TEMPORALLY TRANSFERABLE MACHINE LEARNING MODEL FOR TOTAL SUSPENDED MATTER RETRIEVAL FROM SENTINEL-2

5 citations as recorded by crossref.

1. Satellite-driven machine learning framework for monitoring river sediment discharge: integrating Sentinel-2 spectral data with hydrometric insights M. Ahmadi et al. https://doi.org/10.1016/j.asr.2025.04.061
2. Deep-Learning-Based Retrieval of an Orange Band Sensitive to Cyanobacteria for Landsat-8/9 and Sentinel-2 M. Niroumand-Jadidi & F. Bovolo https://doi.org/10.1109/JSTARS.2023.3266929
3. From sample to sonde to Sentinel-2: insights from a multi-scale chlorophyll-a monitoring effort in the Hudson River, New York W. Salls et al. https://doi.org/10.1007/s10661-025-14844-3
4. A review and assessment of Copernicus water quality and temperature products in coastal waters: insights from the Adriatic Sea M. Niroumand-Jadidi et al. https://doi.org/10.3389/fmars.2026.1913182
5. WASI-AI: Synergistic Integration of AI and Physics for Retrieving Water Quality and Benthic Parameters from Multi- and Hyperspectral Images M. Niroumand-Jadidi & P. Gege https://doi.org/10.1109/JSTARS.2025.3605061