Articles | Volume 22, issue 8
https://doi.org/10.5194/bg-22-2049-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-2049-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Detection of fast-changing intra-seasonal vegetation dynamics of drylands using solar-induced chlorophyll fluorescence (SIF)
School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, NY, USA
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA
Giulia Tagliabue
Remote Sensing of Environmental Dynamics Laboratory, Department of Environmental and Earth Sciences (DISAT), University of Milano-Bicocca, Milan, Italy
Micol Rossini
Remote Sensing of Environmental Dynamics Laboratory, Department of Environmental and Earth Sciences (DISAT), University of Milano-Bicocca, Milan, Italy
Francesco Pietro Fava
Department of Environmental Science and Policy, Università degli Studi di Milano, Milan, Italy
Cinzia Panigada
Remote Sensing of Environmental Dynamics Laboratory, Department of Environmental and Earth Sciences (DISAT), University of Milano-Bicocca, Milan, Italy
Lutz Merbold
Integrative Agroecology Group, Research Division Agroecology and Environment, Agroscope, Reckenholzstr. 191, 8046 Zurich, Switzerland
Sonja Leitner
International Livestock Research Institute, Mazingira Centre, P.O. Box 30709, 00100 Nairobi, Kenya
School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, NY, USA
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Cited
10 citations as recorded by crossref.
- Resolving sub-daily variability in the GPP–SIF relationship and its implications for GPP estimation at a temperate savanna site J. Zhuang et al. https://doi.org/10.1016/j.srs.2026.100522
- ConvLSTM-GCN-transformer: Spatiotemporal graph-attention model for vegetation index map forecasting M. Bouaziz et al. https://doi.org/10.1016/j.eswa.2026.131596
- Differences in spring green up date and climate sensitivities of forest and grassland ecosystems across China L. Yan et al. https://doi.org/10.1080/10095020.2025.2586390
- Hyperspectral constraints reduce bias in ECOSTRESS evapotranspiration and drought indicators M. Marshall et al. https://doi.org/10.1016/j.rse.2026.115453
- Context-Aware Multimodal Representation Learning for Spatio-Temporally Explicit Environmental Modeling J. Peters et al. https://doi.org/10.1109/TGRS.2026.3707966
- Spatiotemporal Patterns of Fractional Vegetation Cover and Their Associations with Climate and Land Cover in the Yarkant River Basin B. Abudureheman et al. https://doi.org/10.3390/land15101911
- Tracing changes in subsurface water storage through a novel satellite-based time-series of far-red solar-induced fluorescence quantum efficiency D. Herrera et al. https://doi.org/10.1016/j.rse.2026.115456
- Spatial Assessment of Future Drought Risk in Agricultural Lands: A CMIP6-Based Analysis A. Şekeroğlu & R. Öztaş Karlı https://doi.org/10.15832/ankutbd.1842184
- The Downscaled GOME-2 SIF Based on Machine Learning Enhances the Correlation with Ecosystem Productivity C. Hu et al. https://doi.org/10.3390/rs17152642
- Constraining the maximum entropy production approach with sun-induced chlorophyll fluorescence to estimate evapotranspiration in a seasonally dry deciduous forest L. Méndez-Barroso et al. https://doi.org/10.1016/j.jhydrol.2026.136295
10 citations as recorded by crossref.
- Resolving sub-daily variability in the GPP–SIF relationship and its implications for GPP estimation at a temperate savanna site J. Zhuang et al. https://doi.org/10.1016/j.srs.2026.100522
- ConvLSTM-GCN-transformer: Spatiotemporal graph-attention model for vegetation index map forecasting M. Bouaziz et al. https://doi.org/10.1016/j.eswa.2026.131596
- Differences in spring green up date and climate sensitivities of forest and grassland ecosystems across China L. Yan et al. https://doi.org/10.1080/10095020.2025.2586390
- Hyperspectral constraints reduce bias in ECOSTRESS evapotranspiration and drought indicators M. Marshall et al. https://doi.org/10.1016/j.rse.2026.115453
- Context-Aware Multimodal Representation Learning for Spatio-Temporally Explicit Environmental Modeling J. Peters et al. https://doi.org/10.1109/TGRS.2026.3707966
- Spatiotemporal Patterns of Fractional Vegetation Cover and Their Associations with Climate and Land Cover in the Yarkant River Basin B. Abudureheman et al. https://doi.org/10.3390/land15101911
- Tracing changes in subsurface water storage through a novel satellite-based time-series of far-red solar-induced fluorescence quantum efficiency D. Herrera et al. https://doi.org/10.1016/j.rse.2026.115456
- Spatial Assessment of Future Drought Risk in Agricultural Lands: A CMIP6-Based Analysis A. Şekeroğlu & R. Öztaş Karlı https://doi.org/10.15832/ankutbd.1842184
- The Downscaled GOME-2 SIF Based on Machine Learning Enhances the Correlation with Ecosystem Productivity C. Hu et al. https://doi.org/10.3390/rs17152642
- Constraining the maximum entropy production approach with sun-induced chlorophyll fluorescence to estimate evapotranspiration in a seasonally dry deciduous forest L. Méndez-Barroso et al. https://doi.org/10.1016/j.jhydrol.2026.136295
Saved (final revised paper)
Latest update: 15 Oct 2026
Short summary
Solar-induced chlorophyll fluorescence (SIF), a tiny optical signal emitted from the core photosynthetic machinery, has emerged as a promising tool to evaluate vegetation growth from satellites. We find satellite SIF can capture intra-seasonal (i.e., from days to weeks) vegetation dynamics of dryland ecosystems, while greenness-based vegetation indices cannot. This study generates novel insights for developing effective real-time vegetation monitoring systems to inform climate risk management.
Solar-induced chlorophyll fluorescence (SIF), a tiny optical signal emitted from the core...
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