Martin Schwartz

FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach

Martin Schwartz, Philippe Ciais, Aurélien De Truchis et al. · Earth system science data · 2023 · 50 citations · Full text

Natural Resources, Ecology, Forestry +17

High-resolution data reveal a surge of biomass loss from temperate and Atlantic pine forests, contextualizing the 2022 fire season distinctiveness in France

Lilian Vallet, Martin Schwartz, Philippe Ciais et al. · Biogeosciences · 2023 · 17 citations · Full text

Natural Resources, Environmental Sciences, Forestry +17

Retrieving yearly forest growth from satellite data: A deep learning based approach

Martin Schwartz, Philippe Ciais, Ewan Sean et al. · Remote Sensing of Environment · 2025 · 13 citations · Full text

Natural Resources, Earth Sciences, Forestry +17

Remote-sensing-based forest canopy height mapping: some models are useful, but might they provide us with even more insights when combined?

Nikola Bešič, Nicolas Picard, Cédric Vega et al. · Geoscientific model development · 2025 · 12 citations · Full text

Natural Resources, Earth Sciences, Forestry +17

Canopy height and biomass distribution across the forests of Iberian Peninsula

Yang Su, Martin Schwartz, Ibrahim Fayad et al. · Scientific Data · 2025 · 12 citations · Full text

Natural Resources, Earth Sciences, Forestry +17