Spectral reflectance based indices for soil organic carbon quantification

Bartholomeus, Harm; Schaepman, Michael E.; Kooistra, Lammert; Stevens, Antoine; Hoogmoed, W.B.; Spaargaren, O.

doi:10.1016/j.geoderma.2008.01.010

Cited by 185 publications

(118 citation statements)

References 48 publications

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“…Wavelengths at 960 and 1100 nm (Daniel et al, 2004);1109, 1232, 1414, and 1522nm (Mouazen et al, 20071630, 2038, 2164, 2203and 2216nm (Hummel et al, 2001; and 2070 and 2220 nm (Vasques and Grunwald, 2008) are identified as the wavebands most sensitive to SOM levels. It was found that the absorption features related to cellulose (around 2100 nm) are strongly related to SOC (R 2 = 0.81) (Bartholomeus et al, 2008). For our study, the Pearson correlation coefficient at each wavelength between spectroscopic value and SOM was calculated for the whole 350-2500 nm spectrum region.…”

Section: Correlation Analysismentioning

confidence: 96%

“…Cross-validation is a way to predict the fit of a model to a hypothetical validation set when an explicit validation set is not available. Due to the low number of soil samples, all samples were used to develop the calibration model in the study, and the crossvalidation was used to verify the models (Bartholomeus et al, 2008;Melendez-Pastror et al, 2008;Escribano et al, 2010).…”

Section: Evaluation Of Som Prediction Modelmentioning

confidence: 99%

“…Although PLSR has many advantages, such as its simplicity, robustness, predictability, precision, and clearly quantitative explanations, the principal disadvantage is that PLSR does not provide a quantitative explanation for the relationship between predictor variables and response variables, and it does not support re-use of model algorithms between variable instrumentations. In a remotely sensed area, Bartholomeus et al (2008) indicated that an important drawback of PLSR is the complexity of the transfer of prediction models from one sensor to another. Sensor characteristics such as wavelength position, bandwidth, or number of bands often differ from one remote sensing instrument to another, which requires new model calibration for each sensor.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Chen

Peng

et al. 2012

Soil and Tillage Research

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Section: Correlation Analysismentioning

confidence: 96%

Section: Evaluation Of Som Prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Chen

Peng

et al. 2012

Soil and Tillage Research

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“…For the VNIR estimation of SOM content, transformations, such as absorbance (log 1/R, R = reflectance), are wildly used [34,35]. MATLAB ® (R2008a, MathWorks, Inc.)…”

Section: Spectral Pre-processingmentioning

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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“…5) is between 0.1 and 0.3 (e.g. Stoner and Baumgardner, 1981;Curran, 1985;Bartholomeus et al, 2008). Here we use a conservative estimate of 0.15.…”

Section: Dynamic Background Albedomentioning

confidence: 99%

The effect of a dynamic background albedo scheme on Sahel/Sahara precipitation during the mid-Holocene

2011

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Abstract.We have implemented a new albedo scheme that takes the dynamic behaviour of the surface below the canopy into account, into the land-surface scheme of the MPI-ESM. The standard (static) scheme calculates the seasonal canopy albedo as a function of leaf area index, whereas the background albedo is a gridbox constant derived from satellite measurements. The new (dynamic) scheme additionally models the background albedo as a slowly changing function of organic matter in the ground and of litter and standing dead biomass covering the ground. We use the two schemes to investigate the interactions between vegetation, albedo and precipitation in the Sahel/Sahara for two time-slices: preindustrial and mid-Holocene. The dynamic scheme represents the seasonal cycle of albedo and the correspondence between annual mean albedo and vegetation cover in a more consistent way than the static scheme. It thus gives a better estimate of albedo change between the two time periods. With the introduction of the dynamic scheme, precipitation is increased by 30 mm yr −1 for the pre-industrial simulation and by about 80 mm yr −1 for the mid-Holocene simulation. The present-day dry bias in the Sahel of standard ECHAM5 is thus reduced and the sensitivity of precipitation to mid-Holocene external forcing is increased by around one third. The locations of mid-Holocene lakes, as estimated from reconstructions, lie south of the modelled desert border in both mid-Holocene simulations. The magnitude of simulated rainfall in this area is too low to fully sustain lakes, however it is captured better with the dynamic scheme. The dynamic scheme leads to increased vegetation variability in the remaining desert region, indicating a higher frequency of green spells, thus reaching a better agreement with the vegetation distribution as derived from pollen records.

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Spectral reflectance based indices for soil organic carbon quantification

Cited by 185 publications

References 48 publications

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

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

The effect of a dynamic background albedo scheme on Sahel/Sahara precipitation during the mid-Holocene

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