Improving land surface parameter retrieval by integrating plant traits priors in the MULTIPLY data assimilation platform

There is a growing demand for accurate land surface parameterization from remote sensing (RS) observations. This demand has not been satisfied, because most estimation schemes apply 1) a single-sensor single-scale approach, and 2) require specific key-variables to be `guessed'. This is because of the relevant observational information required to accurately retrieve parameters of interest. Consequently, many schemes assume specific variables to be constant or not present; subsequently leading to more uncertainty. In this aspect, the MULTIscale SENTINEL land surface information retrieval Platform (MULTIPLY) was created. MULTIPLY couples a variety of RS sources with Radiative Transfer Models (RTM) over varying spectral ranges using data-assimilation to estimate geophysical parameters. In addition, MULTIPLY also uses prior information about the land surface to constrain the retrieval problem. This research aims to improve the retrieval of plant biophysical parameters through the use of priors of biophysical parameters/plant traits. Of particular interest are traits (physical, morphological or chemical trait) affecting individual performance and fitness of species. Plant traits that are able to be retrieved via RS and with RTMs include traits such as leaf-pigments, leaf water, LAI, phenols, C/N, etc. In-situ data for plant traits that are retrievable via RS techniques were collected for a meta-analysis from databases such as TRY, Ecosis, and individual collaborators. Of particular interest are the following traits: chlorophyll, carotenoids, anthocyanins, phenols, leaf water, and LAI. ANOVA statistics were generated for each traits according to species, plant functional groups (such as evergreens, grasses, etc.), and the trait itself. Afterwards, traits were also compared using covariance matrices. Using these as priors, MULTIPLY was is used to retrieve several plant traits in two validation sites in the Netherlands (Speulderbos) and in Finland (Sodankylä). Initial comparisons show significant improved results over non-a priori based retrievals.