Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy

Stevens, Antoine; Udelhoven, Thomas; Denis, Antoine; Tychon, Bernard; Lioy, Rocco; Hoffmann, Lucien; Wesemael, Bas van

doi:10.1016/j.geoderma.2009.11.032

Cited by 254 publications

(215 citation statements)

References 53 publications

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“…In order to address some of the limitations of current approaches highlighted above, spectroscopic techniques using visible and near-infrared or mid-infrared wavelengths have been developed as cost-effective and high-throughput alternatives to conventional SOC analysis (Nocita et al, 2014a,b;Stevens et al, 2010). For example, Doetterl et al, 2013 have shown the potential of using VIS-NIR reflectance to measure SOC profiles with a high vertical resolution of 3 cm.…”

Section: Introductionmentioning

confidence: 99%

High resolution characterization of the soil organic carbon depth profile in a soil landscape affected by erosion

Aldana-Jague

Sommer

Saby

et al. 2016

Soil and Tillage Research

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A B S T R A C TThe identification of soil management strategies as well as the evaluation of their effectiveness requires detailed information on the spatial and temporal patterns of soil organic carbon storage. High-resolution SOC profile data are generally not available and traditional methods for collecting these are time consuming and costly. Recent studies use geo-statistical approaches to assess the three-dimensional patterns of SOC storage. However, there is still a large discrepancy between the continuous and high resolution mapping of the horizontal SOC variability on the one hand, and the coarse and discontinuous mapping of the vertical SOC profile on the other. In this study, we combine spectroscopic techniques with spatial modeling in a small, cultivated catchment in Germany and we evaluate the contribution of soil redistribution processes and topographical parameters to the observed spatial and vertical patterns. Using high-resolution data from soil cores, we evaluated the robustness of a third order polynomial function to model the vertical SOC profile. Using a crossvalidation, our results show that this approach results in a robust model (RSME = 0.24%) and performs better than the widely used exponential depth model (RMSE = 0.39%). In a next step, we evaluated the relationship between the parameters of the SOC depth model and co-variables including soil redistribution (inferred from 137 Cs data) and topographical indices using a multiple linear regression model. The performance was calculated by cross-validation and we found a low robustness of the models because of the low number of profiles (i.e. n = 19). A statistical evaluation of the co-variables highlighted two key factors influencing the SOC vertical distribution. Soil redistribution processes mainly influenced the surface SOC content (first centimeters) whereas the shape of the depth distribution was controlled by slope curvature alone. The mapping of polynomial parameters was validated using an external SOC profile dataset and showed a poor prediction of the surface content but a good prediction of the depth distribution once the surface SOC content is known (RMSE = 0.15-0.25%C). This suggests that estimating the vertical SOC profile from topsoil data by applying remote sensing data, in combination with our SOC profile model, is promising and can will result in an accurate mapping of 3D SOC patterns at a very high resolution.

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Section: Introductionmentioning

confidence: 99%

High resolution characterization of the soil organic carbon depth profile in a soil landscape affected by erosion

Aldana-Jague

Sommer

Saby

et al. 2016

Soil and Tillage Research

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“…Visible and near-infrared (VIS/NIR) spectroscopy, known as a rapid, cost-effective, quantitative and eco-friendly technique, can provide hyperspectral data with narrow and numerous wavebands, both in the laboratory and in the field [1,8]. VIS/NIR spectroscopy has great potential for simultaneously estimating a variety of soil properties [1,4].…”

Section: Introductionmentioning

confidence: 99%

Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

et al. 2014

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With 298 heterogeneous soil samples from Yixing (Jiangsu Province), Zhongxiang and Honghu (Hubei Province), this study aimed to combine a successive projections algorithm (SPA) with a support vector machine regression (SVMR) model (SPA-SVMR model) to improve the estimation accuracy of soil organic carbon (SOC) contents using the laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The effects of eight spectra pre-processing methods, i.e., Log (1/R), Log (1/R) coupled with Savitzky-Golay (SG) smoothing (Log (1/R) + SG), first derivative with SG smoothing (FD), second derivative with SG smoothing (SD), SG, standard normal variate (SNV), mean center (MC) and multiplicative scatter correction (MSC), on SPA-based informative wavelength selection were explored. The SVMR model (i.e., SVMR without SPA) and SPA-PLSR model (i.e., SPA combined with partial least squares regression (PLSR)) were developed and compared with the SPA-SVMR model in order to evaluate the performance of SPA-SVMR. The results indicated that the variables selected by SPA and their distributions were strongly affected by different pre-processing methods, and SG was the optimal pre-processing method for SPA-SVMR model development; the SPA-SVMR model using SG pre-processing and 28 SPA-selected wavelengths obtained a better result (R 2 V = 0.73, RMSE V = 2.78 g·kg and RPD V = 1.63). Most of the spectral bands used by the SPA-SVMR model over the near-infrared region were important wavelengths for SOC content estimation. This study demonstrated that the combination of SPA and SVMR is feasible and reliable for estimating SOC content from the VIS/NIR spectra of soils in regions with multiple soil and land-use types.

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“…Several common methods have been adopted to use multivariate calibration methods to extract the relevant part of the information for a very large dataset in soil applications. These methods include stepwise multiple linear regression (SMLR) (Vasques et al 2008), principle component regression (PCR) (Nocita et al 2013), partial least squares regression (PLSR) (Stevens et al 2010), random forests (RF) (Viscarra Rossel & Behrens 2010;Ji et al 2012), artificial neural networks (ANN) (Hidaka et al 2011), and support vector machine regression (SVMR) (Stevens et al 2010;Chen et al 2012). According to some researchers, using SVMR can overcome the problems of other calibration methods, as the above-mentioned calibration methods require the creation of robust and generalized models due to their potential tendency to over-fit the data (Vapnik 1995;Gholizadeh et al 2013).…”

mentioning

confidence: 99%

Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features

Gholizadeh¹,

Borůvka²,

Saberioon³

et al. 2015

Soil Water Res.

150

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Gholizadeh A., Borůvka L., Saberioon M.M., Kozák J., Vašát R., Němeček K. (2015): Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil & Water Res., 10: 218-227.The lands near mining industries in the Czech Republic are subjected to soil pollution with heavy metals. Excessive heavy metal concentrations in soils not only dramatically impact the soil quality, but also due to their persistent nature and indefinite biological half-lives, potentially toxic metals can accumulate in the food chain and can eventually endanger human health. Monitoring and spatial information of these elements require a large number of samples and cumbersome and time-consuming laboratory measurements. A faster method has been developed based on a multivariate calibration procedure using support vector machine regression (SVMR) with cross-validation, to establish a relationship between reflectance spectra in the visible-near infrared (Vis-NIR) region and concentration of Mn, Cu, Cd, Zn, and Pb in soil. Spectral preprocessing methods, first and second derivatives (FD and SD), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR) were employed after smoothing with Savitzky-Golay to improve the robustness and performance of the calibration models. According to the criteria of maximal coefficient of determination (R 2 cv ) and minimal root mean square error of prediction in cross-validation (RMSEP cv ), the SVMR algorithm with FD preprocessing was determined as the best method for predicting Cu, Mn, Pb, and Zn concentration, whereas the SVMR model with CR preprocessing was chosen as the final method for predicting Cd. Overall, this study indicated that the Vis-NIR reflectance spectroscopy technique combined with a continuously enriched soil spectral library as well as a suitable preprocessing method could be a nondestructive alternative for monitoring of the soil environment. The future possibilities of multivariate calibration and preprocessing with real-time remote sensing data have to be explored.

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Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy

Cited by 254 publications

References 53 publications

High resolution characterization of the soil organic carbon depth profile in a soil landscape affected by erosion

High resolution characterization of the soil organic carbon depth profile in a soil landscape affected by erosion

Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features

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