| 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 | 263 | 2015 |
| CEFLES2: the remote sensing component to quantify photosynthetic efficiency from the leaf to the region by measuring sun-induced fluorescence in the oxygen absorption bands U Rascher, G Agati, L Alonso, G Cecchi, S Champagne, R Colombo, ... Biogeosciences 6 (7), 1181-1198, 2009 | 181 | 2009 |
| National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series D Frantz, F Schug, A Okujeni, C Navacchi, W Wagner, S van der Linden, ... Remote Sensing of Environment 252, 112128, 2021 | 176 | 2021 |
| Support vector regression and synthetically mixed training data for quantifying urban land cover A Okujeni, S van der Linden, L Tits, B Somers, P Hostert Remote Sensing of Environment 137, 184-197, 2013 | 172 | 2013 |
| Influence of neighbourhood information on ‘Local Climate Zone’mapping in heterogeneous cities ML Verdonck, A Okujeni, S van der Linden, M Demuzere, R De Wulf, ... International Journal of Applied Earth Observation and Geoinformation 62 …, 2017 | 105 | 2017 |
| Mapping patterns of urban development in Ouagadougou, Burkina Faso, using machine learning regression modeling with bi-seasonal Landsat time series F Schug, A Okujeni, J Hauer, P Hostert, JØ Nielsen, S van der Linden Remote Sensing of Environment 210, 217-228, 2018 | 99 | 2018 |
| Extending the vegetation–impervious–soil model using simulated EnMAP data and machine learning A Okujeni, S van der Linden, P Hostert Remote Sensing of Environment 158, 69-80, 2015 | 95 | 2015 |
| Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data F Schug, D Frantz, A Okujeni, S van der Linden, P Hostert Remote Sensing of Environment 246, 111810, 2020 | 80 | 2020 |
| 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 …, 2017 | 64 | 2017 |
| 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 | 58 | 2018 |
| A generalized regression-based unmixing model for mapping forest cover fractions throughout three decades of Landsat data C Senf, J Laštovička, A Okujeni, M Heurich, S van der Linden Remote Sensing of Environment 240, 111691, 2020 | 56 | 2020 |
| Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities A Okujeni, F Canters, SD Cooper, J Degerickx, U Heiden, P Hostert, ... Remote Sensing of Environment 216, 482-496, 2018 | 54 | 2018 |
| Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series K Kowalski, A Okujeni, M Brell, P Hostert Remote Sensing of Environment 268, 112781, 2022 | 50 | 2022 |
| Imaging Spectroscopy of Urban Environments S van der Linden, A Okujeni, F Canters, J Degerickx, U Heiden, P Hostert, ... Surveys in Geophysics, 1-18, 2018 | 47 | 2018 |
| A comparison of advanced regression algorithms for quantifying urban land cover A Okujeni, S Van der Linden, B Jakimow, A Rabe, J Verrelst, P Hostert Remote Sensing 6 (7), 6324-6346, 2014 | 47 | 2014 |
| A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover J Degerickx, A Okujeni, MD Iordache, M Hermy, S van der Linden, ... Remote Sensing 9 (6), 565, 2017 | 43 | 2017 |
| Disentangling fractional vegetation cover: Regression-based unmixing of simulated spaceborne imaging spectroscopy data S Cooper, A Okujeni, C Jänicke, M Clark, S van der Linden, P Hostert Remote Sensing of Environment 246, 111856, 2020 | 38 | 2020 |
| 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 | 37 | 2015 |
| Berlin-Urban-Gradient dataset 2009-An EnMAP Preparatory Flight Campaign (Datasets) A Okujeni, S Van Der Linden, P Hostert GFZ Data Services, 2016 | 36 | 2016 |
| Comparing map-based and library-based training approaches for urban land-cover fraction mapping from Sentinel-2 imagery F Priem, A Okujeni, S van der Linden, F Canters International Journal of Applied Earth Observation and Geoinformation 78 …, 2019 | 33 | 2019 |
Sebastian van der LindenUniversity of Greifswalduni-greifswald.de의 이메일 확인됨
