Visible and near infrared diffuse reflectance spectroscopy has produced promising results to infer soil organic carbon (SOC) content in the laboratory. However, using soil spectra measured directly ...in the field or with airborne imaging spectrometers remains challenging due to uncontrolled variations in surface soil conditions, like vegetation cover, soil moisture and roughness. In particular, soil moisture may dramatically degrade predictions of SOC content when using an empirical approach. This study aims to quantify the effect of soil moisture on the accuracy of SOC predictions, and propose a method to determine SOC content for moist samples with unknown moisture content. More than 100 soil samples were collected along a transect, in the Grand-Duchy of Luxembourg. The soil samples were air-dried, moistened in steps of 0.05g water g soil−1 until saturation, and scanned in the laboratory with a visible and near infrared diffuse reflectance spectrometer. We computed the normalized soil moisture index (NSMI) to estimate the soil moisture content of the samples (R2=0.74), and used it to spectrally classify the samples according to their moisture content. SOC content was predicted using separate partial least square regressions developed on groups of samples with similar NSMI values. The root mean square error of prediction (RMSE) after validation was always below 5g C kg−1, with a ratio of prediction to deviation (RPD) greater than 2. The SOC content prediction models with a-priori knowledge of soil moisture gave similar RMSE as the ones after the NSMI classification. Hence, the NSMI might be used as a proxy of moisture content to improve SOC content prediction for spectral data acquired outside the laboratory since the method is simple and does not need other data than a simple band ratio of the spectra.
► Dry calibrations applied to wet samples gave inaccurate SOC predictions. ► We classified the dataset based on NSMI for its good correlation with soil moisture. ► NSMI showed a correlation with SOC, and soil type has a significant impact on NSMI. ► We predicted SOC with similar accuracy from NSMI classes and soil moisture classes. ► This methodology may improve the SOC predictions based on imaging spectroscopy.
Due to the large spatial variation of soil organic carbon (SOC) content, assessing the current state of SOC for large areas is costly and time consuming. Visible and Near Infrared Diffuse Reflectance ...Spectroscopy (Vis-NIR DRS) is a fast and cheap tool for measuring SOC based on empirical equations and spectral libraries. While the approach has been demonstrated to yield accurate predictions for databases containing samples belonging to soils with similar characteristics such as mineralogy, texture, iron, and CaCO3 content, spectroscopic calibrations have been less successful when applied to large and diverse soil spectral libraries. The scope of this study was to predict SOC using a local partial least square regression approach. In total, 19,969 topsoil (0–20 cm) samples collected all over the European Union were analyzed for physical and chemical properties, and scanned with a Vis-NIR spectrometer in a single laboratory. The local regression method builds a different multivariate model for each sample to predict. Each local model is trained with neighbours' samples selected from a large spectral library, based on their spectral similarity with the sample to predict. We modified the local regression procedure by including other covariates (geographical and texture information) in the computation of the distance between samples. The results showed good prediction ability for mineral soils under cropland (RMSE = 3.6 g C kg−1) and grassland (RMSE = 7.2 g C kg−1). Predictions of mineral soils under woodland (RMSE = 11.9 g C kg−1) and organic soils (RMSE = 51.1 g C kg−1) were less accurate. The use of sand content in the computation of the sample similarities provided the most accurate SOC predictions due to its influence on light scattering properties of soils. In large datasets, using additional soil or environmental information allows to select neighbours that have overall the same soil composition as the samples to predict, resulting in more accurate models. This study shows that (i) it is possible to realize low-cost estimations of SOC at continental scale using large spectral libraries with a reasonable accuracy, and (ii) the local approach is a valuable tool to deal with large datasets, especially if existing soil property maps or soil legacy data could be used as covariates in the SOC prediction models.
•We predicted SOC content combining Vis-NIR DRS and a local regression algorithm.•We modified the algorithm using geographical coordinates and sand to infer SOC.•We predicted well cropland (RMSE: 3.6 g C kg−1) and grassland (7.2 g C kg−1) soils.•Woodland (RMSE = 11.9 g C kg−1) and organic soils (51.1 g C kg−1) got larger error.•The best results were obtained when sand content was used as covariate.
Agricultural management has received increased attention over the last decades due to its central role in carbon (C) sequestration and greenhouse gas mitigation. Yet, regardless of the large body of ...literature on the effects of soil erosion by tillage and water on soil organic carbon (SOC) stocks in agricultural landscapes, the significance of soil redistribution for the overall C budget and the C sequestration potential of land management options remains poorly quantified. In this study, we explore the role of lateral SOC fluxes in regional scale modelling of SOC stocks under three different agricultural management practices in central Belgium: conventional tillage (CT), reduced tillage (RT) and reduced tillage with additional carbon input (RT+i). We assessed each management scenario twice: using a conventional approach that did not account for lateral fluxes and an alternative approach that included soil erosion‐induced lateral SOC fluxes. The results show that accounting for lateral fluxes increased C sequestration rates by 2.7, 2.5 and 1.5 g C m−2 yr−1 for CT, RT and RT+i, respectively, relative to the conventional approach. Soil redistribution also led to a reduction of SOC concentration in the plough layer and increased the spatial variability of SOC stocks, suggesting that C sequestration studies relying on changes in the plough layer may underestimate the soil's C sequestration potential due to the effects of soil erosion. Additionally, lateral C export from cropland was in the same of order of magnitude as C sequestration; hence, the fate of C exported from cropland into other land uses is crucial to determine the ultimate impact of management and erosion on the landscape C balance. Consequently, soil management strategies targeting C sequestration will be most effective when accompanied by measures that reduce soil erosion given that erosion loss can balance potential C uptake, particularly in sloping areas.
Soil organic carbon (SOC) is considered to influence important processes affecting soil, air, and water quality. The management of this valuable resource could be assisted by remote sensing ...techniques able to provide high-resolution spatial estimates of SOC. Such estimations are usually based on empirical regressions that are likely to have poor extrapolation abilities and hence it is important to properly estimate their accuracy in unsampled fields. Based on an imaging spectroscopy image acquired over the Luxembourg (c. 420 km2), several multivariate calibration models (partial least square PLSR, penalized-spline signal PSR, and support vector machine SVMR regressions) were developed to predict SOC content of topsoil bare agricultural fields and compared. The performance of the models was evaluated by means of cross-validation (k-foldKFO, leave-one-out LOO, leave-one-group-out LOGO, and leave-one-field-out LOFO) and these estimates were compared with model performance obtained by validation. The validation set excluded the fields used in the training set, to provide realistic measures of prediction error in unsampled fields. All cross-validation techniques, except LOFO, strongly underestimate validation error. In large areas, training samples are often not a representative subset of the soil and spectral variation. Leave-one-field-out cross-validation, by repeatedly leaving samples belonging to one field out of the calibration, better simulates model error at unknown locations than other cross-validation strategies. The root mean square error (RMSE) of the best models, obtained with a stringent validation procedure (leave-fields-out), was equal to 4.7 g C kg−1. This is higher than most of previous studies using imaging spectroscopy for SOC prediction, suggesting that measures of accuracy obtained by KFO, LOO, and LOGO are likely over-optimistic in large areas. Finally, a SOC content map for the topsoil of croplands was produced that may assist soil monitoring and/or management efforts in this region in the future.
•Wet sieving over 20 μm separates stable fine and more reactive coarse organic matter.•These fractions are related to biological indicators and reflect management effects.•The coarse fraction is most ...sensitive, it reflects management changes within 5 years.•A ratio of coarse to total C > 0.5 indicates increase in soil organic matter (SOM).•The coarse fraction is crucial for preserving SOM, it gradually feeds the fine C.
The heterogeneity of soil organic matter (SOM) and the small changes in soil organic carbon (SOC) compared to large total SOC stocks hinder a robust estimation of SOC turnover, in particular for more stable SOC. We developed a simple fractionation protocol for agricultural topsoils and tested it extensively on a range of soils in southern Belgium, including farmed soils, soils from long-term field trials, and paired sites after recent conversion to conservation farming. Our simple fractionation involves shaking the soil, wet sieving over 20 μm and analysing the SOC concentration in the soil as well as in the fine fraction (<20 μm). Eight biological indicators measured in an earlier study across the same monitoring network for the 0–10 cm topsoil were analysed in a conditional inference forest model in order to investigate the factors influencing the SOC fractions. Soil microbial biomass N explained the largest proportion of variation in both fractions. The fine fraction was also associated with factors explaining the regional trend in SOC distribution such as farmyard manure input, precipitation, land use and flow length. The variation in SOC content between treatments both in long-term trials and in farmers’ fields converted to conservation management was mainly attributed to changes within the coarse fraction. Thus, this fraction proves to be sensitive to management changes, although care should be taken to sample deep enough to represent the former plough layer inherited from the conventional tillage practice. Furthermore, the ratio between the coarse and the fine fraction showed a linear relationship (r² = 0.66) with the relative changes in SOC concentration over the last ten years. These fractions derived from a simple analytical approach are thus useful as an indicator for changes in SOC concentration. In analogy to biological indicators such as the soil microbial biomass C, the relationship between the fractions and relative changes in SOC concentration are likely to depend on climate conditions. Our methodology provides an indicator for use in routine analysis of agricultural topsoils, which is capable of predicting the effects of management practices on SOC concentrations in the short to mid-term (5–10 years).
The capacity of soils to store organic carbon represents a key function of soils that is not only decisive for climate regulation but also affects other soil functions. Recent efforts to assess the ...impact of land management on soil functionality proposed that an indicator- or proxy-based approach is a promising alternative to quantify soil functions compared to time- and cost-intensive measurements, particularly when larger regions are targeted. The objective of this review is to identify measurable biotic or abiotic properties that control soil organic carbon (SOC) storage at different spatial scales and could serve as indicators for an efficient quantification of SOC. These indicators should enable both an estimation of actual SOC storage as well as a prediction of the SOC storage potential, which is an important aspect in land use and management planning. There are many environmental conditions that affect SOC storage at different spatial scales. We provide a thorough overview of factors from micro-scales (particles to pedons) to the global scale and discuss their suitability as indicators for SOC storage: clay mineralogy, specific surface area, metal oxides, Ca and Mg cations, microorganisms, soil fauna, aggregation, texture, soil type, natural vegetation, land use and management, topography, parent material and climate. As a result, we propose a set of indicators that allow for time- and cost-efficient estimates of actual and potential SOC storage from the local to the regional and subcontinental scale. As a key element, the fine mineral fraction was identified to determine SOC stabilization in most soils. The quantification of SOC can be further refined by including climatic proxies, particularly elevation, as well as information on land use, soil management and vegetation characteristics. To enhance its indicative power towards land management effects, further “functional soil characteristics”, particularly soil structural properties and changes in the soil microbial biomass pool should be included in this indicator system. The proposed system offers the potential to efficiently estimate the SOC storage capacity by means of simplified measures, such as soil fractionation procedures or infrared spectroscopic approaches.
•Indicators for current and potential SOC storage at various scales were identified•An indicator system for SOC storage from local to regional scales is proposed•The fine mineral fraction was identified as a key indicator•Soil structural properties and microbial biomass are further promising indicators•Simplified soil fractionation or infrared spectroscopic approaches are needed
Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor ...the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg(-1) for mineral soils and a root mean square error of 50 g C kg(-1) for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.
Visible, Near and Short Wave Infrared (VNSWIR) diffuse reflectance spectroscopy (350nm to 2500nm) has been proven to be an efficient tool to determine the Soil Organic Carbon (SOC) content. SOC ...assessment (SOCa) is usually done by using calibration samples and multivariate models. However one of the major constraints of this technique, when used in field conditions is the spatial variation in surface soil properties (soil water content, roughness, vegetation residue) which induces a spectral variability not directly related to SOC and hence reduces the SOCa accuracy. This study focuses on the impact of soil roughness on SOCa by outdoor VIS-NIR-SWIR spectroscopy and is based on the assumption that soil roughness effect can be approximated by its related shadowing effect.
A new method for identifying and correcting the effect of soil shadow on reflectance spectra measured with an Analytical Spectral Devices (ASD) spectroradiometer and an Airborne Hyperspectral Sensor (AHS-160) on freshly tilled fields in the Grand Duchy of Luxembourg was elaborated and tested. This method is based on the shooting of soil vertical photographs in the visible spectrum and the derivation of a shadow correction factor resulting from the comparison of “reflectance” of shadowed and illuminated soil areas.
Moreover, the study of laboratory ASD reflectance of shadowed soil samples showed that the influence of shadow on reflectance varies according to wavelength. Consequently a correction factor in the entire 350–2500nm spectral range was computed to translate this differential influence.
Our results showed that SOCa was improved by 27% for field spectral data and by 25% for airborne spectral data by correcting the effect of soil relative shadow. However, compared to simple mathematical treatment of the spectra (first derivative, etc.) able to remove variation in soil albedo due to roughness, the proposed method, leads only to slightly more accurate SOCa.
•Soil roughness effect on SOCa by VNSWIR spectroscopy is studied.•Soil roughness is estimated by its shadowing effect on field vertical photographs.•Outdoor field and airborne and laboratory spectroscopy are used for SOCa.•SOCa by spectroscopy was improved with a shadow correction factor.•The shadow disturbing effect for SOCa was found wavelength dependent.
We investigated the effect of both the calibration set size (number of samples) and the calibration sampling strategy on the performance of vis–NIR models to predict clay content and exchangeable Ca ...(Ca++). We evaluated the following calibration sampling algorithms: Kenard–Stone (KSS), conditioned Latin hypercube (cLHS) and fuzzy c-means (FCMS), which are commonly used in spectroscopy and digital soil mapping. These algorithms were tested separately using a field-scale dataset and a regional scale dataset. For each dataset we randomly selected a validation subset and the remaining samples were used as candidates for calibration sampling. The accuracy of vis–NIR models of clay content and Ca++ were compared on the basis of the sampling algorithms used for selecting the calibration samples. We also tested 38 different calibration set sizes varying from 10 to 380 samples. The vis–NIR models were calibrated by using the support vector regression machine (SVM) algorithm. The training root mean square error (RMSE), the normalized RMSE and the prediction RMSE were used to evaluate the sensitivity of the models to both the sampling algorithm and the calibration set size. In addition, we investigated the sample representativeness of each algorithm and we suggest a novel and simple methodology to identify an adequate calibration set size based only on the vis–NIR data (i.e. without prior knowledge of the response variables).
As expected, our results show that the error of the soil vis–NIR models depends on the calibration set size. When the number of calibration samples is relatively small the sampling algorithm may play an important role on the accuracy of the vis–NIR models. On the other hand, if the calibration set size is large enough, the sampling method is not a critical issue. Concerning the sample representativeness, we found for all the algorithms that the original distribution of the vis–NIR data can be better replicated by increasing the calibration set size. The results indicate that the calibration samples selected by the cLHS and by the FCMS algorithms better replicate the original vis–NIR distribution of all the samples, in comparison to those samples selected by the KSS algorithm.
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•The effect of the calibration set size on vis–NIR model's accuracy is evaluated.•Different calibration sampling strategies are tested.•We propose a spectral-based methodology to identify an adequate calibration set size.
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