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Remote Sens. 2014, 6(6), 4660-4686;

Evaluating Remotely Sensed Phenological Metrics in a Dynamic Ecosystem Model

1,* , 1,* and 2,3
Department of Soil, Water and Climate, University of Minnesota, Saint Paul, MN 55108, USA
Department of Geological Sciences, Brown University, Providence, RI 02912, USA
The Ecosystem Center, Marine Biological Laboratory, Woods Hole, MA 02543, USA
Authors to whom correspondence should be addressed.
Received: 28 March 2014 / Revised: 16 May 2014 / Accepted: 19 May 2014 / Published: 26 May 2014
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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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. View Full-Text
Keywords: phenology; remote sensing; dynamic ecosystem model; Agro-IBIS; MODIS phenology; remote sensing; dynamic ecosystem model; Agro-IBIS; MODIS

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Xu, H.; Twine, T.E.; Yang, X. Evaluating Remotely Sensed Phenological Metrics in a Dynamic Ecosystem Model. Remote Sens. 2014, 6, 4660-4686.

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