Advanced Very High Resolution Radiometer (AVHRR) data were obtained from the USGS Global AVHRR 10-day composite data. (http://edcdaac.usgs.gov/1KM/1kmhomepage.asp) (Markon et al. 1995). Glaciers and oceans were masked out using information from the Digital Chart of the World (ESRI 1993). The image is composed of 1 x 1-km pixels. The color of each pixel was determined by its reflectance at the time of maximum greenness, selected from 10-day composite images from 11 July to 30 August 1993 and 1995. These intervals cover the vegetation green-up-to-senescence period during two relatively warm years when summer-snow cover was at a minimum in the Arctic. Maximum greenness was determined from the normalized difference vegetation index (NDVI). Vegetation greenness is calculated as: NDVI = (NIR - R) / (NIR + R), where NIR is the spectral reflectance in the AVHRR near-infrared channel (0.725-1.1 µ, channel 2) where light-reflectance from the plant canopy is dominant, and R is the reflectance in the red channel (0.58 to 0.68 µ, channel 1), the portion of the spectrum where chlorophyll absorbs maximally. The resulting image shows the Arctic with minimum snow and cloud cover. The channel 1 and channel 2 values were then stacked to create as a false-color CIR image (RGB = ch. 2, ch. 1, ch. 1). Back to Circumpolar Arctic Vegetation Map Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes: AVHRR Biomass 2010, AVHRR Biomass Trend 1982-2010, AVHRR False Color Infrared 1993-1995, AVHRR NDVI 1993-1995, AVHRR NDVI Trend 1982-2010, AVHRR Summer Warmth Index 1982-2003, Bioclimate Subzone, Coastline and Treeline Map, Elevation, Floristic Provinces, Lake Cover, Landscape Physiography, Landscape Age, Substrate Chemistry, Vegetation References Markon, C. J., M. D. Fleming, and E. F. Binnian. 1995. Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data. Polar Record 31:179-190.

Statewide Imagery flown in 2020 for South Carolina. This layer references service published by SC Revenue and Fiscal Affairs.This data was collected between January 7 and March 7, 2020, during leaf-off and cloud free conditions by Kucera International Inc Using both frame (Vexcel Eagle M1) and push-broom (Leica ADS100) based digital mapping cameras, all of the imagery was collected at 6 inch resolution with 4-radiometric bands producing True and False Color datasets. 6 Inch, 4-Band (R, G, B, NIR) imagery. Cloud free conditions with a sun angle of 30 Degrees or higher to reduce shadows as much as possible. Additionally, the northing and easting offsets cannot exceed 3 pixels or 1.5ft Ground Sampled Distance.

We measured the spectra of 4606 surface soil samples from across Australia using a vis-NIR spectrometer. These spectra provide an integrative measure that provides information on the fundamental characteristics and composition of the soil, including colour, iron oxide, clay and carbonate mineralogy, organic matter content and composition, the amount of water present and particle size. This soil information content of the spectra was summarised using a principal component analysis (PCA). We used model trees to derive statistical relationships between the scores of the PCA and 31 predictors that were readily available and we thought might best represent the factors of soil formation (climate, organisms, relief, parent material, time and the soil itself). The models were validated and subsequently used to produce digital maps of the information content of the spectra, as summarised by the PCA, with estimates of prediction error at 3-arc seconds (around 90 m) pixel resolution. The maps might be useful in situations requiring high-resolution, quantitative soil information e.g. in agricultural, environmental and ecologic modelling and for soil mapping and classification.

Attributes: Units of measurement: 1. Principal component 1; 2. Principal component 3; 3. Principal component 3.

For interpretations please see Viscarra Rossel & Chen (2011).

Data Type: Float Grid.

Map Projection: Geographic.

Datum: GDA94.

Map units: Decimal degrees.

Resolution: 0.00083333333 degrees.

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SPOT has two similar instrument on each bus; SPOT 1 launched in February 1986, SPOT 2 launched in January 1990 and SPOT3 launched in September 1994 . The High Resolution Visible Imaging System (HRV) on board on SPOT consists of push broom instruments.

 Each instrument has two spectral modes: 1) panchromatic (P), black and
 white from 510-730.nanometers; and 2) multispectral (XS), color with
 three spectral bands, from 500-590, 610-680 and 790-890
 nanometers. The resolution in P mode is 10 meters while the resolution
 in XS mode is 20 meters. The P mode number of pixels per line can vary
 from 6000 to 10400 and the number of lines per scene from 6000 to 9800
 depending on the surveying method (vertical or oblique) and the
 prepocessing level (note, for LEVEL 2, scenes are aligned with true
 North and resampled accordingly). The volume of data per scene can
 therefore vary from 36 to 100 Mbytes. In XS mode, the number of pixels
 per lines varies from 3000 to 5200 and the number of lines per scenes
 from 300 to 4900 for each of the three spectral bands. The total
 volume of data can therefore vary from 27 to 76.5 Mbytes. Each SPOT
 scene acquired in P or XS mode covers an area of 60 X 60 km2 in
 near-vertical viewing, and up to 60 x 80 km2 in oblique viewing at
 viewing angles up to 27 degrees either side of the vertical. The
 oblique viewing offers two possibilities: 1) to increase the
 revisiting capability up to five times per 26 days; and 2) to acquire
 stereoscopic pairs by imaging the same portion of landscape from two
 different revolutions. SPOT circles the Earth 14 and 5/26 times each
 day at an altitude of 830 km in a sun-synchronous (polar) orbit.

 All scenes are nominally centered in latitude on the J rows of the
 SPOT Grid Reference System (GRS). By opting for a SAT
 (Shift-Along-Track) scene, rather than a standard scene, the customer
 can, however, obtain scenes that are, as the name implies, shifted
 along the satellite track. The center of a SAT scene lies on a line
 parallel to the satellite track at a latitude corresponding to a
 whole-number multiple of one-tenth of the standard scene length
 (i.e. 6 km) relative to the GRS (K,J) position. These positions are
 numbered from 0 to 9, from North to South, between a given (K,J)and
 the next (K,J) to the South.

 The LEVEL 2 differs from the LEVEL 1B preprocessing as follows: 1)
 geometry and location accuracy enhanced by internal data; 2) pixel
 resampling along X and Y axes according to a map projection system
 selected by the client (LEVEL 2 scenes are aligned with North); 3)
 location of a point by its rectangular coordinates using CARTOGRAPHIC
 marks (these replace the geographic marks used in LEVEL 1B); and 4)
 possibility of joining two LEVEL 2 scenes, pixel to pixel, by
 processing them as a single strip. A series of consecutive scenes from
 the same satellite pass can be modeled just once (maximum 10
 scenes). RADIOMETRIC corrections are the same as for LEVEL
 1B. GEOMETRIC corrections are bidirectional and you can use an image
 deformation law established from a viewing model. The model is
 computed from the SPOT system internal data and can be applied to a
 single scene or a strip of several scenes.

 The LEVEL 2A product is a precision preprocessing level. In addition
 to LEVEL 1B radiometric corrections, bidirectional geometric
 corrections are performed in order to plot the scene according to a
 given map projection (conformal Lambert, transverse Mercator, oblique
 equatorial, polar stereographic, polyconic). Corrections are made
 using satellite attitude data and viewing geometry. GCPs are not used
 which means that topographical maps are not required. Although the
 internal accuracy of the image is enhanced (0.5 10-3) absolute
 location accuracy remains the same as for LEVEL 1B (about 800 m).
 Location accuracy can, however, be optimized by a simple XY adjustment
 based on known points.

 The LEVEL 2B product is a precision preprocessing level in which
 bidirectional geometric corrections are performed using
 GCPs. Topographic maps of sufficient accuracy will therefore be
 required to extract GCPs. The image is rectified according to the
 projection of the map used. The location accuracy is 30 m (rms error)
 for vertical viewing where the difference in elevation between the
 highest and lowest points does not exceed 1250 m. For oblique viewing
 at maximum angle (27 degrees), this accuracy can be met provided that
 maximum elevation minus minimum elevation does not exceed 170 m. Note
 that this level, like LEVEL 1B, does not consider distortion due to
 terrain relief. The less pronouced the relief and the closer the
 viewing angle to the vertical, the more accurate the product.

 LEVEL 2 scenes are aligned with true North and images are resampled
 accordingly. LEVEL 2 scenes can be processed in the form of a strip
 along the satellite track comprising several perfectly matched scenes
 (pixel to pixel). As an option and for the same price, LEVEL 2A and
 2B scenes can be supplied with a 2 km x 2 km geographic grid in the
 form of continuous lines or crosses at 2 km intervals.

 LEVEL S scenes are rectified using control points for registration
 with a reference scene. Registration accuracy is 0.5 pixels when both
 scenes are recorded at the same viewing angle. This product is
 intended primarily for multidate studies. LEVEL S1 scenes are
 rectified with respect to LEVEL 1B reference scenes. LEVEL S2 scenes
 are rectified with respect to LEVEL 2 reference scenes. The GEOSPOT
 PRODUCTS including SPOT view basic, SPOT view plus and SPOT MAP are
 delivered according to the GIS_Geospot digital format (for additional
 technical information about the GIS_Geospot format, please contact :
 tel (33)62194242 or fax (33)62194056, these new products include
 orthoimage, layout in raster format,digital elevation model in raw
 raster format, vector files in Arc/Info Export 6.0 and digital quick
 look in TIFF format.

 One way to access to the SPOT IMAGE Catalogue (DALI) is via the
 Prototype International Directory using the 'LINK' facility.

Traditional soil maps have helped us to better understand soil, to form our concepts and to teach and transfer our ideas about it, and so they have been used for many purposes. Although, soil maps are available in many countries, there is a need for them to be updated because they are often deficient in that their spatial delineations and their descriptions are subjective and lack assessments of uncertainty. Updating them is a priority for federal soil surveys worldwide as well as for research, teaching and communication. New data from sensors and quantitative ‘digital’ methods provide us with the tools to do so. Here, we present an approach to update large scale, national soil maps with data derived from a combination of traditional soil profile classifications, classifications made with visible–near infrared (vis–NIR) spectroscopy, and digital soil class mapping (DSM). Our results present an update of the Australian Soil Classification (ASC) orders map. The overall error rate of the DSM model, tested on an independent validation set, was 55.6%, and a few of the orders were poorly classified. We discuss the possible reasons for these errors, but argue that compared to the previous ASC maps, our classification was derived objectively, using currently best available data sets and methods, the classification model was interpretable in terms of the factors of soil formation, the modelling produced a 1×1 km resolution soil map with estimates of spatial uncertainty for each soil order and our map has no artefacts at state and territory borders.

Date of Images:1/10/2024Date of Next Image:UnknownSummary:This PlanetScope imagery captured by Planet Labs Inc. on January 10, 2024 shows the post-event conditions after the Southeast United States severe storms.The color infrared image is created using the near-infrared, red, and green channels from the Planet instrument allowing for the ability to see areas impacted from the flooding. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue.Suggested Use:A false color composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred.Satellite/Sensor:PlanetScopeResolution:3 metersCredits:NASA Disasters Program, Includes copyrighted material of Planet Labs PBC. All rights reserved.Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags03/services/se_us_severestorms_202401/planet_colorinfrared_20240110/ImageServer/WMSServer

The normalized difference vegetation index (NDVI) is a measure of greenness. NDVI was calculated as: NDVI = (NIR - R) / (NIR + R), where NIR is the spectral reflectance in the AVHRR near-infrared channel (0.725-1.1 µm, channel 2) where light-reflectance from the plant canopy is dominant, and R is the reflectance in the red channel (0.5 to 0.68 µm, channel 1), the portion of the spectrum where chlorophyll absorbs maximally. Advanced Very High Resolution Radiometer (AVHRR) data were obtained from the USGS Global AVHRR 10-day composite data website. Glaciers and oceans were masked out using information from the Digital Chart of the World (ESRI 1993). The image is composed of 1 x 1-km pixels. The color of each pixel was determined by its reflectance at the time of maximum greenness, selected from 10-day composite images from 11 July to 30 August 1993 and 1995. These intervals cover the vegetation green-up-to-senescence period during two relatively warm years when summer-snow cover was at a minimum in the Arctic (Markon et al. 1995). Back to Alaska Arctic Tundra Vegetation Map (Raynolds et al. 2006) Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes AVHRR NDVI, Bioclimate Subzone, Elevation, False Color-Infrared CIR, Floristic Province, Lake Cover, Landscape, Substrate Chemistry, Vegetation References Markon, C. J., M. D. Fleming, and E. F. Binnian. 1995. Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data. Polar Record 31:179-190.

Iron (Fe) oxide mineralogy in most Australian soils is poorly characterized, even though Fe oxides play an important role in soil function. Fe oxides reflect the conditions of pH, redox potential, moisture, and temperature in the soil environment. The strong pigmenting effect of Fe oxides gives most soils their color, which is largely a reflection of the soil’s Fe mineralogy. Visible-near-infrared (vis-NIR) spectroscopy can be used to identify and measure the abundance of certain Fe oxides in soil, and the visible range can be used to derive tristimuli soil color information. We measured the spectra of 4606 surface soil samples from across Australia using a vis-NIR spectrometer with a wavelength range of 350-2500 nm. We determined the Fe oxide abundance for each sample using the diagnostic absorption features of hematite (near 880 nm) and goethite (near 920 nm) and derived a normalized iron oxide difference index (NIODI) to better discriminate between them. The NIODI was generalized across Australia with its spatial uncertainty using sequential indicator simulation, which resulted in a map of the probability of the occurrence of hematite and goethite. We also derived soil RGB color from the spectra and mapped its distribution and uncertainty across the country using sequential Gaussian simulations. The simulated RGB color values were made into a composite true color image and were also converted to Munsell hue, value, and chroma. These color maps were compared to the map of the NIODI, and both were used to interpret our results. The maps were validated by randomly splitting the data into training and test data sets, as well as by comparing our results to existing studies on the distribution of Fe oxides in Australian soils. Attributes: Units of measurement: 1. Munsell Hue; 2. Munsell Chroma; 3. Munsell value; 4. NIODI; 5. NIODI uncertainty. For details please see Viscarra Rossel et al. (2010).

Data Type: Float Grid.

Map projection: Lambert Conformal Conic.

Datum: GDA94.

Map units: Decimal degrees.

Resolution: 10,000 metres.

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Near Infrared Imagery for the State of Georgia (NAIP 2010). This service is based on 1 meter resolution, 4 band (RGBI) imagery from the National Agriculture Imagery Program (NAIP 2010). A band extraction process reduces the 4-band (RGBI) output to 3-band (NIR, 4-1-2). NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. NAIP imagery may contain as much as 10% cloud cover per source tile. Although these data have been processed successfully on a computer system at the Georgia GIS Data Clearinghouse, no warranty expressed or implied is made by Georgia GIS Data Clearinghouse regarding the utility of the data on any other system, nor shall the act of distribution constitute any such warranty.

The high resolution sensor HRV operates in two modes: panchromatic (0.51-0.73 micrometer) with 10 meter resolution and multispectral (0.50-0.59, 0.61-0.68, and 0.79-0.89 micrometer) with 20 meter resolution. Each scene is normally 60x60 km.

 More than 700 000 SPOT images (over 100 000 per year) from all parts of the
 world are archived at SSC Satellitbild. About 40% of the total number is from
 the onboard recorder of the satellite.
 The ratio between multispectral and panchromatic data is 40/60.
 Approximately 3000 scenes per year are processed and equally many films
 produced.

 The different processing levels include various radiometric and geometric
 corrections, precision correction using group control points, with or without
 digital terrain model, and output in any standard map projection.

 The data is produced as digital data or as a variety of photographic products.
 A unique product at Satellitbild is the Satellite Image Map which, as a
 complement to the scene format, is imagery in map sheet format. Another product
 is digital terrain models from SPOT data.

 The most common digital distribution media is magnetic tape at 1600 or 6250 bpi.
 The production system is able to read and produce all of the most frequently
 used formats for satellite imagery, i.e. CRIS-format, SPOT IMAGE-format,
 LTWG-format, FAST-format, ERDAS-format, etc.

 For input to ARC/INFO GIS system, the GISIMAGE format is available. GISIMAGE
 is an ERDAS image format delivered on 1/4 inch cassettes or CCTs for input
 on both SUN and VAX stations. The GISIMAGE is ideal as a background image for
 integrated raster/vector updating of map information.

Metadata: NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 1b RadiancesMore information about this imagery can be found here.This satellite imagery combines data from the NOAA GOES East and West satellites and the JMA Himawari satellite, providing full coverage of weather events for most of the world, from the west coast of Africa west to the east coast of India. The tile service updates to the most recent image every 10 minutes at 1.5 km per pixel resolution.The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.The Advanced Baseline Imager (ABI) instrument samples the radiance of the Earth in sixteen spectral bands using several arrays of detectors in the instrument’s focal plane. Single reflective band ABI Level 1b Radiance Products (channels 1 - 6 with approximate center wavelengths 0.47, 0.64, 0.865, 1.378, 1.61, 2.25 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for visible and near-infrared (IR) bands. Single emissive band ABI L1b Radiance Products (channels 7 - 16 with approximate center wavelengths 3.9, 6.185, 6.95, 7.34, 8.5, 9.61, 10.35, 11.2, 12.3, 13.3 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for IR bands. Detector samples are compressed, packetized and down-linked to the ground station as Level 0 data for conversion to calibrated, geo-located pixels (Level 1b Radiance data). The detector samples are decompressed, radiometrically corrected, navigated and resampled onto an invariant output grid, referred to as the ABI fixed grid.McIDAS merge technique and color mapping provided by the Cooperative Institute for Meteorological Satellite Studies (Space Science and Engineering Center, University of Wisconsin - Madison) using satellite data from SSEC Satellite Data Services and the McIDAS visualization software.

Approximately 1,900 square kilometers of imagery were collected from July 14 to July 21, 2014 using a HyMap™ sensor (Cocks and others, 1998) mounted on a modified Piper Navajo aircraft. The survey area covered parts of the Wrangell and Nutzotin Mountains in the eastern Alaska Range near Nabesna, Alaska. The aircraft was flown at an altitude of approximately 5,050 meters (m) (3,480 m above the mean ground surface elevation of 1570 m) resulting in average ground spatial resolution of 6.7 m. HyMap measured reflected sunlight in 126 narrow channels that cover the wavelength region of 455 to 2,483 nanometers (nm). Data were delivered by the operators of the sensor (HyVista Corp., Australia) in units of radiance (Kokaly and others, 2017). Radiance data were converted to reflectance with procedures adapted from Kokaly and others (2013). They are described and documented in this data release. Reflectance data from HyMap were processed using the Material Identification and Characterizatio ...

This Near Real Time (NRT) data type (MYD02SSH) is a subsample from the MODIS Level 1B 1-km data. Every fifth pixel is taken from the MYD021KM product and written out to MYD02SSH. The subsampling starts at the third frame, and at the third line. Here, "frame" and "line" are naming conventions for pixels along and across the scan, respectively. Since MYD02SSH is a subsampled Level 1B , many things from the Level 1B documentation apply as well. That is, the MYD02SSH data productcontains calibrated and geolocated at-aperture radiances for 36 bands generated from MODIS Level 1A scans of raw radiance (MOD 01). The radiance units are in W/(m ^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared (SWIR), and Near Infrared (NIR) measurements are made during daytime only, while radiances for Thermal Infrared (TIR) are measured continuously.As it's parent, the MYD02SSH is in HDF-EOS format, and all metadata structures and names are preserved for better convenience. However, some relevant changes are made where appropriate, e.g. the dimension mappings are updated to reflect the new one-to-one correspondance between the data and geolocations. The latter is one of the most important differences: in the MYD02SSH, there is no offset between data and geolocation pixels. The spatial coverage is almost similar to that from MYD021KM (nominally it is 2330 by 2030 km, cross-track by along-track, respectively). The MYD02SSH is produced continuously, and thus the processing provides 2-day repeat observations of the Earth with a repeat orbitpattern every 16 days.The shortname for this product is MYD02SSH

Metadata: NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 1b RadiancesMore information about this imagery can be found here.This satellite imagery combines data from the NOAA GOES East and West satellites and the JMA Himawari satellite, providing full coverage of weather events for most of the world, from the west coast of Africa west to the east coast of India. The tile service updates to the most recent image every 10 minutes at 1.5 km per pixel resolution.The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.The Advanced Baseline Imager (ABI) instrument samples the radiance of the Earth in sixteen spectral bands using several arrays of detectors in the instrument’s focal plane. Single reflective band ABI Level 1b Radiance Products (channels 1 - 6 with approximate center wavelengths 0.47, 0.64, 0.865, 1.378, 1.61, 2.25 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for visible and near-infrared (IR) bands. Single emissive band ABI L1b Radiance Products (channels 7 - 16 with approximate center wavelengths 3.9, 6.185, 6.95, 7.34, 8.5, 9.61, 10.35, 11.2, 12.3, 13.3 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for IR bands. Detector samples are compressed, packetized and down-linked to the ground station as Level 0 data for conversion to calibrated, geo-located pixels (Level 1b Radiance data). The detector samples are decompressed, radiometrically corrected, navigated and resampled onto an invariant output grid, referred to as the ABI fixed grid.Data source and merge technique provided by the Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin- Madison.

This is Version 1 of the Australian Soil Bulk Density - Whole Earth product of the Soil and Landscape Grid of Australia.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.

Attribute Definition: Bulk Density of the whole soil (including coarse fragments) in mass per unit volume by a method equivalent to the core method; Units: g/cm3; Period (temporal coverage; approximately): 1950-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Variance explained (cross-validation): 0.4%; Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. Lineage: The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being;

1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a); 2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).

Version 1 of the Australian Soil Property Maps combines mapping from the:

1) Australia-wide three-dimensional Digital Soil Property Maps; 2) Western Australia Polygon Disaggregation Maps; 3) South Australian Agricultural Areas Polygon Disaggregation Maps; 4) Tasmanian State-wide DSM Maps.

These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.

This is Version 1 of the Australian Soil Organic Carbon product of the Soil and Landscape Grid of Australia.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.

Attribute Definition: Mass fraction of carbon by weight in the less than 2 mm soil material as determined by dry combustion at 900° C; Units: %; Period (temporal coverage; approximately): 2000-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. Lineage: The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being;

1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a); 2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).

Version 1 of the Australian Soil Property Maps combines mapping from the:

1) Australia-wide three-dimensional Digital Soil Property Maps; 2) South Australian Agricultural Areas Polygon Disaggregation Maps; 3) Tasmanian State-wide DSM Maps.

These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. Initially, the 2005 New Mexico statewide acquisition Digital Ortho-photo Quarter Quads (DOQQs) were used as the foundation imagery for the vegetation map . These DOQQs came as separate natural color and color-infrared sets which altogether gathered four separate bands of spectral reflectance values from the visible blue to the near-infrared (NIR) wavelengths at a 1-m spatial resolution. These four separate bands were combined into one file and then mosaicked together. The ortho-photo mosaic was resampled to a 2-m spatial resolution. All imagery and other spatial data layers were compiled into a geodatabase and GIS using ArcGIS 10.4 (ESRI 2008). To support the mapping process, we acquired a standard set of relevant spatial data layers including digital elevation models, digital raster graphics (DRGs) of 1:24,000-scale USGS topographic maps, roads, ownership, geology (Hawley et al. 2005; Fryberger 2001) and soils (USDA-NRCS 2017).

NOTE: This services contains a large amount of data and may be slow to load. For best results and faster loading, zoom into an area of interest.Date of Images:10/3/2022Date of Next Image:None ExpectedSummary:This PlanetScope imagery captured by Planet Labs Inc. on October 3, 2022 shows the impacts from Hurricane Ian across Florida.The True Color RGB provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred.The color infrared image is created using the near-infrared, red, and green channels from the Planet instrument allowing for the ability to see areas impacted from the hurricane. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue.Suggested Use:True Color:True Color RGB provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred.Color Infrared:A false color composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred.Satellite/Sensor:PlanetScopeResolution:3 metersCredits:NASA Disasters Program, Includes copyrighted material of Planet Labs PBC. All rights reserved.Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/rest/services/hurricane_ian_2022/planet_20221003/MapServer

This is Version 1 of the Australian Soil Total Phosphorus product of the Soil and Landscape Grid of Australia.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.

Attribute Definition: Total phosphorus; Units: %; Period (temporal coverage; approximately): 1950-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. Lineage: The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. For this soil attribute the Australia-wide three-dimensional Digital Soil Property Maps are the only maps available. Thus the modelling for this soil attribute only used Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a).

This is Version 1 of the Australian Soil Silt product of the Soil and Landscape Grid of Australia.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.

Attribute Definition: 2-200 μm mass fraction of the less than 2 mm soil material determined using the pipette method; Units: %; Period (temporal coverage; approximately): 1950-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF.

Lineage: The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being:

1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a); 2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).

Version 1 of the National Digital Soil Property Maps combines mapping from the:

1) Australia-wide three-dimensional Digital Soil Property Maps; 2) Western Australia Polygon Disaggregation Maps; 3) South Australian Agricultural Areas Polygon Disaggregation Maps; 4) Tasmanian State-wide DSM Maps.

These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.