Dynamical Approach for Real-Time Monitoring of Agricultural Crops

In this paper, a novel approach for exploiting multitemporal remote sensing data focused on real-time monitoring of agricultural crops is presented. The methodology is defined in a dynamical system context using state-space techniques, which enables the possibility of merging past temporal information with an update for each new acquisition. The dynamic system context allows us to exploit classical tools in this domain to perform the estimation of relevant variables. A general methodology is proposed, and a particular instance is defined in this study based on polarimetric radar data to track the phenological stages of a set of crops. A model generation from empirical data through principal component analysis is presented, and an extended Kalman filter is adapted to perform phenological stage estimation. Results employing quad-pol Radarsat-2 data over three different cereals are analyzed. The potential of this methodology to retrieve vegetation variables in real time is shown.

Pursuing Chemical Efficiency by Using Supported Organocatalysts for Asymmetric Reactions under Aqueous Conditions

Over the past decade, a great effort has been made by the chemical community to improve the efficiency of organic transformations and allow sustainable processes. Merging the use of supported and recyclable organocatalysts and aqueous conditions for the asymmetric synthesis of valuable molecules, has led to outstanding contributions in the area of green chemistry. Recent progresses in the field include the implementation of these methodologies in the large scale production of chiral molecules using automated flow chemistry.