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Biogeosciences An interactive open-access journal of the European Geosciences Union
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We present an exhaustive comparative survey of eight proximal methods to estimate forest phenology. We focused on methodological aspects and assessed deeply deviations between predicted and observed phenological dates and pointed out their main causes. We show that proximal methods provide robust phenological metrics. They can be used to retrieve long-term phenological series at flux measurement sites and help interpret the interannual variability and trends of mass and energy exchanges.
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Preprints
https://doi.org/10.5194/bg-2020-389
https://doi.org/10.5194/bg-2020-389

  02 Dec 2020

02 Dec 2020

Review status: this preprint is currently under review for the journal BG.

A survey of proximal methods for monitoring leaf phenology in temperate deciduous forests

Kamel Soudani1, Nicolas Delpierre1, Daniel Berveiller1, Gabriel Hmimina2, Jean-Yves Pontailler1, Lou Seureau1, Gaëlle Vincent1, and Éric Dufrêne1 Kamel Soudani et al.
  • 1Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et Evolution, 91405, Orsay, France
  • 2Laboratoire de Météorologie Dynamique, IPSL, CNRS/UPMC, Paris, France

Abstract. Tree phenology is a major driver of forest-atmosphere mass and energy exchanges. Yet tree phenology has historically not been recorded at flux measurement sites. Here, we used seasonal time-series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (Greenness Chromatic Coordinate), broad-band NDVI, LAI (Leaf Area Index), fAPAR (fraction of Absorbed Photosynthetic Active Radiation), CC (Canopy Closure), fRvis (fraction of Reflected Radiation) and GPP (Gross Primary Productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured very well the interannual variability of spring phenology (R2 > 0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) less than 4 days. For the CC and GCC methods, mid-amplitude (50 %) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25 % of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 days. GPP provides the most biased estimates. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R2 = 0.63 and 0.80 for NDVI and CC, respectively), with MAD less than 7 days. Our results show that proximal sensing methods can be used to derive robust phenological metrics. They can be used to retrieve long-term phenological series at flux measurement sites and help interpret the interannual variability and trends of mass and energy exchanges.

Kamel Soudani et al.

 
Status: open (extended)
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Kamel Soudani et al.

Kamel Soudani et al.

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Short summary
We present an exhaustive comparative survey of eight proximal methods to estimate forest phenology. We focused on methodological aspects and assessed deeply deviations between predicted and observed phenological dates and pointed out their main causes. We show that proximal methods provide robust phenological metrics. They can be used to retrieve long-term phenological series at flux measurement sites and help interpret the interannual variability and trends of mass and energy exchanges.
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