Journal ArticleDOI
Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006
Michael A. White,Kirsten M. de Beurs,Kamel Didan,David W. Inouye,Andrew D. Richardson,Olaf P. Jensen,John O'Keefe,G. Zhang,Ramakrishna R. Nemani,Willem J. D. van Leeuwen,Jesslyn F. Brown,Allard de Wit,Michael E. Schaepman,Xioamao Lin,Michael D. Dettinger,Amey S. Bailey,John S. Kimball,Mark D. Schwartz,Dennis D. Baldocchi,J. T. Lee,William K. Lauenroth +20 more
TLDR
In this paper, the authors assess 10 start-of-spring (SOS) methods for North America between 1982 and 2006 and find that SOS estimates were more related to the first leaf and first flowers expanding phenological stages.Abstract:
Shifts in the timing of spring phenology are a central feature of global change research. Long-term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start-of-spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8 km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16 000 individual measurements of ground-based phenology, and two temperature-driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day-of-year estimates by � 60 days and in standard deviation by � 20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground- or model-based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOSread more
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Journal ArticleDOI
Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis
Andrew D. Richardson,Ryan S. Anderson,M. Altaf Arain,Alan G. Barr,Gil Bohrer,Guangsheng Chen,Jing M. Chen,Philippe Ciais,Kenneth J. Davis,Ankur R. Desai,Michael Dietze,Danilo Dragoni,S. R. Garrity,Christopher M. Gough,Robert F. Grant,David Y. Hollinger,Hank A. Margolis,Harry McCaughey,Mirco Migliavacca,Russell K. Monson,J. William Munger,Benjamin Poulter,Brett Raczka,Daniel M. Ricciuto,A. K. Sahoo,Kevin Schaefer,Hanqin Tian,Rodrigo Vargas,Hans Verbeeck,Jingfeng Xiao,Yongkang Xue +30 more
TL;DR: In this article, the authors evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis.
Journal ArticleDOI
Analysis of monotonic greening and browning trends from global NDVI time-series
Rogier de Jong,Sytze de Bruin,Allard de Wit,Michael E. Schaepman,Michael E. Schaepman,David Dent +5 more
TL;DR: In this article, the authors applied harmonic analysis and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006) to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981.
Journal ArticleDOI
Widespread climate change in the Himalayas and associated changes in local ecosystems.
TL;DR: This is the first time that large scale climatic and phenological changes at the landscape level have been documented for the Himalayas, confirming that the Himalayan regions are among the regions most vulnerable to climate change.
Journal ArticleDOI
Near real-time disturbance detection using satellite image time series
TL;DR: In this article, a multi-purpose time-series-based disturbance detection approach is proposed to identify and model stable historical variation to enable change detection within newly acquired data, which can analyse in-situ or satellite data time series of biophysical indicators from local to global scale since it is fast, does not depend on thresholds and does not require time series gap filling.
Journal ArticleDOI
Land Surface Phenology from MODIS: Characterization of the Collection 5 Global Land Cover Dynamics Product
TL;DR: The MODIS Global Land Cover Dynamics Product (GCDP) as mentioned in this paper was developed to support investigations that require regional to global scale information related to spatiotemporal dynamics in land surface phenology.
References
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Journal ArticleDOI
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Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity
Anthony L. Westerling,Anthony L. Westerling,Hugo G. Hidalgo,Daniel R. Cayan,Daniel R. Cayan,Thomas W. Swetnam +5 more
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Journal ArticleDOI
FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities
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TL;DR: The FLUXNET project as mentioned in this paper is a global network of micrometeorological flux measurement sites that measure the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere.
Journal ArticleDOI
North american regional reanalysis
Fedor Mesinger,Geoff DiMego,Eugenia Kalnay,Kenneth E. Mitchell,Perry Shafran,Wesley Ebisuzaki,Dusan Jovic,John S. Woollen,Eric Rogers,Ernesto Hugo Berbery,Michael Ek,Yun Fan,Robert Grumbine,Wayne Higgins,Hong Li,Ying Lin,Geoff Manikin,David F. Parrish,Wei Shi +18 more
TL;DR: The North American Regional Reanalysis (NARR) project as mentioned in this paper uses the NCEP Eta model and its Data Assimilation System (at 32-km-45-layer resolution with 3-hourly output) to capture regional hydrological cycle, the diurnal cycle and other important features of weather and climate variability.
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