Articles | Volume 13, issue 11
https://doi.org/10.5194/bg-13-3305-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-13-3305-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
06 Jun 2016
Research article |
| 06 Jun 2016
Modelling interannual variation in the spring and autumn land surface phenology of the European forest
Victor F. Rodriguez-Galiano
CORRESPONDING AUTHOR
Physical Geography and Regional Geographic Analysis, University of
Seville, Seville 41004, Spain
Global Environmental Change and Earth Observation Research Group,
Geography and Environment, University of Southampton, Southampton SO17 1BJ,
UK
Manuel Sanchez-Castillo
Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell
Institute and Cambridge Institute for Medical Research, University of
Cambridge, Cambridge CB2 0XY, UK
Jadunandan Dash
Global Environmental Change and Earth Observation Research Group,
Geography and Environment, University of Southampton, Southampton SO17 1BJ,
UK
Peter M. Atkinson
Faculty of Science and Technology, Engineering Building, Lancaster
University, Lancaster LA1 4YR, UK
Faculty of Geosciences, University of Utrecht, Heidelberglaan 2, 3584 CS
Utrecht, the Netherlands
School of Geography, Archaeology and Palaeoecology, Queen's University
Belfast, Belfast BT7 1NN, Northern Ireland, UK
Global Environmental Change and Earth Observation Research Group,
Geography and Environment, University of Southampton, Southampton SO17 1BJ,
UK
Jose Ojeda-Zujar
Physical Geography and Regional Geographic Analysis, University of
Seville, Seville 41004, Spain
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Cited
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- Estimación de la fenología de la vegetación a partir de imágenes de satélite: el caso de la península ibérica e islas Baleares (2001-2017) J. Caparros-Santiago & V. Rodríguez-Galiano 10.4995/raet.2020.13632
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- Predicting coal elemental components from proximate analysis: Explicit versus implicit nonlinear models A. Akkaya 10.1080/15567036.2019.1640812
- Solar radiation and ENSO predict fruiting phenology patterns in a 15‐year record from Kibale National Park, Uganda C. Chapman et al. 10.1111/btp.12559
- Predicting the onset of Betula pendula flowering in Poznań (Poland) using remote sensing thermal data P. Bogawski et al. 10.1016/j.scitotenv.2018.12.295
- Machine Learning for Modeling Water Demand M. Villarin & V. Rodriguez-Galiano 10.1061/(ASCE)WR.1943-5452.0001067
- A comparative approach of methods to estimate machine productivity in wood cutting I. Lopes et al. 10.1080/14942119.2021.1952520
- Specific Drivers and Responses to Land Surface Phenology of Different Vegetation Types in the Qinling Mountains, Central China J. Guo et al. 10.3390/rs13224538
- Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics E. Berra & R. Gaulton 10.1016/j.foreco.2020.118663
- Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia M. Ghorbani et al. 10.1007/s00500-019-04648-2
- Comparative analysis of orbital sensors in soybean yield estimation by the random forest algorithm D. Batistella et al. 10.1590/1413-7054202347002423
- Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review J. Caparros-Santiago et al. 10.1016/j.isprsjprs.2020.11.019
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- Biological and climate factors co-regulated spatial-temporal dynamics of vegetation autumn phenology on the Tibetan Plateau J. Zu et al. 10.1016/j.jag.2018.03.006
- Accumulated Heating and Chilling Are Important Drivers of Forest Phenology and Productivity in the Algonquin-to-Adirondacks Conservation Corridor of Eastern North America M. Stefanuk & R. Danby 10.3390/f12030282
- Physics-informed machine learning-aided framework for prediction of minimum film boiling temperature K. Kim et al. 10.1016/j.ijheatmasstransfer.2022.122839
- Optimal sensor placement using machine learning R. Semaan 10.1016/j.compfluid.2017.10.002
- Spring and Autumn Phenological Variability across Environmental Gradients of Great Smoky Mountains National Park, USA S. Norman et al. 10.3390/rs9050407
- Change point estimation of deciduous forest land surface phenology Y. Xie & A. Wilson 10.1016/j.rse.2020.111698
- Machine Learning Approaches to Model Galvanic Corrosion of Coated Al Alloy Systems M. Jokar et al. 10.5006/4175
- Big Data and Machine Learning to Improve European Grapevine Moth (Lobesia botrana) Predictions J. Balduque-Gil et al. 10.3390/plants12030633
- Retrieval of high spatial resolution precipitable water vapor maps using heterogeneous earth observation data X. Ma et al. 10.1016/j.rse.2022.113100
- Interpretable machine learning algorithms to predict leaf senescence date of deciduous trees C. Gao et al. 10.1016/j.agrformet.2023.109623
- Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues . Bajocco et al. 10.3390/rs11232751
30 citations as recorded by crossref.
- High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement K. Kim et al. 10.1016/j.ijheatmasstransfer.2021.122338
- Bedload transport rate prediction: Application of novel hybrid data mining techniques K. Khosravi et al. 10.1016/j.jhydrol.2020.124774
- Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning R. Brackenridge et al. 10.3390/en15031070
- Estimación de la fenología de la vegetación a partir de imágenes de satélite: el caso de la península ibérica e islas Baleares (2001-2017) J. Caparros-Santiago & V. Rodríguez-Galiano 10.4995/raet.2020.13632
- All-weather precipitable water vapor map reconstruction using data fusion and machine learning-based spatial downscaling Y. Ma et al. 10.1016/j.atmosres.2023.107068
- Land cover composition, climate, and topography drive land surface phenology in a recently burned landscape: An application of machine learning in phenological modeling J. Wang et al. 10.1016/j.agrformet.2021.108432
- Testing machine learning algorithms on a binary classification phenological model W. Dai et al. 10.1111/geb.13612
- Machine learning to identify risk factors associated with the development of ventilated hospital-acquired pneumonia and mortality: implications for antibiotic therapy selection A. Sophonsri et al. 10.3389/fmed.2023.1268488
- Predicting coal elemental components from proximate analysis: Explicit versus implicit nonlinear models A. Akkaya 10.1080/15567036.2019.1640812
- Solar radiation and ENSO predict fruiting phenology patterns in a 15‐year record from Kibale National Park, Uganda C. Chapman et al. 10.1111/btp.12559
- Predicting the onset of Betula pendula flowering in Poznań (Poland) using remote sensing thermal data P. Bogawski et al. 10.1016/j.scitotenv.2018.12.295
- Machine Learning for Modeling Water Demand M. Villarin & V. Rodriguez-Galiano 10.1061/(ASCE)WR.1943-5452.0001067
- A comparative approach of methods to estimate machine productivity in wood cutting I. Lopes et al. 10.1080/14942119.2021.1952520
- Specific Drivers and Responses to Land Surface Phenology of Different Vegetation Types in the Qinling Mountains, Central China J. Guo et al. 10.3390/rs13224538
- Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics E. Berra & R. Gaulton 10.1016/j.foreco.2020.118663
- Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia M. Ghorbani et al. 10.1007/s00500-019-04648-2
- Comparative analysis of orbital sensors in soybean yield estimation by the random forest algorithm D. Batistella et al. 10.1590/1413-7054202347002423
- Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review J. Caparros-Santiago et al. 10.1016/j.isprsjprs.2020.11.019
- A Simple Method of Predicting Autumn Leaf Coloring Date Using Machine Learning with Spring Leaf Unfolding Date S. Lee et al. 10.1007/s13143-021-00251-4
- A framework for monitoring movements of pandemic disease patients based on GPS trajectory datasets P. Ugwoke et al. 10.1007/s11276-021-02819-4
- Biological and climate factors co-regulated spatial-temporal dynamics of vegetation autumn phenology on the Tibetan Plateau J. Zu et al. 10.1016/j.jag.2018.03.006
- Accumulated Heating and Chilling Are Important Drivers of Forest Phenology and Productivity in the Algonquin-to-Adirondacks Conservation Corridor of Eastern North America M. Stefanuk & R. Danby 10.3390/f12030282
- Physics-informed machine learning-aided framework for prediction of minimum film boiling temperature K. Kim et al. 10.1016/j.ijheatmasstransfer.2022.122839
- Optimal sensor placement using machine learning R. Semaan 10.1016/j.compfluid.2017.10.002
- Spring and Autumn Phenological Variability across Environmental Gradients of Great Smoky Mountains National Park, USA S. Norman et al. 10.3390/rs9050407
- Change point estimation of deciduous forest land surface phenology Y. Xie & A. Wilson 10.1016/j.rse.2020.111698
- Machine Learning Approaches to Model Galvanic Corrosion of Coated Al Alloy Systems M. Jokar et al. 10.5006/4175
- Big Data and Machine Learning to Improve European Grapevine Moth (Lobesia botrana) Predictions J. Balduque-Gil et al. 10.3390/plants12030633
- Retrieval of high spatial resolution precipitable water vapor maps using heterogeneous earth observation data X. Ma et al. 10.1016/j.rse.2022.113100
- Interpretable machine learning algorithms to predict leaf senescence date of deciduous trees C. Gao et al. 10.1016/j.agrformet.2023.109623
Saved (preprint)
Latest update: 18 Apr 2024
Short summary
This research reveals new insights into the weather drivers of land surface phenology (LSP) across the entire European forest, while at the same time it establishes a new conceptual framework for modelling LSP. Specifically, a sophisticated machine learning regression method (RF) was introduced for LSP modelling across very large areas and across multiple years simultaneously. The RF models explained 81 and 62 % of the variance in the spring and autumn LSP interannual variation.
This research reveals new insights into the weather drivers of land surface phenology (LSP)...
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