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
Citations
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Journal ArticleDOI
Evaluating the accuracy of and evaluating the potential errors in extracting vegetation phenology through remote sensing in China
TL;DR: Using remote sensing to study vegetation phenology faces a problem related to extraction methods as discussed by the authors, and there is a lack of widely recognized methods for obtaining phenology data from different methods.
Journal ArticleDOI
Response of Vegetation Photosynthetic Phenology to Urbanization in Dongting Lake Basin, China
TL;DR: Wang et al. as discussed by the authors analyzed the changing trend of vegetation photosynthetic phenology in Dongting Lake basin, China, and its response to urbanization using nighttime light and chlorophyll fluorescence datasets.
Journal ArticleDOI
Assessing the inter-annual variability of vegetation phenological events observed from satellite vegetation index time series in dryland sites
TL;DR: In this paper, the authors used satellite vegetation indices (VIs) to track the number and timing of phenological transition dates in dryland ecosystems, including the start, peak, and end of season (SOS, POS, and EOS) across large temporal and spatial scales.
Journal ArticleDOI
Challenges in Complementing Data from Ground-Based Sensors with Satellite-Derived Products to Measure Ecological Changes in Relation to Climate-Lessons from Temperate Wetland-Upland Landscapes.
TL;DR: The effectiveness of decisions and assumptions made in applying the remotely sensed data for the assessment and the value of integrating observations across scales, sensors, and disciplines are evaluated.
Journal ArticleDOI
Modeling temporal growth profile of vegetation index from Indian geostationary satellite for assessing in-season progress of crop area
Rahul Nigam,Swapnil Vyas,Bimal K. Bhattacharya,M. P. Oza,Shailendra S. Srivastava,Nita Bhagia,Debajyoti Dhar,K. R. Manjunath +7 more
TL;DR: In this paper, a new methodology has been developed with high temporal vegetation index data at 1000m spatial resolution from Indian geostationary satellite (INSAT 3A) to track progress of country-scale rabi (post-rainy) crop area in six agriculturally dominant states of India.
References
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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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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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