Deconvolution of remotely sensed spectral mixtures for retrieval of LAI, fAPAR and soil brightness

Linear mixture models have been used to invert spectral reflectances of targets at the Earth's surface into proportions of plant and soil components. However, operational use of mixture models has been limited by a lack of biophysical interpretation of the results. The main objectives of this study were (1) to relate the deconvolved components of mixture model with biophysical properties of vegetation and soil at the surface and (2) to apply the mixture model results to remotely sensed imagery. A radiative transfer model (SAIL: Scattering by Arbitrarily Inclined Leaves) was used to generate reflectance 'mixtures' from leaf and bare soil spectral measurements made at HAPEX-Sahel (Hydrological Atmospheric Pilot EXperiment) study sites. The SAIL model was used to create canopy reflectances and fractions of absorbed photosynthetically active radiation (fAPAR) for a range of mixed targets with varying leaf area index (LAI) and soils. A spectral mixture model was used to deconvolve the simulated reflectance data into component fractions, which were then calibrated to the SAIL-generated LAI, fAPAR and soil brightness. The calibrated relationships were validated with observational ground data (LAI, fAPAR and reflectance) measured at the HAPEX Sahel fallow bush/grassland, fallow grassland and millet sites. Both the vegetation and soil component fractions were found to be dependent upon soil background brightness, such that inclusion of the soil fraction information significantly improved the derivation of vegetation biophysical parameters. Soil brightness was also shown to be a useful parameter to infer soil properties. The deconvolution methodology was then applied to a nadir image of a HAPEX-Sahel site measured by the Advanced Solid State Array Spectroradiometer (ASAS). Site LAI and fAPAR were successfully estimated by combining the fractional estimates of vegetation and soils, obtained through deconvolution of the ASAS image, with the calibrated relationships between vegetation fraction, LAI and fAPAR, obtained from the SAIL data

<em>flp-32</em> Ligand/Receptor Silencing Phenocopy Faster Plant Pathogenic Nematodes

Restrictions on nematicide usage underscore the need for novel control strategies for plant pathogenic nematodes such as Globodera pallida (potato cyst nematode) that impose a significant economic burden on plant cultivation activities. The nematode neuropeptide signalling system is an attractive resource for novel control targets as it plays a critical role in sensory and motor functions. The FMRFamide-like peptides (FLPs) form the largest and most diverse family of neuropeptides in invertebrates, and are structurally conserved across nematode species, highlighting the utility of the FLPergic system as a broad-spectrum control target. flp-32 is expressed widely across nematode species. This study investigates the role offlp-32 in G. pallida and shows that: (i) Gp-flp-32 encodes the peptide AMRNALVRFamide; (ii)Gp-flp-32 is expressed in the brain and ventral nerve cord of G. pallida; (iii) migration rate increases in Gp-flp-32-silenced worms; (iv) the ability of G. pallida to infect potato plant root systems is enhanced in Gp-flp-32-silenced worms; (v) a novel putative Gp-flp-32 receptor (Gp-flp-32R) is expressed in G. pallida; and, (vi) Gp-flp-32R-silenced worms also display an increase in migration rate. This work demonstrates that Gp-flp-32 plays an intrinsic role in the modulation of locomotory behaviour in G. pallida and putatively interacts with at least one novel G-protein coupled receptor (Gp-flp-32R). This is the first functional characterisation of a parasitic nematode FLP-GPCR