Estimation of soil organic carbon in arable soil in Belgium and Luxembourg with the LUCAS topsoil database

Abstract

Summary Quantification of the soil organic carbon (SOC) content over large areas is mandatory to obtain accurate soil characterization and classification, which can improve site‐specific management at local or regional scales. In this context, soil spectroscopy is a well‐consolidated and widespread method to estimate soil variables, and in particular SOC content, at a low cost for routine analysis. The increasing number of large soil spectral libraries collected worldwide reflects the importance of spectroscopy in soil science. These large libraries contain soil samples derived from a large number of pedological regions and thus from different parent materials and soil types. In the light of the huge variation in the spectral responses to SOC content and composition, a rigorous process is necessary to subdivide large spectral libraries to avoid calibration with global models that fail to predict local variation in SOC content. Here, we propose to classify the European LUCAS topsoil database with a cluster analysis based on a large number of soil properties. The soil samples collected from arable land in the LUCAS database were chosen to apply a standardized multivariate calibration approach, valid for large areas, to calibrate local models without the need for further field and laboratory work. Cluster analysis detected seven soil classes and the samples belonging to each class were used to calibrate specific partial least squares regression (PLSR) models to estimate SOC content in three spectral libraries collected in Belgium and Luxembourg. Soil organic carbon was predicted with good accuracy, both within each library (root mean square error (RMSE), 1.2–5.1 g kg−1; ratio of performance to prediction (RPD), 1.41–2.24) and for the samples of the three libraries together (RMSE, 3.7 g kg−1; RPD, 2.54). The proposed approach could enable SOC to be estimated for arable soils in Europe with only the spectra of soil samples and without the need for laboratory analyses. Highlights We investigated the potential of the LUCAS database to estimate SOC in spectral libraries. We proposed a routine approach to estimate SOC with less laboratory work. The classification of the LUCAS topsoil dataset improved estimation accuracy of SOC SOC content can be predicted from soil spectra with models calibrated on the LUCAS database.

Sampling Strategies for Soil Property Mapping Using Multispectral Sentinel-2 and Hyperspectral EnMAP Satellite Data

“…The results highlighted that the distance between spectra is directly correlated with the differences in terms of SOC content, meaning that it could be feasible to exploit both multispectral and hyperspectral data to select pixels having a wide SOC range. This is due to the large heterogeneity of the organic matter, which entails the absence of well-defined spectral features within a specific spectral region.Consequently, the wavelengths linked to the SOC content can be detected along the whole VNIR-SWIR (400-2500 nm) spectrum [14,15,36]. However, the assumption that larger spectral differences result in more different soil properties weakens if the spectral response of the target variable is mainly influenced by functional groups located in narrow spectral regions and not throughout the spectrum as for SOC.…”

“…Since only rarely a single clay mineral is present in the soil, the quantitative estimation of clay content uses all spectral data having good signal quality. Similarly, Soil Organic Carbon (SOC) prediction models exploit most of the spectral regions across the electromagnetic spectrum between 400 and 2500 nm [14,15] and this is due to the large heterogeneity of the components of the organic matter. So given the link between spectral characteristics and soil variability, the absorbance/reflectance values at a given wavelength can be considered as covariates related to the target variable and consequently the spectral variability can be exploited for sampling strategies based on feature space.…”

Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects

“…However, a new field campaign and the related soil chemical analysis are time-consuming and expensive in comparison to using an existing soil spectral library. Castaldi et al [10,11] conceived the bottom-up approach that allows exploiting the Land Use and Coverage Area frame Survey (LUCAS) European topsoil dataset and airborne data for SOC mapping without spectral transfer function between laboratory and remote spectral data. While this approach proved to be successful in a relatively small pilot area restricted to bare cropland soils, the main challenge for the SOC estimation from an entire Sentinel-2 tile (i.e., 100 by 100 km) is the minimization of disturbing factors, such as vegetation, residues, roughness, and soil moisture.…”

Robustness of visible near‐infrared and mid‐infrared spectroscopic models to changes in the quantity and quality of crop residues in soil

“…Calibration stability (i.e., the robustness of models to application in altered contexts) determines the frequency with which models must be recalibrated (Stevens, van Wesemael, Vandenschrick, Touré, & Tychon, 2006) and thus the efficiency of spectroscopy in comparison to traditional laboratory methods. Soil type and mineralogy, but also management aspects, such as land use, tillage, fertilization, and handling of crop residues, may need to be considered in creating a suitable calibration model (Araújo, Wetterlind, Demattê, & Stenberg, 2014;Castaldi et al, 2018;Zeng et al, 2016). In this context, several studies have been conducted to determine whether visNIRS and MIRS can be used to estimate SOC content with sufficient accuracy to distinguish between the effects of various management strategies and document change in C stocks, with implications for both soil productivity and climate change.…”