Regional predictions of soil organic carbon content from spectral reflectance measurements

Aïchi, Hamouda; Fouad, Youssef; Walter, Christian; Rossel, Raphael A. Viscarra; Lili-Chabaane, Zohra; Sanaa, Mustapha

doi:10.1016/j.biosystemseng.2009.08.002

Cited by 47 publications

(23 citation statements)

References 9 publications

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“…A simple way of SOM analyses could be using extraction indexes derived from ultraviolet (UV) and visible (VIS) absorbance spectra (Chin et al, 1994;Her et al, 2008;Tan, 2003). The application of UV, VIS and near infrared (NIR) reflectance of the soil is also a widespread method for the survey of soil properties by remote sensing (Aichi et al, 2009;Conforti et al, 2013). This method is applicable only to establish the soil surface parameters (Gomez et al, 2008).…”

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confidence: 99%

Redistribution of Soil Organic Carbon Triggered by Erosion at Field Scale Under Subhumid Climate, Hungary

et al. 2016

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Soil organic carbon (SOC) has primary importance in terms of soil physics, fertility and even of climate change control. An intensively cultivated Cambisol was studied in order to quantify SOC redistribution under subhumid climate. One hundred soil samples were taken from the representative points of the solum along the slopes from the depth of 20-300 cm with a mean 1.2 % SOC content. They were measured by the simultaneous application of diffuse reflectance (240-1900 nm) and traditional physico-chemical methods in order to compare the results. On the basis of the results hierarchical cluster analyses were performed. The spatial pattern of the groups created were similar, and even though the classifications were not the same, diffuse reflectance has proven to be a suitable method for soil/sediment classification even within a given arable field. Both organic and inorganic carbon distribution was found a proper tool for estimations of past soil erosion process. Results show SOC enrichment on two sedimentary spots with different geomorphological positions. Soil organic matter compound also differs between the two spots due to selective deposition of the delivered organic matter. The components of low molecular weight reach the bottom of the slope and there can leach into the profile, while the more polymerised organic matter compounds are delivered and deposited even before, on a higher segment of the slope in an aggregated form. This spatial difference appears below the uppermost tilled soil layer as well; referring the lower efficiency of conventional ploughing tillage in spatial soil homogenisation.

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confidence: 99%

Redistribution of Soil Organic Carbon Triggered by Erosion at Field Scale Under Subhumid Climate, Hungary

et al. 2016

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“…Thirty samples in the calibration set may be not enough to encompass all variations of soil characteristics and, as a consequence, new independent samples are not predicted satisfactorily. However, accurate regional prediction of SOC in Brittany (France) has been accomplished with a calibration dataset of 32 (Aïchi et al, 2009).…”

Section: Independent Validation Set Predictionmentioning

confidence: 99%

“…VIS-NIRS can aid in estimating soil carbon content (Aïchi et al, 2009;Ladoni et al, 2010) and these data are particularly important for future global carbon research as soils are regarded as potential sinks or sources of atmospheric carbon. Partial least square (PLS) regression is one of the most popular methods for calibration used in combination with VIS-NIRS (Brown et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Use of Vis-Nirs for Land Management Classification With a Support Vector Machine and Prediction of Soil Organic Carbon and Other Soil Properties

Debaene¹,

Pikuła²,

Niedźwiecki³

2014

Cienc. Inv. Agr.

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G. Debaene, D. Pikuła, and J. Niedźwiecki. 2014. Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties. Cien. Inv. Agr. 41(1): 21-32. The objective of this research was to investigate the effects of a long-term experiment on soil spectral properties and to develop prediction models of these properties (soil organic carbon (SOC), N, pH, Hh, P 2 O 5 , K 2 O, Ca, Mg, K, and Na content) from texturally homogeneous samples (loamy sand). To this aim, chemometric techniques, such as partial least square (PLS) regression and support vector machine (SVM) classification, were used. Our results show that visible and near infrared spectroscopy (VIS-NIRS) is suitable for the prediction of properties of texturally homogeneous samples. The effects of fertilizer applications were sufficient to modify the soil chemical composition to a range suitable for using VIS-NIRS for calibration and modeling purposes. The best results were obtained for SOC and N content prediction using the full dataset with cross-validation (r 2 = 0.76, RMSECV = 0.04, RPD = 2.02 and r 2 = 0.81, RMSECV = 0.01, RPD = 2.20, respectively) and with an independent validation dataset (r 2 = 0.70, RMSEP = 0.04, RPD = 1.80 and r 2 = 0.73, RMSEP = 0.03, RPD = 1.22, for SOC and N content, respectively). The use of fertilizers and the type of crop rotation appear to have a significant impact on soil spectral properties; the SVM methodology with a linear kernel function was able to classify soil samples as functions of the applied doses of organic and inorganic fertilizers with 75% accuracy with cross-validation and the type of crop rotation with more than 90% accuracy with full validation of separate datasets.

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“…Soil carbon also serves as a primary source of energy and nutrients for many soil organisms (Aïchi et al 2009) and is inconstant in both space and time (Batchily et al 2003). Timely and continuous assessment of soil C requires state of the art technologies that can quantify the relationships between leaf nutrient concentrations and soil nutrients (He et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Aïchi et al (2009) attempted to develop a regional predictive model of soil organic carbon (SOC) content, based on laboratory measurements of spectra within the visible and near-infrared spectral ranges (400-2500nm). The authors reported relatively high coefficients of determination for both calibration (R 2 =0.91, RMSE = 0.36 %) and validation (R 2 = 0.83, RMSE = 0.46 %) datasets.…”

Section: Introductionmentioning

confidence: 99%

Soil carbon estimation from <i>eucalyptus grandis</i> using canopy spectra

Mzinyane¹,

Aardt²,

Gebreslasie³

2015

SA J of Geomatics

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Regional predictions of soil organic carbon content from spectral reflectance measurements

Cited by 47 publications

References 9 publications

Redistribution of Soil Organic Carbon Triggered by Erosion at Field Scale Under Subhumid Climate, Hungary

Redistribution of Soil Organic Carbon Triggered by Erosion at Field Scale Under Subhumid Climate, Hungary

Use of Vis-Nirs for Land Management Classification With a Support Vector Machine and Prediction of Soil Organic Carbon and Other Soil Properties

Soil carbon estimation from <i>eucalyptus grandis</i> using canopy spectra

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