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		<Name>WMS</Name>
		<Title>GSKY Web Map Service</Title>
		<Abstract>This service relies on GSKY - A Scalable, Distributed Geospatial Data Service. https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/dc9fb2db-8d6f-4b76-a734-93ac7fbc9201</Abstract>
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			<Keyword>WFS</Keyword>
			<Keyword>WMS</Keyword>
			<Keyword>GSKY</Keyword>
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		<OnlineResource xlink:type="simple" xlink:href="https://gsky.nci.org.au/ows/geoglam" />
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		        <ContactOrganization>National Computational Infrastructure</ContactOrganization>
			<ContactPerson>GSKY Developers</ContactPerson>
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				<Address>143 Ward Road</Address>
				<City>Acton</City>
				<StateOrProvince>ACT</StateOrProvince>
				<PostCode>2601</PostCode>
				<Country>Australia</Country>
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		<Layer>
			<Title>GSKY Web Map Service</Title>
			<Abstract>A compliant implementation of WMS</Abstract>
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				<Name>chirps</Name>
				<Title>Monthly Precipitation CHIRPS v2.0</Title>
				<Abstract>The CHIRPS dataset was obtained from http://chg.geog.ucsb.edu/data/chirps/
What is CHIRPS? Latest Preliminary CHIRPS v2.0 Africa Pentad Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. Spanning 50°S-50°N (and all longitudes), starting in 1981 to near-present, CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. As of February 12th, 2015, version 2.0 of CHIRPS is complete and available to the public. For detailed information on CHIRPS, please refer to our paper in Scientific Data.
History and Intent - Since 1999, U.S. Geological Survey (USGS) and CHG scientists, supported by funding from the U.S. Agency for International Development (USAID), the National Aeronautics and Space Administration (NASA), and the National Oceanic and Atmospheric Administration (NOAA), have been developing techniques for producing rainfall maps, especially where surface data is sparse. Estimating rainfall variations in space and time is an important aspect of drought early warning and environmental monitoring. An evolving dryer-than-normal season must be placed in historical context so that the severity of rainfall deficits may be quickly evaluated. However, estimates derived from satellite data provide areal averages that suffer from biases due to complex terrain which often underestimate the intensity of extreme precipitations events. Conversely, precipitation grids produced from station data suffer in more rural regions where there are less rain gauge stations. CHIRPS was created in collaboration with scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in order to deliver reliable, up to date, and more complete datasets for a number of early warning objectives (such as trend analysis and seasonal drought monitoring). Early research focused on combining models of terrain-induced precipitation enhancement with interpolated station data. More recently, new resources of satellite observations such as gridded satellite-based precipitation estimates from NASA and NOAA have been leveraged to build high resolution (0.05°) gridded precipitation climatologies. When applied to satellite-based precipitation fields, these improved climatologies can remove systematic bias, a key technique in the production of the 1981 to near-present CHIRPS dataset. The creation of CHIRPS has supported drought monitoring efforts by the USAID Famine Early Warning Systems Network (FEWS NET).
The current best publication of record for CHIRPS is: Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, James Rowland, Laura Harrison, Andrew Hoell and Joel Michaelsen. "The climate hazards infrared precipitation with stations - a new environmental record for monitoring extremes". Scientific Data 2, 150066. doi:10.1038/sdata.2015.66 2015.</Abstract>
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				<Name>modis_fract_cov_8day</Name>
				<Title>MODIS Vegetation Fractional Cover 8-day v3.1</Title>
				<Abstract>MODIS, CSIRO Land and Water algorithm, Global coverage. 8-day - Vegetation Fractional Cover represents the exposed proportion of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and Bare Soil (BS) within each pixel. The sum of the three fractions is 100% (+/- 3%) and shown in Red/Green/Blue colors. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. This product is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6 and has 500 meters spatial resolution. Access to the data is available in https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/catalog.html. The following publications provide details of the product: http://dx.doi.org/10.1016/j.rse.2015.01.021, http://doi.org/10.1080/2150704X.2018.1465611 and https://doi.org/10.25919/5bf84026e556d</Abstract>
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				<Name>modis_fract_cov_monthly</Name>
				<Title>MODIS Vegetation Fractional Cover Monthly v3.1</Title>
				<Abstract>MODIS, CSIRO Land and Water algorithm, Global coverage. Monthly - Vegetation Fractional Cover represents the exposed proportion of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and Bare Soil (BS) within each pixel. The sum of the three fractions is 100% (+/- 3%) and shown in Red/Green/Blue colors. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. This product is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6 and has 500 meters spatial resolution. The monthly product is aggregated from the 8-day composites using the medoid method. Access to the data is available in https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/catalog.html. The following publications provide details of the product: http://dx.doi.org/10.1016/j.rse.2015.01.021, http://doi.org/10.1080/2150704X.2018.1465611 and https://doi.org/10.25919/5bf84026e556d</Abstract>
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				<Name>modis_tot_cov_8day</Name>
				<Title>MODIS Total Vegetation Cover 8-day v3.1</Title>
				<Abstract>MODIS, CSIRO Land and Water algorithm, Global coverage. 8-day - Total Vegetation Cover represents the sum of the exposed proportion of Photosynthetic Vegetation (PV) and Non-Photosynthetic Vegetation (NPV) within each pixel and is calculated from the Vegetation Fractional Cover product. The sum of Total Vegetation cover and Bare Soil (BS) is 100%. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. This product is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6 and has 500 meters spatial resolution. Access to the data is available in https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/catalog.html. The following publications provide details of the product: http://dx.doi.org/10.1016/j.rse.2015.01.021, http://doi.org/10.1080/2150704X.2018.1465611 and https://doi.org/10.25919/5bf84026e556d</Abstract>
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				<Name>modis_tot_cov_anomaly</Name>
				<Title>MODIS Total Vegetation Cover Anomaly Monthly v3.1</Title>
				<Abstract>MODIS, CSIRO Land and Water algorithm, Global coverage. Monthly - Total Vegetation Cover anomaly represents the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available, expressed in units of cover. For example, if the mean vegetation cover in January (2001-current year) was 40% and the vegetation cover for the pixel in January 2018 was 30%, the anomaly for the pixel in Jan 2018 would be -10%. This product is derived from the Total Vegetation Cover (monthly) product and has 500 meters spatial resolution. Access to the data is available in https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/catalog.html. The following publications provide details of the product: http://dx.doi.org/10.1016/j.rse.2015.01.021, http://doi.org/10.1080/2150704X.2018.1465611 and https://doi.org/10.25919/5bf84026e556d</Abstract>
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				<Name>modis_tot_cov_decile</Name>
				<Title>MODIS Total Vegetation Cover Decile Monthly v3.1</Title>
				<Abstract>MODIS, CSIRO Land and Water algorithm, Global coverage. Monthly - Total Vegetation Cover decile represents the ranking (in ten value intervals) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time-series. This product is derived from the Total Vegetation Cover (monthly) product and has 500 meters spatial resolution. Access to the data is available in https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/catalog.html. The following publications provide details of the product: http://dx.doi.org/10.1016/j.rse.2015.01.021, http://doi.org/10.1080/2150704X.2018.1465611 and https://doi.org/10.25919/5bf84026e556d</Abstract>
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				<Title>MODIS Total Vegetation Cover Monthly v3.1</Title>
				<Abstract>MODIS, CSIRO Land and Water algorithm, Global coverage. Monthly - Total Vegetation Cover represents the sum of the exposed proportion of Photosynthetic Vegetation (PV) and Non-Photosynthetic Vegetation (NPV) within each pixel and is calculated from the Vegetation Fractional Cover product. The sum of Total Vegetation cover and Bare Soil (BS) is 100%. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. The monthly product is aggregated from the 8-day composites using the medoid method. This product is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6 and has 500 meters spatial resolution. Access to the data is available in https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/catalog.html. The following publications provide details of the product: http://dx.doi.org/10.1016/j.rse.2015.01.021, http://doi.org/10.1080/2150704X.2018.1465611 and https://doi.org/10.25919/5bf84026e556d</Abstract>
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