Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy

Bartholomeus, Harm; Kooistra, Lammert; Stevens, Antoine; Leeuwen, Martin van; Wesemael, Bas van; Ben‐Dor, Eyal; Tychon, Bernard

doi:10.1016/j.jag.2010.06.009

Cited by 117 publications

(69 citation statements)

References 47 publications

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“…Only three out of 19 estimated SOC over areas partially covered by vegetation, all of them using PLSR [36][37][38]. The results obtained in this study using AHS data (R 2 ≥0.60 and RPD ≥ 1.60) were in the range of those found in the literature.…”

Section: Discussionsupporting

confidence: 69%

“…The results obtained in this study using AHS data (R 2 ≥0.60 and RPD ≥ 1.60) were in the range of those found in the literature. Bartholomeus et al [37] used AHS data and the residual spectral unmixing technique to estimate SOC in fields partially covered by maize, and obtained slightly worse results in the validation (R 2 = 0.56 and RPD = 1.50). Fernández et al [38] calibrated PLSR models using laboratory reflectance spectra that were subsequently applied to an AHS image to estimate SOC.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

et al. 2017

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Abstract:The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R 2 = 0.60-0.62, while models for the Hyperion sensor showed R 2 = 0.49-0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600-700 nm), and the short wave infrared region betweeñ 2000-2250 nm. The use of SMLR and spectral indices based on reference channels at~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method.

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Section: Discussionsupporting

confidence: 69%

Section: Discussionmentioning

confidence: 99%

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

et al. 2017

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“…Similar effects were described by Ben-Dor et al (1997) for absorption features of organic matter at 664 nm during decomposition process. Also recently revealed by Bartholomeus et al (2011), vegetation cover strongly influences the prediction accuracy of SOC based on PLSR. Therefore, model parameters at Sinsteden VC indicate a slightly worse SOC prediction compared to Sinsteden BF (Table 1).…”

Section: Influence Of Soil Surface Conditionsmentioning

confidence: 89%

“…However, reports in the literature regarding the influence of vegetation cover on the evaluation of (hyper-) spectral data are inconsistent. Bartholomeus et al (2007Bartholomeus et al ( , 2011 showed that even a vegetation cover of 5% leads to large variations in the estimation of soil properties such as iron content and SOC, while Chabrillat et al (2002) reported that clay minerals can still be satisfactorily detected with a dry vegetation cover of 20-30% and a green vegetation cover of 40-50%.…”

Section: Influence Of Soil Surface Conditionsmentioning

confidence: 99%

Airborne hyperspectral imaging of spatial soil organic carbon heterogeneity at the field-scale

et al. 2012

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“…Additionally, the reflectance of the air dried samples is measured (further referred to as labs pectra for all soil properties (Total C, Total N, pH, Total P, and Total K and moisture). This method has been used frequently to develop soil property models to determine, for example, organic carbon in laboratory, field and airborne settings [27][28][29]. PLSR is done in Parles [30], where all reflectance spectra are converted to apparent absorbance, mean centre transformation is done, and spectra are denoised using a Savitzky-Golay filter.…”

Section: Soil Description and Samplingmentioning

confidence: 99%

Spectral Estimation of Soil Properties in Siberian Tundra Soils and Relations with Plant Species Composition

Bartholomeus

Schaepman‐Strub

Blok

et al. 2012

Applied and Environmental Soil Science

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Predicted global warming will be most pronounced in the Arctic and will severely affect permafrost environments. Due to its large spatial extent and large stocks of soil organic carbon, changes to organic matter decomposition rates and associated carbon fluxes in Arctic permafrost soils will significantly impact the global carbon cycle. We explore the potential of soil spectroscopy to estimate soil carbon properties and investigate the relation between soil properties and vegetation composition. Soil samples are collected in Siberia, and vegetation descriptions are made at each sample point. First, laboratory-determined soil properties are related to the spectral reflectance of wet and dried samples using partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR). SMLR, using selected wavelengths related with C and N, yields high calibration accuracies for C and N. PLSR yields a good prediction model for K and a moderate model for pH. Using these models, soil properties are determined for a larger number of samples, and soil properties are related to plant species composition. This analysis shows that variation of soil properties is large within vegetation classes, but vegetation composition can be used for qualitative estimation of soil properties.

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Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy

Cited by 117 publications

References 47 publications

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

Airborne hyperspectral imaging of spatial soil organic carbon heterogeneity at the field-scale

Spectral Estimation of Soil Properties in Siberian Tundra Soils and Relations with Plant Species Composition

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