Spiking of NIR regional models using samples from target sites: Effect of model size on prediction accuracy

Guerrero, C.; Zornoza, Raúl; Gómez, Ignacio; Mataix‐Beneyto, J.

doi:10.1016/j.geoderma.2009.12.021

Cited by 145 publications

(111 citation statements)

References 29 publications

(54 reference statements)

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“…Better proportions were obtained by Shepherd and Walsh (2002), about 18%, Fystro (2002) about 20%, andWetterlind et al (2008) about 8%. Most of the high proportions are from studies covering large areas as does the present study, which suggests room for further improvement by spiking (Guerrero et al, 2010), i.e., inclusion of a few local samples.…”

Section: Prediction Of Soc From Nir Spectramentioning

confidence: 66%

Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique

et al. 2012

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Section: Prediction Of Soc From Nir Spectramentioning

confidence: 66%

Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique

et al. 2012

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“…Most recently, many researchers have investigated the effect of SM on reflectance spectra, and some methods for removing or minimizing the SM and improving the prediction accuracy of SOM have been also put forward and explored, such as external parameter orthogonalization (EPO) [9,[13][14][15], direct standardization (DS) and piecewise direct standardization (PDS) [11,[16][17][18], "spiking" method [19,20], first derivative [21], slope bias correction (SB) [22], orthogonal signal correction (OSC) and generalized least squares weighting (GLSW) [23,24], spectral classification [25,26] and so on. The above-mentioned EPO, DS and PDS strategies usually require dry soil spectral libraries (SSLs) at a specific scales (global, continental, national or regional) and then use a projection matrix (or transfer matrix) to correct the moist spectra.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

Hong

Chen

et al. 2017

Remote Sensing

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Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS-NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in the moist conditions to minimize the influence of SM is growing. The objective of this study was to investigate the transferability of two approaches, SM-based cluster method with known SM (classifying the VIS-NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)-based cluster method with unknown SM (utilizing NSMI to indicate the SM and establish models separately), to predict SOM directly in moist soil spectra. One hundred and twenty one soil samples were collected from Central China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOM concentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOM prediction models. Specifically, prediction models were developed for NSMI-based clusters with unknown SM data. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R 2 ), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SM led to reduced VIS-NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS-NIR spectra into different SM clusters in known SM states could improve the performance of PLS-SVM models to acceptable prediction accuracies (R 2 cv = 0.69-0.77, RPD = 1.79-2.08). The estimation of SOM, when using the NSMI-based cluster method with unknown SM (RPD = 1.95-2.04), was similar to the use of the SM-based cluster method with known SM (RPD = 1.79-2.08). The predictive results (RPD = 1.87-2.06) demonstrated that the NSMI-based cluster method has potential for application outside the laboratory for SOM prediction without knowing the SM explicitly, and this method is also easy to carry out and only requires spectral information.

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“…In order to take full advantage of the VNIR spectroscopy for soil characterization, it would be desirable to minimize the number of calibration samples [21]. Recent efforts have been made for better predictions of soil properties, with the assistance of soil spectral libraries at continental [7], national [21,22], regional [23] and local scales [22]. Nevertheless, it remains a challenge to develop an effective strategy for the VNIR estimation of SOM when the local soil spectral libraries are unavailable and in the areas where the soils are largely influenced by human activities.…”

Section: Introductionmentioning

confidence: 99%

Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes

et al. 2014

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Abstract:The transferability of a visible and near-infrared (VNIR) model for soil organic matter (SOM) estimation in riparian landscapes is explored. The results indicate that for the soil samples with air-drying, grinding and 2-mm sieving pretreatment, the model calibrated from the soil sample set with mixed land-use types can be applied in the SOM prediction of cropland soil samples (r 2 Pre = 0.66, RMSE = 2.78 g•kg −1 , residual prediction deviation (RPD) = 1.45). The models calibrated from cropland soil samples, however, cannot be transferred to the SOM prediction of soil samples with diverse land-use types and different SOM ranges. Wavelengths in the region of 350-800 nm and around 1900 nm are important for SOM estimation. The correlation analysis reveals that the spectral wavelengths from the soil samples with and without the air-drying, grinding and 2-mm sieving pretreatment are not linearly correlated at each wavelength in the region of 350-1000 nm, which is an important spectral region for SOM estimation in riparian landscapes. This result explains why the models calibrated from samples without pretreatment fail in the SOM estimation. The Kennard-Stone algorithm performed well in the selection of a representative subset for SOM estimation using the spectra of soil samples with pretreatment, but failed in soil samples without the pretreatment. Our study also demonstrates that a widely applicable SOM prediction model for riparian landscapes should be based on a wide range of SOM content.

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Spiking of NIR regional models using samples from target sites: Effect of model size on prediction accuracy

Cited by 145 publications

References 29 publications

Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique

Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes

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