Deriving land surface phenology indicators from CO2 eddy covariance measurements

Gonsamo, Alemu; Chen, Jing M.; D’Odorico, Petra

doi:10.1016/j.ecolind.2012.12.026

Cited by 88 publications

(91 citation statements)

References 16 publications

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“…We further refer to length of season (LOS) as the land surface phenology season length or the photosynthesis phenology season length. For the extraction of phenological transition dates we used an objective method based on analytical formulas we derived from the extremes of the first, second and third derivatives of the fitted logistic functions (Gonsamo et al, 2013a). For GPP, GF and PI time series, SOS, SOP, EOP and EOS dates are derived as:…”

Section: Extraction Of Land Surface Phenology Datesmentioning

confidence: 99%

“…Eddy covariance (EC) measurements of carbon dioxide (CO 2 ) exchange between terrestrial ecosystems and the atmosphere for numerous sites offers a spatially and temporally broad perspective for extracting photosynthesis phenological dates through gross primary productivity (GPP) (Garrity et al, 2011;Gonsamo et al, 2013aGonsamo et al, , 2012aNoormets et al, 2009;Richardson et al, 2010). The long-term availability of CO 2 flux measurements at EC tower sites now allows for comparison with remote sensing based LSP estimates over significant time periods and at a spatial footprint similar to coarse and medium resolution satellite pixels.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The match and mismatch between photosynthesis and land surface phenology of deciduous forests

D’Odorico

Gonsamo

Gough

et al. 2015

Agricultural and Forest Meteorology

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Section: Extraction Of Land Surface Phenology Datesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The match and mismatch between photosynthesis and land surface phenology of deciduous forests

D’Odorico

Gonsamo

Gough

et al. 2015

Agricultural and Forest Meteorology

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“…It has been observed by many researchers in the past [22,24,31,32] that different LSP-SOS derivation methods provide different results and therefore no single method can be claimed to best describe the phenology from satellite NDVI data. In this context Schwartz et al [26] notes, "though all (methods of LSP-SOS are) assessing the start of spring vegetation growth in some fashion, are effectively measuring different processes".…”

Section: Determination Of Satellite Start Of Season (Lsp-sos)mentioning

confidence: 99%

“…The delayed moving average [6,9,26,28] method used in this study is established from auto regressive moving average (ARMA) models that compare the NDVI time series with its moving average to determine the start of season. The derivatives, namely 1st [37][38][39], 2nd [38,40] and 3rd derivatives [32,38], determine the start of season as the date of the maximum increase in the respective NDVI derivative curve. As explained by Tan et al, 2011 [38], the local maxima of 1st derivative corresponds to the maximum rate of increase of green up phase, whereas, the local maxima of 2nd and 3rd derivative corresponds to the beginning of green up.…”

Section: Determination Of Satellite Start Of Season (Lsp-sos)mentioning

confidence: 99%

Effects of Different Methods on the Comparison between Land Surface and Ground Phenology—A Methodological Case Study from South-Western Germany

2016

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Several methods exist for extracting plant phenological information from time series of satellite data. However, there have been only a few successful attempts to temporarily match satellite observations (Land Surface Phenology or LSP) with ground based phenological observations (Ground Phenology or GP). The classical pixel to point matching problem along with the temporal and spatial resolution of remote sensing data are some of the many issues encountered. In this study, MODIS-sensor's Normalised Differenced Vegetation Index (NDVI) time series data were smoothed using two filtering techniques for comparison. Several start of season (SOS) methods established in the literature, namely thresholds of amplitude, derivatives and delayed moving average, were tested for determination of LSP-SOS for broadleaf forests at a site in southwestern Germany using 2001-2013 time series of NDVI data. The different LSP-SOS estimates when compared with species-rich GP dataset revealed that different LSP-SOS extraction methods agree better with specific phases of GP, and the choice of data processing or smoothing strongly affects the LSP-SOS extracted. LSP methods mirroring late SOS dates, i.e., 75% amplitude and 1st derivative, indicated a better match in means and trends, and high, significant correlations of up to 0.7 with leaf unfolding and greening of late understory and broadleaf tree species. GP-SOS of early understory leaf unfolding partly were significantly correlated with earlier detecting LSP-SOS, i.e., 20% amplitude and 3rd derivative. Early understory SOS were, however, more difficult to detect from NDVI due to the lack of a high resolution land cover information.

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“…Conversely, vegetation plays a role in impacting the carbon cycle, thus completing a "feedback loop" with the climate [8][9][10][11]. For this reason, the response of vegetation dynamics, for different land cover types, to precipitation and temperature anomalies is a subject of current climate research [12][13][14][15][16][17][18][19], aimed at understanding (and predicting) how the biosphere interacts with the atmosphere through the carbon, water and energy cycles [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

On the Response of European Vegetation Phenology to Hydroclimatic Anomalies

2014

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Climate change is expected to alter vegetation and carbon cycle processes, with implications for ecosystems. Notably, understanding the sensitivity of vegetation to the anomalies of precipitation and temperature over different land cover classes and the corresponding temporal response is essential for improved climate prediction. In this paper, we analyze vegetation response to hydroclimatic forcings using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) derived from SeaWiFS (Sea-viewing Wide Field-of-view Sensor) (1998)(1999)(2000)(2001)(2002) and (Medium Resolution Imaging Spectrometer) (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)) satellite sensors at ∼1-km resolution. Based on land cover and pixel-wise analysis, we quantify the extent of the dependence of the FAPAR and, ultimately, the phenology on the anomalies of precipitation and temperature over Europe. Statistical tests are performed to establish where this correlation may be regarded as statistically significant. Furthermore, we assess a statistical link between the climate variables and a set of phenological metrics defined from FAPAR measurement. Variation in the phenological response to the unusual values of precipitation and temperature can be interpreted as the result of the balanced opposite effects of water and temperature on vegetation processes. Results suggest very different responses for different land cover classes and seasons. Correlation analysis also indicates that European phenology may be quite sensitive to perturbations in precipitation and temperature regimes, such as those induced by climate change.Remote Sens. 2014, 6 3144

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Deriving land surface phenology indicators from CO2 eddy covariance measurements

Cited by 88 publications

References 16 publications

The match and mismatch between photosynthesis and land surface phenology of deciduous forests

The match and mismatch between photosynthesis and land surface phenology of deciduous forests

Effects of Different Methods on the Comparison between Land Surface and Ground Phenology—A Methodological Case Study from South-Western Germany

On the Response of European Vegetation Phenology to Hydroclimatic Anomalies

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