Global mapping of vegetation parameters from POLDER multiangular measurements for studies of surface‐atmosphere interactions: A pragmatic method and its validation

Abstract

[1] This paper presents a pragmatic method to produce global maps of vegetation parameters, which offer essential data for weather forecast and climate modeling. The crucial variables are leaf area index (LAI), fractional vegetation cover (FVC), and fraction of absorbed photosynthetically active radiation (fAPAR). The approach relies on the use of spectral and directional vegetation indices simulated by a bidirectional reflectance model and calibrated against sets of satellite data. The model belongs to the kernel-driven category, and the coefficients obtained, as the result of linear inversion, are the basis of the proposed method. The strategy presented relies upon the existence of suitable angular measurements to derive each biophysical parameter. An application is shown with the global POLDER/ADEOS-I database. Special attention is given here to the future production of LAI and fAPAR since the albedo is a product already disseminated by the POLDER production center. Terrestrial ecosystems show a high level of aggregation and, in practice, only effective LAI can be measured. Therefore a correction factor, namely the clumping index, must be applied to help resolve the scaling issue. Clumping corrections are performed biome by biome, using empirical equations where it appears that LAI assessments for boreal and tropical forests would otherwise be significantly inaccurate. However, the effect of clumping will be less on FVC and fAPAR. The relevance of the proposed method is demonstrated through a comparison of POLDER-derived LAI values with a varied set of ground LAI measurements, including their coherence with the corresponding fAPAR.

Analysis of leaf area index in the ECMWF land surface model and impact on latent heat and carbon fluxes: Application to West Africa

“…Version 3 algorithm used recalibrated and atmospheric corrected reflectances. The LAI is derived from fCover using the semiempirical approach of Roujean and Lacaze [2002]. Further details can be found in the algorithm description [Lacaze, 2004] (available at http://postel.mediasfrance.org/IMG/pdf/CYCL_ATBD-DirectionalNormalisation_I2.0.pdf) and in the work of Baret et al [2008].…”

CO<sub>2</sub> budgeting at the regional scale using a Lagrangian experimental strategy and meso-scale modeling

“…The Leaf Area Index (LAI) was taken from the remote sensing observations of the Normalized Difference Vegetation Index (NDVI) from the MODIS sensor (Roujean and Lacaze, 2002).…”

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

“…However, because of the complexity of the physical models, direct inversion is generally complex. Usually, machine learning methods, such as artificial neural networks (ANNs) and lookup table method (LUT), are used for indirect inversion of the physical models by training with a pre-computed reflectance database from the physical models [22]. Machine learning methods have the advantages of computational efficiency and robustness to noisy data and can approximate multivariate nonlinear relationships, which make them popular choices for large-area FVC estimation from remote-sensing data [1,[23][24][25].…”

“…However, in terms of FVC products, the current FVC products were obtained mainly from low-or medium-resolution remote sensing data such as SPOT-VGT, SEAWIFS, MERIS, MODIS and AVHRR data [1,22,[28][29][30], which limits the FVC applications to the regional and local scales [31]. The development of FVC products from decametric spatial resolution sensors will be better for addressing these applications closely related to agriculture, ecosystem and environmental management.…”