LS5_GM_NBART_9_-39_19880101.nc

dataset of product ls5_nbart_geomedian_annual
Indexed by dra547 , created
Archived
for 1st January 1988

Fields 🔗

creation_time 2018-07-05 17:07:27 🔍 format NetCDF 🔍 id 8fa65709-05f7-4aeb-be7e-adeca4e44bda 🔍 instrument TM label • 🔍 lat -35.386 to -34.417 🔍 lon 141.848 to 143.032 🔍 platform LANDSAT_5 product_type surface_reflectance_statistical_summary time 1988-01-01 00:00:00 to 1988-01-01 00:00:00 🔍

Related Datasets 🔗

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LS5_TM_PQ_3577_9_-39_1988_v1501858717.nc#part=47
LS5_TM_PQ_3577_9_-39_1988_v1501858717.nc#part=1
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LS5_TM_NBART_3577_9_-39_1988_v1493704101.nc#part=5
LS5_TM_PQ_3577_9_-39_1988_v1501858717.nc#part=21
LS5_TM_PQ_3577_9_-39_1988_v1501858717.nc#part=24
LS5_TM_PQ_3577_9_-39_1988_v1501858717.nc#part=15
LS5_TM_PQ_3577_9_-39_1988_v1501858717.nc#part=25
LS5_TM_PQ_3577_9_-39_1988_v1501858717.nc#part=27
LS5_TM_NBART_3577_9_-39_1988_v1493704101.nc#part=17
LS5_TM_NBART_3577_9_-39_1988_v1493704101.nc#part=56
LS5_TM_NBART_3577_9_-39_1988_v1493704101.nc#part=50
LS5_TM_PQ_3577_9_-39_1988_v1501858717.nc#part=65
LS5_TM_NBART_3577_9_-39_1988_v1493704101.nc#part=35
LS5_TM_PQ_3577_9_-39_1988_v1501858717.nc#part=29
LS5_TM_PQ_3577_9_-39_1988_v1501858717.nc#part=46
LS5_TM_NBART_3577_9_-39_1988_v1493704101.nc#part=63
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... 102 more
LS5_GM_NBART_9_-39_19880101.nc
ga_ls_landcover_class_cyear_2_1-0-0_au_1988-01-01

Location 🔗

file:///g/data/fk4/datacube/002/stats/geomedian/annual/ls5_gm/9_-39/LS5_GM_NBART_9_-39_19880101.nc

Metadata Document 🔗

RAW As Stac
id: 8fa65709-05f7-4aeb-be7e-adeca4e44bda
product_type: surface_reflectance_statistical_summary
creation_dt: 2018-07-05T17:07:27.388652
platform:
code: LANDSAT_5
instrument:
name: TM
format:
name: NetCDF
extent:
coord:
ll:
lat: -35.385573628005034
lon: 141.93306989200025
lr:
lat: -35.31183966512113
lon: 143.03153836844618
ul:
lat: -34.48952257568112
lon: 141.84816204491463
ur:
lat: -34.41657401717461
lon: 142.93732850210924
to_dt: 1988-01-01T00:00:00
from_dt: 1988-01-01T00:00:00
center_dt: 1988-01-01T00:00:00
grid_spatial:
projection:
valid_data:
type: Polygon
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lineage:
algorithm:
name: datacube-stats
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
swir1
swir2
]
source_filter:
product: ls5_level1_scene
gqa_iterative_mean_xy: [
0
1
]
]
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
y
x
]
location: /g/data/u46/users/ia1511/Work/data/geomedian/output
computation:
chunking:
x: 2000
y: 2000
date_ranges:
end_date: 1990-01-01
step_size: 1y
start_date: 1987-01-01
stats_duration: 1y
input_region: null
filter_product:
var_attributes:
output_products: [
name: ls5_nbart_geomedian_annual
metadata:
format:
name: NetCDF
platform:
code: LANDSAT_5
instrument:
name: TM
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 r2990 3.10.0-862.3.3.el6.x86_64 #1 SMP Wed Jun 27 18:22:35 AEST 2018 x86_64
hostname: r2990
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