| The EnMAP-Box—A toolbox and application programming interface for EnMAP data processing S Van der Linden, A Rabe, M Held, B Jakimow, PJ Leitão, A Okujeni, ... Remote Sensing 7 (9), 11249-11266, 2015 | 251 | 2015 |
| Estimating fractional shrub cover using simulated EnMAP data: A comparison of three machine learning regression techniques M Schwieder, PJ Leitão, S Suess, C Senf, P Hostert Remote Sensing 6 (4), 3427-3445, 2014 | 69 | 2014 |
| Ensemble learning from synthetically mixed training data for quantifying urban land cover with support vector regression A Okujeni, S van der Linden, S Suess, P Hostert IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2016 | 59 | 2016 |
| Characterizing 32 years of shrub cover dynamics in southern Portugal using annual Landsat composites and machine learning regression modeling S Suess, S van der Linden, A Okujeni, P Griffiths, PJ Leitão, M Schwieder, ... Remote Sensing of Environment 219, 353-364, 2018 | 53 | 2018 |
| The value of satellite-based active fire data for monitoring, reporting and verification of REDD+ in the Lao PDR D Müller, S Suess, AA Hoffmann, G Buchholz Human ecology 41, 7-20, 2013 | 50 | 2013 |
| Monitoring natural ecosystem and ecological gradients: perspectives with EnMAP PJ Leitão, M Schwieder, S Suess, A Okujeni, LS Galvão, S Linden, ... Remote Sensing 7 (10), 13098-13119, 2015 | 33 | 2015 |
| Using class probabilities to map gradual transitions in shrub vegetation from simulated EnMAP data S Suess, S Van der Linden, A Okujeni, PJ Leitão, M Schwieder, P Hostert Remote Sensing 7 (8), 10668-10688, 2015 | 29 | 2015 |
| Mapping beta diversity from space: Sparse Generalised Dissimilarity Modelling (SGDM) for analysing high‐dimensional data PJ Leitao, M Schwieder, S Suess, I Catry, EJ Milton, F Moreira, ... Methods in Ecology and Evolution 6 (7), 764-771, 2015 | 28 | 2015 |
| Import vector machines for quantitative analysis of hyperspectral data S Suess, S van der Linden, PJ Leitão, A Okujeni, B Waske, P Hostert IEEE Geoscience and Remote Sensing Letters 11 (2), 449-453, 2013 | 20 | 2013 |
| imageSVM regression, application manual: imageSVM Version 2.1 S van der Linden, A Rabe, F Wirth, S Suess, A Okujeni, P Hostert Humboldt-Universität zu Berlin, Germany, 2010 | 18* | 2010 |
| imageSVM Classification, Manual for Application: imageSVM version 3.0 S Van der Linden, A Rabe, M Held, F Wirth, S Suess, A Okujeni, P Hostert Humboldt-Universität zu Berlin, Germany, 2014 | 11 | 2014 |
| Can the MODIS active fire hotspots be used to monitor vegetation fires in the Lao PDR D Müller, S Suess Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) Climate …, 2011 | 6 | 2011 |
| Data from: Mapping beta diversity from space: Sparse Generalized Dissimilarity Modelling (SGDM) for analysing high-dimensional data PJ Leitão, S Suess, M Schwieder, I Catry, E Milton, F Moreira, PE Osborne, ... DRYAD, 2016 | 1 | 2016 |
| Mapping Spatial and Temporal Transitions in Shrublands with Cover Fractions from Space S Süß Humboldt-Universität zu Berlin, 2019 | | 2019 |
| Hyperspectral satellite data for modelling spatial beta diversity patterns of birds along an environmental gradient PJ Leitao, S Suess, M Schwieder, EJ Milton, S van der Linden, P Hostert | | 2013 |
