On the bias of instantaneous FAPAR estimates in open-canopy forests.

Abstract Global products of the fraction of absorbed photosynthetically active radiation (FAPAR) are operationally available from a variety of space agencies. A proper validation of these products is essential and hinges on the acquisition of accurate ground-based FAPAR estimates of the vegetation contained within the field of view of the space sensor at the time of satellite overpass. Often remotely sensed FAPAR products are defined with respect to theoretical rather than ambient illumination conditions which complicates in situ validation efforts. Similarly, the spatial complexity and substantial heights of certain plant environments may prevent the reliable sampling of certain radiation fluxes. As a consequence, many field campaigns are carried out on agricultural crops or within young tree plantations where canopy height is not an issue. This contribution compares different approaches for estimating instantaneous FAPAR in tall, open-canopy forest stands under a variety of architectural, spectral and illumination related conditions. The bias associated with these estimations is separated into a sampling error and a transfer bias. The former relates to the impact of both the number and location of the measurements whereas the latter addresses the quality of the theory that relates these measurements to the actual canopy FAPAR. Among the various methods tested it was the 2-flux FAPAR estimator (1 − TPAR) that performs best in open forest canopies under typical summer conditions. The quality of the 1 − TPAR canopy FAPAR estimator changes, however, with illumination conditions, foliage colour and especially with the background brightness. Similarly, the smaller the size of the area for which the FAPAR is to be estimated the larger the variability of the bias is going to be (and this irrespective of the choice of in situ estimation techniques). Evidence is provided that working under overcast sky conditions will reduce the sampling error but may well increase the transfer bias when compared to clear sky conditions. A parametric relationship is developed that allows to predict the instantaneous canopy FAPAR for arbitrary diffuse-to-total-incident-radiation ratios (at any given solar zenith angle). This approach has a similar transfer bias as the 1 − TPAR method when the forest floor is dark but dramatically outperforms the 2-flux approach under snowy background conditions (RMSE = 0.9934 versus 0.5801, respectively). The number of samples acquired was found to be crucial in reducing the variability of the bias of a given FAPAR estimator. Both random and grid-based sampling schemes result in similar FAPAR biases but do not lend themselves easily to the acquisition of hundreds of data points needed for reliable estimations under direct-only illumination conditions. Transect sampling—which is shown to deliver best results if carried out at ninety degrees to the solar azimuth angle—appears ideally suited to acquire the necessary numbers of samples enabling the generation of accurate quasi-instantaneous FAPAR estimates in open-canopy forests.

[1]  Frédéric Baret,et al.  Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography , 2004 .

[2]  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 .

[3]  Zhi-min Liu,et al.  [Research progress on plant diversity conservation in sand dune areas]. , 1982, Ying yong sheng tai xue bao = The journal of applied ecology.

[4]  G. Asrar,et al.  Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat1 , 1984 .

[5]  P. Jarvis,et al.  Radiation interception measurement in poplar: sample size and comparison between tube solarimeters and quantum sensors , 1997 .

[6]  G. Russell,et al.  Plant Canopies: Their Growth, Form and Function: Absorption of radiation by canopies and stand growth , 1989 .

[7]  E. Vermote,et al.  Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part II. Homogeneous Lambertian and anisotropic surfaces. , 2007, Applied optics.

[8]  Hideki Kobayashi,et al.  A coupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape , 2008 .

[9]  R. Fensholt,et al.  Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements , 2004 .

[10]  Josep Peñuelas,et al.  ESTIMATION OF CANOPY PHOTOSYNTHETIC AND NONPHOTOSYNTHETIC COMPONENTS FROM SPECTRAL TRANSMITTANCE , 2000 .

[11]  Frédéric Baret,et al.  Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Graham Russell,et al.  Plant Canopies: Their Growth, Form and Function: Contents , 1989 .

[13]  C. Woodcock,et al.  Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland , 2004 .

[14]  D. Matt,et al.  The Distribution of Solar Radiation within a Deciduous Forest , 1977 .

[15]  M. Sulev,et al.  Sources of errors in measurements of PAR , 2000 .

[16]  William J. Emery,et al.  Optimal sampling conditions for estimating grassland parameters via reflectance , 1996, IEEE Trans. Geosci. Remote. Sens..

[17]  Paul J. Curran,et al.  Spatial correlation in reflected radiation from the ground and its implications for sampling and mapping by ground-based radiometry , 1989 .

[18]  Ranga B. Myneni,et al.  Time‐series validation of MODIS land biophysical products in a Kalahari woodland, Africa , 2005 .

[19]  Serge Collineau,et al.  2 – The Physical Nature of Solar Radiation in Heterogeneous Canopies: Spatial and Temporal Attributes , 1994 .

[20]  L. Alados-Arboledas,et al.  Parametric models to estimate photosynthetically active radiation in Spain. , 2000 .

[21]  N. Gobron,et al.  On the need to observe vegetation canopies in the near-infrared to estimate visible light absorption , 2009 .

[22]  S. Prince A model of regional primary production for use with coarse resolution satellite data , 1991 .

[23]  Will Steffen,et al.  Establishing A Earth Observation Product Service For The Terrestrial Carbon Community: The Globcarbon Initiative , 2006 .

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

[25]  N. Gobron,et al.  Global-Scale Drought Caused Atmospheric CO2 Increase , 2005 .

[26]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[27]  Nadine Gobron,et al.  Towards a high spatial resolution limit for pixel-based interpretations of optical remote sensing data , 2008 .

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

[29]  Nadine Gobron,et al.  Radiation transfer model intercomparison (RAMI) exercise , 2001 .

[30]  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.

[31]  J. Goudriaan,et al.  SEPARATING THE DIFFUSE AND DIRECT COMPONENT OF GLOBAL RADIATION AND ITS IMPLICATIONS FOR MODELING CANOPY PHOTOSYNTHESIS PART I. COMPONENTS OF INCOMING RADIATION , 1986 .

[32]  D. Baldocchi,et al.  Leaf area distribution and radiative transfer in open-canopy forests: implications for mass and energy exchange. , 2001, Tree physiology.

[33]  F. W. Bell,et al.  Comparison of photosynthetically active radiation and cover estimation for measuring the effects of interspecific competition on jack pine seedlings , 1999 .

[34]  E. Walter-Shea,et al.  Biophysical properties affecting vegetative canopy reflectance and absorbed photosynthetically active radiation at the FIFE site , 1992 .

[35]  Jean-Luc Widlowski,et al.  Third Radiation Transfer Model Intercomparison (RAMI) exercise: Documenting progress in canopy reflectance models , 2007 .

[36]  S. T. Gower,et al.  Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems , 1999 .

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

[38]  N. Gobron,et al.  An automatic procedure to identify key vegetation phenology events using the JRC-FAPAR products , 2008 .

[39]  J. C. Price,et al.  Examples of high resolution visible to near-infrared reflectance spectra and a standardized collection for remote sensing studies , 1995 .

[40]  Jing M. Chen,et al.  Canopy architecture and remote sensing of the fraction of photosynthetically active radiation absorbed by boreal conifer forests , 1996, IEEE Trans. Geosci. Remote. Sens..

[41]  Philippe Martin,et al.  A Model of Light Scattering in Three-Dimensional Plant Canopies: a Monte Carlo Ray Tracing Approach , 2007 .

[42]  Richard H. Grant,et al.  Photosynthetically-active radiation: sky radiance distributions under clear and overcast conditions , 1996 .

[43]  K. I. Kondratʹev Radiation processes in the atmosphere , 1972 .

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

[45]  N. Breda Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. , 2003, Journal of experimental botany.

[46]  Nadine Gobron,et al.  Uniqueness of multiangular measurements. I. An indicator of subpixel surface heterogeneity from MISR , 2002, IEEE Trans. Geosci. Remote. Sens..

[47]  John A. Gamon,et al.  A mobile tram system for systematic sampling of ecosystem optical properties , 2006 .

[48]  Nadine Gobron,et al.  Horizontal radiation transport in 3-D forest canopies at multiple spatial resolutions: Simulated impact on canopy absorption , 2006 .

[49]  M. Awal,et al.  Radiation interception and use by maize/peanut intercrop canopy , 2006 .

[50]  J. Ross The radiation regime and architecture of plant stands , 1981, Tasks for vegetation sciences 3.

[51]  Scott J. Goetz,et al.  Validation of MODIS F/sub PAR/ products in boreal forests of Alaska , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[52]  Valerie A. Thomas,et al.  Spatial modelling of the fraction of photosynthetically active radiation absorbed by a boreal mixedwood forest using a lidar–hyperspectral approach , 2006 .

[53]  J. Chen Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands , 1996 .

[54]  John J. Barnett,et al.  Traceable Radiometry Underpinning Terrestrial- and Helio-Studies (TRUTHS): An Element of a Space-Based Climate and Calibration Observatory , 2003, Remote. Sens..

[55]  C. S. T. Daughtry,et al.  Techniques for Measuring Intercepted and Absorbed Photosynthetically Active Radiation in Corn Canopies1 , 1986 .

[56]  Oliver Sonnentag,et al.  Leaf area index measurements at Fluxnet-Canada forest sites , 2006 .

[57]  William E. Reifsnyder,et al.  Spatial and temporal distribution of solar radiation beneath forest canopies , 1970 .

[58]  Jean-Luc Widlowski,et al.  The RAMI On-line Model Checker (ROMC): A web-based benchmarking facility for canopy reflectance models , 2008 .

[59]  H. Rahman,et al.  Coupled surface‐atmosphere reflectance (CSAR) model: 1. Model description and inversion on synthetic data , 1993 .

[60]  D. Baldocchi,et al.  Seasonal variation in the statistics of photosynthetically active radiation penetration in an oak-hickory forest , 1986 .

[61]  Lu Su,et al.  Radiation Transfer Model Intercomparison (RAMI) exercise: Results from the second phase , 2004 .

[62]  Michel M. Verstraete,et al.  Raytran: a Monte Carlo ray-tracing model to compute light scattering in three-dimensional heterogeneous media , 1998, IEEE Trans. Geosci. Remote. Sens..

[63]  Elizabeth A. Walter-Shea,et al.  The EOS Prototype Validation Exercise (PROVE) at Jornada: Overview and Lessons Learned , 2000 .

[64]  Benjamin Smith,et al.  Combining remote sensing data with process modelling to monitor boreal conifer forest carbon balances , 2008 .