| A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level H Jiang, H Hu, R Zhong, J Xu, J Xu, J Huang, S Wang, Y Ying, T Lin Global change biology 26 (3), 1754-1766, 2020 | 167 | 2020 |
| DeepCropMapping: A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping J Xu, Y Zhu, R Zhong, Z Lin, J Xu, H Jiang, J Huang, H Li, T Lin Remote Sensing of Environment 247, 111946, 2020 | 161 | 2020 |
| DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation T Lin, R Zhong, Y Wang, J Xu, H Jiang, J Xu, Y Ying, L Rodriguez, ... Environmental research letters 15 (3), 034016, 2020 | 59 | 2020 |
| Double cropping and cropland expansion boost grain production in Brazil J Xu, J Gao, HV de Holanda, LF Rodríguez, JV Caixeta-Filho, R Zhong, ... Nature Food 2 (4), 264-273, 2021 | 37 | 2021 |
| Understanding the non-stationary relationships between corn yields and meteorology via a spatiotemporally varying coefficient model H Jiang, H Hu, B Li, Z Zhang, S Wang, T Lin Agricultural and Forest Meteorology 301, 108340, 2021 | 18 | 2021 |
| A spatiotemporal assessment of field residues of rice, maize, and wheat at provincial and county levels in China T Lin, J Xu, X Shen, H Jiang, R Zhong, S Wu, Z Du, L Rodriguez, KC Ting GCB Bioenergy 11 (10), 1146-1158, 2019 | 13 | 2019 |
| Understanding the impact of sub-seasonal meteorological variability on corn yield in the US Corn Belt H Jiang, H Hu, S Wang, Y Ying, T Lin Science of the Total Environment 724, 138235, 2020 | 7 | 2020 |
| Crop yield estimation method based on deep temporal and spatial feature combined learning T Lin, R Zhong, XU Jinfan, H Jiang, Y Ying, KC Ting US Patent 11,741,555, 2023 | | 2023 |
| Large-scale crop phenology extraction method based on shape model fitting method T Lin, Z Lin, H Jiang, Y Ying US Patent 11,734,925, 2023 | | 2023 |
Tao LinZhejiang University在 zju.edu.cn 的电子邮件经过验证
