Class imbalance: A crucial factor affecting the performance of tea plantations mapping by machine learning. - Yuanjun Xiao; Jingfeng Huang; Wei Weng 005; Ran Huang; Qi Shao; Chang Zhou; Shengcheng Li (Int. J. Appl. Earth Obs. Geoinformation, 2024)

The illusion of success: Test set disproportion causes inflated accuracy in remote sensing mapping research. - Yuanjun Xiao; Zhen Zhao; Jingfeng Huang; Ran Huang; Wei Weng 005; Gerui Liang; Chang Zhou; Qi Shao; Qiyu Tian (Int. J. Appl. Earth Obs. Geoinformation, 2024)

A novel framework for dynamic and quantitative mapping of damage severity due to compound Drought-Heatwave impacts on tea Plantations, integrating Sentinel-2 and UAV images. - Ran Huang; Yuanjun Xiao; Shengcheng Li; Jianing Li; Wei Weng 005; Qi Shao; Jingcheng Zhang; Yao Zhang; Lingbo Yang; Chao Huang; Weiwei Sun; Weiwei Liu; et al. (Comput. Electron. Agric., 2025)

A novel scheme for seamless global mapping of daily mean air temperature (SGM_DMAT) at 1-km spatial resolution using satellite and auxiliary data. - Ran Huang; Shengcheng Li; Xin Zhu; Jianing Li; Yuanjun Xiao; Wei Weng 005; Qi Shao; Dengfeng Chai; Jingcheng Zhang; Yao Zhang; Lingbo Yang; Kaihua Wu; et al. (Ecol. Informatics, 2025)

Selecting of global phenological field observations for validating coarse AVHRR-derived forest phenology products based on spatial heterogeneity and temporal consistency. - Qi Shao; Chao Huang; Yuanjun Xiao; Li Liu; Weiwei Liu; Ran Huang; Chang Zhou; Wei Weng 005; Jingfeng Huang (Ecol. Informatics, 2025)