Prediction of chemical and biological variables of soil in grazing areas with visible- and near-infrared spectroscopy

Gandariasbeitia, Maite; Besga, G.; Albizu, Isabel; Larregla, Santiago; Mendarte, S.

doi:10.1016/j.geoderma.2017.05.045

Cited by 21 publications

(4 citation statements)

References 41 publications

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“…Albeit, Bünemann et al [16] stated that there are a few clear interpretation schemes of measured indicator values in relation to soil threats, soil functions, and ecosystem services. The mapping of indictors of soil quality, functions, and/or ecosystem services is becoming more feasible with the increasing adoption of spectroscopic and other remote and proximal soil sensing techniques [16,24,25]. The increasing abilities of digital soil mapping have the potential to map soil functions [26], and it is suggested that a shift in focus to digital soil assessment will focus mapping of soil functions and threats [27].…”

Section: Numbermentioning

confidence: 99%

On Soil Capability, Capacity, and Condition

McBratney

Field

Morgan³

et al. 2019

Sustainability

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The role of soil in the existential environmental problems of declining biodiversity, climate change, water and energy security, impacting on food security has highlighted the need to link the soil functions to ecosystem services. We describe and illustrate by a limited example, the concepts and assessment of soil’s capacity measured through its capability and condition as contributors to an overall soil security framework. The framework is based on the concepts of genosoils and phenosoils. The links to other notions, such as threats to soil and soil functions are made. The framework can be potentially applied elsewhere to quantify soil changes under natural processes and human activities.

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Section: Numbermentioning

confidence: 99%

On Soil Capability, Capacity, and Condition

McBratney

Field

Morgan³

et al. 2019

Sustainability

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“…In recent years, laboratory visible-near-infrared (VIS-NIR)/short-wave infrared (SWIR) spectroscopy data have been viewed as a potential cost-effective option to the fieldbased survey and traditional laboratory approach for soil analysis [29,[31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Spectral Discrimination of Archaeological Sites Previously Occupied by Farming Communities Using In Situ Hyperspectral Data

Thabeng

Adam

Merlo

2019

Journal of Spectroscopy

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This study investigates the ability of field spectra measurements to discriminate between soils from non-sites (natural soils) and from archaeological sites, such as middens (rubbish-dumping areas) and animal byres. First, we tested whether there is a difference in the concentration of elements between different soil types using analysis of variance while random forest (RF) and forward variable selection (FVS) methods were used to select important soil elements for the classification of the archaeological sites. In the second approach, we evaluated the ability of field spectroscopy reflectance measurements to discriminate among nonsites, middens, vitrified dung, and nonvitrified dung byres. The guided regularised random forest (GRRF) was used to identify important wavelengths for the discrimination of abovementioned archaeological and nonarchaeological soils. Thereafter, the selected soil elements and wavelengths were used as input variables in the RF classification algorithm to classify the nonsites, middens, vitrified dung, and nonvitrified dung. The findings reveal that there is a significant difference in the composition of chemical elements and spectral signatures of nonsites, middens, vitrified dung, and nonvitrified dung. In summary, high classification accuracies achieved when using field spectroscopy data prove that remote sensing techniques can be used to exploit the spectral differences among the abovementioned soil types in mapping archaeological feature characteristics of farming communities’ settlements.

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“…Near-infrared (NIR) spectroscopy is a fast, efficient, nondestructive technology suitable for on-line analysis of processes. 1,2 The basic principle of this technology is to obtain sample information by measuring and analyzing absorption spectra generated by the interaction of chemical bonds in sample molecules (mainly C-H, O-H, and N-H covalent bonds) with transmitted or reflected light. 3 Compared to traditional sensors that measure process parameters and variables (e.g., temperature, pressure, flow, and liquid level) from macroscopic perspectives, which inevitably contain external noise and interference information, NIR spectroscopy reveals composition and structural information about samples from a microscopic molecular perspective; thus, it can measure and provide essential information more accurately and effectively than other methods.…”

Section: Introductionmentioning

confidence: 99%

Process Pattern-Based Near-Infrared Spectroscopy (NIRS) Fault Detection Using a Potential Function

2018

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This paper proposes a near-infrared (NIR) fault detection technology based on a process pattern via a potential function. Near-infrared spectroscopy is used to acquire process information at the molecular level. In this study, the process pattern concept is first introduced in the field of process control and a process pattern construction method based on elastic net-PCA is put forth. Next, the potential function discriminant method is applied to distinguish and classify the constructed process pattern and identify the running state of the industrial system. Finally, the proposed method is verified and analyzed using spectra data of the crude oil desalination and dehydration process. Compared with existing fault detection methods, the proposed approach offers the following advantages: (1) potential function discrimination achieves nonlinear process classification with better fault detection accuracy and good visualization performance; (2) fault detection based on NIR spectra is faster with and possesses greater accuracy because it acquires process information from a microscopic molecular perspective; and (3) the process pattern contains more effective process information and can more comprehensively characterize the essential features of processes.

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Prediction of chemical and biological variables of soil in grazing areas with visible- and near-infrared spectroscopy

Cited by 21 publications

References 41 publications

On Soil Capability, Capacity, and Condition

On Soil Capability, Capacity, and Condition

Spectral Discrimination of Archaeological Sites Previously Occupied by Farming Communities Using In Situ Hyperspectral Data

Process Pattern-Based Near-Infrared Spectroscopy (NIRS) Fault Detection Using a Potential Function

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