| SoilGrids250m: Global gridded soil information based on machine learning T Hengl, J Mendes de Jesus, GBM Heuvelink, M Ruiperez Gonzalez, ... PLoS one 12 (2), e0169748, 2017 | 3686 | 2017 |
| A generic framework for spatial prediction of soil variables based on regression-kriging T Hengl, GBM Heuvelink, A Stein Geoderma 120 (1-2), 75-93, 2004 | 1405 | 2004 |
| SoilGrids1km—global soil information based on automated mapping T Hengl, JM De Jesus, RA MacMillan, NH Batjes, GBM Heuvelink, ... PloS one 9 (8), e105992, 2014 | 1244 | 2014 |
| SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty L Poggio, LM De Sousa, NH Batjes, GBM Heuvelink, B Kempen, ... Soil 7 (1), 217-240, 2021 | 1180 | 2021 |
| About regression-kriging: From equations to case studies T Hengl, GBM Heuvelink, DG Rossiter Computers & geosciences 33 (10), 1301-1315, 2007 | 1159 | 2007 |
| Error propagation in environmental modelling with GIS GBM Heuvelink CRC press, 1998 | 1007 | 1998 |
| Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions T Hengl, GBM Heuvelink, B Kempen, JGB Leenaars, MG Walsh, ... PloS one 10 (6), e0125814, 2015 | 975 | 2015 |
| Spatio-temporal interpolation using gstat B Gräler, E Pebesma, G Heuvelink | 963 | 2016 |
| Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables T Hengl, M Nussbaum, MN Wright, GBM Heuvelink, B Gräler PeerJ 6, e5518, 2018 | 838 | 2018 |
| An integrated pan‐tropical biomass map using multiple reference datasets V Avitabile, M Herold, GBM Heuvelink, SL Lewis, OL Phillips, GP Asner, ... Global change biology 22 (4), 1406-1420, 2016 | 712 | 2016 |
| Modelling soil variation: past, present, and future GBM Heuvelink, R Webster Geoderma 100 (3-4), 269-301, 2001 | 568 | 2001 |
| Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network PH Hiemstra, EJ Pebesma, CJW Twenhöfel, GBM Heuvelink Computers & Geosciences 35 (8), 1711-1721, 2009 | 530 | 2009 |
| Propagation of errors in spatial modelling with GIS GBM Heuvelink, PA Burrough, A Stein International Journal of Geographical Information System 3 (4), 303-322, 1989 | 521 | 1989 |
| Optimization of sample patterns for universal kriging of environmental variables DJ Brus, GBM Heuvelink Geoderma 138 (1-2), 86-95, 2007 | 466 | 2007 |
| GlobalSoilMap: Toward a fine-resolution global grid of soil properties D Arrouays, MG Grundy, AE Hartemink, JW Hempel, GBM Heuvelink, ... Advances in agronomy 125, 93-134, 2014 | 457 | 2014 |
| Sampling for validation of digital soil maps DJ Brus, B Kempen, GBM Heuvelink European Journal of Soil Science 62 (3), 394-407, 2011 | 455 | 2011 |
| DEM resolution effects on shallow landslide hazard and soil redistribution modelling L Claessens, GBM Heuvelink, JM Schoorl, A Veldkamp Earth Surface Processes and Landforms: The Journal of the British …, 2005 | 332 | 2005 |
| Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning T Hengl, JGB Leenaars, KD Shepherd, MG Walsh, GBM Heuvelink, ... Nutrient Cycling in Agroecosystems 109, 77-102, 2017 | 327 | 2017 |
| Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images T Hengl, GBM Heuvelink, M Perčec Tadić, EJ Pebesma Theoretical and applied climatology 107, 265-277, 2012 | 308 | 2012 |
| Updating the 1: 50,000 Dutch soil map using legacy soil data: A multinomial logistic regression approach B Kempen, DJ Brus, GBM Heuvelink, JJ Stoorvogel Geoderma 151 (3-4), 311-326, 2009 | 305 | 2009 |
