Evaluation of soil structural quality using VIS–NIR spectra

Askari, Mohammad Sadegh; Cui, Jiangwei; O’Rourke, Sharon; Holden, Nicholas M.

doi:10.1016/j.still.2014.03.006

Cited by 77 publications

(46 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the best of our knowledge, at present, most studies dealing with optical remote sensing technologies for SQ assessment focus on several soil properties as indicators of SQ for a specific soil type or land use [10], and much less research exists on the potential of IS for developing SQ surface maps. These studies mainly use point spectroscopy, either in the laboratory or in the field, and do not implement IS on a regional scale.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, after finalizing the SQI, the PLS-R technique can be used rapidly, making it possible to analyze a large number of samples in a practical and timely manner as a prediction model. These qualities make spectroscopic analysis, combined with PLS-R, an attractive method for environmental monitoring, especially for modeling SQ in changed land uses [10].…”

Section: Predictability Of Indicators and Indicesmentioning

confidence: 99%

“…Thus, methodologies for SQ assessment should be able to measure the specific soil functions and the soil ecosystem services associated with these management goals. The ability of spectral analysis for evaluating SQ in point spectroscopy and IS will allow field implementation with IS and portable spectrometer equipment as new ways for monitoring and mapping SQ in order to address soil threats in a near real-time manner [10]. This is an important implication for sustainable agricultural management, in particular, and land use, in general, because greater spatial and temporal resolution sampling will be possible at a lower cost.…”

Section: Monitoring Soil Quality With Ismentioning

confidence: 99%

“…Spectroscopy techniques that include the visible (VIS, 400-700 nm), near-infrared (NIR, 700-1100 nm), shortwave infrared (SWIR, 1100-2500 nm), and thermal infra-red (TIR, 8000-12000 nm) spectral ranges are well-known tools for monitoring and studying different aspects of soil surface properties and SQ with various levels of prediction accuracy [10][11][12][13][14]. Spectroscopy is a rapid, non-destructive, reproducible, and cost-effective analytical method that is used in food science, medical science, and all geoscientific disciplines, in general, and in soil science, in particular [11,14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Their study evaluated the spectral predictability of 14 SQ indicators along with an integrated spectral soil quality index (SSQI). Askari et al [10] used spectroscopic and chemometric analyses to classify soil structural quality using 40 sites in Ireland under arable and grassland management systems. They found that spectroscopy is a suitable technique for the quantitative assessment of SQ.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

et al. 2015

View full text Add to dashboard Cite

Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and demonstrate the ability of IS to evaluate soil properties and quality across anthropogenically induced land-use changes. This aim was fulfilled by developing and implementing a spectral soil quality index (SSQI) using IS obtained by a laboratory and field spectrometer (point scale) as well as by airborne hyperspectral imaging (local scale), in two experimental sites located in Israel and Germany. In this regard, 13 soil physical, biological, and chemical properties and their derived soil quality index (SQI) were measured. Several mathematical/statistical procedures, consisting of a series of operations, including a principal component analysis OPEN ACCESSRemote Sens. 2015, 7 15749(PCA), a partial least squares-regression (PLS-R), and a partial least squares-discriminate analysis (PLS-DA), were used. Correlations between the laboratory spectral values and the calculated SQI coefficient of determination (R 2 ) and ratio of performance to deviation (RPD) were R 2 = 0.84; RPD = 2.43 and R 2 = 0.78; RPD = 2.10 in the Israeli and the German study sites, respectively. The PLS-DA model that was used to develop the SSQI showed high classification accuracy in both sites (from laboratory, field, and imaging spectroscopy). The correlations between the SSQI and the SQI were R 2 = 0.71 and R 2 = 0.7, in the Israeli and the German study sites, respectively. It is concluded that soil quality can be effectively monitored using the spectral-spatial information provided by the IS technology. IS-based classification of soils can provide the basis for a spatially explicit and quantitative approach for monitoring SQ and function at a local scale.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Predictability Of Indicators and Indicesmentioning

confidence: 99%

Section: Monitoring Soil Quality With Ismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

et al. 2015

View full text Add to dashboard Cite

show abstract

Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy

Meng,

Feng,

Tan

et al. 2024

Food Science & Nutrition

View full text Add to dashboard Cite

Detection of the moisture content (MC) and freshness for loquats is crucial for achieving optimal taste and economic efficiency. Traditional methods for evaluating the MC and freshness of loquats have disadvantages such as destructive sampling and time‐consuming. To investigate the feasibility of rapid and non‐destructive detection of the MC and freshness for loquats, optical fiber spectroscopy in the range of 200–1000 nm was used in this study. The full spectra were pre‐processed using standard normal variate method, and then, the effective wavelengths were selected using competitive adaptive weighting sampling (CARS) and random frog algorithms. Based on the selected effective wavelengths, prediction models for MC were developed using partial least squares regression (PLSR), multiple linear regression, extreme learning machine, and back‐propagation neural network. Furthermore, freshness level discrimination models were established using simplified k nearest neighbor, support vector machine (SVM), and partial least squares discriminant analysis. Regarding the prediction models, the CARS‐PLSR model performed relatively better than the other models for predicting the MC, with R2P and RPD values of 0.84 and 2.51, respectively. Additionally, the CARS‐SVM model obtained superior discrimination performance, with 100% accuracy for both calibration and prediction sets. The results demonstrated that optical fiber spectroscopy technology is an effective tool to fast detect the MC and freshness for loquats.

show abstract

Evaluating consistency across multiple NeoSpectra (compact Fourier transform near‐infrared) spectrometers for estimating common soil properties

Mitu,

Smith,

Sanderman

et al. 2024

Soil Science Soc of Amer J

View full text Add to dashboard Cite

Rapid and cost‐effective techniques for soil analysis are essential to guide sustainable land management and production agriculture. This study aimed at evaluating the performance and consistency of portable handheld Fourier‐transform near‐infrared spectrometers and the NeoSpectra scanners in estimating 12 common soil physical and chemical properties including pH; organic carbon (OC); inorganic carbon (IC); total nitrogen (TN); cation exchange capacity (CEC); clay, silt, and sand fractions; and exchangeable potassium (K), phosphorus (P), calcium (Ca), and magnesium (Mg). A diverse set of samples (n = 600) were retrieved from a national‐scale soil archive of the Kellogg Soil Survey Laboratory of USDA‐NRCS and scanned with five NeoSpectra scanners. Predictive models for the soil properties were developed using partial least squares regression (PLSR), Cubist, and memory‐based learning (MBL). Cubist outperformed PLSR and MBL, with the best prediction performance for clay, OC, and CEC (R2 > 0.7), followed by IC, sand, silt, and Mg (R2 > 0.6), and then pH, TN, and Ca (R2 > 0.5). K and P were predicted somewhat poorly with R2 of 0.48 and 0.22. All five NeoSpectra yielded comparable near‐infrared (NIR) spectral data and the PLSR models for the soil properties (in terms of model regression coefficients). However, the consistency assessment showed that the model performance was significantly decreased when the training and testing spectra were from different NeoSpectra scanners. It is concluded that NeoSpectra scanners could be rapid and cost effective for estimating certain soil properties, and calibration transfer should be considered for applications where multiple devices are involved and high estimation accuracy from NIR data is required.

show abstract

Evaluation of soil structural quality using VIS–NIR spectra

Cited by 77 publications

References 83 publications

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy

Evaluating consistency across multiple NeoSpectra (compact Fourier transform near‐infrared) spectrometers for estimating common soil properties

Contact Info

Product

Resources

About