Evaluating Remotely Sensed Phenological Metrics in a Dynamic Ecosystem Model

Hong Xu,Tracy Twine,Xi Yang

doi:10.3390/rs6064660

Abstract

Vegetation phenology plays an important role in regulating processes of terrestrial ecosystems. Dynamic ecosystem models (DEMs) require representation of phenology to simulate the exchange of matter and energy between the land and atmosphere. Location-specific parameterization with phenological observations can potentially improve the performance of phenological models embedded in DEMs. As ground-based phenological observations are limited, phenology derived from remote sensing can be used as an alternative to parameterize phenological models. It is important to evaluate to what extent remotely sensed phenological metrics are capturing the phenology observed on the ground. We evaluated six methods based on two vegetation indices (VIs) (i.e., Normalized Difference Vegetation Index and Enhanced Vegetation Index) for retrieving the phenology of temperate forest in the Agro-IBIS model. First, we compared the remotely sensed phenological metrics with observations at Harvard Forest and found that most of the methods have large biases regardless of the VI used. Only two methods for the leaf onset and one method for the leaf offset showed a moderate performance. When remotely sensed phenological metrics were used to parameterize phenological models, the bias is maintained, and errors propagate to predictions of gross primary productivity and net ecosystem production. Our results show that Agro-IBIS has different sensitivities to leaf onset and offset in terms of carbon assimilation, suggesting it might be better to examine the respective impact of leaf onset and offset rather than the overall impact of the growing season length.

Highlights

Vegetation phenology, or the timing of plant growth stages, is considered a robust indicator of short-term climate variation and long-term climate trends because it is driven by environmental factors, such as temperature, precipitation and photoperiod
Bias in vegetation phenology may lead to errors in carbon and water exchange and energy budgets simulated in dynamic ecosystem models (DEMs) [10] as well as climate patterns simulated in coupled global climate models (GCMs) [11]
Because least 75% of their total size (L75) and L95 occur around the time when LAI nearly reaches its peak value, which is much later than the leaf onset defined in Agro-IBIS, they were excluded from the analysis

Summary

Introduction

Vegetation phenology, or the timing of plant growth stages (e.g., the timing of budburst, flowering, leaf coloring), is considered a robust indicator of short-term climate variation and long-term climate trends because it is driven by environmental factors, such as temperature, precipitation and photoperiod. Shifts in vegetation phenology can exert strong control on the feedbacks between the biosphere and atmosphere by affecting biogeochemical processes (e.g., exchange of carbon dioxide, production of biogenic volatile organic compounds) and biophysical properties (e.g., seasonal variation in albedo) of ecosystems [8,9]. A multi-model synthesis study has shown that vegetation phenology is poorly represented in many terrestrial biosphere models [10], which highlighted the urgency of improving phenological models embedded in DEMs. Phenological models can potentially be improved by reducing the uncertainties that stem from model structure, model parameters, or drivers [12]. Location-specific parameterization has the potential to reduce the uncertainty associated with model parameters

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