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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Digital mapping of peatlands – A critical review
Minasny, Budiman ; Berglund, Örjan ; Connolly, John ; Hedley, Carolyn ; Vries, Folkert de; Gimona, Alessandro ; Kempen, Bas ; Kidd, Darren ; Lilja, Harry ; Malone, Brendan ; McBratney, Alex ; Roudier, Pierre ; O'Rourke, Sharon ; Rudiyanto, ; Padarian, José ; Poggio, Laura ; Caten, Alexandre ten; Thompson, Daniel ; Tuve, Clint ; Widyatmanti, Wirastuti - \ 2019
Earth-Science Reviews 196 (2019). - ISSN 0012-8252

Peatlands offer a series of ecosystem services including carbon storage, biomass production, and climate regulation. Climate change and rapid land use change are degrading peatlands, liberating their stored carbon (C) into the atmosphere. To conserve peatlands and help in realising the Paris Agreement, we need to understand their extent, status, and C stocks. However, current peatland knowledge is vague—estimates of global peatland extent ranges from 1 to 4.6 million km2, and C stock estimates vary between 113 and 612 Pg (or billion tonne C). This uncertainty mostly stems from the coarse spatial scale of global soil maps. In addition, most global peatland estimates are based on rough country inventories and reports that use outdated data. This review shows that digital mapping using field observations combined with remotely-sensed images and statistical models is an avenue to more accurately map peatlands and decrease this knowledge gap. We describe peat mapping experiences from 12 countries or regions and review 90 recent studies on peatland mapping. We found that interest in mapping peat information derived from satellite imageries and other digital mapping technologies is growing. Many studies have delineated peat extent using land cover from remote sensing, ecology, and environmental field studies, but rarely perform validation, and calculating the uncertainty of prediction is rare. This paper then reviews various proximal and remote sensing techniques that can be used to map peatlands. These include geophysical measurements (electromagnetic induction, resistivity measurement, and gamma radiometrics), radar sensing (SRTM, SAR), and optical images (Visible and Infrared). Peatland is better mapped when using more than one covariate, such as optical and radar products using nonlinear machine learning algorithms. The proliferation of satellite data available in an open-access format, availability of machine learning algorithms in an open-source computing environment, and high-performance computing facilities could enhance the way peatlands are mapped. Digital soil mapping allows us to map peat in a cost-effective, objective, and accurate manner. Securing peatlands for the future, and abating their contribution to atmospheric C levels, means digitally mapping them now.

Multi-source data integration for soil mapping using deep learning
Wadoux, A.M.J.C. ; Padarian, José ; Minasny, Budiman - \ 2018
SOIL (2018). - ISSN 2199-3971 - 19 p.
With the advances of new proximal soil sensing technologies, soil properties can be inferred by a variety of sensors, each having its distinct level of accuracy. This measurement error affects subsequent modelling and therefore must be integrated when calibrating a spatial prediction model. This paper introduces a deep learning model for contextual Digital Soil Mapping (DSM) using uncertain measurements of the soil property. The deep learning model, called Convolutional Neural Network (CNN), has the advantage that it uses as input a local representation of environmental covariates to leverage the spatial information contained in the vicinity of a location. Spatial non-linear relationships between covariate pixel values and measured soil properties are found by optimizing an objective function, which can be weighted with respect to a measurement error of soil observations. In addition, a single model can be trained to predict a soil property at different soil depths. This method is tested in mapping top- and subsoil organic carbon using laboratory analyzed and spectroscopically inferred measurements. Results show that CNNs significantly increased prediction accuracy as indicated by the coefficient of determination and concordance correlation coefficient, when compared to a conventional DSM technique. Deeper soil layer prediction error decreased, while preserving the interrelation between soil property and depths. The tests conducted using different window size of input covariates matrix to predict organic carbon suggest that CNN benefits from using local contextual information up to 260 to 360 metres. We conclude that CNN is a flexible, effective and promising model to predict soil properties at multiple depths while accounting for contextual covariates information and measurement error.
Pedotransfer Functions in Earth System Science : Challenges and Perspectives
Looy, Kris Van; Bouma, Johan ; Herbst, Michael ; Koestel, John ; Minasny, Budiman ; Mishra, Umakant ; Montzka, Carsten ; Nemes, Attila ; Pachepsky, Yakov A. ; Padarian, José ; Schaap, Marcel G. ; Tóth, Brigitta ; Verhoef, Anne ; Vanderborght, Jan ; Ploeg, Martine J. van der; Weihermüller, Lutz ; Zacharias, Steffen ; Zhang, Yonggen ; Vereecken, Harry - \ 2017
Reviews of Geophysics 55 (2017)4. - ISSN 8755-1209 - p. 1199 - 1256.
Biogeochemical processes - Extrapolation - Heat flow - Hydraulic properties - Land surface model - Soil properties
Soil, through its various functions, plays a vital role in the Earth's ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. In this paper, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscaling techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration, and organic carbon content, root density, and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale.
GlobalSoilMap for Soil Organic Carbon Mapping and as a Basis for Global Modeling
Arrouays, D. ; Minasny, B. ; McBratney, A. ; Grundy, Mike ; McKenzie, Neil ; Thompson, James ; Gimona, Alessandro ; Hong, Suk Young ; Smith, Scott ; Hartemink, A.E. ; Chen, Songchao ; Martin, Manuel P. ; Mulder, V.L. ; Richer-de-Forges, A.C. ; Odeh, Inakwu ; Padarian, José ; Lelyk, Glenn ; Poggio, Laura ; Savin, Igor ; Stolbovoy, Vladimir ; Leenaars, J.G.B. ; Heuvelink, G.B.M. ; Montanarella, Luca ; Panagos, P. ; Hempel, Jon - \ 2017
In: Proceedings of the global symposium on soil organic carbon 2017. - FAO - p. 27 - 30.
The demand for information on functional soil properties is high and has increased over time. This is especially true for soil organic carbon (SOC) in the framework of food security and climate change. The GlobalSoilMap consortium was established in response to such a soaring demand for up-to-date and relevant soil information. The majority of the data needed to produce GlobalSoilMap soil property maps will, at least for the first generation, come mainly from archived soil legacy data, which could include polygon soil maps and point pedon data, and from available co-variates such as climatic data, remote sensing information, geological data, and other forms of environmental information.
Several countries have already released products according to the GlobalSoilMap specifications and the project is rejuvenating soil survey and mapping in many parts of the world. Functional soil property maps have been produced using digital soil mapping techniques and existing legacy information and made available to the user community for application. In addition, uncertainty has been provided as a 90% prediction interval based on estimated upper and lower class limits. We believe that GlobalSoilMap constitutes the best available framework and methodology to address global issues about SOC mapping. Main scientific challenges include time related and uncertainties issues.
Modeling soil processes : Review, key challenges, and new perspectives
Vereecken, H. ; Schnepf, A. ; Hopmans, J.W. ; Javaux, M. ; Or, D. ; Roose, T. ; Vanderborght, J. ; Young, M.H. ; Amelung, W. ; Aitkenhead, M. ; Allison, S.D. ; Assouline, S. ; Baveye, P. ; Berli, M. ; Brüggemann, N. ; Finke, P. ; Flury, M. ; Gaiser, T. ; Govers, G. ; Ghezzehei, T. ; Hallett, P. ; Hendricks Franssen, H.J. ; Heppell, J. ; Horn, R. ; Huisman, J.A. ; Jacques, D. ; Jonard, F. ; Kollet, S. ; Lafolie, F. ; Lamorski, K. ; Leitner, D. ; Mcbratney, A. ; Minasny, B. ; Montzka, C. ; Nowak, W. ; Pachepsky, Y. ; Padarian, J. ; Romano, N. ; Roth, K. ; Rothfuss, Y. ; Rowe, E.C. ; Schwen, A. ; Šimůnek, J. ; Tiktak, A. ; Dam, Jos van; Zee, S.E.A.T.M. van der; Vogel, H.J. ; Vrugt, J.A. ; Wöhling, T. ; Wöhling, T. ; Young, I.M. - \ 2016
Vadose Zone Journal 15 (2016)5. - ISSN 1539-1663 - 57 p.

The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate-change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges.

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