LS8_GM_NBART_-6_-36_20190101.nc

dataset of product ls8_nbart_geomedian_annual
Indexed by lpgs , created
Archived
for 1st January 2019

Fields 🔗

creation_time 2020-02-21 03:39:52 🔍 format NetCDF 🔍 id 8ac78c17-6e8b-4def-a24e-cf4e34d4ab07 🔍 instrument OLI label • 🔍 lat -32.913 to -31.979 🔍 lon 125.538 to 126.659 🔍 platform LANDSAT_8 product_type surface_reflectance_statistical_summary time 2019-01-01 00:00:00 to 2019-01-01 00:00:00 🔍

Related Datasets 🔗

LS8_OLI_NBART_3577_-6_-36_20190303012803000000_v1552535500.nc
LS8_OLI_NBART_3577_-6_-36_20190328012209000000_v1554710327.nc
LS8_OLI_PQ_3577_-6_-36_20190709012842000000_v1563940188.nc
LS8_OLI_NBART_3577_-6_-36_20190130012833000000_v1550125175.nc
LS8_OLI_PQ_3577_-6_-36_20190709012818000000_v1563940188.nc
LS8_OLI_NBART_3577_-6_-36_20190312012212500000_v1553976634.nc
LS8_OLI_PQ_3577_-6_-36_20191006012258000000_v1573617919.nc
LS8_OLI_PQ_3577_-6_-36_20190920012230000000_v1573117147.nc
LS8_OLI_NBART_3577_-6_-36_20190208012221000000_v1551790549.nc
LS8_OLI_NBART_3577_-6_-36_20190420012748000000_v1557719056.nc
LS8_OLI_NBART_3577_-6_-36_20190215012831000000_v1551790549.nc
LS8_OLI_NBART_3577_-6_-36_20191209012230000000_v1576724423.nc
LS8_OLI_NBART_3577_-6_-36_20190515012146000000_v1559109089.nc
LS8_OLI_NBART_3577_-6_-36_20191107012234000000_v1574062320.nc
LS8_OLI_NBART_3577_-6_-36_20190607012809000000_v1561023766.nc
LS8_OLI_PQ_3577_-6_-36_20190303012803000000_v1552535501.nc
LS8_OLI_PQ_3577_-6_-36_20190616012225500000_v1561023770.nc
LS8_OLI_PQ_3577_-6_-36_20190319012758000000_v1554171200.nc
LS8_OLI_PQ_3577_-6_-36_20190607012809000000_v1561023770.nc
LS8_OLI_NBART_3577_-6_-36_20190709012842000000_v1563940186.nc
LS8_OLI_PQ_3577_-6_-36_20190911012837500000_v1569401634.nc
LS8_OLI_NBART_3577_-6_-36_20190718012233000000_v1564996465.nc
LS8_OLI_NBART_3577_-6_-36_20190616012201500000_v1561023766.nc
LS8_OLI_NBART_3577_-6_-36_20190725012846500000_v1564996465.nc
LS8_OLI_PQ_3577_-6_-36_20190130012809000000_v1550125175.nc
LS8_OLI_PQ_3577_-6_-36_20190522012825000000_v1560314570.nc
LS8_OLI_PQ_3577_-6_-36_20190702012230000000_v1563940188.nc
LS8_OLI_NBART_3577_-6_-36_20190215012807000000_v1551790549.nc
LS8_OLI_NBART_3577_-6_-36_20191107012258000000_v1574062320.nc
LS8_OLI_NBART_3577_-6_-36_20190718012209000000_v1564996465.nc
... 106 more
LS8_GM_NBART_-6_-36_20190101.nc

Location 🔗

file:///g/data/fk4/datacube/002/stats/geomedian/annual/ls8_gm/-6_-36/LS8_GM_NBART_-6_-36_20190101.nc

Metadata Document 🔗

RAW As Stac
id: 8ac78c17-6e8b-4def-a24e-cf4e34d4ab07
product_type: surface_reflectance_statistical_summary
creation_dt: 2020-02-21T03:39:52.210357
platform:
code: LANDSAT_8
instrument:
name: OLI
format:
name: NetCDF
extent:
coord:
ll:
lat: -32.8719305269816
lon: 125.53804848569112
lr:
lat: -32.913418447999724
lon: 126.61363727082221
ul:
lat: -31.978703919062355
lon: 125.59202260818066
ur:
lat: -32.01979035804176
lon: 126.65865057186201
to_dt: 2019-01-01T00:00:00
from_dt: 2019-01-01T00:00:00
center_dt: 2019-01-01T00:00:00
grid_spatial:
projection:
valid_data:
type: Polygon
coordinates: [
[
[
-600000.0
-3533486.9463724545
]
[
-600000.0
-3500000.0
]
[
-500000.0
-3500000.0
]
[
-500000.0
-3600000.0
]
[
-600000.0
-3600000.0
]
[
-600000.0
-3533486.9463724545
]
]
]
geo_ref_points:
ll:
x: -600000.0
y: -3600000.0
lr:
x: -500000.0
y: -3600000.0
ul:
x: -600000.0
y: -3500000.0
ur:
x: -500000.0
y: -3500000.0
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:
name: datacube-stats
version: 1.0.1
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: ls8_pq_albers
group_by: solar_day
fuse_func: datacube.helpers.ga_pq_fuser
measurement: pixelquality
]
product: ls8_nbart_albers
group_by: solar_day
measurements: [
blue
green
red
nir
swir1
swir2
]
source_filter:
product: ls8_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: ./output
computation:
chunking:
x: 4000
y: 4000
date_ranges:
end_date: 2020-01-01
step_size: 1y
start_date: 2019-01-01
stats_duration: 1y
input_region: null
filter_product:
var_attributes:
output_products: [
name: ls8_nbart_geomedian_annual
metadata:
format:
name: NetCDF
platform:
code: LANDSAT_8
instrument:
name: OLI
statistic: new_geomedian
product_type: surface_reflectance_statistical_summary
output_params:
zlib: True
fletcher32: True
statistic_args:
num_threads: 24
file_path_template: ls8_gm/{x}_{y}/LS8_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-0365.gadi.nci.org.au 4.18.0-80.11.2.el8_0.x86_64 #1 SMP Tue Sep 24 11:32:19 UTC 2019 x86_64
hostname: gadi-cpu-clx-0365.gadi.nci.org.au
software_versions:
python:
version: 3.6.7 | packaged by conda-forge | (default, Nov 6 2019, 16:19:42) [GCC 7.3.0]
datacube:
version: 1.7+253.ga031f3f4
repo_url: https://github.com/opendatacube/datacube-core.git
source_datasets:
statistics:
step: 1y
period: 1y