On the potential of CHRIS/PROBA for estimating vegetation canopy properties from space

Abstract

The Compact High Resolution Imaging Spectrometer (CHRIS), to be launched on board the PROBA (Project for On-Board Autonomy) satellite in 2001/2002, will provide remotely-sensed data for terrestrial and atmospheric applications. The mission is intended to demonstrate the potential of a compact, low-cost, imaging spectrometer when combined with a small, agile satellite platform. CHRIS will provide data in 18-62 user-selectable spectral channels in the range 400 nm to 1050 nm (1.25 nm -11 nm intervals) at a nominal spatial resolution of either 25 m or 50 m. Since PROBA can be pointed off-nadir in both the along-track and across-track directions, it will be possible to use CHRIS to sample the Bidirectional Reflectance Distribution Function (BRDF) of the land surface. This combination of an agile satellite and a highly configurable sensor offers the unique potential to acquire high spatial resolution, spectral BRDF data sets and, from these, to study the biophysical and biochemical properties of vegetation canopies. It will also provide an important means of validating similar data sets from other, coarser spatial resolution sensors, such as VEGETATION, POLDER2, MODIS and MISR. This paper presents key features of the instrument, and explores the potential of CHRIS for estimating canopy biophysical parameters from space by means of a LUT-based BRDF model inversion scheme.

Satellite-driven modelling of Net Primary Productivity (NPP): Theoretical analysis

“…Although these are not very realistic scenarios, they allow us to examine the effect of multiangular sensing in the most extreme case of backward and forward scattering. The range of spectral wavebands used here correspond to those employed by a range of current satellite-sensors (Table 2; AVHRR, MODIS, MISR, MERIS, VEGETATION and CHRIS), which have proved to be useful in the retrieval of surface biophysical properties (Abuelgasim et al, 2006;Barnsley et al, 2000;Knyazikhin et al, 1998b).…”

Investigating the Utility of Wavelet Transforms for Inverting a 3-D Radiative Transfer Model Using Hyperspectral Data to Retrieve Forest LAI

“…The parameter combination that yields the closest spectrum in the database is considered to be the inversion solution [26]. The major advantage of the LUT-based approaches is that the forward modeling is divorced from the inversion procedure, and hence can be used for inversion of any complex model like DART [15,27].…”

“…Weiss et al [32] reported that only a limited number of wavebands are required for canopy biophysical variable estimation. Other studies have stated that the selection of a subset of spectral bands can lead to a more stable and accurate inversion [27,31]. However, a general criterion for the selection of bands has not yet been defined.…”

Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer

“…While much work exists in the domain of retrieving the canopy biophysical variables from model inversion methods using a variety of optimization methods (Baret et al, 1995;Barnsley et al, 2000;Bicheron and Leroy, 1999;Combal et al, 2002;Goel et al, 1984;Jacquemoud & Baret, 1993;Knyazikhin et al, 1998a;Kuusk, 1991), applications of support vector machines in remote sensing problems are focused mainly towards classification (Banerjee et al, 2006;Durbha & King, 2005 Wohlberg et al, 2006) and to the best of our knowledge little work has been reported towards the application of support vector regression (SVR) for the retrieval of biophysical variables from inversion of canopy radiative transfer models.…”