A Method of Biophysical Parameter Retrieval at Global Scale by Inversion of a Vegetation Reflectance Model

Bicheron, P.

doi:10.1016/s0034-4257(98)00083-2

Cited by 121 publications

(63 citation statements)

References 42 publications

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“…canopy structure and vegetation type) or can be obtained from mathematical inversion of reflectance measurements (e.g. Wanner et al, 1995), allowing the estimation of both leaf and canopy parameters in a predictive mode, thereby overcoming the need of parameterisation required for the use of regressive semi-empirical models (Privette et al, 1996, Bicheron andLeroy 1999). Over the last decade, models allowing for parameter acquisition from mathematical inversion have arisen as the most promising technique for retrieving f PAR (Meroni et al, 2004) and several studies have been successfully conducted for crop and forestred environments (e.g.…”

Section: Physical Models For Determination Of Absorbed Parmentioning

confidence: 99%

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Hilker

Coops

Wulder

et al. 2008

Science of The Total Environment

255

187

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Section: Physical Models For Determination Of Absorbed Parmentioning

confidence: 99%

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Hilker

Coops

Wulder

et al. 2008

Science of The Total Environment

255

187

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“…To reduce the dimensionality of the inverse problem and to assess the canopy biochemistry, SAIL was coupled with PROSPECT to derive PROSAIL (Baret et al 1992). The inversion of PROSAIL allows the prediction of both leaf and canopy parameters (Bicheron and Leroy 1999;Jacquemoud et al 2009;Privette et al 1996). Mathematically, the inversion of PROSAIL is a non-linear minimization problem that can be solved through iterative adjustment of estimated a priori inputs (Verstraete et al 1996).…”

Section: Inversion Of the Prosail Canopy Modelmentioning

confidence: 99%

“…Image spectroscopy is a powerful technique for monitoring plant physiological properties and measuring changes in plant pigment contents over time (Bicheron and Leroy 1999). Broadly, remote sensing of leaf and canopy biochemical composition can be classified into empirical, semi-analytical and analytical approaches (Ustin et al 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Analytical techniques infer biochemical properties from the inversion of canopy reflectance (CR) models, which, based on radiative transfer theory and coupled with leaf optical models, simulate the reflectance and the transmittance of a leaf as a function of its constituents (Meroni et al 2004). While these models require additional input parameters to determine the radiative transfer through the canopy, they allow the estimation of both leaf and canopy parameters from model inversion, thereby overcoming the need to parameterize empirical models (Bicheron and Leroy 1999;Jacquemoud et al 2009;Privette et al 1996).…”

Section: Introductionmentioning

confidence: 99%

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Tracking plant physiological properties from multi-angular tower-based remote sensing

et al. 2011

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Imaging spectroscopy is a powerful technique for monitoring the biochemical constituents of vegetation and is critical for understanding the fluxes of carbon and water between the land surface and the atmosphere. However, spectral observations are subject to the sunobserver geometry and canopy structure which impose confounding effects on spectral estimates of leaf pigments. For instance, the sun-observer geometry influences the spectral brightness measured by the sensor. Likewise, when considering pigment distribution at the stand level scale, the pigment content observed from single view angles may not necessarily be representative of stand-level conditions as some constituents vary as a function of the degree of leaf illumination and are therefore not isotropic. As an alternative to mono-angle observations, multi-angular remote sensing can describe the anisotropy of surface reflectance and yield accurate information on canopy structure. These observations can also be used to describe the bi-directional reflectance distribution which then allows the modeling of reflectance independently of the observation geometry. In this paper, we demonstrate a method for estimating pigment contents of chlorophyll and carotenoids continuously over a year from tower-based, multi-angular spectro-radiometer observations. Estimates of chlorophyll and carotenoid content were derived at two flux-tower sites in western Canada. Pigment contents derived from inversion of a CR model (PROSAIL) compared well to those estimated using a semi-analytical approach (r 2 = 0.90 and r 2 = 0.69, P \ 0.05 for both sites, respectively). Analysis of the seasonal dynamics indicated that net ecosystem productivity was strongly related to total canopy chlorophyll content at the deciduous site (r 2 = 0.70, P \ 0.001), but not at the coniferous site. Similarly, spectral estimates of photosynthetic light-use efficiency showed strong seasonal patterns in the deciduous stand, but not in conifers. We conclude that multi-angular, spectral observations can play a key role in explaining seasonal dynamics of fluxes of carbon and water and provide a valuable addition to fluxtower-based networks.

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“…Accurate LAI retrieval is always a key task of such remotesensing applications, and it is also a precondition of cropyield estimation, vegetative evapotranspiration calculation and the energy-exchange evaluation of terrestrial vegetation (Badhwar et al, 1986;Townshend et al, 1988;Baret et al, 1991;Wood et al, 1993;Bonan, 1995;Bicheron et al, 1999;Buermann et al, 2001;Kamalesh et al, 2008). In general, there are three categories of LAI retrieval methods.…”

Section: Introductionmentioning

confidence: 99%

Accurate LAI retrieval method based on PROBA/CHRIS data

Fan

Liu

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. Leaf area index (LAI) is one of the key structural variables in terrestrial vegetation ecosystems. Remote sensing offers an opportunity to accurately derive LAI at regional scales. The anisotropy of canopy reflectance, variations in background characteristics, and variability in atmospheric conditions constitute three factors that can strongly constrain the accuracy of retrieved LAI. Based on a hybrid canopy reflectance model, a new hyperspectral directional second derivative method (DSD) is proposed in this paper. This method can estimate LAI accurately through analyzing the canopy anisotropy. The effect of the background can also be effectively removed. With the aid of a widely-accepted atmospheric model, the influence of atmospheric conditions can be minimized as well. Thus the inversion precision and the dynamic range can be markedly improved, which has been proved by numerical simulations. As the derivative method is very sensitive to random noise, we put forward an innovative filtering approach, by which the data can be de-noised in spectral and spatial dimensions synchronously. It shows that the filtering method can remove random noise effectively; therefore, the method can be applied to hyperspectral images. The study region was situated in Zhangye, Gansu Province, China; hyperspectral and multi-angular images of the study region were acquired via the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA), on 4 June 2008. After the pre-processing procedures, the DSD method was applied, and the retrieved LAI was validated by ground reference data at 11 sites. Results show that the new LAI inversion method is accurate and effective with the aid of the innovative filtering method.

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A Method of Biophysical Parameter Retrieval at Global Scale by Inversion of a Vegetation Reflectance Model

Cited by 121 publications

References 42 publications

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Tracking plant physiological properties from multi-angular tower-based remote sensing

Accurate LAI retrieval method based on PROBA/CHRIS data

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