Characterization of seasonal variation of forest canopy in a temperate deciduous broadleaf forest, using daily MODIS data

In this paper, we present an improved procedure for collecting no or little atmosphere- and snow-contaminated observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The resultant time series of daily MODIS data of a temperate deciduous broadleaf forest (the Bartlett Experimental Forest) in 2004 show strong seasonal dynamics of surface reflectance of green, near infrared and shortwave infrared bands, and clearly delineate leaf phenology and length of plant growing season. We also estimate the fractions of photosynthetically active radiation (PAR) absorbed by vegetation canopy (FAPARcanopy), leaf (FAPARleaf), and chlorophyll (FAPARchl), respectively, using a coupled leafcanopy radiative transfer model (PROSAIL-2) and daily MODIS data. The Markov Chain Monte Carlo (MCMC) method (the Metropolis algorithm) is used for model inversion, which provides probability distributions of the retrieved variables. A two-step procedure is used to estimate the fractions of absorbed PAR: (1) to retrieve biophysical and biochemical variables from MODIS images using the PROSAIL-2 model; and (2) to calculate the fractions with the estimated model variables from the first step. Inversion and forward simulations of the PROSAIL-2 model are carried out for the temperate deciduous broadleaf forest during day of year (DOY) 184 to 201 in 2005. The reproduced reflectance values from the PROSAIL-2 model agree well with the observed MODIS reflectance for the five spectral bands (green, red, NIR1, NIR2, and SWIR1). The estimated leaf area index, leaf dry matter, leaf chlorophyll content and FAPARcanopy values are close to field measurements at the site. The results also showed significant differences between FAPARcanopy and FAPARchl at the site. Our results show that MODIS imagery provides important information on biophysical and biochemical variables at both leaf and canopy levels.

[1]  Lorraine Remer,et al.  Angular and seasonal variation of spectral surface reflectance ratios: implications for the remote sensing of aerosol over land , 2001, IEEE Trans. Geosci. Remote. Sens..

[2]  Yoram J. Kaufman,et al.  Remote sensing of subpixel snow cover using 0.66 and 2.1 μm channels , 2002 .

[3]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[4]  David M. Lawrence,et al.  An annual cycle of vegetation in a GCM. Part I: implementation and impact on evaporation , 2004 .

[5]  S. Ollinger,et al.  Net Primary Production and Canopy Nitrogen in a Temperate Forest Landscape: An Analysis Using Imaging Spectroscopy, Modeling and Field Data , 2005, Ecosystems.

[6]  Andrew J. Challinor,et al.  Influence of vegetation on the local climate and hydrology in the tropics: sensitivity to soil parameters , 2004 .

[7]  Roger A. Pielke,et al.  Interactions between the atmosphere and terrestrial ecosystems: influence on weather and climate , 1998 .

[8]  S. Ustin,et al.  Estimating leaf biochemistry using the PROSPECT leaf optical properties model , 1996 .

[9]  Andrew D Richardson,et al.  Reflectance of Alaskan black spruce and white spruce foliage in relation to elevation and latitude. , 2003, Tree physiology.

[10]  S. Ustin,et al.  Seasonal changes observed in AVIRIS images of Jasper Ridge, California , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[11]  A. Huete,et al.  Normalization of multidirectional red and NIR reflectances with the SAVI , 1992 .

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

[13]  M. Steven,et al.  Evaluation of an improved version of SAIL model for simulating bidirectional reflectance of sugar beet canopies , 1997 .

[14]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[15]  S. Frolking,et al.  Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest , 2005 .

[16]  S. Goward,et al.  Global Primary Production: A Remote Sensing Approach , 1995 .

[17]  J. Townshend,et al.  Carbon emissions from tropical deforestation and regrowth based on satellite observations for the 1980s and 1990s , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[18]  N. Goel,et al.  Evaluation of a Canopy Reflectance Model for LAI Estimation through Its Inversion , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[19]  S. Goward,et al.  Vegetation canopy PAR absorptance and the normalized difference vegetation index - An assessment using the SAIL model , 1992 .

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

[21]  P. Bicheron.  A Method of Biophysical Parameter Retrieval at Global Scale by Inversion of a Vegetation Reflectance Model , 1999 .

[22]  D. Diner,et al.  Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere‐corrected MISR data , 1998 .

[23]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .

[24]  N. Pettorelli,et al.  Using the satellite-derived NDVI to assess ecological responses to environmental change. , 2005, Trends in ecology & evolution.

[25]  Naoto Matsumura,et al.  Seasonal patterns of canopy structure, biochemistry and spectral reflectance in a broad-leaved deciduous Fagus crenata canopy , 2002 .

[26]  K. E. Moore,et al.  Climatic Consequences of Leaf Presence in the Eastern United States , 2001 .

[27]  M. Weiss,et al.  Reliability of the estimation of vegetation characteristics by inversion of three canopy reflectance models on airborne POLDER data , 2002 .

[28]  W. Verhoef,et al.  Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models , 2003 .

[29]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

[30]  Eric F. Lambin,et al.  Land‐cover change and vegetation dynamics across Africa , 2005 .

[31]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modeling: The Scattering by Arbitrarily Inclined Leaves (SAIL) model , 1984 .

[32]  W. Cohen,et al.  Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation , 2003 .

[33]  Gérard Dedieu,et al.  TURC: A diagnostic model of continental gross primary productivity and net primary productivity , 1996 .

[34]  S. Ustin,et al.  Water content estimation in vegetation with MODIS reflectance data and model inversion methods , 2003 .

[35]  J. Privette,et al.  Extracting ecological and biophysical information from AVHRR optical data: An integrated algorithm based on inverse modeling , 1996 .

[36]  Yujie Wang.  Assessment of the MODIS LAI and FPAR algorithm: Retrieval quality, theoretical basis and validation , 2002 .

[37]  R. Colwell.  Remote sensing of the environment , 1980, Nature.

[38]  J. Randerson,et al.  Terrestrial ecosystem production: A process model based on global satellite and surface data , 1993 .

[39]  R. Myneni,et al.  Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data , 2000 .

[40]  Valérie Demarez,et al.  Seasonal variation of leaf chlorophyll content of a temperate forest. Inversion of the PROSPECT model , 1999 .

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

[42]  W. Verhoef.  Earth observation modelling based on layer scattering matrices , 1984 .

[43]  Richard L. Thompson,et al.  Inversion of vegetation canopy reflectance models for estimating agronomic variables. V. Estimation of leaf area index and average leaf angle using measured canopy reflectances , 1984 .

[44]  V. Salomonson.  The Moderate Resolution Imaging Spectrometer (MODIS) , 1990 .

[45]  Samuel N. Goward,et al.  Transient Effects of Climate on Vegetation Dynamics: Satellite Observations , 1995 .

[46]  David M. Lawrence,et al.  An annual cycle of vegetation in a GCM. Part II: global impacts on climate and hydrology , 2004 .

[47]  Andrew D Richardson,et al.  Spectral reflectance and photosynthetic properties of Betula papyrifera (Betulaceae) leaves along an elevational gradient on Mt. Mansfield, Vermont, USA. , 2002, American journal of botany.

[48]  G. Hurtt,et al.  A pelagic ecosystem model calibrated with BATS data , 1996 .

[49]  S. Running,et al.  Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active , 1998 .

[50]  F. Baret,et al.  Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies , 2002 .

[51]  A. Gitelson,et al.  Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction , 2002 .

[52]  Ernst Linder,et al.  Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations , 2005 .

[53]  Frank Veroustraete,et al.  Seasonal variations in leaf area index, leaf chlorophyll, and water content; scaling-up to estimate fAPAR and carbon balance in a multilayer, multispecies temperate forest. , 1999, Tree physiology.

[54]  F. Baret,et al.  Effect of senescent leaves on NDVI-based estimates of fAPAR: Experimental and modelling evidences , 2004 .

[55]  B. Braswell.  Global terrestrial biogeochemistry: Perturbations, interactions, and time scales , 1996 .

[56]  Nadine Gobron,et al.  Joint retrieval of vegetation structure and photosynthetic activity from MISR , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[57]  C. Bacour,et al.  Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. , 2000 .

[58]  J. L. Lovell,et al.  Filtering Pathfinder AVHRR Land NDVI data for Australia , 2001 .

[59]  Richard L. Thompson,et al.  Inversion of vegetation canopy reflectance models for estimating agronomic variables. IV. Total inversion of the SAIL model , 1984 .

[60]  Steven W. Running,et al.  Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products , 2003 .

[61]  Gregory P. Asner,et al.  SCALE DEPENDENCE OF ABSORPTION OF PHOTOSYNTHETICALLY ACTIVE RADIATION IN TERRESTRIAL ECOSYSTEMS , 1998 .

[62]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

[63]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[64]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[65]  F. Baret,et al.  Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model , 2005 .

[66]  S. Goward,et al.  Evaluation of the NOAA/NASA Pathfinder AVHRR Land Data Set for global primary production , 1995 .

[67]  S. Wofsy,et al.  Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data , 2004 .

[68]  Nadine Gobron,et al.  Advanced vegetation indices optimized for up-coming sensors: Design, performance, and applications , 2000, IEEE Trans. Geosci. Remote. Sens..

[69]  W. Verhoef,et al.  Comparative study of suits and sail canopy reflectance models , 1985 .

[70]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[71]  C. L. Wiegand,et al.  Accuracy and sensitivity analyses of SAIL model-predicted reflectance of maize , 1992 .

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

[73]  Gilles Lemaire,et al.  Diagnosis of the Nitrogen Status in Crops , 1997, Springer Berlin Heidelberg.

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

[75]  Qin Wenhan,et al.  Modeling bidirectional reflectance of multicomponent vegetation canopies , 1993 .

[76]  Frédéric Baret,et al.  Radiometric Estimates of Nitrogen Status of Leaves and Canopies , 1997 .

[77]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[78]  W. Oechel,et al.  Seasonal patterns of reflectance indices, carotenoid pigments and photosynthesis of evergreen chaparral species , 2002, Oecologia.