OR/15/030 Results and their interpretation - Revision history

Dbk: /* Bulk density and compaction */

2015-07-28T12:55:08Z

Bulk density and compaction

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	The bulk density (of topsoil) is the mass of soil material per unit volume and is strongly related to the soil organic carbon concentration (and thus land cover type), land management and to a lesser extent soil texture (Hollis et al., 2012<ref name="Hollis">HOLLIS, J M, HANNAM, J & BELLAMY, P H. 2012. Empirically-derived pedotransfer functions for predicting bulk density in European soils. ''European Journal of Soil Science, ''63, 96–109. </ref>). Soils which have been subject to compaction have larger bulk densities than those which have not and so we consider compaction in this section.		The bulk density (of topsoil) is the mass of soil material per unit volume and is strongly related to the soil organic carbon concentration (and thus land cover type), land management and to a lesser extent soil texture (Hollis et al., 2012<ref name="Hollis">HOLLIS, J M, HANNAM, J & BELLAMY, P H. 2012. Empirically-derived pedotransfer functions for predicting bulk density in European soils. ''European Journal of Soil Science, ''63, 96–109. </ref>). Soils which have been subject to compaction have larger bulk densities than those which have not and so we consider compaction in this section.

	There were no published papers in the scientific literature (nor was there evidence elsewhere) which showed that soil bulk density or compaction could be predicted on a defined scale with a specified accuracy using remotely sensed signals. In their study on prediction of soil physical properties using spectral approaches, Minasny et al. (2008)<ref name="Minasny"></ref> state that 'soil physical properties that are based on pore-space relationships such as bulk density, water retention and hydraulic conductivity cannot be predicted well using [MIR] spectroscopy'. The soil property which accounts for the greatest (typically around 65%) proportion of variation in bulk density is SOC concentration (Hollis et al., 2012<ref name="Hollis"></ref>). Hence, it may be possible to use pedo-transfer functions based on SOC concentration to predict bulk density with errors somewhat larger 0.17 gm cm-3 (Hollis et al., 2012<ref name="Hollis"></ref>) However, such large errors would mean that monitoring change by remote sensing would be impractical, and therefore it is advisable to use field-based sampling and analysis to monitor this property.		There were no published papers in the scientific literature (nor was there evidence elsewhere) which showed that soil bulk density or compaction could be predicted on a defined scale with a specified accuracy using remotely sensed signals. In their study on prediction of soil physical properties using spectral approaches, Minasny et al. (2008)<ref name="Minasny 2008">MINASNY, B, MCBRATNEY, A B, TRANTER, G & MURPHY, B W. 2008. Using soil knowledge for the evaluation of mid-infrared diffuse reflectance spectroscopy for predicting soil physical and mechanical properties. ''European Journal of Soil Science, ''59, 960–971. </ref> state that 'soil physical properties that are based on pore-space relationships such as bulk density, water retention and hydraulic conductivity cannot be predicted well using [MIR] spectroscopy'. The soil property which accounts for the greatest (typically around 65%) proportion of variation in bulk density is SOC concentration (Hollis et al., 2012<ref name="Hollis"></ref>). Hence, it may be possible to use pedo-transfer functions based on SOC concentration to predict bulk density with errors somewhat larger 0.17 gm cm-3 (Hollis et al., 2012<ref name="Hollis"></ref>) However, such large errors would mean that monitoring change by remote sensing would be impractical, and therefore it is advisable to use field-based sampling and analysis to monitor this property.

	Given the current absence of effective techniques for measuring bulk density by remote methods, and no immediate prospect of this being addressed in the short-term by new technologies, we have not addressed the set of five questions relating to this indicator, but summarise them in Table 1. Soil compaction leads to small reductions in soil surface elevation so it may also be possible to detect compaction-induced changes through remote monitoring of elevation using the InSAR methods described in the study by Rawlins et al., (2014)<ref name="Rawlins 2014">RAWLINS, B G., CIGNA, F, JORDAN, C J, SOWTER, A, EVANS, C, ROBINSON, D A & TEAM, G. 2014. Monitoring changes in surface elevation of blanket peat and other land cover types using a novel InSAR processing technique.''Geophysical Research Abstracts, ''Vol. 16, EGU2014-3047, 2014. </ref>, however such an approach would need considerable research and testing before it could be deployed.		Given the current absence of effective techniques for measuring bulk density by remote methods, and no immediate prospect of this being addressed in the short-term by new technologies, we have not addressed the set of five questions relating to this indicator, but summarise them in Table 1. Soil compaction leads to small reductions in soil surface elevation so it may also be possible to detect compaction-induced changes through remote monitoring of elevation using the InSAR methods described in the study by Rawlins et al., (2014)<ref name="Rawlins 2014">RAWLINS, B G., CIGNA, F, JORDAN, C J, SOWTER, A, EVANS, C, ROBINSON, D A & TEAM, G. 2014. Monitoring changes in surface elevation of blanket peat and other land cover types using a novel InSAR processing technique.''Geophysical Research Abstracts, ''Vol. 16, EGU2014-3047, 2014. </ref>, however such an approach would need considerable research and testing before it could be deployed.

Dbk: /* Extractable K */

2015-07-28T12:54:11Z

Extractable K

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	===Extractable K ===		===Extractable K ===
	The only study to have demonstrated that extractable K could be measured based on the VNIR spectra of soil samples was published by Daniel et al. (2003)<ref ~~nmae~~="Daniel">DANIEL, K W, TRIPATHI, X K & HONDA, K. 2003. Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand). ''Australian Journal Of Soil Research, ''41, 47–59. </ref> based on 41 samples collected from a region of Thailand. In their study, Daniel et al. (2003)<ref name="Daniel"></ref> found that soil spectra accounted for 80% of the variance in extractable K; there was no attempt to relate the ground-based spectra to remotely sensed sources such as hyperspectral satellite data. A total sample size of 41 is a relatively small number upon which to develop and independently validate predictions of extractable soil K. It is somewhat surprising that, if there was a general relationship between soil VNIR spectra and extractable K, it has not been reported more widely given its potential importance for soil fertility and agricultural production. We believe that the relationship reported by Daniel et al, (2003)<ref name="Daniel"></ref> was unusual (confined to these local soils) and that if tested more widely, the relationship could not be deployed to accurately predict extractable K in soils across broad landscapes. Furthermore, the relationship with hyperspectral satellite data and extractable K would likely be even more tenuous.		The only study to have demonstrated that extractable K could be measured based on the VNIR spectra of soil samples was published by Daniel et al. (2003)<ref name="Daniel">DANIEL, K W, TRIPATHI, X K & HONDA, K. 2003. Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand). ''Australian Journal Of Soil Research, ''41, 47–59. </ref> based on 41 samples collected from a region of Thailand. In their study, Daniel et al. (2003)<ref name="Daniel"></ref> found that soil spectra accounted for 80% of the variance in extractable K; there was no attempt to relate the ground-based spectra to remotely sensed sources such as hyperspectral satellite data. A total sample size of 41 is a relatively small number upon which to develop and independently validate predictions of extractable soil K. It is somewhat surprising that, if there was a general relationship between soil VNIR spectra and extractable K, it has not been reported more widely given its potential importance for soil fertility and agricultural production. We believe that the relationship reported by Daniel et al, (2003)<ref name="Daniel"></ref> was unusual (confined to these local soils) and that if tested more widely, the relationship could not be deployed to accurately predict extractable K in soils across broad landscapes. Furthermore, the relationship with hyperspectral satellite data and extractable K would likely be even more tenuous.

	By contrast, it is possible to accurately predict total K based on ''airborne ''radiometric survey data which relies on the detection of gamma radiation from 40K emitted from the ground surface. Airborne radiometric survey data have been used to determine the distribution of plant available K at farm-scale (Pracilio et al., 2006<ref name="Pracilio">PRACILIO, G, ADAMS, M L, SMETTEM, K R J & HARPER, R J. 2006. Determination of spatial distribution patterns of clay and plant available potassium contents in surface soils at the farm scale using high resolution gamma ray spectrometry. ''Plant and Soil, ''282, 67–82. </ref>); gamma-K concentrations accounted for 60% of the variation in bicarbonate-extractable K across three farms in Australia. Specific regions of England and Wales have been flown by airborne radiometric survey by the British Geological		By contrast, it is possible to accurately predict total K based on ''airborne ''radiometric survey data which relies on the detection of gamma radiation from 40K emitted from the ground surface. Airborne radiometric survey data have been used to determine the distribution of plant available K at farm-scale (Pracilio et al., 2006<ref name="Pracilio">PRACILIO, G, ADAMS, M L, SMETTEM, K R J & HARPER, R J. 2006. Determination of spatial distribution patterns of clay and plant available potassium contents in surface soils at the farm scale using high resolution gamma ray spectrometry. ''Plant and Soil, ''282, 67–82. </ref>); gamma-K concentrations accounted for 60% of the variation in bicarbonate-extractable K across three farms in Australia. Specific regions of England and Wales have been flown by airborne radiometric survey by the British Geological

Dbk: /* Olsen P, extractable MG, extractable K */

2015-07-28T12:53:58Z

Olsen P, extractable MG, extractable K

← Older revision		Revision as of 13:53, 28 July 2015
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	==Olsen P, extractable MG, extractable K==		==Olsen P, extractable MG, extractable K==
	The extractable concentrations of these three nutrients inform us about the fertility of the soil. Extractable concentrations are quite different from total concentrations, because the former are held in ‘exchangeable’ forms associated with the surfaces of soil minerals and organic matter. The concentrations of extractable nutrients do not have such strong relationships with bulk soil properties, because they can reflect recent applications of manufactured fertilisers and organic materials or transitory soil hydrological conditions (e.g. wetting and drying cycles). The basis of predicting soil properties based on VNIR spectroscopy relies on the overall reflectance properties of the dominant soil mineral and organic phase composition, not the composition of ‘exchangeable’ elements on their surfaces. A review study by Ge et al. (2011)<ref ~~names~~="Ge">GE, Y, THOMASSON, J A & SUI, R. 2011. Remote sensing of soil properties in precision agriculture: A review. ''Frontiers of Earth Science, ''5, 229–238. </ref> summarised approaches to estimate a range of soil properties by use of remote sensing including total P, total K and Mg, but not extractable concentrations. The bonds that respond to infra-red spectroscopy are predominantly between carbon, nitrogen, hydrogen and oxygen which are present in substantial quantities in certain soil phases, whilst bonds for P, K and Mg are much less responsive.		The extractable concentrations of these three nutrients inform us about the fertility of the soil. Extractable concentrations are quite different from total concentrations, because the former are held in ‘exchangeable’ forms associated with the surfaces of soil minerals and organic matter. The concentrations of extractable nutrients do not have such strong relationships with bulk soil properties, because they can reflect recent applications of manufactured fertilisers and organic materials or transitory soil hydrological conditions (e.g. wetting and drying cycles). The basis of predicting soil properties based on VNIR spectroscopy relies on the overall reflectance properties of the dominant soil mineral and organic phase composition, not the composition of ‘exchangeable’ elements on their surfaces. A review study by Ge et al. (2011)<ref name="Ge">GE, Y, THOMASSON, J A & SUI, R. 2011. Remote sensing of soil properties in precision agriculture: A review. ''Frontiers of Earth Science, ''5, 229–238. </ref> summarised approaches to estimate a range of soil properties by use of remote sensing including total P, total K and Mg, but not extractable concentrations. The bonds that respond to infra-red spectroscopy are predominantly between carbon, nitrogen, hydrogen and oxygen which are present in substantial quantities in certain soil phases, whilst bonds for P, K and Mg are much less responsive.

	===Olsen P ===		===Olsen P ===

Dbk at 12:53, 28 July 2015

2015-07-28T12:53:27Z

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	===Opportunities ===		===Opportunities ===
	The most promising approach for quantifying the state of soil pH using remote sensed methods is the application of hyperspectral remote sensing in combination with ground-based measurements (Lu et al., 2013). In their study, Lu et al. (2013) showed that using Hyperion imagery, 60% of the variation on pH was accounted for by a statistical model fitted between the measured pH of the field samples and the remote sensing data from the VNIR region. To robustly detect a change in soil pH, the method for its measurement would need to have a significantly smaller error than that which would be associated with spectral approaches that relay in ground-based calibration of remote sensed data which could have RMSE values as large as 0.3 pH units (Lu et al., 2013]). So it is unlikely that hyperspectral imagery (in combination with field sampling) could be used to measure change of soil pH. Based on our literature survey, only one study has demonstrated that remote sensed data can improve the prediction of soil pH (state) and further studies are required across a range of soil types to prove that this approach is more generally applicable.		The most promising approach for quantifying the state of soil pH using remote sensed methods is the application of hyperspectral remote sensing in combination with ground-based measurements (Lu et al., 2013<ref name="Lu">LU, P, WANG, L, NIU, Z., LI, L H & ZHANG, W H. 2013. Prediction of soil properties using laboratory VIS-NIR spectroscopy and Hyperion imagery. ''Journal of Geochemical Exploration, ''132, 26–33. </ref>). In their study, Lu et al. (2013)<ref name="Lu"></ref> showed that using Hyperion imagery, 60% of the variation on pH was accounted for by a statistical model fitted between the measured pH of the field samples and the remote sensing data from the VNIR region. To robustly detect a change in soil pH, the method for its measurement would need to have a significantly smaller error than that which would be associated with spectral approaches that relay in ground-based calibration of remote sensed data which could have RMSE values as large as 0.3 pH units (Lu et al., 2013<ref name="Lu"></ref>). So it is unlikely that hyperspectral imagery (in combination with field sampling) could be used to measure change of soil pH. Based on our literature survey, only one study has demonstrated that remote sensed data can improve the prediction of soil pH (state) and further studies are required across a range of soil types to prove that this approach is more generally applicable.

	===Can Earth Observation replace field sampling? ===		===Can Earth Observation replace field sampling? ===
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	\| '''Platform'''		\| '''Platform'''
	\| '''Can detect state and/or change'''		\| '''Can detect state and/or change'''
	\| '''Will also require ground- based soil sampling and analysis at national scale?'''		\| '''Will also require ground-based soil sampling and analysis at national scale?'''
	\| '''Further development required? Practicality or technical considerations to be addressed'''		\| '''Further development required? Practicality or technical considerations to be addressed'''
	\|-valign="top"		\|-valign="top"
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	\| <nowiki>*</nowiki>State (not change)		\| <nowiki>*</nowiki>State (not change)
	\| <p align="center">Yes</p>		\| <p align="center">Yes</p>
	\| This technique would need testing in a pilot study – there are sufficient data across England and Wales for this to be undertaken.		\| This technique would need testing in a pilot study — there are sufficient data across England and Wales for this to be undertaken.
	\|-valign="top"		\|-valign="top"
	\| Extractable Mg		\| Extractable Mg
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	NA = Not applicable		NA = Not applicable

			==References==


	[[category: OR/15/030 Identify the opportunities provided by developments in earth observation and remote sensing for national scale monitoring of soil quality \| 04]]		[[category: OR/15/030 Identify the opportunities provided by developments in earth observation and remote sensing for national scale monitoring of soil quality \| 04]]

Dbk at 12:51, 28 July 2015

2015-07-28T12:51:18Z

Show changes

Dbk: /* Summary */

2015-06-15T07:23:55Z

Summary

← Older revision		Revision as of 08:23, 15 June 2015
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	\| '''Will also require ground- based soil sampling and analysis at national scale?'''		\| '''Will also require ground- based soil sampling and analysis at national scale?'''
	\| '''Further development required? Practicality or technical considerations to be addressed'''		\| '''Further development required? Practicality or technical considerations to be addressed'''
	\|-		\|-valign="top"
	\| Organic carbon and total N		\| Organic carbon and total N
	\| Hyperspectral		\| Hyperspectral
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	\| <p align="center">Yes</p>		\| <p align="center">Yes</p>
	\| Outcome: smaller errors in predicting concentrations in bare soil (partially vegetation free regions). New satellites (e.g. HySPIRI) may enhance spectral signal-to-noise ratios.		\| Outcome: smaller errors in predicting concentrations in bare soil (partially vegetation free regions). New satellites (e.g. HySPIRI) may enhance spectral signal-to-noise ratios.
	\|-		\|-valign="top"
	\| Bulk density		\| Bulk density
	\| None available		\| None available
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	\| <p align="center">NA</p>		\| <p align="center">NA</p>
	\| <p align="center">NA</p>		\| <p align="center">NA</p>
	\|-		\|-valign="top"
	\| Soil depth (and erosion)		\| Soil depth (and erosion)
	\| InSAR (using IsBAS)		\| InSAR (using IsBAS)
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	\| <p align="center">No</p>		\| <p align="center">No</p>
	\| This technique needs further testing before it could be used to monitor either peat depth or estimate the magnitude and extent of soil erosion.		\| This technique needs further testing before it could be used to monitor either peat depth or estimate the magnitude and extent of soil erosion.
	\|-		\|-valign="top"
	\| Total metals (Cu, Zn, Cd, Ni)		\| Total metals (Cu, Zn, Cd, Ni)
	\| None available		\| None available
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	\| <p align="center">NA</p>		\| <p align="center">NA</p>
	\| <p align="center">NA</p>		\| <p align="center">NA</p>
	\|-		\|-valign="top"
	\| Extractable K		\| Extractable K
	\| Radiometric survey (gamma radiation)		\| Radiometric survey (gamma radiation)
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	\| <p align="center">Yes</p>		\| <p align="center">Yes</p>
	\| This technique would need testing in a pilot study – there are sufficient data across England and Wales for this to be undertaken.		\| This technique would need testing in a pilot study – there are sufficient data across England and Wales for this to be undertaken.
	\|-		\|-valign="top"
	\| Extractable Mg		\| Extractable Mg
	\| Hyperspectral		\| Hyperspectral
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	\| <p align="center">Yes</p>		\| <p align="center">Yes</p>
	\| This approach needs to be explored further as there are currently insufficient data from England and Wales to determine whether it could be effective.		\| This approach needs to be explored further as there are currently insufficient data from England and Wales to determine whether it could be effective.
	\|-		\|-valign="top"
	\| Soil pH		\| Soil pH
	\| Hyperspectral		\| Hyperspectral
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	\| <p align="center">Yes</p>		\| <p align="center">Yes</p>
	\| Smaller errors in predicting pH values in bare soil (or partially vegetation free regions). New satellites (e.g. HySPIRI) may enhance spectral signal-to-noise ratios.		\| Smaller errors in predicting pH values in bare soil (or partially vegetation free regions). New satellites (e.g. HySPIRI) may enhance spectral signal-to-noise ratios.
	\|-		\|-valign="top"
	\| Olsen P		\| Olsen P
	\| None available		\| None available

Dbk: /* Summary */

2015-06-15T07:23:14Z

Summary

← Older revision		Revision as of 08:23, 15 June 2015
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	\| Hyperspectral		\| Hyperspectral
	\| Satellite		\| Satellite
	\| * State (not change)		\| <nowiki>*</nowiki>State (not change)
	\| <p align="center">Yes</p>		\| <p align="center">Yes</p>
	\| Outcome: smaller errors in predicting concentrations in bare soil (partially vegetation free regions). New satellites (e.g. HySPIRI) may enhance spectral signal-to-noise ratios.		\| Outcome: smaller errors in predicting concentrations in bare soil (partially vegetation free regions). New satellites (e.g. HySPIRI) may enhance spectral signal-to-noise ratios.
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	\| Radiometric survey (gamma radiation)		\| Radiometric survey (gamma radiation)
	\| Airborne		\| Airborne
	\| *State (not change)		\| <nowiki>*</nowiki>State (not change)
	\| <p align="center">Yes</p>		\| <p align="center">Yes</p>
	\| This technique would need testing in a pilot study – there are sufficient data across England and Wales for this to be undertaken.		\| This technique would need testing in a pilot study – there are sufficient data across England and Wales for this to be undertaken.
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	\| Hyperspectral		\| Hyperspectral
	\| Satellite		\| Satellite
	\| *Maybe state (not change)		\| <nowiki>*</nowiki>Maybe state (not change)
	\| <p align="center">Yes</p>		\| <p align="center">Yes</p>
	\| This approach needs to be explored further as there are currently insufficient data from England and Wales to determine whether it could be effective.		\| This approach needs to be explored further as there are currently insufficient data from England and Wales to determine whether it could be effective.
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	\| Hyperspectral		\| Hyperspectral
	\| Satellite		\| Satellite
	\| <p align="center">*State (not change)</p>		\| <p align="center"><nowiki>*</nowiki>State (not change)</p>
	\| <p align="center">Yes</p>		\| <p align="center">Yes</p>
	\| Smaller errors in predicting pH values in bare soil (or partially vegetation free regions). New satellites (e.g. HySPIRI) may enhance spectral signal-to-noise ratios.		\| Smaller errors in predicting pH values in bare soil (or partially vegetation free regions). New satellites (e.g. HySPIRI) may enhance spectral signal-to-noise ratios.
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	\|}		\|}

	*the remotely sensed data would most likely be used here as a secondary covariate to reduce the uncertainty in the prediction of the primary indicator property (measured by ground-based sampling plus analysis) at locations across the landscape where the remotely sensed data was available, but the primary soil indicator had not been measured.		<nowiki>*</nowiki>the remotely sensed data would most likely be used here as a secondary covariate to reduce the uncertainty in the prediction of the primary indicator property (measured by ground-based sampling plus analysis) at locations across the landscape where the remotely sensed data was available, but the primary soil indicator had not been measured.

	NA = Not applicable		NA = Not applicable

Dbk: /* Tagged database surmmary */

2015-05-18T13:33:02Z

Tagged database surmmary

← Older revision		Revision as of 14:33, 18 May 2015
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	We have presented our findings by first summarising the counts of the tagged keywords in the reference database through combining the information from the literature search, the internet search and the expert survey. Secondly, we have assessed each of the indicators separately through addressing the list of '''5 key questions which were presented in the introduction'''. We summarise the information in a table describing the most promising remote sensing approaches which might be used for each indicator and the practicality of implementation. We chose to combine some of the indicators into two groups: i) nutrients (extractable K, extractable Mg, Olsen extractable P, total N) and ii) metals (Cd, Zn, Cu, Ni).		We have presented our findings by first summarising the counts of the tagged keywords in the reference database through combining the information from the literature search, the internet search and the expert survey. Secondly, we have assessed each of the indicators separately through addressing the list of '''5 key questions which were presented in the introduction'''. We summarise the information in a table describing the most promising remote sensing approaches which might be used for each indicator and the practicality of implementation. We chose to combine some of the indicators into two groups: i) nutrients (extractable K, extractable Mg, Olsen extractable P, total N) and ii) metals (Cd, Zn, Cu, Ni).

	==Tagged database ~~surmmary~~==		==Tagged database summary==

	[[Image:OR15030fig2.jpg\|thumb\|center\|400px\| '''Figure 2''' The number of soil monitoring and remote sensing studies which refer to or utilise the specified a) soil indicators, and b) sensors/platforms (based on a total of 185 references selected from peer-reviewed published literature).]]		[[Image:OR15030fig2.jpg\|thumb\|center\|400px\| '''Figure 2''' The number of soil monitoring and remote sensing studies which refer to or utilise the specified a) soil indicators, and b) sensors/platforms (based on a total of 185 references selected from peer-reviewed published literature).]]

Dbk at 13:32, 18 May 2015

2015-05-18T13:32:53Z

← Older revision		Revision as of 14:32, 18 May 2015
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	==Tagged database surmmary==		==Tagged database surmmary==

	[[Image:~~15030fig2~~.jpg\|thumb\|center\|400px\| '''Figure 2''' The number of soil monitoring and remote sensing studies which refer to or utilise the specified a) soil indicators, and b) sensors/platforms (based on a total of 185 references selected from peer-reviewed published literature).]]		[[Image:OR15030fig2.jpg\|thumb\|center\|400px\| '''Figure 2''' The number of soil monitoring and remote sensing studies which refer to or utilise the specified a) soil indicators, and b) sensors/platforms (based on a total of 185 references selected from peer-reviewed published literature).]]

	Figure 2a shows that overwhelmingly the most common soil indicator which has been measured (or referred to) by soil remote sensing approaches is SOC (n=118 keyword citations). This dominance is likely for two main reasons: i) the importance of soil organic carbon as a mediator of global climate change and its general use as a soil quality indicator, ii) organic carbon signatures can be detected using hyperspectral data because the frequency of the associated bonds occur within the visible and near infra-red spectral range (0.35–2.5 µm).		Figure 2a shows that overwhelmingly the most common soil indicator which has been measured (or referred to) by soil remote sensing approaches is SOC (n=118 keyword citations). This dominance is likely for two main reasons: i) the importance of soil organic carbon as a mediator of global climate change and its general use as a soil quality indicator, ii) organic carbon signatures can be detected using hyperspectral data because the frequency of the associated bonds occur within the visible and near infra-red spectral range (0.35–2.5 µm).

Dbk: /* Summary */

2015-05-18T09:57:26Z

Summary

← Older revision		Revision as of 10:57, 18 May 2015
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	\| '''Most promising RS technique (or none available)'''		\| '''Most promising RS technique (or none available)'''
	\| '''Platform'''		\| '''Platform'''
	\| '''Can~~'''''' ''''''~~detect~~'''''' ''''''~~state~~'''''' ''''''~~and/or~~'''''' ''''''~~change'''		\| '''Can detect state and/or change'''
	\| '''Will~~'''''' ''''''~~also~~'''''' ''''''~~require~~'''''' ''''''~~ground-~~'''''' ''''''~~based soil sampling and analysis at national scale?'''		\| '''Will also require ground- based soil sampling and analysis at national scale?'''
	\| '''Further development~~'''''' ''''''~~required?~~'''''' ''''''~~Practicality~~'''''' ''''''~~or technical considerations to be addressed'''		\| '''Further development required? Practicality or technical considerations to be addressed'''
	\|-		\|-
	\| Organic carbon and total N		\| Organic carbon and total N