Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring

Nocita, Marco; Stevens, Antoine; Wesemael, Bas van; Aitkenhead, Matt; Bachmann, Martin; Barthès, Bernard; Dor, Eyal Ben; Brown, David J.; Clairotte, Michaël; Csorba, Ádám; Dardenne, Pierre; Demattê, José A.M.; Genot, Valérie; Guerrero, C.; Knadel, Maria; Montanarella, Luca; Noon, Carole; López, Luciano Espino; Robertson, Jean; Sakai, Hiro; Soriano-Disla, José M.; Shepherd, Keith D.; Stenberg, Bo; Towett, Erick K.; Vargas, Ronald; Wetterlind, Johanna

doi:10.1016/bs.agron.2015.02.002

Cited by 334 publications

(186 citation statements)

References 57 publications

Supporting

Mentioning

170

Contrasting

Unclassified

Order By: Relevance

“…The comparison of different data mining algorithms for prediction of soil properties from the spectral reflectance data showed regression performance via Support Vector Machine(0.92%, RMSE) followed respectively by the partial least square regression (0.96%, RMSE) and the Stochastic gradient boosting (1.02%, RMSE) [16]. One of the advantages of PLSR compared to other chemometric methods like principal component analysis is the possibility to interpret the first few latent variables (LV), because they show the correlations between the property values and the spectral features [22].…”

Section: The Plsr Modelmentioning

confidence: 99%

“…In the international context, mathematical and statistical methods of prediction are increasingly tested in the soil properties analysis protocols in relation to spectroscopy data [10]- [15]. The possibility offered by spectroscopy to generate reflectance and luminance spectra in different wavelengths 250 -400 nm (ultraviolet; UV), 400 -700 nm (visible; VIS), 700 -2500 nm (near infrared; NIR), 2500 -25,000 nm (med infrared, MIR) allows an extraction of useful information about soil components at lower cost [16] [17]. Hence, the interest of pursuing research in spectroscopy was to implement more accurate and reproducible model estimation of soil properties.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effect of the Continuum Removal in Predicting Soil Organic Carbon with Near Infrared Spectroscopy (NIRS) in the Senegal Sahelian Soils

Loum¹,

Diack²,

Ndour³

et al. 2016

OJSS

View full text Add to dashboard Cite

Spectroscopy plays a major role in the access of the analytical parameters of the soil. It tends to substitute the conventional laboratory analysis because hyperspectral data were least expensive and easier to obtain. The objective of this study was to evaluate the effect of the continuum removal (CR) in the validation of the accurate prediction model of the soil properties with Vis-NIR spectroscopy data. Few studies using Vis-NIR reflectance spectroscopy have well focused the calculation of the CR method; its effect in the calibration of the accurate models was also not well emphasized. In this study, we used the remote sensing software ENVI 4.7 to compute the CR function where the value of the continuum for each sample and for each spectral wavelength was obtained by dividing the reflectance values of the full spectrum (FS) with those of the continuum curve (CC). The partial least square regression (PLSR) model was applied in the spectral data from the soil of the Senegal Sahelian region. It was calibrated with both data from the full spectrum (FS) and those obtained after the application of the continuum removal. With the application of the CR, ultraviolet wavelengths (350 -429 nm) and those of near infrared (2491 -2500 nm) were removed from the explanatory variables of PLSR model. With the FS, all wavelengths between 350 and 2500 nm were taken into account in predicting soil properties. Our findings show a positive effect of the application of CR in the estimation of soil organic carbon. In calibration, the R2 increased up to 10% with the continuum removal in the model of 12 components (CP). In terms of validation, it's the 15-component model which is the most accurate with the same range in calibration between the FS and the CR. The lowest RMSE ranged from 0.04 with the FS to 0.03 with the application of the CR in calibration and validation. These results show that the interest of this study as soil organic carbon is recognized as a key indicator of fertility of the soil 136in Sahelian-African regions. For future studies, it's important to apply the model of neural networks to better evaluate the effect of continuum removal in predicting soil properties from the spectral data and other methods of preprocessing like the multiplicative scatter correction (msc).

show abstract

Section: The Plsr Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of the Continuum Removal in Predicting Soil Organic Carbon with Near Infrared Spectroscopy (NIRS) in the Senegal Sahelian Soils

Loum¹,

Diack²,

Ndour³

et al. 2016

OJSS

View full text Add to dashboard Cite

show abstract

“…This has found applications in soil surveying, agriculture, land management and mineral exploration (for extensive reviews, we refer to [5][6][7][8]). Today, however, multispectral sensors provide invaluable time series of data since the 1970s with global coverage.…”

Section: Introductionmentioning

confidence: 99%

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

et al. 2017

View full text Add to dashboard Cite

Many soil remote sensing applications rely on narrow-band observations to exploit molecular absorption features. However, broadband sensors are invaluable for soil surveying, agriculture, land management and mineral exploration, amongst others. These sensors provide denser time series compared to high-resolution airborne imaging spectrometers and hold the potential of increasing the observable bare-soil area at the cost of spectral detail. The wealth of data coming along with these applications can be handled using cloud-based processing platforms such as Earth Engine. We present a method for identifying the least-vegetated observation, or so called barest pixel, in a dense time series between January 1985 and March 2017, based on Landsat 5, 7 and 8 observations. We derived a Barest Pixel Composite and Bare Soil Composite for the agricultural area of the Swiss Plateau. We analysed the available data over time and concluded that about five years of Landsat data were needed for a full-coverage composite (90% of the maximum bare soil area). Using the Swiss harmonised soil data, we derived soil properties (sand, silt, clay, and soil organic matter percentages) and discuss the contribution of these soil property maps to existing conventional and digital soil maps. Both products demonstrate the substantial potential of Landsat time series for digital soil mapping, as well as for land management applications and policy making.

show abstract

“…To overcome these difficulties that block any attempt for reliability and comparability of results [19], some solutions were suggested by researchers; these included the selection of proper instrumentation, standards and protocol development [20], improved spectra filtering and preprocessing [21], better control of ambient conditions [8] and the appropriate selection of multivariate statistical analysis [15,22]. These approaches can significantly reduce differences between spectral measurements of the same samples by different operators in different laboratories.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring

Cited by 334 publications

References 57 publications

Effect of the Continuum Removal in Predicting Soil Organic Carbon with Near Infrared Spectroscopy (NIRS) in the Senegal Sahelian Soils

Effect of the Continuum Removal in Predicting Soil Organic Carbon with Near Infrared Spectroscopy (NIRS) in the Senegal Sahelian Soils

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

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

Contact Info

Product

Resources

About