| Comparison of machine learning algorithms random forest, artificial neural network and support vector machine to maximum likelihood for supervised crop type classification I Nitze, U Schulthess, H Asche Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil 79, 3540, 2012 | 309 | 2012 |
| Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland JUH Eitel, LA Vierling, ME Litvak, DS Long, U Schulthess, AA Ager, ... Remote Sensing of Environment 115 (12), 3640-3646, 2011 | 297 | 2011 |
| Evolution of CO2 and soil carbon dynamics in biologically managed, row-crop agroecosystems EA Paul, D Harris, HP Collins, U Schulthess, GP Robertson Applied Soil Ecology 11 (1), 53-65, 1999 | 218 | 1999 |
| Variable rate nitrogen fertilizer response in wheat using remote sensing B Basso, C Fiorentino, D Cammarano, U Schulthess Precision agriculture 17, 168-182, 2016 | 127 | 2016 |
| Sustainable crop intensification through surface water irrigation in Bangladesh? A geospatial assessment of landscape-scale production potential TJ Krupnik, U Schulthess, ZU Ahmed, AJ McDonald Land use policy 60, 206-222, 2017 | 104 | 2017 |
| Yield‐independent variation in grain nitrogen and phosphorus concentration among Ethiopian wheats U Schulthess, B Feil, SC Jutzi Agronomy Journal 89 (3), 497-506, 1997 | 100 | 1997 |
| Vernalization in wheat I. A model based on the interchangeability of plant age and vernalization duration SY Wang, RW Ward, JT Ritchie, RA Fischer, U Schulthess Field Crops Research 41 (2), 91-100, 1995 | 76 | 1995 |
| Role of modelling in international crop research: overview and some case studies M Reynolds, M Kropff, J Crossa, J Koo, G Kruseman, A Molero Milan, ... Agronomy 8 (12), 291, 2018 | 65 | 2018 |
| Mapping field-scale yield gaps for maize: An example from Bangladesh U Schulthess, J Timsina, JM Herrera, A McDonald Field Crops Research 143, 151-156, 2013 | 62 | 2013 |
| Multi-temporal and spectral analysis of high-resolution hyperspectral airborne imagery for precision agriculture: assessment of wheat grain yield and grain protein content FA Rodrigues Jr, G Blasch, P Defourny, JI Ortiz-Monasterio, U Schulthess, ... Remote Sensing 10 (6), 930, 2018 | 60 | 2018 |
| Rapid estimation of canopy nitrogen of cereal crops at paddock scale using a Canopy Chlorophyll Content Index EM Perry, GJ Fitzgerald, JG Nuttall, GJ O’Leary, U Schulthess, A Whitlock Field Crops Research 134, 158-164, 2012 | 59 | 2012 |
| Harnessing translational research in wheat for climate resilience MP Reynolds, JM Lewis, K Ammar, BR Basnet, L Crespo-Herrera, ... Journal of Experimental Botany 72 (14), 5134-5157, 2021 | 42 | 2021 |
| Integrated wheat crop management based on generic task knowledge-based systems and CERES numerical simulation. A Kamel, K Schroeder, J Sticklen, A Rafea, A Salah, U Schulthess, ... | 40* | 1995 |
| Increased ranking change in wheat breeding under climate change W Xiong, MP Reynolds, J Crossa, U Schulthess, K Sonder, C Montes, ... Nature plants 7 (9), 1207-1212, 2021 | 39 | 2021 |
| Detecting mortality induced structural and functional changes in a piñon-juniper woodland using Landsat and RapidEye time series DJ Krofcheck, JUH Eitel, LA Vierling, U Schulthess, TM Hilton, ... Remote sensing of environment 151, 102-113, 2014 | 33 | 2014 |
| Estimating adoption and impacts of agricultural management practices in developing countries using satellite data. A scoping review C Kubitza, VV Krishna, U Schulthess, M Jain Agronomy for Sustainable Development 40, 1-21, 2020 | 30 | 2020 |
| NEPER‐Weed: A Picture‐Based Expert System for Weed Identification U Schulthess, K Schroeder, A Kamel, AEGM AbdElGhani, ... Agronomy journal 88 (3), 423-427, 1996 | 27 | 1996 |
| Radiative transfer model inversion using high-resolution hyperspectral airborne imagery–Retrieving maize LAI to access biomass and grain yield A Kayad, FA Rodrigues Jr, S Naranjo, M Sozzi, F Pirotti, F Marinello, ... Field Crops Research 282, 108449, 2022 | 26 | 2022 |
| Detecting functional field units from satellite images in smallholder farming systems using a deep learning based computer vision approach: A case study from Bangladesh R Yang, ZU Ahmed, UC Schulthess, M Kamal, R Rai Remote Sensing Applications: Society and Environment 20, 100413, 2020 | 26 | 2020 |
| Farming on the fringe: Shallow groundwater dynamics and irrigation scheduling for maize and wheat in Bangladesh’s coastal delta U Schulthess, ZU Ahmed, S Aravindakshan, GM Rokon, ASMA Kurishi, ... Field crops research 239, 135-148, 2019 | 25 | 2019 |
Timothy J. KrupnikRegional Director, Sustainable Agrifood Systems Program (Asia), CIMMYT / CGIAR在 cgiar.org 的电子邮件经过验证
