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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Inferring causation from time series in Earth system sciences
Runge, Jakob ; Bathiany, Sebastian ; Bollt, Erik ; Camps-Valls, Gustau ; Coumou, Dim ; Deyle, Ethan ; Glymour, Clark ; Kretschmer, Marlene ; Mahecha, Miguel D. ; Muñoz-Marí, Jordi ; Nes, Egbert H. van; Peters, Jonas ; Quax, Rick ; Reichstein, Markus ; Scheffer, Marten ; Schölkopf, Bernhard ; Spirtes, Peter ; Sugihara, George ; Sun, Jie ; Zhang, Kun ; Zscheischler, Jakob - \ 2019
Nature Communications 10 (2019)1. - ISSN 2041-1723

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.

A Deep Network Approach to Multitemporal Cloud Detection
Tuia, Devis ; Kellenberger, Benjamin ; Perez-suey, Adrian ; Camps-valls, Gustau - \ 2018
In: 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings. - IEEE Xplore - ISBN 9781538671511 - p. 4351 - 4354.
We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite. The model provides pixel-level cloud maps with related confidence and propagates information in time via a recurrent neural network structure. With a single model, we are able to outline clouds along all year and during day and night with high accuracy.
A methodology to derive global maps of leaf traits using remote sensing and climate data
Moreno-Martínez, Álvaro ; Camps-Valls, Gustau ; Kattge, Jens ; Robinson, Nathaniel ; Reichstein, Markus ; Bodegom, Peter van; Kramer, Koen ; Cornelissen, J.H.C. ; Reich, Peter ; Bahn, Michael ; Niinemets, Ülo ; Peñuelas, Josep ; Craine, Joseph M. ; Cerabolini, Bruno E.L. ; Minden, Vanessa ; Laughlin, Daniel C. ; Sack, Lawren ; Allred, Brady ; Baraloto, Christopher ; Byun, Chaeho ; Soudzilovskaia, Nadejda A. ; Running, Steve W. - \ 2018
Remote Sensing of Environment 218 (2018). - ISSN 0034-4257 - p. 69 - 88.
Climate - Landsat - Machine learning - MODIS - Plant ecology - Plant traits - Random forests - Remote sensing

This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE≤ 20%) and goodness of fit (averaged Pearson's correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products – the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps – are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system.

Advances in Kernel Machines for Image Classification and Biophysical Parameter Retrieval
Tuia, Devis ; Volpi, Michele ; Verrelst, Jochem ; Camps-valls, Gustau - \ 2017
In: Mathematical Models for Remote Sensing Image Processing / Moser, G., Zerubia, J., Springer (Signals and Communication Technology ) - ISBN 9783319663289 - p. 399 - 441.
Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison
Verrelst, Jochem ; Rivera, Juan Pablo ; Veroustraete, Frank ; Muñoz-Marí, Jordi ; Clevers, J.G.P.W. ; Camps-Valls, Gustau ; Moreno, José - \ 2015
ISPRS Journal of Photogrammetry and Remote Sensing 108 (2015). - ISSN 0924-2716 - p. 260 - 272.
Biophysical variables - Machine learning - Non-parametric - Parametric - Physically-based RTM inversion - Sentinel-2

Given the forthcoming availability of Sentinel-2 (S2) images, this paper provides a systematic comparison of retrieval accuracy and processing speed of a multitude of parametric, non-parametric and physically-based retrieval methods using simulated S2 data. An experimental field dataset (SPARC), collected at the agricultural site of Barrax (Spain), was used to evaluate different retrieval methods on their ability to estimate leaf area index (LAI). With regard to parametric methods, all possible band combinations for several two-band and three-band index formulations and a linear regression fitting function have been evaluated. From a set of over ten thousand indices evaluated, the best performing one was an optimized three-band combination according to (ρ56016102190)/(ρ56016102190) with a 10-fold cross-validation RCV2 of 0.82 (RMSECV: 0.62). This family of methods excel for their fast processing speed, e.g., 0.05s to calibrate and validate the regression function, and 3.8s to map a simulated S2 image. With regard to non-parametric methods, 11 machine learning regression algorithms (MLRAs) have been evaluated. This methodological family has the advantage of making use of the full optical spectrum as well as flexible, nonlinear fitting. Particularly kernel-based MLRAs lead to excellent results, with variational heteroscedastic (VH) Gaussian Processes regression (GPR) as the best performing method, with a RCV2 of 0.90 (RMSECV: 0.44). Additionally, the model is trained and validated relatively fast (1.70s) and the processed image (taking 73.88s) includes associated uncertainty estimates. More challenging is the inversion of a PROSAIL based radiative transfer model (RTM). After the generation of a look-up table (LUT), a multitude of cost functions and regularization options were evaluated. The best performing cost function is Pearson's χ-square. It led to a R2 of 0.74 (RMSE: 0.80) against the validation dataset. While its validation went fast (0.33s), due to a per-pixel LUT solving using a cost function, image processing took considerably more time (01:01:47). Summarizing, when it comes to accurate and sufficiently fast processing of imagery to generate vegetation attributes, this paper concludes that the family of kernel-based MLRAs (e.g. GPR) is the most promising processing approach.

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review
Verrelst, Jochem ; Camps-Valls, Gustau ; Muñoz-Marí, Jordi ; Rivera, Juan Pablo ; Veroustraete, Frank ; Clevers, J.G.P.W. ; Moreno, José - \ 2015
ISPRS Journal of Photogrammetry and Remote Sensing 108 (2015). - ISSN 0924-2716 - p. 273 - 290.
Bio-geophysical variables - Hybrid - Machine learning - Non-parametric - Operational variable retrieval - Parametric - Physical

Forthcoming superspectral satellite missions dedicated to land monitoring, as well as planned imaging spectrometers, will unleash an unprecedented data stream. The processing requirements for such large data streams involve processing techniques enabling the spatio-temporally explicit quantification of vegetation properties. Typically retrieval must be accurate, robust and fast. Hence, there is a strict requirement to identify next-generation bio-geophysical variable retrieval algorithms which can be molded into an operational processing chain. This paper offers a review of state-of-the-art retrieval methods for quantitative terrestrial bio-geophysical variable extraction using optical remote sensing imagery. We can categorize these methods into (1) parametric regression, (2) non-parametric regression, (3) physically-based and (4) hybrid methods. Hybrid methods combine generic capabilities of physically-based methods with flexible and computationally efficient methods, typically non-parametric regression methods. A review of the theoretical basis of all these methods is given first and followed by published applications. This paper focusses on: (1) retrievability of bio-geophysical variables, (2) ability to generate multiple outputs, (3) possibilities for model transparency description, (4) mapping speed, and (5) possibilities for uncertainty retrieval. Finally, the prospects of implementing these methods into future processing chains for operational retrieval of vegetation properties are presented and discussed.

Cloud screening and multitemporal unmixing of MERIS FR data
Gomez-Chova, L. ; Zurita Milla, R. ; Camps-Valls, G. ; Guanter, L. ; Clevers, J.G.P.W. ; Calpe, J. ; Schaepman, M.E. ; Moreno, J. - \ 2007
- 6 p.
Multitemporal unmixing of MERIS FR data
Zurita Milla, R. ; Gomez-Chova, L. ; Clevers, J.G.P.W. ; Schaepman, M.E. ; Camps-Valls, G. - \ 2007
In: Proceedings of the 10th International Symposium on Physical Measurements and Spectral Signatures in Remote Sensing (ISPMSRS'07), Davos, 12-14 March 2007. - Davos (CH) : ISPRS - p. 238 - 243.
Multitemporal validation of an unmixing-based MERIS cloud screening algorithm
Gomez-Chova, L. ; Zurita Milla, R. ; Camps-Valls, G. ; Guanter, L. ; Clevers, J.G.P.W. ; Calpe, J. ; Schaepman, M.E. ; Moreno, J. - \ 2007
In: Proceedings 2nd International Symposium on Recent Advances in Quantitative Remote Sensing: RAQRS'II, Valencia, 25-27 September 2006. - Valencia (Spain) : Universitat de Valencia - p. 119 - 124.
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