Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Evaluation and implementation of vis-NIR spectroscopy models to determine workability
    Mahmood, H.S. ; Bartholomeus, H. ; Hoogmoed, W.B. ; Henten, E. van - \ 2013
    Soil & Tillage Research 134 (2013). - ISSN 0167-1987 - p. 172 - 179.
    near-infrared spectroscopy - diffuse-reflectance spectroscopy - soil organic-carbon - spectral library - workable range - tropical zone - prediction - field - calibrations - veracruz
    Quantitative information of soil properties and their spatial distribution is needed for site-specific soil management. Conventional laboratory methods to obtain high-resolution soil data are expensive and labour intensive. Visible-near infrared (vis-NIR) reflectance spectroscopy is a rapid and cost-effective technique for successful soil characterisation. The objective of this study was to determine the accuracy of vis-NIR reflectance models to predict tillage (workability) related soil properties, such as texture and total organic carbon (TOC) and other common soil properties on a field scale using different types of modelling strategies. For prediction of these properties, spectral data were related to soil properties using support vector regression. For this method, the influence of calibration set on the accuracy of prediction for independent samples was evaluated. The types of models included local models (LMs; models of individual fields), general models (GMs; models of combining equal proportions of samples from all fields), spiked models (SMs; using 10 samples from the target field and all samples from other fields) and true validation models (TVMs; calibration from four fields and validation in the remaining field). The main difference between these models lies in the number of soil samples that need to be taken from a specific field of interest, which determines the investments that have to be made. Results revealed that LMs gave the best results (e.g. the RMSEP was less than 1.7% for clay in all fields), but a large number of samples has to be taken from each field, which costs a lot of time and money. Therefore, this type of models may not be so practical for a farmer having multiple fields. The GMs showed variable accuracies for different sized models, where the accuracy increases with increasing the number of samples in the calibration subset. This means that a large number of samples is needed for making a good calibration model and therefore GMs may also not be so effective. The TVMs are cheap to make, but the risk of wrong predictions in the target field, which is different from the calibration fields, is present. The SMs yielded predictions comparable to the LMs and yielded an acceptable RMSEP with a limited number of samples per field (10 samples) for clay and TOC. This makes SMs very effective with the potential to predict workability related soil properties with a limited number of samples in the target fields
    Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique. August 2012
    Cambule, A. ; Rossiter, D.G. ; Stoorvogel, J.J. ; Smaling, E.M.A. - \ 2012
    Geoderma 183-184 (2012). - ISSN 0016-7061 - p. 41 - 48.
    diffuse-reflectance spectroscopy - quality indicators - quantitative-evaluation - spatial-distribution - nir spectroscopy - prediction - fertility - samples - model - mineralization
    Soil organic carbon (SOC) is a key soil property and particularly important for ecosystem functioning and the sustainable management of agricultural systems. Conventional laboratory analyses for the determination of SOC are expensive and slow. Laboratory spectroscopy in combination with chemometrics is claimed to be a rapid, cost-effective and non-destructive method for measuring SOC. The present study was carried out in Limpopo National Park (LNP) in Mozambique, a data- and access-limited area, with no previous soil spectral library. The question was whether a useful calibration model could be built with a limited number of samples. Across the major landscape units of the LNP, 129 composite topsoil samples were collected and analyzed for SOC, pH and particle sizes of the fine earth fraction. Samples were also scanned in a near-infrared (NIR) spectrometer. Partial least square regression (PLSR) was used on 1037 bands in the wavelength range 1.25–2.5 µm to relate the spectra and SOC concentration. Several models were built and compared by cross-validation. The best model was on a filtered first derivative of the multiplicative scatter corrected (MSC) spectra. It explained 83% of SOC variation and had a root mean square error of prediction (RMSEP) of 0.32% SOC, about 2.5 times the laboratory RMSE from duplicate samples (0.13% SOC). This uncertainty is a substantial proportion of the typical SOC concentrations in LNP landscapes (0.45–2.00%). The model was slightly improved (RMSEP 0.28% SOC) by adding clay percentage as a co-variable. All models had poorer performance at SOC concentrations above 2.0%, indicating a saturation effect. Despite the limitations of sample size and no pre-existing library, a locally-useful, although somewhat imprecise, calibration model could be built. This model is suitable for estimating SOC in further mapping exercises in the LNP
    Geosensors to support crop production: current applications and user requirements
    Thessler, S. ; Kooistra, L. ; Teye, F. ; Huitu, H. ; Bregt, A.K. - \ 2011
    Sensors 11 (2011)7. - ISSN 1424-8220 - p. 6656 - 6684.
    wireless sensor networks - soil organic-carbon - diffuse-reflectance spectroscopy - grain protein-concentration - unmanned aerial vehicles - weed-control - winter-wheat - irrigation management - precision agriculture - imaging spectroscopy
    Sensor technology, which benefits from high temporal measuring resolution, real-time data transfer and high spatial resolution of sensor data that shows in-field variations, has the potential to provide added value for crop production. The present paper explores how sensors and sensor networks have been utilised in the crop production process and what their added-value and the main bottlenecks are from the perspective of users. The focus is on sensor based applications and on requirements that users pose for them. Literature and two use cases were reviewed and applications were classified according to the crop production process: sensing of growth conditions, fertilising, irrigation, plant protection, harvesting and fleet control. The potential of sensor technology was widely acknowledged along the crop production chain. Users of the sensors require easy-to-use and reliable applications that are actionable in crop production at reasonable costs. The challenges are to develop sensor technology, data interoperability and management tools as well as data and measurement services in a way that requirements can be met, and potential benefits and added value can be realized in the farms in terms of higher yields, improved quality of yields, decreased input costs and production risks, and less work time and load
    The use of remote sensing in soil and terrain mapping: Review
    Mulder, V.L. ; Bruin, S. de; Schaepman, M.E. ; Mayr, T. - \ 2011
    Geoderma 162 (2011)1-2. - ISSN 0016-7061 - p. 1 - 19.
    salt-affected soils - diffuse-reflectance spectroscopy - spaceborne thermal emission - digital elevation models - land-cover classification - adjusted vegetation index - imaging spectrometer data - radar topography mission - santa-monica mountains - plant function
    This article reviews the use of optical and microwave remote sensing data for soil and terrain mapping with emphasis on applications at regional and coarser scales. Remote sensing is expected to offer possibilities for improving incomplete spatial and thematic coverage of current regional and global soil databases. Traditionally, remotely sensed imagery have been used to support segmentation of the landscape into rather homogeneous soil–landscape units for which soil composition can be established by sampling. Soil properties have also been inferred from optical and microwave data using physically-based and empirical methods. Used as a secondary data source, remotely sensed imagery may support spatial interpolation of sparsely sampled soil property data. Soil properties that have been measured using remote or proximal sensing approaches include mineralogy, texture, soil iron, soil moisture, soil organic carbon, soil salinity and carbonate content. In sparsely vegetated areas, successful use of space borne, airborne, and in situ measurements using optical, passive and active microwave instruments has been reported. On the other hand, in densely vegetated areas, soil data acquisition typically relied on indirect retrievals using soil indicators, such as plant functional groups, productivity changes, and Ellenberg indicator values. Several forms of kriging, classification and regression tree analyses have been used jointly with remotely sensed data to predict soil properties at unvisited locations aiming at obtaining continuous area coverage. We expect that remotely sensed data from existing platforms and planned missions can provide an important data source supporting digital soil mapping. Yet, most studies so far have been performed on a local scale and only few on regional or smaller map scale. Although progress has been made, current methods and techniques still bear potential to further explore the full range of spectral, spatial and temporal properties of existing data sources. For example, space borne spectroscopy has been of limited use in retrieving soil data when compared to laboratory or field spectroscopy. To date, there is no coherent methodology established, where approaches of spatial segmentation, measurements of soil properties and interpolation using remotely sensed data are integrated in a holistic fashion to achieve complete area coverage. Such approaches will enhance the perspectives of using remotely sensed data for digital soil mapping.
    Prediction of Soil Fertility Properties from a Globally Distributed Soil Mid-Infrared Spectral Library
    Terhoeven-Urselmans, T. ; Vagen, T.G. ; Spaargaren, O. ; Shepherd, K.D. - \ 2010
    Soil Science Society of America Journal 74 (2010)5. - ISSN 0361-5995 - p. 1792 - 1799.
    diffuse-reflectance spectroscopy - partial least-squares - carbon
    Globally applicable calibrations to predict standard soil properties based on infrared spectra may increase the use of this reliable technique. The objective of this study was to evaluate the ability of mid-infrared diffuse reflectance spectroscopy (4000-602 cm(-1)) to predict chemical and textural properties for a globally distributed soil spectral library. We scanned 971 soil samples selected from the International Soil Reference and Information Centre database. A high-throughput diffuse reflectance accessory was used with optics that exclude specular reflectance as a potential source of error. Archived data on soil chemical and physical properties were calibrated to first derivative spectra using partial least-squares regression. Good predictions for the spatially independent validation set were achieved for pH value, organic C content, and cation exchange capacity (CEC) (n = 291, r(2) of linear regression of predicted against measured values >= 0.75 and ratio of standard deviation of measured values to root mean square error of prediction (RPD) >= 2.0). The root mean square errors of prediction (RMSEP) were 0.75 pH units, 9.1 g organic C kg(-1) and 5.5 cmol(c) CEC kg(-1). Satisfactory predictions (r(2) = 0.65-0.75, RPD = 1.4-2.0) were obtained for exchangeable Mg concentration and clay content. The respective RMSEPs were 4.3 cmol(c) kg(-1) and 126 g kg(-1). Poorer predictions (r2 = 0.61 and 0.64) were achieved for sand and exchangeable Ca contents. Although RMSEP values are large relative to laboratory analytical errors, our results suggest a marked potential for the global spectral library as a tool for advice on land management, such as the classification of new samples into basic soil fertility classes based on organic C and clay contents, CEC, and pH. Further research is needed to test the stability of this global calibration on new data sets.
    Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils
    Stevens, A. ; Wesemael, B. van; Bartholomeus, H. ; Rosillon, D. ; Tychon, B. ; Ben-Dor, E. - \ 2008
    Geoderma 144 (2008)1-2. - ISSN 0016-7061 - p. 395 - 404.
    diffuse-reflectance spectroscopy - meta analysis - regression - sequestration - validation - matter - sensor - israel
    The temporal evolution in Soil Organic Carbon (SOC) content is often used in estimations of greenhouse gas fluxes and is an important indicator of soil quality. Regional estimates of SOC changes can only be obtained by analyzing very large number of samples over large areas due to the strong spatial variability in SOC contents. Visible and Near Infrared Spectroscopy (VNIRS) provides an alternative to chemical analyses. The benefits of this technique include a reduction of the sampling processing time, an increase of the number of samples that can be analyzed within time and budget constraints and hence an improvement of the detection of small changes in SOC stocks for a given area. Carbon contents are predicted from spectra through Partial Least Square Regressions (PLSR). The performance of three different instrumental settings (laboratory, field and airborne spectroscopy) has been assessed and their relative advantages for soil monitoring studies have been outlined using the concept of Minimal Detectable Difference. It appears that ground-based spectrometers give Root Mean Square Errors of Cross-Validation similar to the limit of repeatability of a routine SOC analytical technique such as the Walkley and Black method (± 1 g C kg¿ 1). The airborne spectrometer, despite its greater potential to cover large areas during a single flight campaign, has some difficulties to reach such values due to a lower Signal-to-Noise Ratio. Because of its statistical nature, the method and its potential rely on the stability of the calibrations obtained. It appears that calibrations are currently site-specific due to variation in soil type and surface condition. However, it is shown that PLSR can take into account both soil and spectral variation caused by different measuring campaigns and study areas. Further research is needed to develop regional spectral libraries in order to be able to use VNIRS as a robust analytical technique for precisely determining the SOC content and its spatial variation.
    The potential of field spectroscopy for the determination of sediment properties in river floodplains
    Kooistra, L. ; Wanders, J. ; Epema, G.F. ; Leuven, R.S.E.W. ; Wehrens, H.R.M.J. ; Buydens, L.M.C. - \ 2003
    Analytica Chimica Acta 484 (2003)2. - ISSN 0003-2670 - p. 189 - 200.
    bodemeigenschappen - geologische sedimentatie - stroomvlakten - rivieren - spectroscopie - klei - organische stof - nederland - bodemkwaliteit - rijn - soil properties - geological sedimentation - floodplains - rivers - spectroscopy - clay - organic matter - netherlands - soil quality - river rhine - diffuse-reflectance spectroscopy - near-infrared spectroscopy - organic-matter - contamination - calibration - networks
    Investigations have shown that visible-near-infrared (VNIR) spectroscopy can accurately determine soil properties under laboratory conditions. In situ assessment of soil properties is of great benefit for several applications, as spectra can be acquired fast and almost continuously. The present study used partial least squares (PLS) regression to establish a relationship between soil reflectance spectra measured under field conditions and the organic matter and clay content of the soil. Spectra were acquired with a fieldspectrometer in a recently reconstructed floodplain along the river Rhine in The Netherlands. Several spectral pre-processing methods were employed to improve the performance and robustness of the models. Results indicate that, under varying surface conditions, field spectroscopy in combination with multivariate calibration does result in a qualitative relation for organic matter (R2=0.45) and clay content (R2=0.43) while under laboratory conditions more accurate results are obtained (R2=0.69 and 0.92, respectively). Soil moisture and vegetation cover had a negative influence on the prediction capabilities for both soil properties. Although the performance of the spectra measured in situ is not as accurate as physical analysis, the accuracy obtained is useful for rapid soil characterisation and remote sensing applications.
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