Antoine Stevens

Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy

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.

Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy

Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping of soil properties, but the applicability decreases rapidly when fields are partially covered with vegetation. In this paper we show that with only a few percent fractional maize cover the accuracy of a Partial Least Square Regression (PLSR) based SOC prediction model drops dramatically. However, this problem can be solved with the use of spectral unmixing techniques. First, the fractional maize cover is determined with linear spectral unmixing, taking the illumination and observation angles into account. In a next step the influence of maize is filtered out from the spectral signal by a new procedure termed Residual Spectral Unmixing (RSU). The residual soil spectra resulting from this procedure are used for mapping of SOC using PLSR, which could be done with accuracies comparable to studies performed on bare soil surfaces (Root Mean Standard Error of Calibration = 1.34 g/kg and Root Mean Standard Error of Prediction = 1.65 g/kg). With the presented RSU approach it is possible to filter out the influence of maize from the mixed spectra, and the residual soil spectra contain enough information for mapping of the SOC distribution within agricultural fields. This can improve the applicability of airborne imaging spectroscopy for soil studies in temperate climates, since the use of the RSU approach can extend the flight-window which is often constrained by the presence of vegetation.

Soil spectroscopy: an opportunity to be seized

c Whashington State University, 405 Johnson Hall, PO Box 646420, Pullman, WA 99164-6420 USA d World Agroforestry Centre (ICRAF), United Nations Avenue, PO Box 30677, 00100 Nairobi Kenya e Food and Agriculture Organization (FAO), Viale delle Terme di Caracalla 00153 Rome, Italy * Corresponding author. Via Enrico Fermi 2749 e TP 280, I-21027 Ispra, VA, Italy. Tel.: +39 0332 78 3682; fax: +39 0332 78 6394. E-mail addresses: marco.nocita@jrc.ec.europa.eu; marco.nocita@gmail.com

DIGISOIL: an integrated system of data collection technologies for mapping soil properties

The multidisciplinary DIGISOIL consortium intends to integrate and improve in situ proximal measurement technologies for assessing soil properties and soil degradation indicators, moving from the sensing technologies themselves to their integration and application in (digital) soil mapping (DSM). The core objective of the project is to explore and exploit new capabilities of advanced geophysical technologies for answering this societal demand. To this aim, DIGISOIL addresses four issues covering technological, soil science, and economic aspects: (i) development and validation of hydrogeophysical technologies and integrated pedo-geophysical inversion techniques; (ii) the relation between geophysical parameters and soil properties; (iii) the integration of derived soil properties for mapping soil functions and soil threats; and (iv) the evaluation, standardisation, and industrialisation of the proposed methodologies, including technical and economic studies.

Topsoil Organic Carbon map of Europe

In 2009, within the framework of the European Land Use/Cover Area frame statistical Survey (LUCAS), a soil sampling campaign was implemented in 25 countries. About 22,000 composite topsoil samples were collected following a standardized sampling methodology and analysed in one laboratory. In this study, we present the first map of topsoil organic carbon (OC) content estimates for part of the European Union based on that comprehensive sampling programme. A digital soil mapping (DSM) by regression kriging (RK) approach was followed, and the covariates selected by the model were: land cover, elevation and slope, accumulated annual average temperature and the precipitation over potential evapotranspiration ratio, lithology, net primary productivity, and sand content. The results show high OC contents in northern latitudes and low contents in southern European countries which corroborates current expert knowledge. The overall model-fitting performance (R2) is 0.52 and the root mean squared error of the RK predictions equals 77 g C kg−1. Kriging of the regression residuals create hot-spots of OC content predictions on the map which are not believed to be realistic. It was concluded that different DSM techniques should be tested on the OC measurements data from the LUCAS database to try and improve the predictions and that validation against national datasets should be performed.

Improving Soil Organic Carbon (SOC) prediction by field spectrometry in bare cropland by reducing the disturbing effect of soil roughness

The spatial estimation of Soil Organic Carbon (SOC) at large scale in outdoor condition is an important issue. It has been largely demonstrated that diffuse reflectance spectroscopic techniques, are efficient for SOC determination in field conditions. However these methods are influenced by disturbing factors such as soil water content, vegetation residues and surface roughness, the later being the object of this study. Our laboratory experiments showed that the accuracy of SOC prediction from shadowed soil samples with spectroscopy techniques decreases with increasing soil shadow. In this study a new methodology using a digital camera for identifying and correcting the effect of soil shadow on field reflectance spectra measured with an Analytical Spectral Devices (ASD) during field campaign in bare crop lands has been elaborated and tested. Results showed that the proposed shadow correction method enables improving significantly SOC prediction accuracy and performs better than traditionally used methods consisting in automatic signal processing.