The effect of small topographic variations on reflectance

The influence of topography on the bidirectional reflectance factors (BRFs) of vegetation canopies is commonly neglected when retrieving canopy parameters by inversion of reflectance models. However, the nonlinear dependence of vegetation BRFs on slope magnitude and orientation means that topography cannot necessarily be assumed to have an insignificant effect, even when slopes are small. The study presented here shows that, at a Sun zenith angle of 60°, the reflectance of a set of terrain elements may differ from that of horizontal terrain by more than 10% at forward scattering directions for slopes of 10° or less. The errors may also be strongly correlated to observation conditions, which raises questions about using least squares minimization schemes to find the best fit between measured and modeled reflectance data     

Retrieval of canopy biophysical variables from bidirectional reflectance: Using prior information to solve the ill-posed inverse problem

Estimation of canopy biophysical variables from remote sensing data was investigated using radiative transfer model inversion. Measurement and model uncertainties make the inverse problem ill posed, inducing difficulties and inaccuracies in the search for the solution. This study focuses on the use of prior information to reduce the uncertainties associated to the estimation of canopy biophysical variables in the radiative transfer model inversion process. For this purpose, lookup table (LUT), quasi-Newton algorithm (QNT), and neural network (NNT) inversion techniques were adapted to account for prior information. Results were evaluated over simulated reflectance data sets that allow a detailed analysis of the effect of measurement and model uncertainties. Results demonstrate that the use of prior information significantly improves canopy biophysical variables estimation. LUT and QNT are sensitive to model uncertainties. Conversely, NNT techniques are generally less accurate. However, in our conditions, its accuracy is little dependent significantly on modeling or measurement error. We also observed that bias in the reflectance measurements due to miscalibration did not impact very much the accuracy of biophysical estimation.     

Remote sensing capabilities to estimate pasture production in France

Pastures constitute an important terrestrial ecosystem. In France, pastures occupy 21% of the total area. A big effort is being made to develop a real-time systematic approach to estimate biomass production at a national level, focusing on spatial and seasonal variability in relation to drought. The absence of indirect low-cost methods that could be applied to large areas contributes to this situation. Advances in remote sensing and crop models offer new methodological and operative possibilities to solve this problem. In this paper, 13 forage regions (FR) in France were selected on the basis of their different geomorphologic, climatic and soil conditions with regard to pastoral production. Images from Système Probatoire de l'Observation de la Terre (SPOT) 4-VEGETATION were used to forecast productive variables estimated by the STICS-Prairie simulation model. In general terms, both satellite and productive data agreed properly. Particularly, the relationship between the middle infrared based vegetation index (SWVI) and the Leaf Area Index (LAI) demonstrated the best results whatever the FR. Results obtained confirm the capabilities of remote sensing data as an accurate predictor of productive variables estimated as from simulation models. Differences between satellite information and model estimations of pasture systems, especially during the harvesting periods, could be good indicators to improve model estimations at a regional scale as well.     

Gene Therapy in Combination with Nitrogen Scavenger Pretreatment Corrects Biochemical and Behavioral Abnormalities of Infant Citrullinemia Type 1 Mice

Citrullinemia type I (CTLN1) is a rare autosomal recessive disorder caused by mutations in the gene encoding argininosuccinate synthetase 1 (ASS1) that catalyzes the third step of the urea cycle. CTLN1 patients suffer from impaired elimination of nitrogen, which leads to neurotoxic levels of circulating ammonia and urea cycle byproducts that may cause severe metabolic encephalopathy, death or irreversible brain damage. Standard of care (SOC) of CTLN1 consists of daily nitrogen-scavenger administration, but patients remain at risk of life-threatening decompensations. We evaluated the therapeutic efficacy of a recombinant adeno-associated viral vector carrying the ASS1 gene under the control of a liver-specific promoter (VTX-804). When administered to three-week-old CTLN1 mice, all the animals receiving VTX-804 in combination with SOC gained body weight normally, presented with a normalization of ammonia and reduction of citrulline levels in circulation, and 100% survived for 7 months. Similar to what has been observed in CTLN1 patients, CTLN1 mice showed several behavioral abnormalities such as anxiety, reduced welfare and impairment of innate behavior. Importantly, all clinical alterations were notably improved after treatment with VTX-804. This study demonstrates the potential of VTX-804 gene therapy for future clinical translation to CTLN1 patients.