Visible, Near-Infrared, and Mid-Infrared Spectroscopy Applications for Soil Assessment with Emphasis on Soil Organic Matter Content and Quality: State-of-the-Art and Key Issues

Gholizadeh, Asa; Borůvka, Luboš; Saberioon, Mohammadmehdi; Vašát, Radim

doi:10.1366/13-07288

Cited by 151 publications

(93 citation statements)

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“…It differentiates materials based on their reflectance in the wavelength range from 400-2500 and can be used to encode information on the inherent composition of soil and determine a wide range of soil parameters in different platforms (in the laboratory, in the field using sampling, on-the-go using sensors embedded on a tractor, as well as airborne measurements) [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

et al. 2017

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Abstract:Soil spectroscopy has shown to be a fast, cost-effective, environmentally friendly, non-destructive, reproducible and repeatable analytical technique. Soil components, as well as types of instruments, protocols, sampling methods, sample preparation, spectral acquisition techniques and analytical algorithms have a combined influence on the final performance. Therefore, it is important to characterize these differences and to introduce an effective approach in order to minimize the technical factors that alter reflectance spectra and consequent prediction. To quantify this alteration, a joint project between Czech University of Life Sciences Prague (CULS) and Tel-Aviv University (TAU) was conducted to estimate Cox, pH-H 2 O, pH-KCl and selected forms of Fe and Mn. Two different soil spectral measurement protocols and two data mining techniques were used to examine seventy-eight soil samples from five agricultural areas in different parts of the Czech Republic. Spectral measurements at both laboratories were made using different ASD spectroradiometers. The CULS protocol was based on employing a contact probe (CP) spectral measurement scheme, while the TAU protocol was carried out using a CP measurement method, accompanied with the internal soil standard (ISS) procedure. Two spectral datasets, acquired from different protocols, were both analyzed using partial least square regression (PLSR) technique as well as the PARACUDA II ® , a new data mining engine for optimizing PLSR models. The results showed that spectra based on the CULS setup (non-ISS) demonstrated significantly higher albedo intensity and reflectance values relative to the TAU setup with ISS. However, the majority of statistics using the TAU protocol was not noticeably better than the CULS spectra. The paper also highlighted that under both measurement protocols, the PARACUDA II ® engine proved to be a powerful tool for providing better results than PLSR. Such initiative is not only a way to unlock current limitations of soil spectroscopy, but also offers considerable efficiency and cost-and time-saving possibilities, which lead to further improvements in prediction performance of spectral models.

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Section: Introductionmentioning

confidence: 99%

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

et al. 2017

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“…Choosing the most robust technique can help to achieve a more reliable prediction model. Spectral preprocessing methods are employed to remove any inappropriate information, which cannot be handled correctly by the modelling techniques (Gholizadeh et al 2013). Actually, these methods aim to decrease the noise and enhance possible spectral features connected with the property studied.…”

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confidence: 99%

“…These methods include stepwise multiple linear regression (SMLR) (Vasques et al 2008), principle component regression (PCR) (Nocita et al 2013), partial least squares regression (PLSR) (Stevens et al 2010), random forests (RF) (Viscarra Rossel & Behrens 2010;Ji et al 2012), artificial neural networks (ANN) (Hidaka et al 2011), and support vector machine regression (SVMR) (Stevens et al 2010;Chen et al 2012). According to some researchers, using SVMR can overcome the problems of other calibration methods, as the above-mentioned calibration methods require the creation of robust and generalized models due to their potential tendency to over-fit the data (Vapnik 1995;Gholizadeh et al 2013). Based on work by Vapnik (1995) and Gholizadeh et al (2015), SVMR is a supervised non-parametric statistical learning technique; thus, it represents a different method class compared with the previous techniques.…”

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confidence: 99%

Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features

Gholizadeh¹,

Borůvka²,

Saberioon³

et al. 2015

Soil Water Res.

142

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Gholizadeh A., Borůvka L., Saberioon M.M., Kozák J., Vašát R., Němeček K. (2015): Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil & Water Res., 10: 218-227.The lands near mining industries in the Czech Republic are subjected to soil pollution with heavy metals. Excessive heavy metal concentrations in soils not only dramatically impact the soil quality, but also due to their persistent nature and indefinite biological half-lives, potentially toxic metals can accumulate in the food chain and can eventually endanger human health. Monitoring and spatial information of these elements require a large number of samples and cumbersome and time-consuming laboratory measurements. A faster method has been developed based on a multivariate calibration procedure using support vector machine regression (SVMR) with cross-validation, to establish a relationship between reflectance spectra in the visible-near infrared (Vis-NIR) region and concentration of Mn, Cu, Cd, Zn, and Pb in soil. Spectral preprocessing methods, first and second derivatives (FD and SD), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR) were employed after smoothing with Savitzky-Golay to improve the robustness and performance of the calibration models. According to the criteria of maximal coefficient of determination (R 2 cv ) and minimal root mean square error of prediction in cross-validation (RMSEP cv ), the SVMR algorithm with FD preprocessing was determined as the best method for predicting Cu, Mn, Pb, and Zn concentration, whereas the SVMR model with CR preprocessing was chosen as the final method for predicting Cd. Overall, this study indicated that the Vis-NIR reflectance spectroscopy technique combined with a continuously enriched soil spectral library as well as a suitable preprocessing method could be a nondestructive alternative for monitoring of the soil environment. The future possibilities of multivariate calibration and preprocessing with real-time remote sensing data have to be explored.

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“…Also, it allows to measure quantitative information about soil parameters such as soil texture or soil organic matter content (Cécillon et al 2008). These techniques are used under field or laboratory conditions (Gholizadeh et al 2013). Visible and near-infrared (VIS-NIR) diffuse reflectance spectroscopy in the wavelength range of 350-2,500 nm has numerous advantages, it is: rapid, non-invasive, reproducible, relatively inexpensive and safe because it replaces the chemical analysis (Reeves 2010).…”

Section: Introductionmentioning

confidence: 99%

Influence of the Distance Between a Reflectance Sensor and Soil Samples with Different Roughness on Their Spectra

Herodowicz

2017

pjss

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The study assessed the influence of the distance between a reflectance sensor and soil samples with various roughness states (R1-the lowest, R2 -the medium, R3 -the highest roughness state) on their spectra level, under laboratory condition. Studied soil samples were illuminated at three light source zenith angles (θ s equal to 20°, 40°, 60°) and observed by the sensor to the nadir, from various distances (H s ) from 10 to 54 cm. These dark (the Mollic Gleyic Fluvisol) and light (the Cutanic Stagnic Luvisol) soil materials with their minimum roughness were characterized by diffused reflectance spectra. The relative differences (RD) between the spectra level of soil samples with R1, R2, R3 roughness states and the diffused reflectance level of soil materials were calculated with 1 nm interval in range of 420-2,300 nm. Higher roughness state and higher θ s , result in higher RD. Thus, for the dark and light soil samples with R3 roughness state and illuminated at θ s = 60°,the RD are the highest reached 63 and 39% (H s =54 cm) and reached 77 and 63% (H s =10 cm), respectively. The spectra level of the soil samples in R1 and R3 roughness states, illuminated at θ s =20° and soil samples with R1 roughness, illuminated at θ s =60°, reached a stable level, at a specific H s . It means, that a spectra does not significantly change with a further increase H s . However, the soil samples in R3 roughness, illuminated at θ s =60° have not reached the stability.

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Visible, Near-Infrared, and Mid-Infrared Spectroscopy Applications for Soil Assessment with Emphasis on Soil Organic Matter Content and Quality: State-of-the-Art and Key Issues

Cited by 151 publications

References 100 publications

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features

Influence of the Distance Between a Reflectance Sensor and Soil Samples with Different Roughness on Their Spectra

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