| 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 | 3541 | 2017 |
| The knowns, known unknowns and unknowns of sequestration of soil organic carbon U Stockmann, MA Adams, JW Crawford, DJ Field, N Henakaarchchi, ... Agriculture, Ecosystems & Environment 164, 80-99, 2013 | 1759 | 2013 |
| Digital mapping of soil carbon B Minasny, AB McBratney, BP Malone, I Wheeler Advances in agronomy 118, 1-47, 2013 | 508 | 2013 |
| 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 | 321 | 2017 |
| African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning T Hengl, MAE Miller, J Križan, KD Shepherd, A Sila, M Kilibarda, ... Scientific reports 11 (1), 6130, 2021 | 203 | 2021 |
| Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential T Hengl, MG Walsh, J Sanderman, I Wheeler, SP Harrison, IC Prentice PeerJ 6, e5457, 2018 | 141 | 2018 |
| Soils and climate change: potential impacts on carbon stocks and greenhouse gas emissions, and future research for Australian agriculture JA Baldock, I Wheeler, N McKenzie, A McBrateny Crop and Pasture Science 63 (3), 269-283, 2012 | 139 | 2012 |
| Farm-scale soil carbon auditing JJ de Gruijter, AB McBratney, B Minasny, I Wheeler, BP Malone, ... Pedometrics, 693-720, 2018 | 124 | 2018 |
| A general method for downscaling earth resource information BP Malone, AB McBratney, B Minasny, I Wheeler Computers & Geosciences 41, 119-125, 2012 | 61 | 2012 |
| Good practice guidance NC Sims, GJ Newnham, JR England, J Guerschman, SJD Cox, ... SDG indicator 15 (1), 2021-03, 2021 | 59 | 2021 |
| Frameworks for digital soil assessment AB McBratney, B Minasny, I Wheeler, BP Malone, D Van Der Linden Digital Soil Assessment and Beyond. Taylor & Francis Group, London, 9-14, 2012 | 47 | 2012 |
| Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolution P Panagos, T Hengl, I Wheeler, P Marcinkowski, MB Rukeza, B Yu, ... Data in brief 50, 109482, 2023 | 25 | 2023 |
| Using model predictions of soil carbon in farm-scale auditing-A software tool JJ de Gruijter, I Wheeler, BP Malone Agricultural Systems 169, 24-30, 2019 | 9 | 2019 |
| Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution T Hengl, I Wheeler (No Title), 2018 | 6 | 2018 |
| A brief introduction to Open Data, Open Source Software and Collective Intelligence for environmental data creators and users T Hengl, I Wheeler, RA MacMillan PeerJ Preprints 6, e27127v2, 2018 | 6 | 2018 |
| Digital soil mapping to inform design-based sampling strategies for estimating total organic carbon stocks at the farm scale I Wheeler, AB McBratney, B Minasny, JJ De Gruijter Digital Soil Assessments and Beyond—Proceedings of the Fifth Global …, 2012 | 6 | 2012 |
| The auditability of soil carbon at the farm scale I Wheeler University of Sydney., 2014 | 4 | 2014 |
| The dramatic effect of preferential sampling of spatial data on variance estimates D Clifford, P Kuhnert, M Dobbie, J Baldock, N McKenzie, B Harch, ... Australian Mathematical Sciences Institute Conference, Queensland University …, 2011 | 3 | 2011 |
| Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution J Hackländer, L Parente, YF Ho, T Hengl, R Simoes, D Consoli, M Şahin, ... PeerJ 12, e16972, 2024 | 2 | 2024 |
| The effect of preferential sampling on sampling variance D Clifford, P Kuhnert, M Dobbie, J Baldock, B Harch, NJ McKenzie, ... | 1 | 2012 |
