Machine-learning framework for cross-scale estimation of desert vegetation aboveground biomass and driver analysis in the Junggar Basin using UAV classification and satellite data fusion

A machine learning framework for predicting agricultural terrace suitability using positive and unlabelled datasets: an application for the Troodos Mountains, Cyprus

A. Meena, C. Zoumides, H. Djuma, I. Sofokleous, C. Camera, A. Bruggeman

A two-stage machine learning framework for Chl-a reconstruction and short-term prediction in eutrophic lakes using GOCI observations

Zehui Huang, Ronghua Ma, Xinhui Chen, Kun Xue, Minqi Hu, Haoze Liu

High-resolution (30-m) multi-decadal reconstruction of coastal water quality via Landsat-Sentinel fusion and ensemble machine learning (1985–2024)

Zihan Chen, Peng Chen, Siqi Zhang, Yunzhou Li, Delu Pan

SAR2LiDAR: A deep learning-based cross-modal matching framework between SAR images and LiDAR point clouds

Yifan Zhang, J. Mallorquí, Wen Wang

Geomorphology-constrained bathymetric inversion of turbid tidal channels using centerline-network priors and machine learning

Jianchun Chen, Chao Gao, Yan Gu, Xiangyu Ou, Xiaoyi Cheng, Shibing Zhu, et al.

Enabling maize mapping in double-cropping regions of Argentina and Brazil without ground reference data by leveraging multiple satellite platforms

Ryoungseob Kwon, Youngryel Ryu, Philippe Ciais, David Makowski, Changhyun Choi, Huaize Feng, et al.

Large-scale maize mapping is challenging due to the lack of field-level datasets, which were essential for training machine learning models. The United States (US) provides abundant in-situ labels and national maize maps while Argentina and Brazil offered limited data and double-…Read more