COST725 – establishing a European phenological data platform for climatological applications: major results

Koch, Elisabeth; Dittmann, E.; Lipa, W.; Menzel, Annette; Nekovář, J.; Sparks, Tim H.; Vliet, A.J.H. van

doi:10.5194/asr-3-119-2009

Cited by 13 publications

(9 citation statements)

References 2 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…() also hypothesized that a carbon sink limitation could be induced by an earlier spring, as carbohydrate reserves reach maximum earlier (Herold, ; Fatichi et al ., ). The ongoing development of new technologies for phenological observation (Richardson et al ., ; Koch et al ., ; Sonnentag et al ., ) and new techniques for data integration and model testing (Graham et al ., ; Jeong et al ., ; Migliavacca et al ., ; Melaas et al ., ) should help further elucidate the processes responsible for the observed response. Until those processes are understood, our proposed model remains but a hypothesis to be tested with further evidence.…”

Section: Discussionmentioning

confidence: 98%

The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models

Keenan

Richardson

2015

Global Change Biology

274

232

View full text Add to dashboard Cite

Autumn senescence regulates multiple aspects of ecosystem function, along with associated feedbacks to the climate system. Despite its importance, current understanding of the drivers of senescence is limited, leading to a large spread in predictions of how the timing of senescence, and thus the length of the growing season, will change under future climate conditions. The most commonly held paradigm is that temperature and photoperiod are the primary controls, which suggests a future extension of the autumnal growing season as global temperatures rise. Here, using two decades of ground- and satellite-based observations of temperate deciduous forest phenology, we show that the timing of autumn senescence is correlated with the timing of spring budburst across the entire eastern United States. On a year-to-year basis, an earlier/later spring was associated with an earlier/later autumn senescence, both for individual species and at a regional scale. We use the observed relationship to develop a novel model of autumn phenology. In contrast to current phenology models, this model predicts that the potential response of autumn phenology to future climate change is strongly limited by the impact of climate change on spring phenology. Current models of autumn phenology therefore may overpredict future increases in the length of the growing season, with subsequent impacts for modeling future CO uptake and evapotranspiration.

show abstract

Section: Discussionmentioning

confidence: 98%

The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models

Keenan

Richardson

2015

Global Change Biology

274

232

View full text Add to dashboard Cite

show abstract

“…The phenological observational network follows the BBCH methodology (abbr. from German: “Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie”), which was akin to most European countries with similar growth stages of plant species (Meier 1997 ; Koch et al 2009 ).…”

Section: Methodsmentioning

confidence: 99%

Machine learning modeling of plant phenology based on coupling satellite and gridded meteorological dataset

2018

View full text Add to dashboard Cite

Changes in the timing of plant phenological phases are important proxies in contemporary climate research. However, most of the commonly used traditional phenological observations do not give any coherent spatial information. While consistent spatial data can be obtained from airborne sensors and preprocessed gridded meteorological data, not many studies robustly benefit from these data sources. Therefore, the main aim of this study is to create and evaluate different statistical models for reconstructing, predicting, and improving quality of phenological phases monitoring with the use of satellite and meteorological products. A quality-controlled dataset of the 13 BBCH plant phenophases in Poland was collected for the period 2007–2014. For each phenophase, statistical models were built using the most commonly applied regression-based machine learning techniques, such as multiple linear regression, lasso, principal component regression, generalized boosted models, and random forest. The quality of the models was estimated using a k-fold cross-validation. The obtained results showed varying potential for coupling meteorological derived indices with remote sensing products in terms of phenological modeling; however, application of both data sources improves models’ accuracy from 0.6 to 4.6 day in terms of obtained RMSE. It is shown that a robust prediction of early phenological phases is mostly related to meteorological indices, whereas for autumn phenophases, there is a stronger information signal provided by satellite-derived vegetation metrics. Choosing a specific set of predictors and applying a robust preprocessing procedures is more important for final results than the selection of a particular statistical model. The average RMSE for the best models of all phenophases is 6.3, while the individual RMSE vary seasonally from 3.5 to 10 days. Models give reliable proxy for ground observations with RMSE below 5 days for early spring and late spring phenophases. For other phenophases, RMSE are higher and rise up to 9–10 days in the case of the earliest spring phenophases.

show abstract

“…First, large international programmes have been launched to observe the phenology of predefined groups of species in various geographical locations. These studies have devoted a great deal of effort to standardising the instructions in order to facilitate the collection of comparable phenological data across all locations in all the participating countries (Koch et al 2009).…”

Section: Large Temporal and Spatial Scalesmentioning

confidence: 99%

The Annual Phenological Cycle

Hänninen

2016

Boreal and Temperate Trees in a Changing Climate

View full text Add to dashboard Cite

COST725 – establishing a European phenological data platform for climatological applications: major results

Cited by 13 publications

References 2 publications

The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models

The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models

Machine learning modeling of plant phenology based on coupling satellite and gridded meteorological dataset

The Annual Phenological Cycle

Contact Info

Product

Resources

About