Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements

[1] In the last two decades the availability of global remote sensing data sets has provided a new means of studying global patterns and dynamics in vegetation. The vast majority of previous work in this domain has used data from the Advanced Very High Resolution Radiometer, which until recently was the primary source of global land remote sensing data. In recent years, however, a number of new remote sensing data sources have become available that have significantly improved the capability of remote sensing to monitor global ecosystem dynamics. In this paper, we describe recent results using data from NASA's Moderate Resolution Imaging Spectroradiometer to study global vegetation phenology. Using a novel new method based on fitting piecewise logistic models to time series data from MODIS, key transition dates in the annual cycle(s) of vegetation growth can be estimated in an ecologically realistic fashion. Using this method we have produced global maps of seven phenological metrics at 1-km spatial resolution for all ecosystems exhibiting identifiable annual phenologies. These metrics include the date of year for (1) the onset of greenness increase (greenup), (2) the onset of greenness maximum (maturity), (3) the onset of greenness decrease (senescence), and (4) the onset of greenness minimum (dormancy). The three remaining metrics are the growing season minimum, maximum, and summation of the enhanced vegetation index derived from MODIS. Comparison of vegetation phenology retrieved from MODIS with in situ measurements shows that these metrics provide realistic estimates of the four transition dates identified above. More generally, the spatial distribution of phenological metrics estimated from MODIS data is qualitatively realistic, and exhibits strong correspondence with temperature patterns in mid- and high-latitude climates, with rainfall seasonality in seasonally dry climates, and with cropping patterns in agricultural areas.

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

[2]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

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

[4]  V. Arora.  MODELING VEGETATION AS A DYNAMIC COMPONENT IN SOIL‐VEGETATION‐ATMOSPHERE TRANSFER SCHEMES AND HYDROLOGICAL MODELS , 2002 .

[5]  J. Privette,et al.  Impact of Tissue, Canopy, and Landscape Factors on the Hyperspectral Reflectance Variability of Arid Ecosystems , 2000 .

[6]  Wolfgang Cramer,et al.  INCORPORATING DYNAMIC VEGETATION COVER WITHIN GLOBAL CLIMATE MODELS , 2000 .

[7]  C. D. Keeling,et al.  Increased activity of northern vegetation inferred from atmospheric CO2 measurements , 1996, Nature.

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

[9]  Jeffrey W. White,et al.  Adaptation of the CROPGRO growth model to velvet bean (Mucuna pruriens): II. Cultivar evaluation and model testing , 2002 .

[10]  J. Keatinge,et al.  Effects of temperature and photoperiod on phenology as a guide to the selection of annual legume cover and green manure crops for hillside farming systems , 1998 .

[11]  D. Randall,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation , 1996 .

[12]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications , 2002 .

[13]  James Rowland,et al.  Assessing land cover performance in Senegal, West Africa using 1-km integrated NDVI and local variance analysis , 2004 .

[14]  Compton J. Tucker,et al.  Seasonality and trends of snow‐cover, vegetation index, and temperature in northern Eurasia , 2002 .

[15]  P. J. Curran,et al.  Multispectral remote sensing for the estimation of green leaf area index , 1983, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[16]  Rui Sun,et al.  Evapotranspiration estimation in the Yellow River Basin, China using integrated NDVI data , 2004 .

[17]  Zhao-Liang Li,et al.  Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data , 2002 .

[18]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[19]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[20]  Rick L. Lawrence,et al.  Wheat yield estimates using multi-temporal NDVI satellite imagery , 2002 .

[21]  Dennis D. Baldocchi,et al.  Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous forest in North America , 2000 .

[22]  Jiri Nekovar,et al.  Phenology in central Europe – differences and trends of spring phenophases in urban and rural areas , 2000, International journal of biometeorology.

[23]  Harry H. Hendon,et al.  A Composite Study of Onset of the Australian Summer Monsoon , 1990 .

[24]  C. J. Tucker,et al.  Relationship between atmospheric CO2 variations and a satellite-derived vegetation index , 1986, Nature.

[25]  Rikie Suzuki,et al.  West–east contrast of phenology and climate in northern Asia revealed using a remotely sensed vegetation index , 2003, International journal of biometeorology.

[26]  G. Posse,et al.  A test of the use of NDVI data to predict secondary productivity , 2004 .

[27]  N. Delbart,et al.  Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982-2004) , 2006 .

[28]  P. Sellers.  Canopy reflectance, photosynthesis and transpiration , 1985 .

[29]  C. Tucker,et al.  North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer , 1985, Vegetatio.

[30]  R. Burgan,et al.  Evaluation of NDVI for monitoring live moisture in three vegetation types of the Western U.S. , 1999 .

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

[32]  G. Badhwar,et al.  Automatic corn-soybean classification using Landsat MSS data. I - Near-harvest crop proportion estimation. II - Early season crop proportion estimation , 1984 .

[33]  Anders Knudby.  An AVHRR-based model of groundnut yields in the Peanut Basin of Senegal , 2004 .

[34]  G. Asner,et al.  Satellite observation of El Niño effects on Amazon Forest phenology and productivity , 2000 .

[35]  R. Primack,et al.  Herbarium specimens demonstrate earlier flowering times in response to warming in Boston. , 2004, American journal of botany.

[36]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 - Theoretical approach , 2002 .

[37]  S. Running,et al.  Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System , 2000 .

[38]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[39]  J. Paruelo,et al.  ANPP ESTIMATES FROM NDVI FOR THE CENTRAL GRASSLAND REGION OF THE UNITED STATES , 1997 .

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

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

[42]  Rudolf de Groot,et al.  The influence of temperature and climate change on the timing of pollen release in the Netherlands , 2002 .

[43]  H. Freeland,et al.  Spring phenology trends in Alberta, Canada: links to ocean temperature , 2000, International journal of biometeorology.

[44]  G. Yohe,et al.  A globally coherent fingerprint of climate change impacts across natural systems , 2003, Nature.

[45]  A. Strahler,et al.  Climate controls on vegetation phenological patterns in northern mid‐ and high latitudes inferred from MODIS data , 2004 .

[46]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[47]  I. C. Prentice,et al.  Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect , 2002, Science.

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

[49]  Ramakrishna R. Nemani,et al.  Regional hydrologic and carbon balance responses of forests resulting from potential climate change , 1991 .

[50]  M. S. Rasmussen.  Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR. , 1992 .

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

[52]  N. Delbart,et al.  El Niño–Southern Oscillation influences on the interannual variability of leaf appearance dates in central Siberia , 2006 .

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

[54]  J. Townshend,et al.  Global discrimination of land cover types from metrics derived from AVHRR pathfinder data , 1995 .

[55]  I. Leinonen,et al.  The importance of phenology for the evaluation of impact of climate change on growth of boreal, temperate and Mediterranean forests ecosystems: an overview , 2000, International journal of biometeorology.

[56]  A. Strahler,et al.  The footprint of urban climates on vegetation phenology , 2004 .