Interannual variations and trends in global land surface phenology derived from enhanced vegetation index during 1982–2010

Land surface phenology is widely retrieved from satellite observations at regional and global scales, and its long-term record has been demonstrated to be a valuable tool for reconstructing past climate variations, monitoring the dynamics of terrestrial ecosystems in response to climate impacts, and predicting biological responses to future climate scenarios. This study detected global land surface phenology from the advanced very high resolution radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 1982 to 2010. Based on daily enhanced vegetation index at a spatial resolution of 0.05 degrees, we simulated the seasonal vegetative trajectory for each individual pixel using piecewise logistic models, which was then used to detect the onset of greenness increase (OGI) and the length of vegetation growing season (GSL). Further, both overall interannual variations and pixel-based trends were examined across Koeppen’s climate regions for the periods of 1982–1999 and 2000–2010, respectively. The results show that OGI and GSL varied considerably during 1982–2010 across the globe. Generally, the interannual variation could be more than a month in precipitation-controlled tropical and dry climates while it was mainly less than 15 days in temperature-controlled temperate, cold, and polar climates. OGI, overall, shifted early, and GSL was prolonged from 1982 to 2010 in most climate regions in North America and Asia while the consistently significant trends only occurred in cold climate and polar climate in North America. The overall trends in Europe were generally insignificant. Over South America, late OGI was consistent (particularly from 1982 to 1999) while either positive or negative GSL trends in a climate region were mostly reversed between the periods of 1982–1999 and 2000–2010. In the Northern Hemisphere of Africa, OGI trends were mostly insignificant, but prolonged GSL was evident over individual climate regions during the last 3 decades. OGI mainly showed late trends in the Southern Hemisphere of Africa while GSL was reversed from reduced GSL trends (1982–1999) to prolonged trends (2000–2010). In Australia, GSL exhibited considerable interannual variation, but the consistent trend lacked presence in most regions. Finally, the proportion of pixels with significant trends was less than 1 % in most of climate regions although it could be as large as 10 %.

[1]  Geoffrey M. Henebry,et al.  Land surface phenology and temperature variation in the International Geosphere–Biosphere Program high‐latitude transects , 2005 .

[2]  Andrew D Richardson,et al.  Near-surface remote sensing of spatial and temporal variation in canopy phenology. , 2009, Ecological applications : a publication of the Ecological Society of America.

[3]  Pavel Ya. Groisman,et al.  Prolonged Dry Episodes over the Conterminous United States: New Tendencies Emerging during the Last 40 Years , 2008 .

[4]  S. Running,et al.  A continental phenology model for monitoring vegetation responses to interannual climatic variability , 1997 .

[5]  H. Lieth,et al.  Modeling Important Phytophenological Events in Eastern North America , 1974 .

[6]  Youngwook Kim,et al.  Spectral compatibility of vegetation indices across sensors: band decomposition analysis with Hyperion data , 2010 .

[7]  J. Mustard,et al.  Green leaf phenology at Landsat resolution: Scaling from the field to the satellite , 2006 .

[8]  R. Stöckli,et al.  European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset , 2004 .

[9]  Stein Rune Karlsen,et al.  Satellite‐based mapping of the growing season and bioclimatic zones in Fennoscandia , 2006 .

[10]  Yanhong Tang,et al.  Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau , 2011 .

[11]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[12]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[13]  T. Sparks,et al.  The Responses of Species to Climate Over Two Centuries: An Analysis of the Marsham Phenological Record, 1736-1947 , 1995 .

[14]  Benjamin W. Heumann,et al.  AVHRR Derived Phenological Change in the Sahel and Soudan, Africa, 1982 - 2005 , 2007 .

[15]  Mark A. Friedl,et al.  Sensitivity of vegetation phenology detection to the temporal resolution of satellite data , 2009 .

[16]  Ramakrishna R. Nemani,et al.  A global framework for monitoring phenological responses to climate change , 2005 .

[17]  R. Tateishi,et al.  Analysis of phenological change patterns using 1982–2000 Advanced Very High Resolution Radiometer (AVHRR) data , 2004 .

[18]  C. Tucker,et al.  Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 , 2001 .

[19]  M. Schaepman,et al.  Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006 , 2009 .

[20]  H. Lieth Phenology and Seasonality Modeling , 1974, Ecological Studies.

[21]  Mark A. Friedl,et al.  Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements , 2006 .

[22]  C. Tucker,et al.  Coupled vegetation‐precipitation variability observed from satellite and climate records , 2003 .

[23]  A. Gitelson,et al.  AVHRR-Based Spectral Vegetation Index for Quantitative Assessment of Vegetation State and Productivity: Calibration and Validation , 2003 .

[24]  G. Powell,et al.  Terrestrial Ecoregions of the World: A New Map of Life on Earth , 2001 .

[25]  Mark A. Friedl,et al.  Drought-induced vegetation stress in southwestern North America , 2010 .

[26]  N. Delbart,et al.  Determination of phenological dates in boreal regions using normalized difference water index , 2005 .

[27]  José A. Sobrino,et al.  Global land surface phenology trends from GIMMS database , 2009 .

[28]  Xiangming Xiao,et al.  Land Surface Phenology , 2009 .

[29]  J. O'keefe,et al.  Phenology of a northern hardwood forest canopy , 2006 .

[30]  G. Dedieu,et al.  Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements , 1997 .

[31]  Adrian V. Rocha,et al.  Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes , 2009 .

[32]  S. Kalluri,et al.  The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring , 1994 .

[33]  Bradley C. Reed,et al.  Trend Analysis of Time-Series Phenology of North America Derived from Satellite Data , 2006 .

[34]  David P. Roy,et al.  Generating a long-term land data record from the AVHRR and MODIS Instruments , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[35]  Dan Tarpley,et al.  Diverse responses of vegetation phenology to a warming climate , 2007 .

[36]  Tomoaki Miura,et al.  Derivation of Relationships between Spectral Vegetation Indices from Multiple Sensors Based on Vegetation Isolines , 2012, Remote. Sens..

[37]  Jan de Leeuw,et al.  Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series , 2013, Remote. Sens..

[38]  Liang Liang,et al.  Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest , 2011 .

[39]  A. Fischer A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters , 1994 .

[40]  M. Goulden,et al.  Standing litter as a driver of interannual CO2 exchange variability in a freshwater marsh , 2008 .

[41]  Xiaoqiu Chen,et al.  Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China , 2005 .

[42]  F. Lauscher Neue Analysen ältester und neuerer phänologischer Reihen , 1978 .

[43]  D. Roy,et al.  The suitability of multi-temporal web-enabled Landsat data NDVI for phenological monitoring – a comparison with flux tower and MODIS NDVI , 2012 .

[44]  Douglas A. Wiens,et al.  Tilt recorded by a portable broadband seismograph: The 2003 eruption of Anatahan Volcano, Mariana Islands , 2005 .

[45]  Per Jönsson,et al.  Seasonality extraction by function fitting to time-series of satellite sensor data , 2002, IEEE Trans. Geosci. Remote. Sens..

[46]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[47]  S. Bruin,et al.  Analysis of monotonic greening and browning trends from global NDVI time-series , 2011 .

[48]  Larry L. Stowe,et al.  Scientific basis and initial evaluation of the CLAVR-1 global clear cloud classification algorithm f , 1999 .

[49]  J. Ardö,et al.  A recent greening of the Sahel—trends, patterns and potential causes , 2005 .

[50]  E. Vermote,et al.  Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views , 1995 .

[51]  G. Henebry,et al.  Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan , 2004 .

[52]  W. Hargrove,et al.  Toward a national early warning system for forest disturbances using remotely sensed canopy phenology , 2009 .

[53]  C. Tucker,et al.  Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999 , 2001, International journal of biometeorology.

[54]  Maosheng Zhao,et al.  Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009 , 2010, Science.

[55]  J. Eidenshink The 1990 conterminous U. S. AVHRR data set , 1992 .

[56]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..

[57]  S. Piao,et al.  Variations in Vegetation Net Primary Production in the Qinghai-Xizang Plateau, China, from 1982 to 1999 , 2006 .

[58]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[59]  Alan H. Strahler,et al.  Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments , 2005 .

[60]  D. Lloyd,et al.  A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery , 1990 .

[61]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[62]  A. Hopkins,et al.  Bioclimatics: A Science of Life and Climate Relations , 1938 .

[63]  A. Huete,et al.  Amazon rainforests green‐up with sunlight in dry season , 2006 .

[64]  Ranga B. Myneni,et al.  Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS , 2006 .

[65]  C. Tucker,et al.  Increased plant growth in the northern high latitudes from 1981 to 1991 , 1997, Nature.