Digital Soil Mapping Technologies for Countries with Sparse Data Infrastructures
2.2 Hardware
Figure 2.2 shows the electromagnetic spectrum, highlighting those parts where soil information can be obtained. Matter emits electromagnetic radiation in different parts of the spectrum, and this radiation can be measured by different types of
2 Digital Soil Mapping Technologies for Countries with Sparse Data Infrastructures 17
Gamma radiometrics EM
induction
NMR Energy of one photon (eV)
GPR, TDR Ultrasound
JERS RADARSAT PolSAR
Landsat, TM, SPOT AVHRR, Ikonos
XRF, XRD Frequency (Hz)
Wavelength (m) 103
106 105 104 103 102 10–1 10–2 10–3 10–4 10–5
10–1110–1010–9 10–8 10–7 10–6 10–5 10–4 10–3 10–2 10–1 1 10 102 103 104 105 106 107 10–6 10–7 10–8 10–910–1010–1110–1210–13 10 1
104 105 106 107 108 109 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022
Soft X Rays Visible
Infrared Microwaves
Radio waves Ultraviolet
Hard X Rays Gamma rays
Fig. 2.2 Electromagnetic spectrum, highlighting instruments for obtaining soil information
spectroscopy depending on the wavelength. It provides a basis for remote sensing of the properties of matter. A sensing system might measure the radiation emitted by an object after the object has itself been irradiated. Two examples of this are the optical remote sensing systems that measure the solar radiation reflected by an object, and the synthetic aperture radar systems (SAR) that measure deliberately long-wave radiation backscattered by an object. Alternatively, it may be possible to measure radiation emitted by an object because of its temperature (emitted in ther-mal infra-red frequencies) or because of radioactive decay (decay of uranium tho-rium and potassium isotopes are widely measured by “passive” gamma radiometry in geophysics). The electromagnetic radiation emitted from an object will therefore depend on its physico-chemical properties, some of which are of direct interest in soil studies such as temperature, mineralogy, organic content, physical structure, or the chlorophyll content of the vegetation.
Some examples of the instruments used in soil science, grouped by their wave-lengths are:
– Radiowaves, wavelengths about 300,000–0.3 m (Fig. 2.3).
This includes sensors in the low frequency (about 10 kHz), e.g. electromagnetic induction (EMI), and high frequency (about 100 MHz), e.g. ground penetrat-ing radar (GPR), time domain reflectometer (TDR), frequency domain moisture sensors (FD), which detect variations in soil dielectric constant. Radiowave or microwave radiation can be applied in the presence of a magnetic field to excite nuclear and electron magnetic resonances (e.g., Nuclear Magnetic Resonance (NMR) at radiowaves, and Electron Spin Resonance (ESR) at microwaves) that are sensitive to the surrounding molecules, from which information about local bonding of atoms can be obtained (O’Day, 1999). Another application in the radio frequency band is transmission of information via wireless sensor networks (Wang et al., 2006).
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Fig. 2.3 Realε’ and imaginary ε” permittivity as a function of frequency in the radio-microwave spectrum
– Microwaves, wavelengths 0.3–0.0003 m (Fig. 2.3).
Information can be obtained by radar especially if there is a contrast change in dielectric constant. Passive and active microwave imaging systems have been built and experimented with for imaging. The application of radar to active imag-ing systems has resulted in SAR (Synthetic Aperture RADAR). SAR has been used in mapping soil with rough or impenetrable terrain such as the Amazon Basin (EMBRAPA, 1981).
– Infrared (0.7–300m wavelength), visible light (about 400 nm to about 700 nm), ultraviolet (3–400 nm).
Soil scientists in the field mostly used the visible light spectrum through the Munsell soil colour chart to determine soil colour and the presence of some pedological features. Infrared energy radiation may cause vibrational excitation of covalently bonded atoms, and give rise to absorption spectra. Lasers, which emit radiation at a single frequency, are commonly used as a radiation source in the ultraviolet, visible, and infrared regions. Absorption spectra of compounds are a unique reflection of their molecular structure. Recent work has shown how diffuse reflectance spectroscopy in the visual-near-infrared and mid-infrared regions can be used to predict various soil physical, and chemical properties (Viscarra Rossel et al., 2006). Chapter 13 shows the use of diffuse reflectance spectroscopy for rapid acquisition of soil information.
2 Digital Soil Mapping Technologies for Countries with Sparse Data Infrastructures 19 – X-rays (wavelengths 10–0.01 nm).
Radiation in this frequency band causes atomic level excitations (absorption and emission of radiation) that are used to probe bonding states (core or valence level) around an atom. This is mainly used in the laboratory for characterisation of mineral structure, such as X-ray diffraction, XAFS (x-ray absorption fine-structure spectroscopy) for probing physical and chemical fine-structure of minerals at an atomic scale.
– Gamma rays (wavelengths shorter than about 0.01 nm).
All soils contain concentrations of naturally occurring radioisotopes which can decay to produce gamma rays. Gamma-ray spectrometry measures the natu-ral emission of gamma-ray radiation of the earth’s surface, it estimates the abundances of potassium (40K), thorium (232Th) and uranium (238U) (Cook et al., 1996; Wilford et al., 1997). Gamma-ray spectroscopy mapping can be conducted using remote sensing (low flying aircraft, helicopter), or proximal sensing (vehicle mounted).
There are two general models for the development of “technologies” for acquiring soil information. These are:
1. Top-Down
We first seek variables that can be measured that might be related to scorpan factors or target variables by looking to other disciplines such as geophysics, and explore the possibilities of extracting information from these technologies that is pertinent to practical problems. A classical example (where this has been reasonably successful) is EMI technology and a more recent example is the use of gamma radiometrics, which has been used in geological prospecting for over 30 years to detect anomalies associated with exploitable ore deposits.
2. Bottom-up
We start from a problem that we analyse systematically so as to identify the information that is really needed to solve it. We then tackle the general technical problems of collecting this information, and only at the end move to develop-ing the field technology. An excellent example of this is the development of the on-the-go pH and lime-requirement sensing system at Australian Centre for Precision Agriculture. The approach is now mostly proximal sensors developed for precision agriculture, to obtain high resolution soil information (Adamchuk et al., 2004) such as soil moisture, mechanical resistance, organic matter content, soil texture, and nutrients concentration.
Both approaches have been successful, although the bottom-up approach is more intellectually satisfying. Currently, the bottom-up approach is mainly for develop-ing proximal sensors for high resolution digital soil mappdevelop-ing. High resolution soil data are often needed in areas where the land has high value or poses high risk.
The applications are precision agriculture, assessment of contamination sites, and in urban and industrial areas where land is valuable. It falls into soil mapping category D1 of McBratney et al. (2003) with a pixel resolution of (1 m× 1 m)
20 B. Minasny et al.
to (10 m× 10 m). A bottom-up approach also works at coarser-resolution studies for D3 and D4, catchment or environmental mapping. While it is not soil map-ping, the use of remote sensor imagery to parameterize soil-vegetation-atmosphere transfer models illustrates how process-based understanding might be used to ex-tract soil information from remote sensor data at coarse scales (e.g. Verhoef, 2004).
2.2.1 Data Sources for Scorpan
The scorpan factors can be obtained from various sensors, either remotely or prox-imally sensed. Remote sensing for soil properties is reviewed by Ben-Dor (2002), while proximal sensing is given by Adamchuk et al. (2004). Here we list some sensors, based on their platform, that are commonly used for digital soil mapping:
Satellite based (Fig. 2.4):
– Hyperion http://eo1.usgs.gov/hyperion.php
The Hyperion from EO-1 satellite provides a high resolution hyperspectral im-ager capable of resolving 220 spectral bands from 400 to 2500 nm with a 30 m spatial resolution, and image swath width 7.5 km. Hyperspectral images measure reflected radiation at a series of narrow and contiguous wavelength bands. Its use for digital soil mapping is still limited (Datt et al., 2003) and can be challenging as noise of the spectra and the influence of vegetation.
– Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) http://asterweb.jpl.nasa.gov/
Fig. 2.4 Satellite based remote sensing instruments as a function of wavelengths. The gray curves represents atmospheric electromagnetic opacity
2 Digital Soil Mapping Technologies for Countries with Sparse Data Infrastructures 21 ASTER is a multispectral imaging system (Yamaguchi et al., 1998). Multispec-tral imagers measure radiation reflected from a surface at a few wide, separated wavelength bands. ASTER measures visible reflected radiation in three spec-tral bands (VNIR between 0.52 and 0.86 m, with 15-m spatial resolution), and infrared reflected radiation in six spectral bands (SWIR between 1.6 and 2.43 m, with 30-m spatial resolution). In addition, ASTER records the data in band 3B (0.76–0.86 m) with a backward looking that enables the calculation of digital elevation model (DEM). ASTER also receives emitted radiation in five spectral bands (TIR between 8.125 and 11.65 m, with 90-m spatial resolution).
It has been used for mapping geological units (Gomez et al., 2004), and areas of degraded land (Chikhaoui et al., 2006).
– Landsat TM, and Enhanced Thematic Mapper Plus (ETM+) http://landsat7.usgs .gov/
The Enhanced Thematic Mapper Plus (ETM+) is a multispectral scanning ra-diometer that is carried on board the Landsat 7 satellite. It provides images with spatial resolution of 30 m for the visible and near-infrared, and 60 m for the ther-mal infrared, and 15 m for the panchromatic. Landsat has been used most often in digital soil mapping. Chapter 16 and Cole and Boettinger (2007) discussed its practical use for digital soil mapping. Chapter 22 illustrates its application for land-use mapping.
– Satellites Pour l’Observation de la Terre or Earth-observing Satellites (SPOT) http://www.spot.com
SPOT provides high-resolution multispectral images with resolution of 10 m in the visible and near infra-red (0.50–0.89 m), and 20 m in the short wave infra-red (1.58–1.75 m). Barnes and Baker (2000) investigated its use for mapping soil texture class.
– Advanced Very High Resolution Radiometer (AVHRR) http://noaasis.noaa.gov/
NOAASIS/ml/avhrr.html
The AVHRR provides four to six bands of multispectral images (visible red, near infra-red, short-wave infra-red, and thermal infra-red) with 1.1 km resolu-tion from the NOAA polar-orbiting satellite series. The AVHRR data have been collected to monitor global change information, however the data can be used as a cost-effective way of estimating soil properties at regional level (Odeh and McBratney, 2000). Its use is illustrated in Chapter 21.
– Moderate Resolution Imaging Spectroradiometer (MODIS) (http://modis.gsfc.nasa.gov/)
MODIS is an instrument aboard the Terra and Aqua satellites. Terra’s orbit around the Earth passes from north to south across the equator in the morn-ing, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth’s surface every 1–2 days, acquiring data in 36 spectral bands at a resolution of 250 m (620–876 nm), 500 m (459–2155 nm), and 1000 m (405–14385 nm). Its use mainly for monitoring veg-etation activity via NDVI (Huete et al., 1994). Tsvetsinskaya et al. (2002) used MODIS data to derive surface albedo for the arid areas of Northern Africa and
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the Arabian peninsula. This enabled them to relate the surface albedo statistics to FAO soil groups. See also Chapter 30.
Airborne based:
– HyMapTM(Hyperspectral Mapping) http://www.intspec.com/
is an airborne imaging VNIR-SWIR spectrometer, with 450–2500 nm spectral coverage, 128 spectral bands of 10–20 nm bandwiths. Examples of its use for mapping soil are Madeira Netto et al. (2007) and Selige et al. (2006).
– AVIRIS (Airborne Visible Infrared Imaging Spectrometer) http://aviris.jpl.nasa .gov/ is an airborne imaging instrument producing 224 spectral bands ranging from 400 to 2500 nm, with a spatial resolution of 20 m. Palacios-Orueta and Ustin (1996) showed that AVIRIS spectra can be used to discriminate between soil types.
– Airborne gamma radiometrics
Variations of gamma radiation has been found to correspond with the distribution of soil-forming materials over the landscape (Cook et al., 1996).
– Aerial photography
This technique, providing images in the visual light, is still being used in soil surveys and with interpretation is used to generate soil maps (Bie and Beckett, 1973).
Proximal, ground-based:
– Electrical magnetic induction (EMI) (http://www.geonics.com/) or electrical resistance measurement. These instruments measure the bulk soil electrical con-ductivity, it has been successful for high resolution digital soil mapping for prop-erties such as clay and water content (Corwin and Lesch, 2005).
– Gamma radiometrics
Gamma-ray spectrometers can measure an energy spectrum ranging from 0 to 3 MeV. The value of gamma-ray spectrometry lies due to the different rock types contain varying amounts of radioisotopes of K, U and Th. Ground-based gamma-ray spectrometers have been used for mapping soil properties (Viscarra Rossel et al., 2007; Wong and Harper, 1999).
2.2.2 Better Soil Data
By better soil data we mean data obtained more efficiently, so that a larger num-ber of samples are analysed at lower costs, in less time and with higher accuracy.
Spectroscopic techniques are being used and explored as possible alternatives to en-hance or replace conventional laboratory methods of soil analysis. Viscarra Rossel et al. (2006) provides a review on visible, near-infrared, and mid-infrared diffuse reflectance spectroscopy for simultaneous assessment of various soil properties in
2 Digital Soil Mapping Technologies for Countries with Sparse Data Infrastructures 23 laboratory, thus will not be reviewed here (see also Chapter 13). Several instruments that can be used for field measurement of soil properties:
r
FieldSpec FR spectroradiometer (Analytical Spectral Devices Inc., Boulder, Colorado http://www.asdi.com) provides diffuse reflectance spectrospcopy of soil samples from 350 to 2500 nm and sampling resolution of 1 nm. Waiser et al. (2007) found that this field instrument produces acceptable estimation of clay content at various water contents and parent materials.r
Spectral core scanners (Spectral Imaging Ltd., Finland, http://www.specim.fi) is an imaging spectrograph which produces the image of a soil core at the visible and near infrared regions. It is still under experimental testing for use in soil sensing.r
Electrochemical methods have been successfully used to directly evaluate soil fertility (Adamchuk et al., 2005). This is done by using an ion-selective electrode (glass or polymer membrane), or an ion-selective field effect transistor (ISFET).The principle involved measurement of potential difference (voltage) between sensor and reference parts of the system is related to the concentration of specific ions (e.g. H+, NO3−).
Table 2.1 gives a summary of the hardware and its use in digital soil mapping.
2.2.3 Current Problems
While the above methods are becoming available to provide better soil data in fu-ture surveys, we often have to start with legacy data. Legacy soil data arise from traditional soil survey (Bui and Moran, 2001). Methods of soil survey are generally empirical and based on a conceptual model developed by the surveyor, correlating soil with underlying geology, landforms, vegetation and air-photo interpretation.
Under traditional free survey samples are located to confirm the surveyor’s interpre-tation of the landscape and not in accordance with a statistical design. This will lead to bias in the areas that are sampled. Carr´e et al. (2007) examined this problem in more detail. It should be noted that, while soil observations collected in free survey pose a problem to the statistician, as soil scientists we recognize that the conceptual (or mental) models developed by soil surveyors in the past (and represented in map legends, map memoirs and map boundaries) can be highly informative. The main problem that legacy data pose us is how to ensure that this information is transferred effectively into the digital soil mapping framework (e.g. see Chapters 25 and 27).
Another set of variables that is missed from remotely or proximally-sensed in-struments are soil properties at depth. Remote-sensing images only tells us the surface condition, while soil is a three-dimensional body, and in many cases the properties in the subsoil hold lots of information we wish to know. Most satellite images working in the visible and infrared regions are influenced by crop cover (see Chapter 30). The use of instruments such as the spectral core scanners may be useful to quick acquisition of soil profile information.
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Table 2.1 Example of hardware used for digital soil mapping Instrument Platform, wavelength,
measurement
Typical spatial resolution
Example of application
Radarsat Satellitte, Radar CB and 8–100 m Li and Chen (2005)
EM-38 Ground-based,
14.6 kHz, bulk EC
1 m2
Depth exploration:
0.75 & 1.5 m
Corwin and Lesch (2005)
EM-31 Ground-based,
9.8 kHz, bulk EC
4 m2
Depth exploration: 6 m
Corwin and Lesch (2005)
GPR Ground-based,
Microwave (100–1000 MHz), dielectric constant
0.25–1 m2 Davis and Annan (1989)
Gamma radiometrics
Aerial or ground based, Gamma rays
(0–3 MeV), Radiometric K, U, Th
Aerial: 100 m Ground based: 10 m
Wilford and Minty (2007) Viscarra Rossel et al. (2007)
Aerial Photography
Visible, R, G, B Channel
5–10 m Bie and Beckett (1973)
SPOT Satellite,
Multispectral VNIR, SWIR
10 m Agbu et al. (1990)
Landsat Satellite, Multispectral VNIR, SWIR, TIR
30 m (VNIR, SWIR) 60 m (TIR)
Chapter 16
ASTER Satellite,
Multispectral, VNIR, SWIR, TIR
15 m (VNIR), 30 m (SWIR), 90 m (TIR)
Chapter 4
MODIS Satellite,
Multispectral 405–14385 nm
250 m (620–876 nm), 500 m (459–2155 nm), 1000 m
(405–14385 nm)
Tsvetsinskaya et al. (2002) Chapter 30
Hyperion Satellite, Hyperspectral, 400–2500 nm
30 m
Hymap Airborne,
Hyperspectral, 450–2500 nm
5 m Madeira Netto et al. (2007)
AVIRIS Airborne,
Hyperspectral, 400–2500 nm
20 m Palacios-Orueta and
Ustin (1996) FieldSpec Handheld,
350–2500 nm
∼ 0.001 m2 Waiser et al. (2007) Chapter 13
2 Digital Soil Mapping Technologies for Countries with Sparse Data Infrastructures 25