| MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments S Watanabe, T Hajima, K Sudo, T Nagashima, T Takemura, H Okajima, ... Geoscientific Model Development 4 (4), 845-872, 2011 | 1162 | 2011 |
| High sensitivity of peat decomposition to climate change through water-table feedback T Ise, AL Dunn, SC Wofsy, PR Moorcroft Nature Geoscience 1 (11), 763-766, 2008 | 458 | 2008 |
| MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845–872 S Watanabe, T Hajima, K Sudo, T Nagashima, T Takemura, H Okajima, ... | 337 | 2011 |
| Early stage litter decomposition across biomes I Djukic, S Kepfer-Rojas, IK Schmidt, KS Larsen, C Beier, B Berg, ... Science of the total environment 628, 1369-1394, 2018 | 259 | 2018 |
| The global-scale temperature and moisture dependencies of soil organic carbon decomposition: an analysis using a mechanistic decomposition model T Ise, PR Moorcroft Biogeochemistry 80, 217-231, 2006 | 188 | 2006 |
| Explainable identification and mapping of trees using UAV RGB image and deep learning M Onishi, T Ise Scientific reports 11 (1), 903, 2021 | 173 | 2021 |
| MIROC-ESM: model description and basic results of CMIP5-20c3m experiments S Watanabe, T Hajima, K Sudo, T Nagashima, T Takemura, H Okajima, ... Geosci. Model Dev. Discuss 4 (2), 1063-1128, 2011 | 133 | 2011 |
| Comparison of modeling approaches for carbon partitioning: impact on estimates of global net primary production and equilibrium biomass of woody vegetation from MODIS GPP T Ise, CM Litton, CP Giardina, A Ito Journal of Geophysical Research: Biogeosciences 115 (G4), 2010 | 89 | 2010 |
| Effect of plant dynamic processes on African vegetation responses to climate change: Analysis using the spatially explicit individual‐based dynamic global vegetation model … H Sato, T Ise Journal of Geophysical Research: Biogeosciences 117 (G3), 2012 | 61 | 2012 |
| Effect of plant dynamic processes on African vegetation responses to climate change: Analysis using the spatially explicit individual‐based dynamic global vegetation model … H Sato, T Ise Journal of Geophysical Research: Biogeosciences 117 (G3), 2012 | 61 | 2012 |
| Automatic classification of trees using a UAV onboard camera and deep learning M Onishi, T Ise arXiv preprint arXiv:1804.10390, 2018 | 60 | 2018 |
| Forecasting climatic trends using neural networks: an experimental study using global historical data T Ise, Y Oba Frontiers in Robotics and AI 6, 446979, 2019 | 36 | 2019 |
| Identifying 3 moss species by deep learning, using the" chopped picture" method T Ise, M Minagawa, M Onishi arXiv preprint arXiv:1708.01986, 2017 | 33 | 2017 |
| Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for Japanese bamboo forests S Watanabe, K Sumi, T Ise BMC ecology 20, 1-14, 2020 | 30 | 2020 |
| Simulating boreal forest dynamics from perspectives of ecophysiology, resource availability, and climate change T Ise, PR Moorcroft Ecological research 25, 501-511, 2010 | 26 | 2010 |
| MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 2011; 4: 845–872. doi: 10.5194 S Watanabe, T Hajima, K Sudo, T Nagashima, T Takemura, H Okajima, ... gmd-4-845-2011.[CrossRef][Google Scholar], 0 | 25 | |
| Reconciliation of top-down and bottom-up CO2 fluxes in Siberian larch forest K Takata, PK Patra, A Kotani, J Mori, D Belikov, K Ichii, T Saeki, T Ohta, ... Environmental Research Letters 12 (12), 125012, 2017 | 20 | 2017 |
| Explainable deep learning reproduces a ‘professional eye’on the diagnosis of internal disorders in persimmon fruit T Akagi, M Onishi, K Masuda, R Kuroki, K Baba, K Takeshita, T Suzuki, ... Plant and Cell Physiology 61 (11), 1967-1973, 2020 | 19 | 2020 |
| Climate change, allowable emission, and earth system response to representative concentration pathway scenarios T Hajima, T Ise, K Tachiiri, E Kato, S Watanabe, M Kawamiya Journal of the Meteorological Society of Japan. Ser. II 90 (3), 417-434, 2012 | 16 | 2012 |
| Practicality and robustness of tree species identification using UAV RGB image and deep learning in temperate forest in Japan M Onishi, S Watanabe, T Nakashima, T Ise Remote Sensing 14 (7), 1710, 2022 | 14 | 2022 |
