 3.10.0-862.3.3.el6.x86_64 #1 SMP Wed Jun 27 18:22:35 AEST 2018 x86_64

hostname: r2955

software_versions:

python:

version: 3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 13:39:56) [GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]

datacube:

version: 1.6rc2+2.g6aa9d16

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

source_datasets:

statistics:

step: 1y

period: 1y