Articles | Volume 21, issue 2
https://doi.org/10.5194/bg-21-473-2024
© Author(s) 2024. 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-21-473-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Reviews and syntheses
| 
25 Jan 2024
Reviews and syntheses |
| 25 Jan 2024
| 25 Jan 2024
Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity
Lammert Kooistra
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
Katja Berger
CORRESPONDING AUTHOR
Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain
Benjamin Brede
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Lukas Valentin Graf
Earth Observation of Agroecosystems Team, Division Agroecology and Environment, Agroscope, Zurich, Switzerland
Department of Environmental Systems Sciences, Institute of Agricultural Science, Crop Science, ETH Zürich, Zurich, Switzerland
Helge Aasen
Earth Observation of Agroecosystems Team, Division Agroecology and Environment, Agroscope, Zurich, Switzerland
Department of Environmental Systems Sciences, Institute of Agricultural Science, Crop Science, ETH Zürich, Zurich, Switzerland
Jean-Louis Roujean
CESBIO, CNES, CNRS, INRAE, IRD, UT3, 18 avenue Edouard Belin, BPI 2801, TOULOUSE Cedex 9, 31401, France
Miriam Machwitz
Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, 4422 Belvaux, Luxembourg
Martin Schlerf
Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, 4422 Belvaux, Luxembourg
Clement Atzberger
Mantle Labs Ltd 29 Farm Street, London W1J 5RL, UK
Egor Prikaziuk
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE, Enschede, the Netherlands
Dessislava Ganeva
Space Research and Technology Institute – Bulgarian Academy of Sciences, Georgi Bonchev bl. 1, 1113 Sofia, Bulgaria
Enrico Tomelleri
Faculty of Agricultural, Environmental and Food Sciences; Free University of Bozen/Bolzano, Bolzano, Italy
Holly Croft
School of Biosciences, University of Sheffield, Sheffield, S10 2TN, UK
Institute for Sustainable Food, University of Sheffield, Sheffield, S10 2TN, UK
Pablo Reyes Muñoz
Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain
Virginia Garcia Millan
European Topic Centre, University of Malaga, Arquitecto Francisco Peñalosa, 18, 29010 Málaga, Spain
Roshanak Darvishzadeh
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE, Enschede, the Netherlands
Gerbrand Koren
Copernicus Institute of Sustainable Development, Utrecht University, 3584 CB Utrecht, the Netherlands
Ittai Herrmann
The Plant Sensing Laboratory, The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel
Offer Rozenstein
Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization – Volcani Institute, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7528809, Israel
Santiago Belda
UAVAC, Applied Mathematics Department, University of Alicante, 03080 Alicante, Spain
Miina Rautiainen
School of Engineering, Department of Built Environment, Aalto University, 02150 Espoo, Finland
Stein Rune Karlsen
NORCE Norwegian Research Centre AS, P.O. Box 6434, 9294 Tromsø, Norway
Cláudio Figueira Silva
Forest Research Centre (CEF) and Associated Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal
Sofia Cerasoli
Forest Research Centre (CEF) and Associated Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal
Jon Pierre
Mantle Labs Ltd 29 Farm Street, London W1J 5RL, UK
Emine Tanır Kayıkçı
Karadeniz Technical University, Engineering Faculty, Department of Geomatics Engineering, Trabzon, Turkey
Andrej Halabuk
Institute of Landscape Ecology, Slovak Academy of Sciences, 814 99 Bratislava, Slovakia
Esra Tunc Gormus
Karadeniz Technical University, Engineering Faculty, Department of Geomatics Engineering, Trabzon, Turkey
Frank Fluit
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
Zhanzhang Cai
Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 223 62 Lund, Sweden
Marlena Kycko
Department of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmieście 26/28, 00-927, Warsaw, Poland
Thomas Udelhoven
EOCP – Earth Observation and Climate Processes, Environmental Remote Sensing & Geoinformatics, Trier University, 54296 Trier, Germany
Jochem Verrelst
Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain
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- Salix polaris growth rings are an unreliable proxy for satellite-derived tundra productivity on Svalbard T. Verstraten et al. https://doi.org/10.1016/j.dendro.2026.126538
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- Exploring the effects of soil structure, nutrients, and farm management on crop root biomass and depth distribution T. Mattila & L. Häkkinen https://doi.org/10.1016/j.fcr.2025.109909
- Analysis of net primary productivity trends in india by incorporating the direct effect of CO2 fertilization in MODIS data R. Das et al. https://doi.org/10.1088/2515-7620/ae2e98
- Continuous frost causes a greater reduction in forest growth than isolated frost in the Northern Hemisphere H. Yang et al. https://doi.org/10.1038/s41467-025-67861-8
- Identifying Coupled Cropland Abandonment–Land Productivity Trajectories and Restoration Priority Areas Under Dynamic Cropland Boundary Constraints: A Case Study of the Huai River Basin, China X. Zhang et al. https://doi.org/10.1002/ldr.70677
- Advances in vegetation mapping through remote sensing and machine learning techniques: a scientometric review C. Matyukira & P. Mhangara https://doi.org/10.1080/22797254.2024.2422330
- Enhanced gap-filling for satellite-derived crop monitoring using temperature-driven reconstruction techniques F. Tschurr et al. https://doi.org/10.1016/j.atech.2025.100816
- Drone imaging can accurately assess coral cover, bleaching, and growth form for shallow coral reefs L. Himes & T. Rueger https://doi.org/10.1007/s00338-026-02864-5
- Mapping interannual changes of evergreen forests in Beijing, China at 30-m spatial resolution during 1990–2023 Z. Li et al. https://doi.org/10.1080/10106049.2026.2685918
- Evaluating the carbon and nitrogen cycles of the QUINCY terrestrial biosphere model using space-born optical remotely-sensed data T. Miinalainen et al. https://doi.org/10.5194/bg-22-6937-2025
- Nationwide operational mapping of grassland first mowing dates combining machine learning and Sentinel-2 time series H. Rivas et al. https://doi.org/10.1016/j.rse.2024.114476
- Comparing satellite and ED flux tower data with simulated vegetation spectral reflectance in relation to ecosystem energy, water and carbon dynamics in Banksia woodland, Perth, Australia M. Boori & K. Choudhary https://doi.org/10.1080/00049182.2026.2613673
- Land Surface Phenology Reveals Region-Specific Hurricane Impacts Across the North Atlantic Basin (2001–2022) C. Topete-Pozas & S. Norman https://doi.org/10.3390/f17040419
- Greening of Svalbard S. Karlsen et al. https://doi.org/10.1016/j.scitotenv.2024.174130
- Multi-scale greening dynamics in Türkiye’s Ecoregions (2001–2024): the effects of elevation and land cover on NDVI change tendencies F. Işık & S. Eraslan https://doi.org/10.1007/s10661-026-15173-9
- Self-Supervised Learning and Multi-Sensor Fusion for Alpine Wetland Vegetation Mapping: Bayinbuluke, China M. Zaka et al. https://doi.org/10.3390/plants14203153
- Cloud-Native Earth Observation for Quantitative Vegetation Science: Architectures, Workflows, and Scientific Implications J. Verrelst et al. https://doi.org/10.3390/rs18081154
Saved (final revised paper)
Latest update: 29 Jul 2026
Short summary
We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.
We reviewed optical remote sensing time series (TS) studies for monitoring vegetation...
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