The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-3/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 467–472, 2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 467–472, 2015

  20 Aug 2015

20 Aug 2015


S. Jay, R. Bendoula, X. Hadoux, and N. Gorretta S. Jay et al.
  • UMR ITAP, Irstea, 361 rue J.F. Breton, 34196 Montpellier, France

Keywords: Close-range remote sensing, Hyperspectral, Leaf optical properties, Radiative transfer model, Vegetation

Abstract. Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we present a method for estimating leaf optical properties from hyperspectral images having a spatial resolution of a few millimeters or centimeters. In presence of a single light source assumed to be directional, it is shown that leaf hyperspectral measurements can be related to the directional hemispherical reflectance simulated by the PROSPECT radiative transfer model using two other parameters. The first one is a multiplicative term that is related to local leaf angle and illumination zenith angle. The second parameter is an additive specular-related term that models BRDF effects.

Our model was tested on visible and near infrared hyperspectral images of leaves of various species, that were acquired under laboratory conditions. Introducing these two additional parameters into the inversion scheme leads to improved estimation results of PROSPECT parameters when compared to original PROSPECT. In particular, the RMSE for local chlorophyll content estimation was reduced by 21% (resp. 32%) when tested on leaves placed in horizontal (resp. sloping) position. Furthermore, inverting this model provides interesting information on local leaf angle, which is a crucial parameter in classical remote-sensing.