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

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

Earth Observation–Based Machine learning for operational forecasting of harmful algal blooms in the Arabian Gulf and Sea of Oman

Kaltham A. Ismail, Roqaya A. Ismail, M. Salim

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

Linlin Sun, Renping Zhang, Jing Guo, Yaming Zhang, Huaqing Liu, Xiaoyu Yu, et al.

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.

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

MFRXG: A multi-feature stacking ensemble RF-XGBoost model for total nitrogen retrieval in the Yellow River from Landsat 8/9 data

Y Zhang, Shuanggen Jin

Total nitrogen (TN) is a key indicator for assessing eutrophication in aquatic systems, and its concentration dynamics are influenced by a combination of hydrometeorological factors, terrain, and other pollution sources. However, traditional retrieval models based solely on remot…Read more