LS_FC_PC_3577_-13_-35_20160301_20160531.nc
dataset of product fc_percentile_albers_seasonal
Indexed by lpgs
,
created
Archived
for
1st March 2016
Fields 🔗
creation_time
2018-09-17 03:20:43 🔍
format
NetCDF
id
f7c6384e-140d-441d-a9b6-ef0905f0cd7d 🔍
instrument
TM,ETM+,OLI
label
• 🔍
lat
-31.576 to -30.596 🔍
lon
118.159 to 119.321 🔍
platform
LANDSAT_5,LANDSAT_7,LANDSAT_8
product_type
fractional_cover_seasonal_summary
time
2016-03-01 00:00:00 to 2016-03-01 00:00:00 🔍
Location 🔗
Metadata Document 🔗
id:
f7c6384e-140d-441d-a9b6-ef0905f0cd7d
product_type:
fractional_cover_seasonal_summary
creation_dt:
2018-09-17T03:20:43.415274
platform:
code:
LANDSAT_5,LANDSAT_7,LANDSAT_8
instrument:
name:
TM,ETM+,OLI
format:
name:
NetCDF
extent:
coord:
ll:
lat:
-31.483345573051338
lon:
118.1588045168983
lr:
lat:
-31.576260473638293
lon:
119.21612667589093
ul:
lat:
-30.59572944747405
lon:
118.27276308229636
ur:
lat:
-30.687797050701963
lon:
119.3214999582618
to_dt:
2016-03-01T00:00:00
from_dt:
2016-03-01T00:00:00
center_dt:
2016-03-01T00:00:00
grid_spatial:
projection:
valid_data:
type:
Polygon
coordinates:
[
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spatial_reference:
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image:
bands:
BS_PC_10:
path:
layer:
BS_PC_10
BS_PC_50:
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layer:
BS_PC_50
BS_PC_90:
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layer:
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layer:
PV_PC_10
PV_PC_50:
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layer:
PV_PC_50
PV_PC_90:
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layer:
PV_PC_90
NPV_PC_10:
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layer:
NPV_PC_10
NPV_PC_50:
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layer:
NPV_PC_50
NPV_PC_90:
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layer:
NPV_PC_90
BS_PC_10_source:
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BS_PC_50_source:
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layer:
BS_PC_50_source
BS_PC_90_source:
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layer:
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PV_PC_10_source:
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layer:
PV_PC_10_source
PV_PC_50_source:
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layer:
PV_PC_50_source
PV_PC_90_source:
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layer:
PV_PC_90_source
NPV_PC_10_source:
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layer:
NPV_PC_10_source
NPV_PC_50_source:
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layer:
NPV_PC_50_source
NPV_PC_90_source:
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layer:
NPV_PC_90_source
BS_PC_10_observed_date:
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BS_PC_10_observed_date
BS_PC_50_observed_date:
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BS_PC_50_observed_date
BS_PC_90_observed_date:
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BS_PC_90_observed_date
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PV_PC_10_observed_date
PV_PC_50_observed_date:
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PV_PC_50_observed_date
PV_PC_90_observed_date:
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PV_PC_90_observed_date
NPV_PC_10_observed_date:
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layer:
NPV_PC_10_observed_date
NPV_PC_50_observed_date:
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layer:
NPV_PC_50_observed_date
NPV_PC_90_observed_date:
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layer:
NPV_PC_90_observed_date
lineage:
algorithm:
name:
datacube-stats
version:
0.9b2
repo_url:
https://github.com/GeoscienceAustralia/datacube-stats.git
parameters:
configuration_file:
sources:
[
]
time:
[
2013-01-01
2019-01-01
]
masks:
[
]
time:
[
2013-01-01
2019-01-01
]
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
time:
[
2013-01-01
2019-01-01
]
flags:
cloud:
False
cloud_shadow:
False
noncontiguous:
False
water_observed:
False
product:
wofs_albers
fuse_func:
digitalearthau.utils.wofs_fuser
measurement:
water
product:
ls8_fc_albers
group_by:
solar_day
mask_nodata:
False
measurements:
[
BS
PV
NPV
]
source_filter:
product:
ls8_level1_scene
gqa_iterative_mean_xy:
[
0
1
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time:
[
2010-01-01
2013-06-01
]
masks:
[
]
time:
[
2010-01-01
2013-06-01
]
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:
ls7_pq_albers
group_by:
solar_day
fuse_func:
datacube.helpers.ga_pq_fuser
measurement:
pixelquality
time:
[
2010-01-01
2013-06-01
]
flags:
cloud:
False
cloud_shadow:
False
noncontiguous:
False
water_observed:
False
product:
wofs_albers
fuse_func:
digitalearthau.utils.wofs_fuser
measurement:
water
product:
ls7_fc_albers
group_by:
solar_day
mask_nodata:
False
measurements:
[
BS
PV
NPV
]
source_filter:
product:
ls7_level1_scene
gqa_iterative_mean_xy:
[
0
1
]
time:
[
2003-01-01
2012-01-01
]
masks:
[
]
time:
[
2003-01-01
2012-01-01
]
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
time:
[
2003-01-01
2012-01-01
]
flags:
cloud:
False
cloud_shadow:
False
noncontiguous:
False
water_observed:
False
product:
wofs_albers
fuse_func:
digitalearthau.utils.wofs_fuser
measurement:
water
product:
ls5_fc_albers
group_by:
solar_day
mask_nodata:
False
measurements:
[
BS
PV
NPV
]
source_filter:
product:
ls5_level1_scene
gqa_iterative_mean_xy:
[
0
1
]
time:
[
1999-01-01
2003-06-01
]
masks:
[
]
time:
[
1999-01-01
2003-06-01
]
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:
ls7_pq_albers
group_by:
solar_day
fuse_func:
datacube.helpers.ga_pq_fuser
measurement:
pixelquality
time:
[
1999-01-01
2003-06-01
]
flags:
cloud:
False
cloud_shadow:
False
noncontiguous:
False
water_observed:
False
product:
wofs_albers
fuse_func:
digitalearthau.utils.wofs_fuser
measurement:
water
product:
ls7_fc_albers
group_by:
solar_day
mask_nodata:
False
measurements:
[
BS
PV
NPV
]
source_filter:
product:
ls7_level1_scene
gqa_iterative_mean_xy:
[
0
1
]
time:
[
1987-01-01
1999-01-01
]
masks:
[
]
time:
[
1987-01-01
1999-01-01
]
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
time:
[
1987-01-01
1999-01-01
]
flags:
cloud:
False
cloud_shadow:
False
noncontiguous:
False
water_observed:
False
product:
wofs_albers
fuse_func:
digitalearthau.utils.wofs_fuser
measurement:
water
product:
ls5_fc_albers
group_by:
solar_day
mask_nodata:
False
measurements:
[
BS
PV
NPV
]
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/v10/users/ea6141/FC-percentile/seasonal
computation:
date_ranges:
end_date:
2017-03-01
step_size:
3m
start_date:
2016-03-01
stats_duration:
3m
input_region:
tiles:
[
[
]
-13
-35
]
filter_product:
var_attributes:
BS_PC_10:
long_name:
Bare Soil 10th percentile
standard_name:
BS_10th_percentile
coverage_content_type:
modelResult
BS_PC_50:
long_name:
Bare Soil 50th percentile
standard_name:
BS_50th_percentile
coverage_content_type:
modelResult
BS_PC_90:
long_name:
Bare Soil 90th percentile
standard_name:
BS_90th_percentile
coverage_content_type:
modelResult
PV_PC_10:
long_name:
Photosynthetic Vegetation 10th percentile
standard_name:
PV_10th_percentile
coverage_content_type:
modelResult
PV_PC_50:
long_name:
Photosynthetic Vegetation 50th percentile
standard_name:
PV_50th_percentile
coverage_content_type:
modelResult
PV_PC_90:
long_name:
Photosynthetic Vegetation 90th percentile
standard_name:
PV_90th_percentile
coverage_content_type:
modelResult
NPV_PC_10:
long_name:
Non-photosynthetic Vegetation 10th percentile
standard_name:
NPV_10th_percentile
coverage_content_type:
modelResult
NPV_PC_50:
long_name:
Non-photosynthetic Vegetation 50th percentile
standard_name:
NPV_50th_percentile
coverage_content_type:
modelResult
NPV_PC_90:
long_name:
Non-photosynthetic Vegetation 90th percentile
standard_name:
NPV_90th_percentile
coverage_content_type:
modelResult
BS_PC_10_source:
long_name:
Bare Soil 10th percentile source
standard_name:
BS_10th_percentile_source
coverage_content_type:
modelResult
BS_PC_50_source:
long_name:
Bare Soil 50th percentile source
standard_name:
BS_50th_percentile_source
coverage_content_type:
modelResult
BS_PC_90_source:
long_name:
Bare Soil 90th percentile source
standard_name:
BS_90th_percentile_source
coverage_content_type:
modelResult
PV_PC_10_source:
long_name:
Photosynthetic Vegetation 10th percentile source
standard_name:
PV_10th_percentile_source
coverage_content_type:
modelResult
PV_PC_50_source:
long_name:
Photosynthetic Vegetation 50th percentile source
standard_name:
PV_50th_percentile_source
coverage_content_type:
modelResult
PV_PC_90_source:
long_name:
Photosynthetic Vegetation 90th percentile source
standard_name:
PV_90th_percentile_source
coverage_content_type:
modelResult
NPV_PC_10_source:
long_name:
Non-photosynthetic Vegetation 10th percentile source
standard_name:
NPV_10th_percentile_source
coverage_content_type:
modelResult
NPV_PC_50_source:
long_name:
Non-photosynthetic Vegetation 50th percentile source
standard_name:
NPV_50th_percentile_source
coverage_content_type:
modelResult
NPV_PC_90_source:
long_name:
Non-photosynthetic Vegetation 90th percentile source
standard_name:
NPV_90th_percentile_source
coverage_content_type:
modelResult
BS_PC_10_observed_date:
long_name:
Bare Soil 10th percentile observed date
standard_name:
BS_10th_percentile_observed_date
coverage_content_type:
modelResult
BS_PC_50_observed_date:
long_name:
Bare Soil 50th percentile observed date
standard_name:
BS_50th_percentile_observed_date
coverage_content_type:
modelResult
BS_PC_90_observed_date:
long_name:
Bare Soil 90th percentile observed date
standard_name:
BS_90th_percentile_observed_date
coverage_content_type:
modelResult
PV_PC_10_observed_date:
long_name:
Photosynthetic Vegetation 10th percentile observed date
standard_name:
PV_10th_percentile_observed_date
coverage_content_type:
modelResult
PV_PC_50_observed_date:
long_name:
Photosynthetic Vegetation 50th percentile observed date
standard_name:
PV_50th_percentile_observed_date
coverage_content_type:
modelResult
PV_PC_90_observed_date:
long_name:
Photosynthetic Vegetation 90th percentile observed date
standard_name:
PV_90th_percentile_observed_date
coverage_content_type:
modelResult
NPV_PC_10_observed_date:
long_name:
Non-photosynthetic Vegetation 10th percentile observed date
standard_name:
NPV_10th_percentile_observed_date
coverage_content_type:
modelResult
NPV_PC_50_observed_date:
long_name:
Non-photosynthetic Vegetation 50th percentile observed date
standard_name:
NPV_50th_percentile_observed_date
coverage_content_type:
modelResult
NPV_PC_90_observed_date:
long_name:
Non-photosynthetic Vegetation 90th percentile observed date
standard_name:
NPV_90th_percentile_observed_date
coverage_content_type:
modelResult
output_products:
[
]
name:
fc_percentile_albers_seasonal
metadata:
format:
name:
NetCDF
platform:
code:
LANDSAT_5,LANDSAT_7,LANDSAT_8
instrument:
name:
TM,ETM+,OLI
statistic:
percentile
product_type:
fractional_cover_seasonal_summary
output_params:
zlib:
True
fletcher32:
True
statistic_args:
q:
[
10
50
90
]
per_pixel_metadata:
[
source
observed_date
]
file_path_template:
LS_FC_PC/{x}_{y}/LS_FC_PC_3577_{x}_{y}_{epoch_start:%Y%m%d}_{epoch_end:%Y%m%d}.nc
global_attributes:
title:
Landsat Fractional Cover Percentiles
source:
percentiles of fractional cover unmixing model v2017_06_09
license:
CC BY Attribution 4.0 International License
summary:
The Fractional Cover (FC) algorithm was developed by the Joint Remote Sensing Research Program and is described in Scarth et al. (2010). It has been implemented by Geoscience Australia for every observation from Landsat Thematic Mapper (Landsat 5), Enhanced Thematic Mapper (Landsat 7) and Operational Land Imager (Landsat 8) acquired since 1987. It is calculated from surface reflectance (SR-N_25_2.0.0).
FC provides fractional cover representation of the proportions of green or photosynthetic vegetation, non-photosynthetic vegetation, and bare surface cover across the Australian continent. The fractions are retrieved by inverting multiple linear regression estimates and using synthetic endmembers in a constrained non-negative least squares unmixing model. For further information please see the articles below describing the method implemented which are free to read:
Scarth, P, Roder, A and Schmidt, M 2010, 'Tracking grazing pressure and climate interaction - the role of Landsat fractional cover in time series analysis', Proceedings of the 15th Australasian Remote Sensing & Photogrammetry Conference
Schmidt, M, Denham, R and Scarth, P 2010, 'Fractional ground cover monitoring of pastures and agricultural areas in Queensland', Proceedings of the 15th Australasian Remote Sensing & Photogrammetry Conference
A summary of the algorithm developed by the Joint Remote Sensing Centre is also available from the AusCover website: http://www.auscover.org.au/purl/landsat-fractional-cover-jrsrp
Fractional cover data can be used to identify large scale patterns and trends and inform evidence based decision making and policy on topics including wind and water erosion risk, soil carbon dynamics, land management practices and rangeland condition. This information could enable policy agencies, natural and agricultural land resource managers, and scientists to monitor land conditions over large areas over long time frames.
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:
- ABARES, 2014 Australian ground cover reference sites database 2014. Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, Australia. (https://remote-sensing.nci.org.au/u39/public/html/modis/fractionalcover-sitedata-abares/index.shtml).
- Flood, N., 2014.Continuity of Reflectance Data between Landsat-7 ETM+ and Landsat-8 OLI, for Both Top-of-Atmosphere and Surface Reflectance: A Study in the Australian Landscape. Remote Sensing, 6, 7952-7970.
- Muir, J., Schmidt, M., Tindall, D., Trevithick, R., Scarth, P., Stewart, J., 2011. Guidelines for Field measurement of fractional ground cover: a technical handbook supporting the Australian collaborative land use and management program. Tech. rep., Queensland Department of Environment and Resource Management for the Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra.
- Scarth, P., Roder, A. and Schmidt, M., 2010. Tracking grazing pressure and climate interaction - the role of Landsat fractional cover in the time series analysis, Proceedings of the 15th Australasian Remote Sensing & Photogrammetry Conference, viewed 4 January 2011, scribd.com/doc/37455672/15arspc-Submission-140.
- Schmidt, M., Denham, R. and Scarth, P., 2010. 'Fractional ground cover monitoring of pastures and agricultural areas in Queensland', Proceedings of the 15th Australasian Remote Sensing & Photogrammetry Conference, viewed 4 January 2011, www.scribd.com/doc/37455826/15arspc-Submission-119.
description:
Landsat Fractional Cover percentile 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577)
institution:
Commonwealth of Australia (Geoscience Australia)
cdm_data_type:
Grid
product_suite:
Fractional Cover Stats 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.
- The fractional cover algorithm was developed by the Joint Remote Sensing Research Program (JRSRP) and is described in Scarth et al. (2010). While originally calibrated in Queensland, a large collaborative effort between The Department of Agriculture & ABARES and State and Territory governments to collect additional calibration data has enabled the calibration to extend to the entire Australian continent.
- FC_25 was made possible by new scientific and technical capabilities, the collaborative framework established by the Terrestrial Ecosystem Research Network (TERN) through the National Collaborative Research Infrastructure Strategy (NCRIS), and collaborative effort between state and Commonwealth governments.
publisher_name:
Section Leader, Operations Section, NEMO, Geoscience Australia
product_version:
2
publisher_email:
earth.observation@ga.gov.au
keywords_vocabulary:
GCMD
coverage_content_type:
modelResult
machine:
uname:
Linux r3703 3.10.0-862.3.3.el6.x86_64 #1 SMP Wed Jun 27 18:22:35 AEST 2018 x86_64
hostname:
r3703
software_versions:
python:
version:
3.6.2 (default, Aug 14 2017, 13:37:56)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-18)]
datacube:
version:
1.6.1
repo_url:
https://github.com/opendatacube/datacube-core.git
source_datasets:
statistics:
step:
3m
period:
3m